NBA_Score_Predictions_Pipeline_V1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e0761179",
   "metadata": {},
   "source": [
    "# NBA Score Predictions Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4539bfc4",
   "metadata": {},
   "source": [
    "### Necessary Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "1bd0028d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import datetime\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from selenium import webdriver\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib import cm \n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import random as rnd\n",
    "import statistics\n",
    "import math\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81add629",
   "metadata": {},
   "source": [
    "### Colors Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "04653f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "class color:\n",
    "    PURPLE = '\\033[95m'\n",
    "    CYAN = '\\033[96m'\n",
    "    DARKCYAN = '\\033[36m'\n",
    "    BLUE = '\\033[94m'\n",
    "    GREEN = '\\033[92m'\n",
    "    YELLOW = '\\033[93m'\n",
    "    RED = '\\033[91m'\n",
    "    BOLD = '\\033[1m'\n",
    "    UNDERLINE = '\\033[4m'\n",
    "    END = '\\033[0m'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7104a348",
   "metadata": {},
   "source": [
    "### Lists/Dicts for Conferences, Teams, Abbrs, Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "5aef0893",
   "metadata": {},
   "outputs": [],
   "source": [
    "conferences = {'GSW':'WEST', 'POR':'WEST', 'SAC':'WEST', 'UTA':'WEST', 'MIA':'EAST', 'DEN':'WEST', 'MIN':'WEST',\n",
    "               'PHI':'EAST', 'NOP':'EAST', 'ORL':'EAST', 'MIL':'EAST', 'CHI':'EAST','DET':'EAST', 'TOR':'EAST',\n",
    "               'PHX':'WEST', 'LAL':'WEST', 'ATL':'EAST', 'WAS':'EAST', 'MEM':'WEST', 'CLE':'EAST', 'LAC':'WEST',\n",
    "               'BOS':'EAST', 'NYK':'EAST', 'IND':'EAST', 'CHA':'EAST', 'SAS':'WEST', 'HOU':'WEST', 'DAL':'WEST',\n",
    "               'OKC':'WEST', 'BKN':'EAST'}\n",
    "\n",
    "team_names =     ['Utah Jazz', 'Sacramento Kings', 'Washington Wizards',\n",
    "                  'Boston Celtics', 'Milwaukee Bucks', 'Oklahoma City Thunder',\n",
    "                  'Chicago Bulls', 'Phoenix Suns', 'Philadelphia 76ers',\n",
    "                  'New Orleans Pelicans', 'Charlotte Hornets', 'Los Angeles Lakers',\n",
    "                  'Indiana Pacers', 'Toronto Raptors', 'Cleveland Cavaliers',\n",
    "                  'Denver Nuggets', 'Minnesota Timberwolves', 'Brooklyn Nets',\n",
    "                  'San Antonio Spurs', 'Dallas Mavericks', 'Houston Rockets',\n",
    "                  'Detroit Pistons', 'Portland Trail Blazers', 'Atlanta Hawks',\n",
    "                  'Golden State Warriors', 'Miami Heat', 'Los Angeles Clippers',\n",
    "                  'New York Knicks', 'Memphis Grizzlies', 'Orlando Magic', 'League Average']\n",
    "\n",
    "team_names_nocity = ['Jazz', 'Kings', 'Wizards', 'Celtics', 'Bucks', 'Thunder',\n",
    "                  'Bulls', 'Suns', '76ers', 'Pelicans', 'Hornets', 'Lakers',\n",
    "                  'Pacers', 'Raptors', 'Cavaliers', 'Nuggets', 'Timberwolves', 'Nets',\n",
    "                  'Spurs', 'Mavericks', 'Rockets', 'Pistons', 'Trail Blazers', 'Hawks',\n",
    "                  'Warriors', 'Heat', 'Clippers','Knicks', 'Grizzlies', 'Magic', 'League Average']\n",
    "\n",
    "team_abbrs =     ['UTA', 'SAC', 'WAS', 'BOS', 'MIL', 'OKC', 'CHI', 'PHX', 'PHI',\n",
    "                  'NOP', 'CHA', 'LAL', 'IND', 'TOR', 'CLE', 'DEN', 'MIN', 'BKN',\n",
    "                  'SAS', 'DAL', 'HOU', 'DET', 'POR', 'ATL', 'GSW', 'MIA', 'LAC',\n",
    "                  'NYK', 'MEM', 'ORL', 'NBA']\n",
    "\n",
    "team_name_abbr_dict = {}\n",
    "team_abbr_name_dict = {}\n",
    "team_names_nocity_dict = {}\n",
    "team_id_dict={}\n",
    "\n",
    "for i in range(len(team_names)):\n",
    "    team_name_abbr_dict[team_names[i]]=team_abbrs[i]\n",
    "    \n",
    "for i in range(len(team_names_nocity)):\n",
    "    team_names_nocity_dict[team_names_nocity[i]]=team_abbrs[i]\n",
    "\n",
    "for i in range(len(team_abbrs)):\n",
    "    team_abbr_name_dict[team_abbrs[i]]=team_names[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d03dca",
   "metadata": {},
   "source": [
    "### Webscrape 2022-2023 NBA Boxscore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "e96a66a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th>MATCHUP</th>\n",
       "      <th>WL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>FG3M</th>\n",
       "      <th>FG3A</th>\n",
       "      <th>FG3_PCT</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT_PCT</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "      <th>FG2M</th>\n",
       "      <th>FG2A</th>\n",
       "      <th>FG2_PTS</th>\n",
       "      <th>FG3_PTS</th>\n",
       "      <th>OPP_TEAM_ABBR</th>\n",
       "      <th>OPP_PTS</th>\n",
       "      <th>FRAC_ATT_2PT</th>\n",
       "      <th>FRAC_ATT_3PT</th>\n",
       "      <th>FRAC_MK_2PT</th>\n",
       "      <th>FRAC_MK_3PT</th>\n",
       "      <th>FRAC_PTS_2PT</th>\n",
       "      <th>FRAC_PTS_3PT</th>\n",
       "      <th>FRAC_PTS_FT</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>HOME_AWAY</th>\n",
       "      <th>CONFERENCE</th>\n",
       "      <th>OPP_CONFERENCE</th>\n",
       "      <th>DATE_MATCHUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22022</td>\n",
       "      <td>1610612748</td>\n",
       "      <td>MIA</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>0022200780</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>MIA @ NYK</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>39</td>\n",
       "      <td>87</td>\n",
       "      <td>0.448</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>0.361</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0.813</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>104</td>\n",
       "      <td>-2</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>NYK</td>\n",
       "      <td>106</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.413793</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>AWAY</td>\n",
       "      <td>EAST</td>\n",
       "      <td>EAST</td>\n",
       "      <td>02-02 @ NYK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22022</td>\n",
       "      <td>1610612746</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>0022200784</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>LAC @ MIL</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>39</td>\n",
       "      <td>95</td>\n",
       "      <td>0.411</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>0.361</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>0.700</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>105</td>\n",
       "      <td>-1</td>\n",
       "      <td>26</td>\n",
       "      <td>59</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>MIL</td>\n",
       "      <td>106</td>\n",
       "      <td>0.621053</td>\n",
       "      <td>0.378947</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.495238</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>AWAY</td>\n",
       "      <td>WEST</td>\n",
       "      <td>EAST</td>\n",
       "      <td>02-02 @ MIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22022</td>\n",
       "      <td>1610612749</td>\n",
       "      <td>MIL</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>0022200784</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>MIL vs. LAC</td>\n",
       "      <td>W</td>\n",
       "      <td>240</td>\n",
       "      <td>39</td>\n",
       "      <td>90</td>\n",
       "      <td>0.433</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>0.243</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>0.704</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>53</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>LAC</td>\n",
       "      <td>105</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.411111</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.566038</td>\n",
       "      <td>0.254717</td>\n",
       "      <td>0.179245</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>HOME</td>\n",
       "      <td>EAST</td>\n",
       "      <td>WEST</td>\n",
       "      <td>02-02 vs. LAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22022</td>\n",
       "      <td>1610612766</td>\n",
       "      <td>CHA</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>0022200781</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>CHA @ CHI</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>39</td>\n",
       "      <td>93</td>\n",
       "      <td>0.419</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>0.200</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>0.867</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>98</td>\n",
       "      <td>-16</td>\n",
       "      <td>32</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>21</td>\n",
       "      <td>CHI</td>\n",
       "      <td>114</td>\n",
       "      <td>0.623656</td>\n",
       "      <td>0.376344</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.179487</td>\n",
       "      <td>0.653061</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>AWAY</td>\n",
       "      <td>EAST</td>\n",
       "      <td>EAST</td>\n",
       "      <td>02-02 @ CHI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22022</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0022200783</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>GSW @ DEN</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>43</td>\n",
       "      <td>85</td>\n",
       "      <td>0.506</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "      <td>0.500</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0.632</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>117</td>\n",
       "      <td>-17</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>57</td>\n",
       "      <td>DEN</td>\n",
       "      <td>134</td>\n",
       "      <td>0.552941</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.441860</td>\n",
       "      <td>0.410256</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>AWAY</td>\n",
       "      <td>WEST</td>\n",
       "      <td>WEST</td>\n",
       "      <td>02-02 @ DEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SEASON_ID     TEAM_ID TEAM_ABBREVIATION              TEAM_NAME     GAME_ID  \\\n",
       "0     22022  1610612748               MIA             Miami Heat  0022200780   \n",
       "1     22022  1610612746               LAC   Los Angeles Clippers  0022200784   \n",
       "2     22022  1610612749               MIL        Milwaukee Bucks  0022200784   \n",
       "3     22022  1610612766               CHA      Charlotte Hornets  0022200781   \n",
       "4     22022  1610612744               GSW  Golden State Warriors  0022200783   \n",
       "\n",
       "    GAME_DATE      MATCHUP WL  MIN  FGM  FGA  FG_PCT  FG3M  FG3A  FG3_PCT  \\\n",
       "0  2023-02-02    MIA @ NYK  L  240   39   87   0.448    13    36    0.361   \n",
       "1  2023-02-02    LAC @ MIL  L  240   39   95   0.411    13    36    0.361   \n",
       "2  2023-02-02  MIL vs. LAC  W  240   39   90   0.433     9    37    0.243   \n",
       "3  2023-02-02    CHA @ CHI  L  240   39   93   0.419     7    35    0.200   \n",
       "4  2023-02-02    GSW @ DEN  L  240   43   85   0.506    19    38    0.500   \n",
       "\n",
       "   FTM  FTA  FT_PCT  OREB  DREB  REB  AST  STL  BLK  TOV  PF  PTS  PLUS_MINUS  \\\n",
       "0   13   16   0.813     9    24   33   24    8    3   11  15  104          -2   \n",
       "1   14   20   0.700     9    40   49   15    8    4    7  22  105          -1   \n",
       "2   19   27   0.704     8    45   53   16    2    0   10  17  106           1   \n",
       "3   13   15   0.867    16    29   45   19    6    4   15  15   98         -16   \n",
       "4   12   19   0.632    10    32   42   25    4    4   17  23  117         -17   \n",
       "\n",
       "   FG2M  FG2A  FG2_PTS  FG3_PTS OPP_TEAM_ABBR  OPP_PTS  FRAC_ATT_2PT  \\\n",
       "0    26    51       52       39           NYK      106      0.586207   \n",
       "1    26    59       52       39           MIL      106      0.621053   \n",
       "2    30    53       60       27           LAC      105      0.588889   \n",
       "3    32    58       64       21           CHI      114      0.623656   \n",
       "4    24    47       48       57           DEN      134      0.552941   \n",
       "\n",
       "   FRAC_ATT_3PT  FRAC_MK_2PT  FRAC_MK_3PT  FRAC_PTS_2PT  FRAC_PTS_3PT  \\\n",
       "0      0.413793     0.666667     0.333333      0.500000      0.375000   \n",
       "1      0.378947     0.666667     0.333333      0.495238      0.371429   \n",
       "2      0.411111     0.769231     0.230769      0.566038      0.254717   \n",
       "3      0.376344     0.820513     0.179487      0.653061      0.214286   \n",
       "4      0.447059     0.558140     0.441860      0.410256      0.487179   \n",
       "\n",
       "   FRAC_PTS_FT  MONTH  YEAR HOME_AWAY CONFERENCE OPP_CONFERENCE   DATE_MATCHUP  \n",
       "0     0.125000      2  2023      AWAY       EAST           EAST    02-02 @ NYK  \n",
       "1     0.133333      2  2023      AWAY       WEST           EAST    02-02 @ MIL  \n",
       "2     0.179245      2  2023      HOME       EAST           WEST  02-02 vs. LAC  \n",
       "3     0.132653      2  2023      AWAY       EAST           EAST    02-02 @ CHI  \n",
       "4     0.102564      2  2023      AWAY       WEST           WEST    02-02 @ DEN  "
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://stats.nba.com/stats/leaguegamelog'\n",
    "headers= {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36',\n",
    "          'Referer': 'https://www.nba.com/'}\n",
    "payload = {\n",
    "    'Counter': '1000',\n",
    "    'DateFrom': '',\n",
    "    'DateTo': '',\n",
    "    'Direction': 'DESC',\n",
    "    'LeagueID': '00',\n",
    "    'PlayerOrTeam': 'T',\n",
    "    'Season': '2022-23',\n",
    "    'SeasonType': 'Regular Season',\n",
    "    'Sorter': 'DATE'}\n",
    "\n",
    "jsonData = requests.get(url, headers=headers, params=payload).json()\n",
    "\n",
    "\n",
    "rows = jsonData['resultSets'][0]['rowSet']\n",
    "columns = jsonData['resultSets'][0]['headers']\n",
    "\n",
    "df = pd.DataFrame(rows, columns=columns)\n",
    "df.drop(['VIDEO_AVAILABLE'], axis=1, inplace=True)\n",
    "\n",
    "\n",
    "df['FG2M'] = df.FGM - df.FG3M\n",
    "df['FG2A'] = df.FGA - df.FG3A\n",
    "\n",
    "df['FG2_PTS'] = df.FG2M * 2\n",
    "df['FG3_PTS'] = df.FG3M * 3\n",
    "\n",
    "df['OPP_TEAM_ABBR'] = df['MATCHUP'].str.strip().str[-3:]\n",
    "df['OPP_PTS'] = df['PTS'] - df['PLUS_MINUS']\n",
    "\n",
    "df['FRAC_ATT_2PT'] = df.FG2A / df.FGA\n",
    "df['FRAC_ATT_3PT'] = df.FG3A / df.FGA\n",
    "\n",
    "df['FRAC_MK_2PT'] = df.FG2M / df.FGM\n",
    "df['FRAC_MK_3PT'] = df.FG3M / df.FGM\n",
    "\n",
    "df['FRAC_PTS_2PT'] = df.FG2_PTS / df.PTS\n",
    "df['FRAC_PTS_3PT'] = df.FG3_PTS / df.PTS\n",
    "df['FRAC_PTS_FT'] = df.FTM / df.PTS \n",
    "\n",
    "df['MONTH'] = pd.DatetimeIndex(df['GAME_DATE']).month\n",
    "df['YEAR'] = pd.DatetimeIndex(df['GAME_DATE']).year\n",
    "\n",
    "def home_or_away(string):\n",
    "    if string[4]=='@': return 'AWAY'\n",
    "    elif string[4]=='v': return 'HOME'\n",
    "    \n",
    "df['HOME_AWAY'] = df['MATCHUP'].map(home_or_away)\n",
    "\n",
    "df['CONFERENCE'] = df['TEAM_ABBREVIATION'].apply(lambda x: conferences.get(x))\n",
    "df['OPP_CONFERENCE'] = df['OPP_TEAM_ABBR'].apply(lambda x: conferences.get(x))\n",
    "\n",
    "\n",
    "df['DATE_MATCHUP'] = df['GAME_DATE'].str[5:] + ' ' + df['MATCHUP'].str[4:]\n",
    "#df['DATE_MATCHUP'] = df['GAME_DATE'] + ' ' + df['MATCHUP'].str[4:]\n",
    "\n",
    "df.TEAM_NAME = df.TEAM_ABBREVIATION.apply(lambda x: team_abbr_name_dict.get(x))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b2287c",
   "metadata": {},
   "source": [
    "### Team Average Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "a22efcb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_team_avg = pd.pivot_table(df, index=['TEAM_NAME','TEAM_ABBREVIATION'], aggfunc = 'mean')\n",
    "df_team_avg = pivot_team_avg.reset_index()\n",
    "df_team_avg.loc[30] = df_team_avg.drop(['TEAM_NAME','TEAM_ABBREVIATION'], axis=1).mean()\n",
    "df_team_avg.loc[30, 'TEAM_NAME']='League Average'\n",
    "df_team_avg.loc[30, 'TEAM_ABBREVIATION']='NBA'\n",
    "df_team_avg = df_team_avg.rename(columns={'TEAM_ABBREVIATION':'TEAM_ABBR'})\n",
    "df_team_avg = df_team_avg.drop(['MONTH','YEAR'],axis=1)\n",
    "cols_to_move = ['TEAM_NAME', 'TEAM_ABBR', 'PTS']\n",
    "df_team_avg = df_team_avg[cols_to_move + [x for x in df_team_avg.columns if x not in cols_to_move]]\n",
    "\n",
    "#df_team_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df6aab7",
   "metadata": {},
   "source": [
    "### Add Defense Allowed to Team Avg DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "bcae05b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_defense_allowed_team_avg = pd.pivot_table(df, index=['OPP_TEAM_ABBR'], aggfunc = 'mean')\n",
    "df_defense_allowed_team_avg = pivot_defense_allowed_team_avg.reset_index()\n",
    "df_defense_allowed_team_avg.loc[30] = df_defense_allowed_team_avg.drop(['OPP_TEAM_ABBR'], axis=1).mean()\n",
    "df_defense_allowed_team_avg = df_defense_allowed_team_avg.rename(columns={'OPP_TEAM_ABBR':'TEAM_ABBREVIATION'})\n",
    "df_defense_allowed_team_avg.loc[30, 'TEAM_NAME']='League Average'\n",
    "df_defense_allowed_team_avg.loc[30, 'TEAM_ABBREVIATION']='NBA'\n",
    "df_defense_allowed_team_avg['TEAM_NAME'] = df_defense_allowed_team_avg['TEAM_ABBREVIATION'].map(team_abbr_name_dict)\n",
    "df_defense_allowed_team_avg = df_defense_allowed_team_avg.drop(['YEAR','TEAM_ID'], axis=1)\n",
    "df_defense_allowed_team_avg = df_defense_allowed_team_avg.rename(columns={'TEAM_ABBREVIATION':'TEAM_ABBR'})\n",
    "cols_to_move = ['TEAM_NAME', 'TEAM_ABBR']\n",
    "df_defense_allowed_team_avg = df_defense_allowed_team_avg[cols_to_move + [x for x in df_defense_allowed_team_avg.columns if x not in cols_to_move]]\n",
    "df_defense_allowed_team_avg.rename(columns=lambda x: 'OPP_'+x, inplace=True)\n",
    "df_defense_allowed_team_avg = df_defense_allowed_team_avg.rename(columns={'OPP_TEAM_ABBR':'TEAM_ABBR','OPP_TEAM_NAME':'TEAM_NAME'})\n",
    "\n",
    "\n",
    "# Merge Offensive and Defensive Avg DFs\n",
    "\n",
    "df_team_avg = pd.merge(df_team_avg, \n",
    "                       df_defense_allowed_team_avg,\n",
    "                       right_on='TEAM_ABBR',\n",
    "                       left_on='TEAM_ABBR',\n",
    "                       how='left')\n",
    "df_team_avg = df_team_avg.drop(['TEAM_NAME_y', 'OPP_PTS_y'],axis=1)\n",
    "df_team_avg = df_team_avg.rename(columns={'TEAM_NAME_x':'TEAM_NAME','OPP_PTS_x':'OPP_PTS'})\n",
    "\n",
    "#df_team_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbc6add",
   "metadata": {},
   "source": [
    "### Webscrape Advanced Shooting DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "dad3790b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tables = pd.read_html(\"https://www.basketball-reference.com/leagues/NBA_2023.html#shooting-team\")\n",
    "\n",
    "df_team_advshot = tables[12]\n",
    "df_team_advshot.columns = [' '.join(col) for col in df_team_advshot.columns]\n",
    "df_team_advshot.columns = df_team_advshot.columns.str.replace(\"Unnamed:\", \"\")\n",
    "df_team_advshot.columns = df_team_advshot.columns.str.replace(\"_level_0\", \"\")\n",
    "df_team_advshot = df_team_advshot.drop([' 0 Rk',\n",
    "                                        ' 6  6_level_1',\n",
    "                                        ' 13  13_level_1',\n",
    "                                        ' 20  20_level_1',\n",
    "                                        ' 23  23_level_1',\n",
    "                                        ' 26  26_level_1',\n",
    "                                        ' 29  29_level_1'], axis=1)\n",
    "\n",
    "df_team_advshot = df_team_advshot.rename(columns={' 1 Team':'TEAM_NAME',\n",
    "                                                  ' 2 G':'G',\n",
    "                                                  ' 3 MP':'MP',\n",
    "                                                  ' 4 FG%':'FG%',\n",
    "                                                  ' 5 Dist.':'Dist'})\n",
    "\n",
    "\n",
    "df_team_advshot['TEAM_ABBR'] = df_team_advshot['TEAM_NAME']\n",
    "df_team_advshot['TEAM_ABBR']=df_team_advshot['TEAM_ABBR'].map(team_name_abbr_dict)\n",
    "\n",
    "cols_to_move = ['TEAM_NAME', 'TEAM_ABBR']\n",
    "df_team_advshot = df_team_advshot[cols_to_move + [x for x in df_team_advshot.columns if x not in cols_to_move]]\n",
    "\n",
    "\n",
    "#df_team_advshot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b62acb6",
   "metadata": {},
   "source": [
    "### Create Master Data Frame with all variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "63851709",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_team_master = pd.merge(df_team_avg, \n",
    "                       df_team_advshot,\n",
    "                       right_on='TEAM_ABBR',\n",
    "                       left_on='TEAM_ABBR',\n",
    "                       how='left')\n",
    "df_team_master = df_team_master.drop(['TEAM_NAME_y'],axis=1)\n",
    "df_team_master = df_team_master.rename(columns={'TEAM_NAME_x':'TEAM_NAME'})\n",
    "\n",
    "#df_team_master"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c4c5bb",
   "metadata": {},
   "source": [
    "### Function to return a teams dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "c66946e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_team_df(TEAM_ABBR):\n",
    "    team_df = df[df['TEAM_ABBREVIATION']==TEAM_ABBR]\n",
    "    return team_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c712498d",
   "metadata": {},
   "source": [
    "### Webscrape NBA Records Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "1d2d2224",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['TEAM_ABBR','TEAM_NAME','CONFERENCE','GAMES','WINS','LOSES','WIN_PER','LAST_10_WINS','LAST_10_LOSES','HOME_WINS','HOME_LOSES','AWAY_WINS','AWAY_LOSES',\n",
    "           'WEST_WINS', 'WEST_LOSES', 'EAST_WINS', 'EAST_LOSES', 'OT_WINS', 'OT_LOSES','WI5_WINS','WI5_LOSES']\n",
    "data_records=[]\n",
    "conferences = {'GSW':'WEST', 'POR':'WEST', 'SAC':'WEST', 'UTA':'WEST', 'MIA':'EAST', 'DEN':'WEST', 'MIN':'WEST',\n",
    "               'PHI':'EAST', 'NOP':'EAST', 'ORL':'EAST', 'MIL':'EAST', 'CHI':'EAST','DET':'EAST', 'TOR':'EAST',\n",
    "               'PHX':'WEST', 'LAL':'WEST', 'ATL':'EAST', 'WAS':'EAST', 'MEM':'WEST', 'CLE':'EAST', 'LAC':'WEST',\n",
    "               'BOS':'EAST', 'NYK':'EAST', 'IND':'EAST', 'CHA':'EAST', 'SAS':'WEST', 'HOU':'WEST', 'DAL':'WEST',\n",
    "               'OKC':'WEST', 'BKN':'EAST'}\n",
    "\n",
    "for i in df.TEAM_ABBREVIATION.unique(): \n",
    "    \n",
    "    team_abbr = i\n",
    "    \n",
    "    team_name = team_abbr_name_dict[i]\n",
    "    \n",
    "    games = df[(df['TEAM_ABBREVIATION']==i)]['WL'].count() \n",
    "    \n",
    "    wins = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W')]['WL'].count()\n",
    "    loses = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L')]['WL'].count()\n",
    "    \n",
    "    win_per = wins/games\n",
    "    \n",
    "    last_10_df = get_team_df(i).head(10)\n",
    "    \n",
    "    last_10_wins = last_10_df[last_10_df['WL']=='W']['WL'].count()\n",
    "    last_10_loses = last_10_df[last_10_df['WL']=='L']['WL'].count()\n",
    "    \n",
    "    home_wins = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W') & (df['HOME_AWAY']=='HOME')]['WL'].count()\n",
    "    home_loses = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L') & (df['HOME_AWAY']=='HOME')]['WL'].count()\n",
    "    \n",
    "    away_wins = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W') & (df['HOME_AWAY']=='AWAY')]['WL'].count()\n",
    "    away_loses = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L') & (df['HOME_AWAY']=='AWAY')]['WL'].count()\n",
    "    \n",
    "    west_wins = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W')  & (df['OPP_CONFERENCE']=='WEST')]['WL'].count()\n",
    "    west_loses = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L')  & (df['OPP_CONFERENCE']=='WEST')]['WL'].count()\n",
    "    \n",
    "    east_wins = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W')  & (df['OPP_CONFERENCE']=='EAST')]['WL'].count()\n",
    "    east_loses = df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L')  & (df['OPP_CONFERENCE']=='EAST')]['WL'].count()\n",
    "    \n",
    "    OT_wins =  df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W')  & (df['MIN']>240)]['WL'].count()\n",
    "    OT_loses =  df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L')  & (df['MIN']>240)]['WL'].count()\n",
    "    \n",
    "    wi5_wins =  df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='W')  &  (abs(df['OPP_PTS']-df['PTS'])<=5)]['WL'].count()\n",
    "    wi5_loses =  df[(df['TEAM_ABBREVIATION']==i) & (df['WL']=='L')  & (abs(df['OPP_PTS']-df['PTS'])<=5)]['WL'].count()\n",
    "    \n",
    "    conference = conferences[i]\n",
    "    \n",
    "    values = [team_abbr, team_name, conference, games, wins, loses, win_per, last_10_wins, last_10_loses, home_wins, home_loses, away_wins, away_loses,\n",
    "              west_wins, west_loses, east_wins, east_loses, OT_wins, OT_loses, wi5_wins, wi5_loses]\n",
    "    zipped = zip(columns, values)\n",
    "    a_dictionary = dict(zipped)\n",
    "    data_records.append(a_dictionary)\n",
    "    \n",
    "df_records=pd.DataFrame(data_records).sort_values('WINS',ascending=False).reset_index(drop=True)\n",
    "\n",
    "#df_records"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccef792",
   "metadata": {},
   "source": [
    "### Merge Records Data onto master dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "08ac0f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM_ABBR</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>CONFERENCE</th>\n",
       "      <th>GAMES</th>\n",
       "      <th>WINS</th>\n",
       "      <th>LOSES</th>\n",
       "      <th>WIN_PER</th>\n",
       "      <th>LAST_10_WINS</th>\n",
       "      <th>LAST_10_LOSES</th>\n",
       "      <th>HOME_WINS</th>\n",
       "      <th>HOME_LOSES</th>\n",
       "      <th>AWAY_WINS</th>\n",
       "      <th>AWAY_LOSES</th>\n",
       "      <th>WEST_WINS</th>\n",
       "      <th>WEST_LOSES</th>\n",
       "      <th>EAST_WINS</th>\n",
       "      <th>EAST_LOSES</th>\n",
       "      <th>OT_WINS</th>\n",
       "      <th>OT_LOSES</th>\n",
       "      <th>WI5_WINS</th>\n",
       "      <th>WI5_LOSES</th>\n",
       "      <th>PTS</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>DREB</th>\n",
       "      <th>FG2A</th>\n",
       "      <th>FG2M</th>\n",
       "      <th>FG2_PTS</th>\n",
       "      <th>FG3A</th>\n",
       "      <th>FG3M</th>\n",
       "      <th>FG3_PCT</th>\n",
       "      <th>FG3_PTS</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>FRAC_ATT_2PT</th>\n",
       "      <th>FRAC_ATT_3PT</th>\n",
       "      <th>FRAC_MK_2PT</th>\n",
       "      <th>FRAC_MK_3PT</th>\n",
       "      <th>FRAC_PTS_2PT</th>\n",
       "      <th>FRAC_PTS_3PT</th>\n",
       "      <th>FRAC_PTS_FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FT_PCT</th>\n",
       "      <th>MIN</th>\n",
       "      <th>OPP_PTS</th>\n",
       "      <th>OREB</th>\n",
       "      <th>PF</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "      <th>REB</th>\n",
       "      <th>STL</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TOV</th>\n",
       "      <th>OPP_AST</th>\n",
       "      <th>OPP_BLK</th>\n",
       "      <th>OPP_DREB</th>\n",
       "      <th>OPP_FG2A</th>\n",
       "      <th>OPP_FG2M</th>\n",
       "      <th>OPP_FG2_PTS</th>\n",
       "      <th>OPP_FG3A</th>\n",
       "      <th>OPP_FG3M</th>\n",
       "      <th>OPP_FG3_PCT</th>\n",
       "      <th>OPP_FG3_PTS</th>\n",
       "      <th>OPP_FGA</th>\n",
       "      <th>OPP_FGM</th>\n",
       "      <th>OPP_FG_PCT</th>\n",
       "      <th>OPP_FRAC_ATT_2PT</th>\n",
       "      <th>OPP_FRAC_ATT_3PT</th>\n",
       "      <th>OPP_FRAC_MK_2PT</th>\n",
       "      <th>OPP_FRAC_MK_3PT</th>\n",
       "      <th>OPP_FRAC_PTS_2PT</th>\n",
       "      <th>OPP_FRAC_PTS_3PT</th>\n",
       "      <th>OPP_FRAC_PTS_FT</th>\n",
       "      <th>OPP_FTA</th>\n",
       "      <th>OPP_FTM</th>\n",
       "      <th>OPP_FT_PCT</th>\n",
       "      <th>OPP_MIN</th>\n",
       "      <th>OPP_MONTH</th>\n",
       "      <th>OPP_OPP_PTS</th>\n",
       "      <th>OPP_OREB</th>\n",
       "      <th>OPP_PF</th>\n",
       "      <th>OPP_PLUS_MINUS</th>\n",
       "      <th>OPP_REB</th>\n",
       "      <th>OPP_STL</th>\n",
       "      <th>OPP_TOV</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG%</th>\n",
       "      <th>Dist</th>\n",
       "      <th>% of FGA by Distance 2P</th>\n",
       "      <th>% of FGA by Distance 0-3</th>\n",
       "      <th>% of FGA by Distance 3-10</th>\n",
       "      <th>% of FGA by Distance 10-16</th>\n",
       "      <th>% of FGA by Distance 16-3P</th>\n",
       "      <th>% of FGA by Distance 3P</th>\n",
       "      <th>FG% by Distance 2P</th>\n",
       "      <th>FG% by Distance 0-3</th>\n",
       "      <th>FG% by Distance 3-10</th>\n",
       "      <th>FG% by Distance 10-16</th>\n",
       "      <th>FG% by Distance 16-3P</th>\n",
       "      <th>FG% by Distance 3P</th>\n",
       "      <th>% of FG Ast'd 2P</th>\n",
       "      <th>% of FG Ast'd 3P</th>\n",
       "      <th>Dunks %FGA</th>\n",
       "      <th>Dunks Md.</th>\n",
       "      <th>Layups %FGA</th>\n",
       "      <th>Layups Md.</th>\n",
       "      <th>Corner %3PA</th>\n",
       "      <th>Corner 3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BOS</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>EAST</td>\n",
       "      <td>52</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>0.711538</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>118.173077</td>\n",
       "      <td>26.346154</td>\n",
       "      <td>5.403846</td>\n",
       "      <td>35.230769</td>\n",
       "      <td>46.519231</td>\n",
       "      <td>26.346154</td>\n",
       "      <td>52.692308</td>\n",
       "      <td>41.942308</td>\n",
       "      <td>15.615385</td>\n",
       "      <td>0.370519</td>\n",
       "      <td>46.846154</td>\n",
       "      <td>88.461538</td>\n",
       "      <td>41.961538</td>\n",
       "      <td>0.475558</td>\n",
       "      <td>0.525228</td>\n",
       "      <td>0.474772</td>\n",
       "      <td>0.628537</td>\n",
       "      <td>0.371463</td>\n",
       "      <td>0.446293</td>\n",
       "      <td>0.394699</td>\n",
       "      <td>0.159008</td>\n",
       "      <td>22.480769</td>\n",
       "      <td>18.634615</td>\n",
       "      <td>0.831481</td>\n",
       "      <td>243.365385</td>\n",
       "      <td>111.980769</td>\n",
       "      <td>9.711538</td>\n",
       "      <td>18.826923</td>\n",
       "      <td>6.192308</td>\n",
       "      <td>44.942308</td>\n",
       "      <td>6.384615</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>22.615385</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>34.057692</td>\n",
       "      <td>57.519231</td>\n",
       "      <td>30.557692</td>\n",
       "      <td>61.115385</td>\n",
       "      <td>32.903846</td>\n",
       "      <td>11.596154</td>\n",
       "      <td>0.353635</td>\n",
       "      <td>34.788462</td>\n",
       "      <td>90.423077</td>\n",
       "      <td>42.153846</td>\n",
       "      <td>0.467365</td>\n",
       "      <td>0.636301</td>\n",
       "      <td>0.363699</td>\n",
       "      <td>0.724365</td>\n",
       "      <td>0.275635</td>\n",
       "      <td>0.547110</td>\n",
       "      <td>0.309414</td>\n",
       "      <td>0.143477</td>\n",
       "      <td>20.576923</td>\n",
       "      <td>16.076923</td>\n",
       "      <td>0.779942</td>\n",
       "      <td>243.365385</td>\n",
       "      <td>8.096154</td>\n",
       "      <td>118.173077</td>\n",
       "      <td>9.615385</td>\n",
       "      <td>19.480769</td>\n",
       "      <td>-6.192308</td>\n",
       "      <td>43.673077</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>12.807692</td>\n",
       "      <td>52</td>\n",
       "      <td>12655</td>\n",
       "      <td>0.466</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.052</td>\n",
       "      <td>223</td>\n",
       "      <td>0.273</td>\n",
       "      <td>702</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEN</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>WEST</td>\n",
       "      <td>52</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>116.980769</td>\n",
       "      <td>28.769231</td>\n",
       "      <td>4.384615</td>\n",
       "      <td>32.653846</td>\n",
       "      <td>55.173077</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>30.961538</td>\n",
       "      <td>12.326923</td>\n",
       "      <td>0.395462</td>\n",
       "      <td>36.980769</td>\n",
       "      <td>86.134615</td>\n",
       "      <td>43.826923</td>\n",
       "      <td>0.510288</td>\n",
       "      <td>0.640573</td>\n",
       "      <td>0.359427</td>\n",
       "      <td>0.717031</td>\n",
       "      <td>0.282969</td>\n",
       "      <td>0.539641</td>\n",
       "      <td>0.315466</td>\n",
       "      <td>0.144893</td>\n",
       "      <td>22.769231</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.748519</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>112.807692</td>\n",
       "      <td>10.038462</td>\n",
       "      <td>19.288462</td>\n",
       "      <td>4.173077</td>\n",
       "      <td>42.692308</td>\n",
       "      <td>7.519231</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.980769</td>\n",
       "      <td>25.653846</td>\n",
       "      <td>4.346154</td>\n",
       "      <td>30.480769</td>\n",
       "      <td>52.865385</td>\n",
       "      <td>29.596154</td>\n",
       "      <td>59.192308</td>\n",
       "      <td>33.538462</td>\n",
       "      <td>11.826923</td>\n",
       "      <td>0.349788</td>\n",
       "      <td>35.480769</td>\n",
       "      <td>86.403846</td>\n",
       "      <td>41.423077</td>\n",
       "      <td>0.480423</td>\n",
       "      <td>0.611265</td>\n",
       "      <td>0.388735</td>\n",
       "      <td>0.715310</td>\n",
       "      <td>0.284690</td>\n",
       "      <td>0.528844</td>\n",
       "      <td>0.310650</td>\n",
       "      <td>0.160506</td>\n",
       "      <td>23.788462</td>\n",
       "      <td>18.134615</td>\n",
       "      <td>0.762481</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>7.884615</td>\n",
       "      <td>116.980769</td>\n",
       "      <td>9.884615</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>-4.173077</td>\n",
       "      <td>40.365385</td>\n",
       "      <td>7.596154</td>\n",
       "      <td>13.961538</td>\n",
       "      <td>52</td>\n",
       "      <td>12530</td>\n",
       "      <td>0.479</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.055</td>\n",
       "      <td>232</td>\n",
       "      <td>0.290</td>\n",
       "      <td>794</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MIL</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>EAST</td>\n",
       "      <td>52</td>\n",
       "      <td>35</td>\n",
       "      <td>17</td>\n",
       "      <td>0.673077</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>114.096154</td>\n",
       "      <td>24.596154</td>\n",
       "      <td>5.038462</td>\n",
       "      <td>37.115385</td>\n",
       "      <td>50.442308</td>\n",
       "      <td>27.269231</td>\n",
       "      <td>54.538462</td>\n",
       "      <td>39.673077</td>\n",
       "      <td>14.211538</td>\n",
       "      <td>0.359154</td>\n",
       "      <td>42.634615</td>\n",
       "      <td>90.115385</td>\n",
       "      <td>41.480769</td>\n",
       "      <td>0.461442</td>\n",
       "      <td>0.559447</td>\n",
       "      <td>0.440553</td>\n",
       "      <td>0.655579</td>\n",
       "      <td>0.344421</td>\n",
       "      <td>0.476679</td>\n",
       "      <td>0.374308</td>\n",
       "      <td>0.149013</td>\n",
       "      <td>22.923077</td>\n",
       "      <td>16.923077</td>\n",
       "      <td>0.742481</td>\n",
       "      <td>241.923077</td>\n",
       "      <td>111.788462</td>\n",
       "      <td>11.807692</td>\n",
       "      <td>18.326923</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>48.923077</td>\n",
       "      <td>6.692308</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.230769</td>\n",
       "      <td>22.903846</td>\n",
       "      <td>4.288462</td>\n",
       "      <td>33.576923</td>\n",
       "      <td>58.596154</td>\n",
       "      <td>30.096154</td>\n",
       "      <td>60.192308</td>\n",
       "      <td>33.480769</td>\n",
       "      <td>11.711538</td>\n",
       "      <td>0.347327</td>\n",
       "      <td>35.134615</td>\n",
       "      <td>92.076923</td>\n",
       "      <td>41.807692</td>\n",
       "      <td>0.455673</td>\n",
       "      <td>0.635905</td>\n",
       "      <td>0.364095</td>\n",
       "      <td>0.718665</td>\n",
       "      <td>0.281335</td>\n",
       "      <td>0.539440</td>\n",
       "      <td>0.312948</td>\n",
       "      <td>0.147612</td>\n",
       "      <td>21.192308</td>\n",
       "      <td>16.461538</td>\n",
       "      <td>0.779269</td>\n",
       "      <td>241.923077</td>\n",
       "      <td>7.923077</td>\n",
       "      <td>114.096154</td>\n",
       "      <td>10.846154</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-2.307692</td>\n",
       "      <td>44.423077</td>\n",
       "      <td>7.326923</td>\n",
       "      <td>12.326923</td>\n",
       "      <td>52</td>\n",
       "      <td>12580</td>\n",
       "      <td>0.454</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.053</td>\n",
       "      <td>214</td>\n",
       "      <td>0.245</td>\n",
       "      <td>640</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PHI</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>EAST</td>\n",
       "      <td>50</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>114.480000</td>\n",
       "      <td>25.300000</td>\n",
       "      <td>4.820000</td>\n",
       "      <td>32.020000</td>\n",
       "      <td>51.220000</td>\n",
       "      <td>27.940000</td>\n",
       "      <td>55.880000</td>\n",
       "      <td>33.420000</td>\n",
       "      <td>12.860000</td>\n",
       "      <td>0.381340</td>\n",
       "      <td>38.580000</td>\n",
       "      <td>84.640000</td>\n",
       "      <td>40.800000</td>\n",
       "      <td>0.483040</td>\n",
       "      <td>0.604168</td>\n",
       "      <td>0.395832</td>\n",
       "      <td>0.682270</td>\n",
       "      <td>0.317730</td>\n",
       "      <td>0.489813</td>\n",
       "      <td>0.335288</td>\n",
       "      <td>0.174899</td>\n",
       "      <td>24.220000</td>\n",
       "      <td>20.020000</td>\n",
       "      <td>0.825140</td>\n",
       "      <td>242.500000</td>\n",
       "      <td>110.700000</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>20.720000</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>40.680000</td>\n",
       "      <td>8.020000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.140000</td>\n",
       "      <td>23.880000</td>\n",
       "      <td>5.040000</td>\n",
       "      <td>32.480000</td>\n",
       "      <td>52.860000</td>\n",
       "      <td>29.060000</td>\n",
       "      <td>58.120000</td>\n",
       "      <td>32.760000</td>\n",
       "      <td>11.220000</td>\n",
       "      <td>0.343640</td>\n",
       "      <td>33.660000</td>\n",
       "      <td>85.620000</td>\n",
       "      <td>40.280000</td>\n",
       "      <td>0.472640</td>\n",
       "      <td>0.617360</td>\n",
       "      <td>0.382640</td>\n",
       "      <td>0.721277</td>\n",
       "      <td>0.278723</td>\n",
       "      <td>0.526374</td>\n",
       "      <td>0.302984</td>\n",
       "      <td>0.170642</td>\n",
       "      <td>24.160000</td>\n",
       "      <td>18.920000</td>\n",
       "      <td>0.777440</td>\n",
       "      <td>242.500000</td>\n",
       "      <td>8.120000</td>\n",
       "      <td>114.480000</td>\n",
       "      <td>9.740000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>-3.780000</td>\n",
       "      <td>42.220000</td>\n",
       "      <td>6.840000</td>\n",
       "      <td>15.260000</td>\n",
       "      <td>50</td>\n",
       "      <td>12125</td>\n",
       "      <td>0.470</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.052</td>\n",
       "      <td>211</td>\n",
       "      <td>0.274</td>\n",
       "      <td>696</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CLE</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>EAST</td>\n",
       "      <td>54</td>\n",
       "      <td>32</td>\n",
       "      <td>22</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>111.555556</td>\n",
       "      <td>24.685185</td>\n",
       "      <td>4.314815</td>\n",
       "      <td>32.351852</td>\n",
       "      <td>52.833333</td>\n",
       "      <td>29.351852</td>\n",
       "      <td>58.703704</td>\n",
       "      <td>31.629630</td>\n",
       "      <td>11.611111</td>\n",
       "      <td>0.369296</td>\n",
       "      <td>34.833333</td>\n",
       "      <td>84.462963</td>\n",
       "      <td>40.962963</td>\n",
       "      <td>0.486926</td>\n",
       "      <td>0.624903</td>\n",
       "      <td>0.375097</td>\n",
       "      <td>0.715238</td>\n",
       "      <td>0.284762</td>\n",
       "      <td>0.527849</td>\n",
       "      <td>0.310901</td>\n",
       "      <td>0.161250</td>\n",
       "      <td>23.296296</td>\n",
       "      <td>18.018519</td>\n",
       "      <td>0.774519</td>\n",
       "      <td>243.240741</td>\n",
       "      <td>106.888889</td>\n",
       "      <td>9.611111</td>\n",
       "      <td>18.814815</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>41.962963</td>\n",
       "      <td>6.870370</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.962963</td>\n",
       "      <td>22.574074</td>\n",
       "      <td>4.185185</td>\n",
       "      <td>31.240741</td>\n",
       "      <td>52.314815</td>\n",
       "      <td>27.370370</td>\n",
       "      <td>54.740741</td>\n",
       "      <td>31.407407</td>\n",
       "      <td>11.685185</td>\n",
       "      <td>0.370519</td>\n",
       "      <td>35.055556</td>\n",
       "      <td>83.722222</td>\n",
       "      <td>39.055556</td>\n",
       "      <td>0.467889</td>\n",
       "      <td>0.623773</td>\n",
       "      <td>0.376227</td>\n",
       "      <td>0.700599</td>\n",
       "      <td>0.299401</td>\n",
       "      <td>0.515696</td>\n",
       "      <td>0.324831</td>\n",
       "      <td>0.159474</td>\n",
       "      <td>22.203704</td>\n",
       "      <td>17.092593</td>\n",
       "      <td>0.770944</td>\n",
       "      <td>243.240741</td>\n",
       "      <td>8.037037</td>\n",
       "      <td>111.555556</td>\n",
       "      <td>9.277778</td>\n",
       "      <td>21.037037</td>\n",
       "      <td>-4.666667</td>\n",
       "      <td>40.518519</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>15.092593</td>\n",
       "      <td>54</td>\n",
       "      <td>13135</td>\n",
       "      <td>0.466</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.058</td>\n",
       "      <td>230</td>\n",
       "      <td>0.264</td>\n",
       "      <td>673</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MEM</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>WEST</td>\n",
       "      <td>52</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>25.384615</td>\n",
       "      <td>5.942308</td>\n",
       "      <td>35.057692</td>\n",
       "      <td>59.923077</td>\n",
       "      <td>32.211538</td>\n",
       "      <td>64.423077</td>\n",
       "      <td>32.865385</td>\n",
       "      <td>11.288462</td>\n",
       "      <td>0.344769</td>\n",
       "      <td>33.865385</td>\n",
       "      <td>92.788462</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>0.469250</td>\n",
       "      <td>0.645591</td>\n",
       "      <td>0.354409</td>\n",
       "      <td>0.740985</td>\n",
       "      <td>0.259015</td>\n",
       "      <td>0.554699</td>\n",
       "      <td>0.288633</td>\n",
       "      <td>0.156668</td>\n",
       "      <td>25.326923</td>\n",
       "      <td>18.211538</td>\n",
       "      <td>0.730615</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>112.769231</td>\n",
       "      <td>13.288462</td>\n",
       "      <td>20.173077</td>\n",
       "      <td>3.730769</td>\n",
       "      <td>48.346154</td>\n",
       "      <td>8.269231</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.403846</td>\n",
       "      <td>26.653846</td>\n",
       "      <td>5.403846</td>\n",
       "      <td>32.557692</td>\n",
       "      <td>53.788462</td>\n",
       "      <td>27.596154</td>\n",
       "      <td>55.192308</td>\n",
       "      <td>36.211538</td>\n",
       "      <td>12.980769</td>\n",
       "      <td>0.358942</td>\n",
       "      <td>38.942308</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>40.576923</td>\n",
       "      <td>0.452269</td>\n",
       "      <td>0.596620</td>\n",
       "      <td>0.403380</td>\n",
       "      <td>0.678676</td>\n",
       "      <td>0.321324</td>\n",
       "      <td>0.489966</td>\n",
       "      <td>0.344254</td>\n",
       "      <td>0.165780</td>\n",
       "      <td>23.692308</td>\n",
       "      <td>18.634615</td>\n",
       "      <td>0.788000</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>7.903846</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>11.096154</td>\n",
       "      <td>21.384615</td>\n",
       "      <td>-3.730769</td>\n",
       "      <td>43.653846</td>\n",
       "      <td>7.230769</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>52</td>\n",
       "      <td>12530</td>\n",
       "      <td>0.451</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.063</td>\n",
       "      <td>263</td>\n",
       "      <td>0.261</td>\n",
       "      <td>657</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BKN</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>EAST</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>0.607843</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>114.196078</td>\n",
       "      <td>26.196078</td>\n",
       "      <td>6.764706</td>\n",
       "      <td>32.647059</td>\n",
       "      <td>52.607843</td>\n",
       "      <td>30.372549</td>\n",
       "      <td>60.745098</td>\n",
       "      <td>31.745098</td>\n",
       "      <td>12.392157</td>\n",
       "      <td>0.390275</td>\n",
       "      <td>37.176471</td>\n",
       "      <td>84.352941</td>\n",
       "      <td>42.764706</td>\n",
       "      <td>0.508039</td>\n",
       "      <td>0.622814</td>\n",
       "      <td>0.377186</td>\n",
       "      <td>0.709872</td>\n",
       "      <td>0.290128</td>\n",
       "      <td>0.531926</td>\n",
       "      <td>0.324461</td>\n",
       "      <td>0.143613</td>\n",
       "      <td>20.686275</td>\n",
       "      <td>16.274510</td>\n",
       "      <td>0.787039</td>\n",
       "      <td>240.490196</td>\n",
       "      <td>112.058824</td>\n",
       "      <td>7.686275</td>\n",
       "      <td>21.764706</td>\n",
       "      <td>2.137255</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.352941</td>\n",
       "      <td>23.235294</td>\n",
       "      <td>3.784314</td>\n",
       "      <td>32.647059</td>\n",
       "      <td>55.392157</td>\n",
       "      <td>27.980392</td>\n",
       "      <td>55.960784</td>\n",
       "      <td>33.039216</td>\n",
       "      <td>12.274510</td>\n",
       "      <td>0.369078</td>\n",
       "      <td>36.823529</td>\n",
       "      <td>88.431373</td>\n",
       "      <td>40.254902</td>\n",
       "      <td>0.455686</td>\n",
       "      <td>0.624672</td>\n",
       "      <td>0.375328</td>\n",
       "      <td>0.693865</td>\n",
       "      <td>0.306135</td>\n",
       "      <td>0.500887</td>\n",
       "      <td>0.326688</td>\n",
       "      <td>0.172426</td>\n",
       "      <td>25.098039</td>\n",
       "      <td>19.274510</td>\n",
       "      <td>0.767373</td>\n",
       "      <td>240.490196</td>\n",
       "      <td>8.196078</td>\n",
       "      <td>114.196078</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>18.156863</td>\n",
       "      <td>-2.137255</td>\n",
       "      <td>44.313725</td>\n",
       "      <td>7.313725</td>\n",
       "      <td>13.960784</td>\n",
       "      <td>51</td>\n",
       "      <td>12265</td>\n",
       "      <td>0.455</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.053</td>\n",
       "      <td>221</td>\n",
       "      <td>0.297</td>\n",
       "      <td>705</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MIA</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>EAST</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>0.547170</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>108.377358</td>\n",
       "      <td>23.566038</td>\n",
       "      <td>2.924528</td>\n",
       "      <td>31.320755</td>\n",
       "      <td>51.320755</td>\n",
       "      <td>27.339623</td>\n",
       "      <td>54.679245</td>\n",
       "      <td>35.169811</td>\n",
       "      <td>11.849057</td>\n",
       "      <td>0.335830</td>\n",
       "      <td>35.547170</td>\n",
       "      <td>86.490566</td>\n",
       "      <td>39.188679</td>\n",
       "      <td>0.453264</td>\n",
       "      <td>0.592078</td>\n",
       "      <td>0.407922</td>\n",
       "      <td>0.694910</td>\n",
       "      <td>0.305090</td>\n",
       "      <td>0.503971</td>\n",
       "      <td>0.327034</td>\n",
       "      <td>0.168995</td>\n",
       "      <td>22.075472</td>\n",
       "      <td>18.150943</td>\n",
       "      <td>0.822245</td>\n",
       "      <td>241.415094</td>\n",
       "      <td>108.113208</td>\n",
       "      <td>9.943396</td>\n",
       "      <td>18.660377</td>\n",
       "      <td>0.264151</td>\n",
       "      <td>41.264151</td>\n",
       "      <td>8.528302</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.452830</td>\n",
       "      <td>25.169811</td>\n",
       "      <td>3.811321</td>\n",
       "      <td>33.641509</td>\n",
       "      <td>46.830189</td>\n",
       "      <td>26.207547</td>\n",
       "      <td>52.415094</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>13.169811</td>\n",
       "      <td>0.364906</td>\n",
       "      <td>39.509434</td>\n",
       "      <td>82.830189</td>\n",
       "      <td>39.377358</td>\n",
       "      <td>0.476415</td>\n",
       "      <td>0.565151</td>\n",
       "      <td>0.434849</td>\n",
       "      <td>0.661823</td>\n",
       "      <td>0.338177</td>\n",
       "      <td>0.485370</td>\n",
       "      <td>0.364750</td>\n",
       "      <td>0.149880</td>\n",
       "      <td>20.905660</td>\n",
       "      <td>16.188679</td>\n",
       "      <td>0.766151</td>\n",
       "      <td>241.415094</td>\n",
       "      <td>8.150943</td>\n",
       "      <td>108.377358</td>\n",
       "      <td>8.622642</td>\n",
       "      <td>19.716981</td>\n",
       "      <td>-0.264151</td>\n",
       "      <td>42.264151</td>\n",
       "      <td>6.886792</td>\n",
       "      <td>16.528302</td>\n",
       "      <td>53</td>\n",
       "      <td>12795</td>\n",
       "      <td>0.475</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.046</td>\n",
       "      <td>190</td>\n",
       "      <td>0.238</td>\n",
       "      <td>633</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SAC</td>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>WEST</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>119.500000</td>\n",
       "      <td>26.940000</td>\n",
       "      <td>3.260000</td>\n",
       "      <td>33.060000</td>\n",
       "      <td>51.160000</td>\n",
       "      <td>29.880000</td>\n",
       "      <td>59.760000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>13.380000</td>\n",
       "      <td>0.367200</td>\n",
       "      <td>40.140000</td>\n",
       "      <td>87.560000</td>\n",
       "      <td>43.260000</td>\n",
       "      <td>0.494540</td>\n",
       "      <td>0.583667</td>\n",
       "      <td>0.416333</td>\n",
       "      <td>0.689504</td>\n",
       "      <td>0.310496</td>\n",
       "      <td>0.500991</td>\n",
       "      <td>0.334624</td>\n",
       "      <td>0.164385</td>\n",
       "      <td>24.760000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>0.799500</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>116.360000</td>\n",
       "      <td>8.960000</td>\n",
       "      <td>19.940000</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>42.020000</td>\n",
       "      <td>6.880000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>25.460000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>32.360000</td>\n",
       "      <td>57.160000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>31.840000</td>\n",
       "      <td>11.460000</td>\n",
       "      <td>0.362200</td>\n",
       "      <td>34.380000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>43.560000</td>\n",
       "      <td>0.490440</td>\n",
       "      <td>0.642228</td>\n",
       "      <td>0.357772</td>\n",
       "      <td>0.734242</td>\n",
       "      <td>0.265758</td>\n",
       "      <td>0.551262</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.152309</td>\n",
       "      <td>22.460000</td>\n",
       "      <td>17.780000</td>\n",
       "      <td>0.786740</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>7.980000</td>\n",
       "      <td>119.500000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>21.240000</td>\n",
       "      <td>-3.140000</td>\n",
       "      <td>41.960000</td>\n",
       "      <td>7.380000</td>\n",
       "      <td>14.320000</td>\n",
       "      <td>50</td>\n",
       "      <td>12050</td>\n",
       "      <td>0.489</td>\n",
       "      <td>13.4</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.054</td>\n",
       "      <td>220</td>\n",
       "      <td>0.279</td>\n",
       "      <td>745</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LAC</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>WEST</td>\n",
       "      <td>55</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>0.527273</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>110.545455</td>\n",
       "      <td>22.727273</td>\n",
       "      <td>4.636364</td>\n",
       "      <td>33.727273</td>\n",
       "      <td>52.072727</td>\n",
       "      <td>27.381818</td>\n",
       "      <td>54.763636</td>\n",
       "      <td>33.490909</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.374236</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>85.563636</td>\n",
       "      <td>39.981818</td>\n",
       "      <td>0.469455</td>\n",
       "      <td>0.607243</td>\n",
       "      <td>0.392757</td>\n",
       "      <td>0.684903</td>\n",
       "      <td>0.315097</td>\n",
       "      <td>0.497907</td>\n",
       "      <td>0.338691</td>\n",
       "      <td>0.163402</td>\n",
       "      <td>23.345455</td>\n",
       "      <td>17.981818</td>\n",
       "      <td>0.766600</td>\n",
       "      <td>240.909091</td>\n",
       "      <td>110.454545</td>\n",
       "      <td>10.036364</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>43.763636</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.363636</td>\n",
       "      <td>24.181818</td>\n",
       "      <td>4.327273</td>\n",
       "      <td>33.018182</td>\n",
       "      <td>53.836364</td>\n",
       "      <td>28.872727</td>\n",
       "      <td>57.745455</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>11.836364</td>\n",
       "      <td>0.353873</td>\n",
       "      <td>35.509091</td>\n",
       "      <td>87.236364</td>\n",
       "      <td>40.709091</td>\n",
       "      <td>0.467691</td>\n",
       "      <td>0.615249</td>\n",
       "      <td>0.384751</td>\n",
       "      <td>0.709643</td>\n",
       "      <td>0.290357</td>\n",
       "      <td>0.524336</td>\n",
       "      <td>0.318495</td>\n",
       "      <td>0.157170</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>0.761945</td>\n",
       "      <td>240.909091</td>\n",
       "      <td>8.072727</td>\n",
       "      <td>110.545455</td>\n",
       "      <td>10.054545</td>\n",
       "      <td>19.145455</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>43.072727</td>\n",
       "      <td>7.709091</td>\n",
       "      <td>12.909091</td>\n",
       "      <td>55</td>\n",
       "      <td>13250</td>\n",
       "      <td>0.467</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.057</td>\n",
       "      <td>246</td>\n",
       "      <td>0.259</td>\n",
       "      <td>706</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NYK</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>EAST</td>\n",
       "      <td>53</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>114.264151</td>\n",
       "      <td>22.528302</td>\n",
       "      <td>4.113208</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>55.283019</td>\n",
       "      <td>29.396226</td>\n",
       "      <td>58.792453</td>\n",
       "      <td>34.735849</td>\n",
       "      <td>11.886792</td>\n",
       "      <td>0.341453</td>\n",
       "      <td>35.660377</td>\n",
       "      <td>90.018868</td>\n",
       "      <td>41.283019</td>\n",
       "      <td>0.459509</td>\n",
       "      <td>0.612658</td>\n",
       "      <td>0.387342</td>\n",
       "      <td>0.708618</td>\n",
       "      <td>0.291382</td>\n",
       "      <td>0.514318</td>\n",
       "      <td>0.311514</td>\n",
       "      <td>0.174167</td>\n",
       "      <td>25.547170</td>\n",
       "      <td>19.811321</td>\n",
       "      <td>0.771585</td>\n",
       "      <td>243.773585</td>\n",
       "      <td>112.547170</td>\n",
       "      <td>13.188679</td>\n",
       "      <td>20.471698</td>\n",
       "      <td>1.716981</td>\n",
       "      <td>47.188679</td>\n",
       "      <td>6.320755</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.113208</td>\n",
       "      <td>24.830189</td>\n",
       "      <td>4.584906</td>\n",
       "      <td>32.509434</td>\n",
       "      <td>51.301887</td>\n",
       "      <td>27.264151</td>\n",
       "      <td>54.528302</td>\n",
       "      <td>37.924528</td>\n",
       "      <td>13.245283</td>\n",
       "      <td>0.347547</td>\n",
       "      <td>39.735849</td>\n",
       "      <td>89.226415</td>\n",
       "      <td>40.509434</td>\n",
       "      <td>0.454887</td>\n",
       "      <td>0.573926</td>\n",
       "      <td>0.426074</td>\n",
       "      <td>0.672377</td>\n",
       "      <td>0.327623</td>\n",
       "      <td>0.485248</td>\n",
       "      <td>0.352396</td>\n",
       "      <td>0.162356</td>\n",
       "      <td>23.773585</td>\n",
       "      <td>18.283019</td>\n",
       "      <td>0.773679</td>\n",
       "      <td>243.773585</td>\n",
       "      <td>8.169811</td>\n",
       "      <td>114.264151</td>\n",
       "      <td>11.264151</td>\n",
       "      <td>21.150943</td>\n",
       "      <td>-1.716981</td>\n",
       "      <td>43.773585</td>\n",
       "      <td>6.018868</td>\n",
       "      <td>12.905660</td>\n",
       "      <td>53</td>\n",
       "      <td>12920</td>\n",
       "      <td>0.454</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.047</td>\n",
       "      <td>206</td>\n",
       "      <td>0.251</td>\n",
       "      <td>663</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MIN</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>WEST</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>115.148148</td>\n",
       "      <td>25.296296</td>\n",
       "      <td>5.351852</td>\n",
       "      <td>32.425926</td>\n",
       "      <td>53.444444</td>\n",
       "      <td>30.796296</td>\n",
       "      <td>61.592593</td>\n",
       "      <td>33.296296</td>\n",
       "      <td>11.777778</td>\n",
       "      <td>0.353870</td>\n",
       "      <td>35.333333</td>\n",
       "      <td>86.740741</td>\n",
       "      <td>42.574074</td>\n",
       "      <td>0.493167</td>\n",
       "      <td>0.616193</td>\n",
       "      <td>0.383807</td>\n",
       "      <td>0.724332</td>\n",
       "      <td>0.275668</td>\n",
       "      <td>0.536826</td>\n",
       "      <td>0.304462</td>\n",
       "      <td>0.158712</td>\n",
       "      <td>23.740741</td>\n",
       "      <td>18.222222</td>\n",
       "      <td>0.763481</td>\n",
       "      <td>241.388889</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>9.018519</td>\n",
       "      <td>21.944444</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>41.444444</td>\n",
       "      <td>8.277778</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.777778</td>\n",
       "      <td>25.425926</td>\n",
       "      <td>3.981481</td>\n",
       "      <td>32.944444</td>\n",
       "      <td>53.277778</td>\n",
       "      <td>28.277778</td>\n",
       "      <td>56.555556</td>\n",
       "      <td>35.444444</td>\n",
       "      <td>12.981481</td>\n",
       "      <td>0.369130</td>\n",
       "      <td>38.944444</td>\n",
       "      <td>88.722222</td>\n",
       "      <td>41.259259</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>0.599643</td>\n",
       "      <td>0.400357</td>\n",
       "      <td>0.682711</td>\n",
       "      <td>0.317289</td>\n",
       "      <td>0.492509</td>\n",
       "      <td>0.337456</td>\n",
       "      <td>0.170034</td>\n",
       "      <td>25.962963</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.754000</td>\n",
       "      <td>241.388889</td>\n",
       "      <td>8.018519</td>\n",
       "      <td>115.148148</td>\n",
       "      <td>11.703704</td>\n",
       "      <td>20.203704</td>\n",
       "      <td>-0.148148</td>\n",
       "      <td>44.648148</td>\n",
       "      <td>8.092593</td>\n",
       "      <td>16.037037</td>\n",
       "      <td>54</td>\n",
       "      <td>13035</td>\n",
       "      <td>0.465</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.059</td>\n",
       "      <td>252</td>\n",
       "      <td>0.281</td>\n",
       "      <td>731</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DAL</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>WEST</td>\n",
       "      <td>53</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>112.320755</td>\n",
       "      <td>21.886792</td>\n",
       "      <td>3.867925</td>\n",
       "      <td>30.811321</td>\n",
       "      <td>42.509434</td>\n",
       "      <td>24.415094</td>\n",
       "      <td>48.830189</td>\n",
       "      <td>40.509434</td>\n",
       "      <td>14.622642</td>\n",
       "      <td>0.361547</td>\n",
       "      <td>43.867925</td>\n",
       "      <td>83.018868</td>\n",
       "      <td>39.037736</td>\n",
       "      <td>0.471245</td>\n",
       "      <td>0.512014</td>\n",
       "      <td>0.487986</td>\n",
       "      <td>0.621425</td>\n",
       "      <td>0.378575</td>\n",
       "      <td>0.434297</td>\n",
       "      <td>0.390617</td>\n",
       "      <td>0.175086</td>\n",
       "      <td>26.415094</td>\n",
       "      <td>19.622642</td>\n",
       "      <td>0.733170</td>\n",
       "      <td>243.301887</td>\n",
       "      <td>111.849057</td>\n",
       "      <td>7.735849</td>\n",
       "      <td>20.924528</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>38.547170</td>\n",
       "      <td>6.547170</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>12.207547</td>\n",
       "      <td>24.094340</td>\n",
       "      <td>4.056604</td>\n",
       "      <td>34.264151</td>\n",
       "      <td>53.396226</td>\n",
       "      <td>29.452830</td>\n",
       "      <td>58.905660</td>\n",
       "      <td>31.264151</td>\n",
       "      <td>11.132075</td>\n",
       "      <td>0.355755</td>\n",
       "      <td>33.396226</td>\n",
       "      <td>84.660377</td>\n",
       "      <td>40.584906</td>\n",
       "      <td>0.480566</td>\n",
       "      <td>0.631157</td>\n",
       "      <td>0.368843</td>\n",
       "      <td>0.725758</td>\n",
       "      <td>0.274242</td>\n",
       "      <td>0.529194</td>\n",
       "      <td>0.296230</td>\n",
       "      <td>0.174576</td>\n",
       "      <td>25.056604</td>\n",
       "      <td>19.547170</td>\n",
       "      <td>0.779981</td>\n",
       "      <td>243.301887</td>\n",
       "      <td>8.207547</td>\n",
       "      <td>112.320755</td>\n",
       "      <td>9.867925</td>\n",
       "      <td>22.943396</td>\n",
       "      <td>-0.471698</td>\n",
       "      <td>44.132075</td>\n",
       "      <td>6.226415</td>\n",
       "      <td>13.679245</td>\n",
       "      <td>53</td>\n",
       "      <td>12895</td>\n",
       "      <td>0.479</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.053</td>\n",
       "      <td>216</td>\n",
       "      <td>0.269</td>\n",
       "      <td>701</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PHX</td>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>WEST</td>\n",
       "      <td>53</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>0.509434</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>112.679245</td>\n",
       "      <td>26.924528</td>\n",
       "      <td>5.037736</td>\n",
       "      <td>31.905660</td>\n",
       "      <td>56.754717</td>\n",
       "      <td>28.811321</td>\n",
       "      <td>57.622642</td>\n",
       "      <td>33.037736</td>\n",
       "      <td>12.679245</td>\n",
       "      <td>0.381943</td>\n",
       "      <td>38.037736</td>\n",
       "      <td>89.792453</td>\n",
       "      <td>41.490566</td>\n",
       "      <td>0.462094</td>\n",
       "      <td>0.631944</td>\n",
       "      <td>0.368056</td>\n",
       "      <td>0.694513</td>\n",
       "      <td>0.305487</td>\n",
       "      <td>0.513426</td>\n",
       "      <td>0.335626</td>\n",
       "      <td>0.150949</td>\n",
       "      <td>21.396226</td>\n",
       "      <td>17.018868</td>\n",
       "      <td>0.793981</td>\n",
       "      <td>241.886792</td>\n",
       "      <td>111.849057</td>\n",
       "      <td>11.962264</td>\n",
       "      <td>20.867925</td>\n",
       "      <td>0.830189</td>\n",
       "      <td>43.867925</td>\n",
       "      <td>6.924528</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.207547</td>\n",
       "      <td>23.566038</td>\n",
       "      <td>4.188679</td>\n",
       "      <td>32.735849</td>\n",
       "      <td>54.886792</td>\n",
       "      <td>29.113208</td>\n",
       "      <td>58.226415</td>\n",
       "      <td>31.188679</td>\n",
       "      <td>11.358491</td>\n",
       "      <td>0.360868</td>\n",
       "      <td>34.075472</td>\n",
       "      <td>86.075472</td>\n",
       "      <td>40.471698</td>\n",
       "      <td>0.470906</td>\n",
       "      <td>0.636455</td>\n",
       "      <td>0.363545</td>\n",
       "      <td>0.716524</td>\n",
       "      <td>0.283476</td>\n",
       "      <td>0.519972</td>\n",
       "      <td>0.304134</td>\n",
       "      <td>0.175894</td>\n",
       "      <td>24.792453</td>\n",
       "      <td>19.547170</td>\n",
       "      <td>0.790717</td>\n",
       "      <td>241.886792</td>\n",
       "      <td>8.188679</td>\n",
       "      <td>112.679245</td>\n",
       "      <td>10.415094</td>\n",
       "      <td>20.245283</td>\n",
       "      <td>-0.830189</td>\n",
       "      <td>43.150943</td>\n",
       "      <td>7.471698</td>\n",
       "      <td>14.811321</td>\n",
       "      <td>53</td>\n",
       "      <td>12820</td>\n",
       "      <td>0.470</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.054</td>\n",
       "      <td>221</td>\n",
       "      <td>0.285</td>\n",
       "      <td>734</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UTA</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>WEST</td>\n",
       "      <td>53</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>0.509434</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>117.754717</td>\n",
       "      <td>25.924528</td>\n",
       "      <td>4.905660</td>\n",
       "      <td>32.716981</td>\n",
       "      <td>49.377358</td>\n",
       "      <td>27.641509</td>\n",
       "      <td>55.283019</td>\n",
       "      <td>39.603774</td>\n",
       "      <td>14.566038</td>\n",
       "      <td>0.369170</td>\n",
       "      <td>43.698113</td>\n",
       "      <td>88.981132</td>\n",
       "      <td>42.207547</td>\n",
       "      <td>0.475038</td>\n",
       "      <td>0.554202</td>\n",
       "      <td>0.445798</td>\n",
       "      <td>0.653671</td>\n",
       "      <td>0.346329</td>\n",
       "      <td>0.470416</td>\n",
       "      <td>0.370682</td>\n",
       "      <td>0.158902</td>\n",
       "      <td>23.886792</td>\n",
       "      <td>18.773585</td>\n",
       "      <td>0.777038</td>\n",
       "      <td>241.415094</td>\n",
       "      <td>116.528302</td>\n",
       "      <td>11.641509</td>\n",
       "      <td>20.735849</td>\n",
       "      <td>1.226415</td>\n",
       "      <td>44.358491</td>\n",
       "      <td>6.773585</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.207547</td>\n",
       "      <td>23.754717</td>\n",
       "      <td>4.754717</td>\n",
       "      <td>31.905660</td>\n",
       "      <td>58.358491</td>\n",
       "      <td>31.792453</td>\n",
       "      <td>63.584906</td>\n",
       "      <td>31.622642</td>\n",
       "      <td>11.415094</td>\n",
       "      <td>0.354830</td>\n",
       "      <td>34.245283</td>\n",
       "      <td>89.981132</td>\n",
       "      <td>43.207547</td>\n",
       "      <td>0.482038</td>\n",
       "      <td>0.646846</td>\n",
       "      <td>0.353154</td>\n",
       "      <td>0.733780</td>\n",
       "      <td>0.266220</td>\n",
       "      <td>0.546459</td>\n",
       "      <td>0.292437</td>\n",
       "      <td>0.161103</td>\n",
       "      <td>24.094340</td>\n",
       "      <td>18.698113</td>\n",
       "      <td>0.778189</td>\n",
       "      <td>241.415094</td>\n",
       "      <td>8.509434</td>\n",
       "      <td>117.754717</td>\n",
       "      <td>11.339623</td>\n",
       "      <td>20.981132</td>\n",
       "      <td>-1.226415</td>\n",
       "      <td>43.245283</td>\n",
       "      <td>7.660377</td>\n",
       "      <td>13.641509</td>\n",
       "      <td>53</td>\n",
       "      <td>12795</td>\n",
       "      <td>0.480</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.069</td>\n",
       "      <td>285</td>\n",
       "      <td>0.304</td>\n",
       "      <td>789</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NOP</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>EAST</td>\n",
       "      <td>53</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>0.490566</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>114.792453</td>\n",
       "      <td>25.830189</td>\n",
       "      <td>4.339623</td>\n",
       "      <td>33.150943</td>\n",
       "      <td>57.679245</td>\n",
       "      <td>31.264151</td>\n",
       "      <td>62.528302</td>\n",
       "      <td>30.584906</td>\n",
       "      <td>10.849057</td>\n",
       "      <td>0.351019</td>\n",
       "      <td>32.547170</td>\n",
       "      <td>88.264151</td>\n",
       "      <td>42.113208</td>\n",
       "      <td>0.478547</td>\n",
       "      <td>0.652209</td>\n",
       "      <td>0.347791</td>\n",
       "      <td>0.741168</td>\n",
       "      <td>0.258832</td>\n",
       "      <td>0.545310</td>\n",
       "      <td>0.283283</td>\n",
       "      <td>0.171407</td>\n",
       "      <td>25.283019</td>\n",
       "      <td>19.716981</td>\n",
       "      <td>0.778245</td>\n",
       "      <td>242.830189</td>\n",
       "      <td>113.603774</td>\n",
       "      <td>11.075472</td>\n",
       "      <td>20.660377</td>\n",
       "      <td>1.188679</td>\n",
       "      <td>44.226415</td>\n",
       "      <td>8.547170</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.207547</td>\n",
       "      <td>25.056604</td>\n",
       "      <td>5.075472</td>\n",
       "      <td>32.433962</td>\n",
       "      <td>51.320755</td>\n",
       "      <td>29.037736</td>\n",
       "      <td>58.075472</td>\n",
       "      <td>36.150943</td>\n",
       "      <td>12.433962</td>\n",
       "      <td>0.341679</td>\n",
       "      <td>37.301887</td>\n",
       "      <td>87.471698</td>\n",
       "      <td>41.471698</td>\n",
       "      <td>0.475170</td>\n",
       "      <td>0.584904</td>\n",
       "      <td>0.415096</td>\n",
       "      <td>0.697899</td>\n",
       "      <td>0.302101</td>\n",
       "      <td>0.512269</td>\n",
       "      <td>0.327484</td>\n",
       "      <td>0.160246</td>\n",
       "      <td>23.358491</td>\n",
       "      <td>18.226415</td>\n",
       "      <td>0.777377</td>\n",
       "      <td>242.830189</td>\n",
       "      <td>7.981132</td>\n",
       "      <td>114.792453</td>\n",
       "      <td>9.679245</td>\n",
       "      <td>20.773585</td>\n",
       "      <td>-1.188679</td>\n",
       "      <td>42.113208</td>\n",
       "      <td>7.471698</td>\n",
       "      <td>15.377358</td>\n",
       "      <td>53</td>\n",
       "      <td>12870</td>\n",
       "      <td>0.474</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.061</td>\n",
       "      <td>264</td>\n",
       "      <td>0.262</td>\n",
       "      <td>736</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>EAST</td>\n",
       "      <td>52</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>116.480769</td>\n",
       "      <td>24.384615</td>\n",
       "      <td>4.846154</td>\n",
       "      <td>33.230769</td>\n",
       "      <td>61.134615</td>\n",
       "      <td>33.269231</td>\n",
       "      <td>66.538462</td>\n",
       "      <td>30.615385</td>\n",
       "      <td>10.730769</td>\n",
       "      <td>0.352365</td>\n",
       "      <td>32.192308</td>\n",
       "      <td>91.750000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.481231</td>\n",
       "      <td>0.665119</td>\n",
       "      <td>0.334881</td>\n",
       "      <td>0.754055</td>\n",
       "      <td>0.245945</td>\n",
       "      <td>0.570771</td>\n",
       "      <td>0.276424</td>\n",
       "      <td>0.152805</td>\n",
       "      <td>21.692308</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>0.819019</td>\n",
       "      <td>242.403846</td>\n",
       "      <td>116.480769</td>\n",
       "      <td>10.307692</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.538462</td>\n",
       "      <td>6.961538</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>12.846154</td>\n",
       "      <td>25.615385</td>\n",
       "      <td>5.019231</td>\n",
       "      <td>34.519231</td>\n",
       "      <td>56.653846</td>\n",
       "      <td>31.230769</td>\n",
       "      <td>62.461538</td>\n",
       "      <td>33.230769</td>\n",
       "      <td>11.576923</td>\n",
       "      <td>0.345712</td>\n",
       "      <td>34.730769</td>\n",
       "      <td>89.884615</td>\n",
       "      <td>42.807692</td>\n",
       "      <td>0.478058</td>\n",
       "      <td>0.629932</td>\n",
       "      <td>0.370068</td>\n",
       "      <td>0.731125</td>\n",
       "      <td>0.268875</td>\n",
       "      <td>0.537699</td>\n",
       "      <td>0.295292</td>\n",
       "      <td>0.167010</td>\n",
       "      <td>23.942308</td>\n",
       "      <td>19.288462</td>\n",
       "      <td>0.802135</td>\n",
       "      <td>242.403846</td>\n",
       "      <td>8.076923</td>\n",
       "      <td>116.480769</td>\n",
       "      <td>11.038462</td>\n",
       "      <td>19.903846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.557692</td>\n",
       "      <td>7.307692</td>\n",
       "      <td>14.634615</td>\n",
       "      <td>52</td>\n",
       "      <td>12605</td>\n",
       "      <td>0.476</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.070</td>\n",
       "      <td>297</td>\n",
       "      <td>0.285</td>\n",
       "      <td>760</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>WEST</td>\n",
       "      <td>52</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>29.384615</td>\n",
       "      <td>3.826923</td>\n",
       "      <td>34.307692</td>\n",
       "      <td>46.903846</td>\n",
       "      <td>26.192308</td>\n",
       "      <td>52.384615</td>\n",
       "      <td>43.134615</td>\n",
       "      <td>16.442308</td>\n",
       "      <td>0.381173</td>\n",
       "      <td>49.326923</td>\n",
       "      <td>90.038462</td>\n",
       "      <td>42.634615</td>\n",
       "      <td>0.474269</td>\n",
       "      <td>0.520180</td>\n",
       "      <td>0.479820</td>\n",
       "      <td>0.613448</td>\n",
       "      <td>0.386552</td>\n",
       "      <td>0.443728</td>\n",
       "      <td>0.416943</td>\n",
       "      <td>0.139329</td>\n",
       "      <td>20.596154</td>\n",
       "      <td>16.288462</td>\n",
       "      <td>0.789942</td>\n",
       "      <td>242.403846</td>\n",
       "      <td>118.346154</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>22.153846</td>\n",
       "      <td>-0.346154</td>\n",
       "      <td>44.307692</td>\n",
       "      <td>6.980769</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>16.653846</td>\n",
       "      <td>25.769231</td>\n",
       "      <td>4.211538</td>\n",
       "      <td>33.596154</td>\n",
       "      <td>54.826923</td>\n",
       "      <td>29.653846</td>\n",
       "      <td>59.307692</td>\n",
       "      <td>35.807692</td>\n",
       "      <td>12.980769</td>\n",
       "      <td>0.366692</td>\n",
       "      <td>38.942308</td>\n",
       "      <td>90.634615</td>\n",
       "      <td>42.634615</td>\n",
       "      <td>0.472212</td>\n",
       "      <td>0.604588</td>\n",
       "      <td>0.395412</td>\n",
       "      <td>0.695415</td>\n",
       "      <td>0.304585</td>\n",
       "      <td>0.502412</td>\n",
       "      <td>0.327382</td>\n",
       "      <td>0.170207</td>\n",
       "      <td>26.096154</td>\n",
       "      <td>20.096154</td>\n",
       "      <td>0.769577</td>\n",
       "      <td>242.403846</td>\n",
       "      <td>8.307692</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>10.634615</td>\n",
       "      <td>18.826923</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>44.230769</td>\n",
       "      <td>7.865385</td>\n",
       "      <td>14.769231</td>\n",
       "      <td>52</td>\n",
       "      <td>12605</td>\n",
       "      <td>0.470</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.053</td>\n",
       "      <td>230</td>\n",
       "      <td>0.264</td>\n",
       "      <td>701</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LAL</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>WEST</td>\n",
       "      <td>53</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>116.943396</td>\n",
       "      <td>25.094340</td>\n",
       "      <td>4.622642</td>\n",
       "      <td>35.584906</td>\n",
       "      <td>58.698113</td>\n",
       "      <td>32.716981</td>\n",
       "      <td>65.433962</td>\n",
       "      <td>31.113208</td>\n",
       "      <td>10.452830</td>\n",
       "      <td>0.338509</td>\n",
       "      <td>31.358491</td>\n",
       "      <td>89.811321</td>\n",
       "      <td>43.169811</td>\n",
       "      <td>0.481925</td>\n",
       "      <td>0.654295</td>\n",
       "      <td>0.345705</td>\n",
       "      <td>0.758058</td>\n",
       "      <td>0.241942</td>\n",
       "      <td>0.560727</td>\n",
       "      <td>0.266983</td>\n",
       "      <td>0.172290</td>\n",
       "      <td>25.830189</td>\n",
       "      <td>20.150943</td>\n",
       "      <td>0.775962</td>\n",
       "      <td>243.301887</td>\n",
       "      <td>118.132075</td>\n",
       "      <td>9.566038</td>\n",
       "      <td>18.490566</td>\n",
       "      <td>-1.188679</td>\n",
       "      <td>45.150943</td>\n",
       "      <td>6.566038</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.056604</td>\n",
       "      <td>25.679245</td>\n",
       "      <td>5.433962</td>\n",
       "      <td>34.471698</td>\n",
       "      <td>57.566038</td>\n",
       "      <td>31.735849</td>\n",
       "      <td>63.471698</td>\n",
       "      <td>36.207547</td>\n",
       "      <td>12.547170</td>\n",
       "      <td>0.342302</td>\n",
       "      <td>37.641509</td>\n",
       "      <td>93.773585</td>\n",
       "      <td>44.283019</td>\n",
       "      <td>0.473585</td>\n",
       "      <td>0.613099</td>\n",
       "      <td>0.386901</td>\n",
       "      <td>0.718353</td>\n",
       "      <td>0.281647</td>\n",
       "      <td>0.540470</td>\n",
       "      <td>0.315305</td>\n",
       "      <td>0.144225</td>\n",
       "      <td>22.245283</td>\n",
       "      <td>17.018868</td>\n",
       "      <td>0.759491</td>\n",
       "      <td>243.301887</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>116.943396</td>\n",
       "      <td>11.245283</td>\n",
       "      <td>21.283019</td>\n",
       "      <td>1.188679</td>\n",
       "      <td>45.716981</td>\n",
       "      <td>7.603774</td>\n",
       "      <td>12.849057</td>\n",
       "      <td>53</td>\n",
       "      <td>12895</td>\n",
       "      <td>0.472</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.052</td>\n",
       "      <td>228</td>\n",
       "      <td>0.252</td>\n",
       "      <td>733</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>POR</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>WEST</td>\n",
       "      <td>51</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>114.274510</td>\n",
       "      <td>24.215686</td>\n",
       "      <td>4.352941</td>\n",
       "      <td>32.156863</td>\n",
       "      <td>50.215686</td>\n",
       "      <td>27.803922</td>\n",
       "      <td>55.607843</td>\n",
       "      <td>34.176471</td>\n",
       "      <td>12.882353</td>\n",
       "      <td>0.376608</td>\n",
       "      <td>38.647059</td>\n",
       "      <td>84.392157</td>\n",
       "      <td>40.686275</td>\n",
       "      <td>0.482353</td>\n",
       "      <td>0.594308</td>\n",
       "      <td>0.405692</td>\n",
       "      <td>0.684468</td>\n",
       "      <td>0.315532</td>\n",
       "      <td>0.488947</td>\n",
       "      <td>0.335742</td>\n",
       "      <td>0.175311</td>\n",
       "      <td>25.313725</td>\n",
       "      <td>20.019608</td>\n",
       "      <td>0.792725</td>\n",
       "      <td>240.980392</td>\n",
       "      <td>113.705882</td>\n",
       "      <td>10.058824</td>\n",
       "      <td>20.784314</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>42.215686</td>\n",
       "      <td>6.372549</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.960784</td>\n",
       "      <td>25.705882</td>\n",
       "      <td>4.392157</td>\n",
       "      <td>30.882353</td>\n",
       "      <td>54.901961</td>\n",
       "      <td>29.960784</td>\n",
       "      <td>59.921569</td>\n",
       "      <td>32.137255</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>0.362412</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>87.039216</td>\n",
       "      <td>41.627451</td>\n",
       "      <td>0.479667</td>\n",
       "      <td>0.630392</td>\n",
       "      <td>0.369608</td>\n",
       "      <td>0.719526</td>\n",
       "      <td>0.280474</td>\n",
       "      <td>0.528262</td>\n",
       "      <td>0.306181</td>\n",
       "      <td>0.165557</td>\n",
       "      <td>24.235294</td>\n",
       "      <td>18.784314</td>\n",
       "      <td>0.770922</td>\n",
       "      <td>240.980392</td>\n",
       "      <td>8.019608</td>\n",
       "      <td>114.274510</td>\n",
       "      <td>10.372549</td>\n",
       "      <td>21.392157</td>\n",
       "      <td>-0.568627</td>\n",
       "      <td>41.254902</td>\n",
       "      <td>7.686275</td>\n",
       "      <td>13.392157</td>\n",
       "      <td>51</td>\n",
       "      <td>12290</td>\n",
       "      <td>0.478</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.074</td>\n",
       "      <td>280</td>\n",
       "      <td>0.283</td>\n",
       "      <td>728</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>IND</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>EAST</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>115.056604</td>\n",
       "      <td>26.358491</td>\n",
       "      <td>5.943396</td>\n",
       "      <td>31.981132</td>\n",
       "      <td>51.396226</td>\n",
       "      <td>27.283019</td>\n",
       "      <td>54.566038</td>\n",
       "      <td>38.264151</td>\n",
       "      <td>13.849057</td>\n",
       "      <td>0.360868</td>\n",
       "      <td>41.547170</td>\n",
       "      <td>89.660377</td>\n",
       "      <td>41.132075</td>\n",
       "      <td>0.460396</td>\n",
       "      <td>0.571578</td>\n",
       "      <td>0.428422</td>\n",
       "      <td>0.662800</td>\n",
       "      <td>0.337200</td>\n",
       "      <td>0.475253</td>\n",
       "      <td>0.360424</td>\n",
       "      <td>0.164323</td>\n",
       "      <td>23.660377</td>\n",
       "      <td>18.943396</td>\n",
       "      <td>0.797943</td>\n",
       "      <td>240.471698</td>\n",
       "      <td>117.094340</td>\n",
       "      <td>10.132075</td>\n",
       "      <td>21.547170</td>\n",
       "      <td>-2.037736</td>\n",
       "      <td>42.113208</td>\n",
       "      <td>8.018868</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.358491</td>\n",
       "      <td>26.339623</td>\n",
       "      <td>5.207547</td>\n",
       "      <td>33.433962</td>\n",
       "      <td>54.830189</td>\n",
       "      <td>29.433962</td>\n",
       "      <td>58.867925</td>\n",
       "      <td>33.867925</td>\n",
       "      <td>12.584906</td>\n",
       "      <td>0.370585</td>\n",
       "      <td>37.754717</td>\n",
       "      <td>88.698113</td>\n",
       "      <td>42.018868</td>\n",
       "      <td>0.475151</td>\n",
       "      <td>0.617348</td>\n",
       "      <td>0.382652</td>\n",
       "      <td>0.699422</td>\n",
       "      <td>0.300578</td>\n",
       "      <td>0.503953</td>\n",
       "      <td>0.320373</td>\n",
       "      <td>0.175674</td>\n",
       "      <td>25.584906</td>\n",
       "      <td>20.471698</td>\n",
       "      <td>0.802208</td>\n",
       "      <td>240.471698</td>\n",
       "      <td>8.150943</td>\n",
       "      <td>115.056604</td>\n",
       "      <td>12.169811</td>\n",
       "      <td>20.188679</td>\n",
       "      <td>2.037736</td>\n",
       "      <td>45.603774</td>\n",
       "      <td>7.905660</td>\n",
       "      <td>15.584906</td>\n",
       "      <td>53</td>\n",
       "      <td>12745</td>\n",
       "      <td>0.474</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.074</td>\n",
       "      <td>291</td>\n",
       "      <td>0.288</td>\n",
       "      <td>740</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>OKC</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>WEST</td>\n",
       "      <td>51</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>116.647059</td>\n",
       "      <td>24.411765</td>\n",
       "      <td>5.039216</td>\n",
       "      <td>32.647059</td>\n",
       "      <td>59.333333</td>\n",
       "      <td>31.235294</td>\n",
       "      <td>62.470588</td>\n",
       "      <td>33.411765</td>\n",
       "      <td>12.058824</td>\n",
       "      <td>0.362529</td>\n",
       "      <td>36.176471</td>\n",
       "      <td>92.745098</td>\n",
       "      <td>43.294118</td>\n",
       "      <td>0.468157</td>\n",
       "      <td>0.640582</td>\n",
       "      <td>0.359418</td>\n",
       "      <td>0.720123</td>\n",
       "      <td>0.279877</td>\n",
       "      <td>0.536307</td>\n",
       "      <td>0.308888</td>\n",
       "      <td>0.154805</td>\n",
       "      <td>22.627451</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.789706</td>\n",
       "      <td>242.941176</td>\n",
       "      <td>115.803922</td>\n",
       "      <td>11.588235</td>\n",
       "      <td>21.705882</td>\n",
       "      <td>0.843137</td>\n",
       "      <td>44.235294</td>\n",
       "      <td>8.156863</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.725490</td>\n",
       "      <td>25.098039</td>\n",
       "      <td>5.470588</td>\n",
       "      <td>35.019608</td>\n",
       "      <td>53.921569</td>\n",
       "      <td>28.980392</td>\n",
       "      <td>57.960784</td>\n",
       "      <td>35.294118</td>\n",
       "      <td>12.509804</td>\n",
       "      <td>0.353451</td>\n",
       "      <td>37.529412</td>\n",
       "      <td>89.215686</td>\n",
       "      <td>41.490196</td>\n",
       "      <td>0.465725</td>\n",
       "      <td>0.603078</td>\n",
       "      <td>0.396922</td>\n",
       "      <td>0.695632</td>\n",
       "      <td>0.304368</td>\n",
       "      <td>0.500957</td>\n",
       "      <td>0.324118</td>\n",
       "      <td>0.174925</td>\n",
       "      <td>25.980392</td>\n",
       "      <td>20.313725</td>\n",
       "      <td>0.782216</td>\n",
       "      <td>242.941176</td>\n",
       "      <td>8.235294</td>\n",
       "      <td>116.647059</td>\n",
       "      <td>12.431373</td>\n",
       "      <td>19.784314</td>\n",
       "      <td>-0.843137</td>\n",
       "      <td>47.450980</td>\n",
       "      <td>7.470588</td>\n",
       "      <td>16.686275</td>\n",
       "      <td>51</td>\n",
       "      <td>12390</td>\n",
       "      <td>0.465</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.062</td>\n",
       "      <td>244</td>\n",
       "      <td>0.300</td>\n",
       "      <td>733</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CHI</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>EAST</td>\n",
       "      <td>51</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>114.274510</td>\n",
       "      <td>24.313725</td>\n",
       "      <td>4.647059</td>\n",
       "      <td>33.960784</td>\n",
       "      <td>58.176471</td>\n",
       "      <td>31.960784</td>\n",
       "      <td>63.921569</td>\n",
       "      <td>29.019608</td>\n",
       "      <td>10.705882</td>\n",
       "      <td>0.363784</td>\n",
       "      <td>32.117647</td>\n",
       "      <td>87.196078</td>\n",
       "      <td>42.666667</td>\n",
       "      <td>0.490137</td>\n",
       "      <td>0.666153</td>\n",
       "      <td>0.333847</td>\n",
       "      <td>0.749482</td>\n",
       "      <td>0.250518</td>\n",
       "      <td>0.561058</td>\n",
       "      <td>0.279402</td>\n",
       "      <td>0.159540</td>\n",
       "      <td>22.411765</td>\n",
       "      <td>18.235294</td>\n",
       "      <td>0.808490</td>\n",
       "      <td>242.450980</td>\n",
       "      <td>114.078431</td>\n",
       "      <td>8.529412</td>\n",
       "      <td>19.745098</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>42.490196</td>\n",
       "      <td>7.588235</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.745098</td>\n",
       "      <td>26.117647</td>\n",
       "      <td>5.058824</td>\n",
       "      <td>34.058824</td>\n",
       "      <td>51.607843</td>\n",
       "      <td>28.549020</td>\n",
       "      <td>57.098039</td>\n",
       "      <td>36.607843</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.354353</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>88.215686</td>\n",
       "      <td>41.549020</td>\n",
       "      <td>0.473000</td>\n",
       "      <td>0.585110</td>\n",
       "      <td>0.414890</td>\n",
       "      <td>0.686959</td>\n",
       "      <td>0.313041</td>\n",
       "      <td>0.502493</td>\n",
       "      <td>0.340431</td>\n",
       "      <td>0.157076</td>\n",
       "      <td>23.137255</td>\n",
       "      <td>17.980392</td>\n",
       "      <td>0.777471</td>\n",
       "      <td>242.450980</td>\n",
       "      <td>8.235294</td>\n",
       "      <td>114.274510</td>\n",
       "      <td>9.843137</td>\n",
       "      <td>18.843137</td>\n",
       "      <td>-0.196078</td>\n",
       "      <td>43.901961</td>\n",
       "      <td>6.725490</td>\n",
       "      <td>14.843137</td>\n",
       "      <td>51</td>\n",
       "      <td>12365</td>\n",
       "      <td>0.471</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.065</td>\n",
       "      <td>267</td>\n",
       "      <td>0.264</td>\n",
       "      <td>691</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>WAS</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>EAST</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>5.420000</td>\n",
       "      <td>34.860000</td>\n",
       "      <td>55.260000</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>61.200000</td>\n",
       "      <td>31.920000</td>\n",
       "      <td>11.220000</td>\n",
       "      <td>0.349800</td>\n",
       "      <td>33.660000</td>\n",
       "      <td>87.180000</td>\n",
       "      <td>41.820000</td>\n",
       "      <td>0.481380</td>\n",
       "      <td>0.634831</td>\n",
       "      <td>0.365169</td>\n",
       "      <td>0.730168</td>\n",
       "      <td>0.269832</td>\n",
       "      <td>0.542624</td>\n",
       "      <td>0.297409</td>\n",
       "      <td>0.159966</td>\n",
       "      <td>23.340000</td>\n",
       "      <td>18.140000</td>\n",
       "      <td>0.778040</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>113.040000</td>\n",
       "      <td>9.520000</td>\n",
       "      <td>19.460000</td>\n",
       "      <td>-0.040000</td>\n",
       "      <td>44.380000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>23.560000</td>\n",
       "      <td>4.740000</td>\n",
       "      <td>32.940000</td>\n",
       "      <td>57.860000</td>\n",
       "      <td>30.220000</td>\n",
       "      <td>60.440000</td>\n",
       "      <td>32.400000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.350820</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>90.260000</td>\n",
       "      <td>41.720000</td>\n",
       "      <td>0.463940</td>\n",
       "      <td>0.640018</td>\n",
       "      <td>0.359982</td>\n",
       "      <td>0.720778</td>\n",
       "      <td>0.279222</td>\n",
       "      <td>0.536062</td>\n",
       "      <td>0.304825</td>\n",
       "      <td>0.159113</td>\n",
       "      <td>22.620000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.799200</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>10.620000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>43.560000</td>\n",
       "      <td>6.660000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>50</td>\n",
       "      <td>12050</td>\n",
       "      <td>0.462</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.058</td>\n",
       "      <td>222</td>\n",
       "      <td>0.300</td>\n",
       "      <td>723</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TOR</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>EAST</td>\n",
       "      <td>53</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>113.075472</td>\n",
       "      <td>23.226415</td>\n",
       "      <td>5.169811</td>\n",
       "      <td>29.943396</td>\n",
       "      <td>58.584906</td>\n",
       "      <td>30.415094</td>\n",
       "      <td>60.830189</td>\n",
       "      <td>32.377358</td>\n",
       "      <td>10.830189</td>\n",
       "      <td>0.336057</td>\n",
       "      <td>32.490566</td>\n",
       "      <td>90.962264</td>\n",
       "      <td>41.245283</td>\n",
       "      <td>0.454792</td>\n",
       "      <td>0.643790</td>\n",
       "      <td>0.356210</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>0.264174</td>\n",
       "      <td>0.538359</td>\n",
       "      <td>0.286486</td>\n",
       "      <td>0.175154</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>19.754717</td>\n",
       "      <td>0.788245</td>\n",
       "      <td>241.886792</td>\n",
       "      <td>112.641509</td>\n",
       "      <td>12.830189</td>\n",
       "      <td>20.283019</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>42.773585</td>\n",
       "      <td>9.245283</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>11.716981</td>\n",
       "      <td>26.075472</td>\n",
       "      <td>4.584906</td>\n",
       "      <td>33.245283</td>\n",
       "      <td>48.698113</td>\n",
       "      <td>27.849057</td>\n",
       "      <td>55.698113</td>\n",
       "      <td>34.094340</td>\n",
       "      <td>12.754717</td>\n",
       "      <td>0.372547</td>\n",
       "      <td>38.264151</td>\n",
       "      <td>82.792453</td>\n",
       "      <td>40.603774</td>\n",
       "      <td>0.491717</td>\n",
       "      <td>0.586859</td>\n",
       "      <td>0.413141</td>\n",
       "      <td>0.684930</td>\n",
       "      <td>0.315070</td>\n",
       "      <td>0.494429</td>\n",
       "      <td>0.338807</td>\n",
       "      <td>0.166764</td>\n",
       "      <td>23.339623</td>\n",
       "      <td>18.679245</td>\n",
       "      <td>0.802811</td>\n",
       "      <td>241.886792</td>\n",
       "      <td>7.962264</td>\n",
       "      <td>113.075472</td>\n",
       "      <td>9.509434</td>\n",
       "      <td>20.735849</td>\n",
       "      <td>-0.433962</td>\n",
       "      <td>42.754717</td>\n",
       "      <td>5.943396</td>\n",
       "      <td>16.566038</td>\n",
       "      <td>53</td>\n",
       "      <td>12820</td>\n",
       "      <td>0.490</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.070</td>\n",
       "      <td>272</td>\n",
       "      <td>0.271</td>\n",
       "      <td>698</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ORL</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>EAST</td>\n",
       "      <td>52</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>110.923077</td>\n",
       "      <td>22.576923</td>\n",
       "      <td>4.653846</td>\n",
       "      <td>32.923077</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>29.096154</td>\n",
       "      <td>58.192308</td>\n",
       "      <td>31.173077</td>\n",
       "      <td>10.865385</td>\n",
       "      <td>0.349577</td>\n",
       "      <td>32.596154</td>\n",
       "      <td>84.923077</td>\n",
       "      <td>39.961538</td>\n",
       "      <td>0.472308</td>\n",
       "      <td>0.631927</td>\n",
       "      <td>0.368073</td>\n",
       "      <td>0.727762</td>\n",
       "      <td>0.272238</td>\n",
       "      <td>0.526337</td>\n",
       "      <td>0.292208</td>\n",
       "      <td>0.181455</td>\n",
       "      <td>25.480769</td>\n",
       "      <td>20.134615</td>\n",
       "      <td>0.788365</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>114.346154</td>\n",
       "      <td>9.673077</td>\n",
       "      <td>20.365385</td>\n",
       "      <td>-3.423077</td>\n",
       "      <td>42.596154</td>\n",
       "      <td>6.961538</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.192308</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.942308</td>\n",
       "      <td>32.346154</td>\n",
       "      <td>49.807692</td>\n",
       "      <td>28.326923</td>\n",
       "      <td>56.653846</td>\n",
       "      <td>37.634615</td>\n",
       "      <td>13.134615</td>\n",
       "      <td>0.346173</td>\n",
       "      <td>39.403846</td>\n",
       "      <td>87.442308</td>\n",
       "      <td>41.461538</td>\n",
       "      <td>0.474519</td>\n",
       "      <td>0.569105</td>\n",
       "      <td>0.430895</td>\n",
       "      <td>0.680049</td>\n",
       "      <td>0.319951</td>\n",
       "      <td>0.494769</td>\n",
       "      <td>0.344064</td>\n",
       "      <td>0.161167</td>\n",
       "      <td>23.346154</td>\n",
       "      <td>18.288462</td>\n",
       "      <td>0.784423</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>8.288462</td>\n",
       "      <td>110.923077</td>\n",
       "      <td>9.769231</td>\n",
       "      <td>20.288462</td>\n",
       "      <td>3.423077</td>\n",
       "      <td>42.115385</td>\n",
       "      <td>7.884615</td>\n",
       "      <td>14.211538</td>\n",
       "      <td>52</td>\n",
       "      <td>12530</td>\n",
       "      <td>0.474</td>\n",
       "      <td>14.8</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.068</td>\n",
       "      <td>280</td>\n",
       "      <td>0.251</td>\n",
       "      <td>692</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CHA</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>EAST</td>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>0.283019</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>25.075472</td>\n",
       "      <td>4.924528</td>\n",
       "      <td>33.150943</td>\n",
       "      <td>59.320755</td>\n",
       "      <td>31.056604</td>\n",
       "      <td>62.113208</td>\n",
       "      <td>33.301887</td>\n",
       "      <td>10.716981</td>\n",
       "      <td>0.319321</td>\n",
       "      <td>32.150943</td>\n",
       "      <td>92.622642</td>\n",
       "      <td>41.773585</td>\n",
       "      <td>0.452547</td>\n",
       "      <td>0.640660</td>\n",
       "      <td>0.359340</td>\n",
       "      <td>0.744143</td>\n",
       "      <td>0.255857</td>\n",
       "      <td>0.556294</td>\n",
       "      <td>0.284446</td>\n",
       "      <td>0.159261</td>\n",
       "      <td>23.547170</td>\n",
       "      <td>17.735849</td>\n",
       "      <td>0.750019</td>\n",
       "      <td>242.830189</td>\n",
       "      <td>118.603774</td>\n",
       "      <td>12.169811</td>\n",
       "      <td>20.981132</td>\n",
       "      <td>-6.603774</td>\n",
       "      <td>45.320755</td>\n",
       "      <td>7.867925</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>13.792453</td>\n",
       "      <td>26.377358</td>\n",
       "      <td>5.886792</td>\n",
       "      <td>35.490566</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>30.037736</td>\n",
       "      <td>60.075472</td>\n",
       "      <td>35.471698</td>\n",
       "      <td>12.962264</td>\n",
       "      <td>0.361774</td>\n",
       "      <td>38.886792</td>\n",
       "      <td>89.471698</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>0.603196</td>\n",
       "      <td>0.396804</td>\n",
       "      <td>0.698089</td>\n",
       "      <td>0.301911</td>\n",
       "      <td>0.508698</td>\n",
       "      <td>0.325361</td>\n",
       "      <td>0.165941</td>\n",
       "      <td>24.886792</td>\n",
       "      <td>19.641509</td>\n",
       "      <td>0.795094</td>\n",
       "      <td>242.830189</td>\n",
       "      <td>8.169811</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>10.735849</td>\n",
       "      <td>20.660377</td>\n",
       "      <td>6.603774</td>\n",
       "      <td>46.226415</td>\n",
       "      <td>6.622642</td>\n",
       "      <td>14.962264</td>\n",
       "      <td>53</td>\n",
       "      <td>12870</td>\n",
       "      <td>0.481</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.063</td>\n",
       "      <td>250</td>\n",
       "      <td>0.268</td>\n",
       "      <td>702</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SAS</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>WEST</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>112.519231</td>\n",
       "      <td>27.153846</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.903846</td>\n",
       "      <td>60.615385</td>\n",
       "      <td>32.519231</td>\n",
       "      <td>65.038462</td>\n",
       "      <td>30.923077</td>\n",
       "      <td>10.615385</td>\n",
       "      <td>0.345346</td>\n",
       "      <td>31.846154</td>\n",
       "      <td>91.538462</td>\n",
       "      <td>43.134615</td>\n",
       "      <td>0.474038</td>\n",
       "      <td>0.661607</td>\n",
       "      <td>0.338393</td>\n",
       "      <td>0.753239</td>\n",
       "      <td>0.246761</td>\n",
       "      <td>0.580151</td>\n",
       "      <td>0.281485</td>\n",
       "      <td>0.138364</td>\n",
       "      <td>21.153846</td>\n",
       "      <td>15.634615</td>\n",
       "      <td>0.734135</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>122.250000</td>\n",
       "      <td>12.057692</td>\n",
       "      <td>19.826923</td>\n",
       "      <td>-9.730769</td>\n",
       "      <td>42.961538</td>\n",
       "      <td>6.826923</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>15.307692</td>\n",
       "      <td>26.615385</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>33.076923</td>\n",
       "      <td>58.269231</td>\n",
       "      <td>33.230769</td>\n",
       "      <td>66.461538</td>\n",
       "      <td>31.846154</td>\n",
       "      <td>12.769231</td>\n",
       "      <td>0.399212</td>\n",
       "      <td>38.307692</td>\n",
       "      <td>90.115385</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.512500</td>\n",
       "      <td>0.644362</td>\n",
       "      <td>0.355638</td>\n",
       "      <td>0.723208</td>\n",
       "      <td>0.276792</td>\n",
       "      <td>0.546114</td>\n",
       "      <td>0.309356</td>\n",
       "      <td>0.144529</td>\n",
       "      <td>22.711538</td>\n",
       "      <td>17.480769</td>\n",
       "      <td>0.766423</td>\n",
       "      <td>240.961538</td>\n",
       "      <td>8.076923</td>\n",
       "      <td>112.519231</td>\n",
       "      <td>10.442308</td>\n",
       "      <td>18.846154</td>\n",
       "      <td>9.730769</td>\n",
       "      <td>43.519231</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>14.019231</td>\n",
       "      <td>52</td>\n",
       "      <td>12530</td>\n",
       "      <td>0.510</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.069</td>\n",
       "      <td>283</td>\n",
       "      <td>0.297</td>\n",
       "      <td>814</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>HOU</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>WEST</td>\n",
       "      <td>51</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>0.254902</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>109.627451</td>\n",
       "      <td>22.274510</td>\n",
       "      <td>4.941176</td>\n",
       "      <td>33.823529</td>\n",
       "      <td>54.490196</td>\n",
       "      <td>28.666667</td>\n",
       "      <td>57.333333</td>\n",
       "      <td>33.980392</td>\n",
       "      <td>11.098039</td>\n",
       "      <td>0.320255</td>\n",
       "      <td>33.294118</td>\n",
       "      <td>88.470588</td>\n",
       "      <td>39.764706</td>\n",
       "      <td>0.449941</td>\n",
       "      <td>0.615189</td>\n",
       "      <td>0.384811</td>\n",
       "      <td>0.720699</td>\n",
       "      <td>0.279301</td>\n",
       "      <td>0.526388</td>\n",
       "      <td>0.300636</td>\n",
       "      <td>0.172976</td>\n",
       "      <td>25.313725</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.750745</td>\n",
       "      <td>240.980392</td>\n",
       "      <td>116.862745</td>\n",
       "      <td>13.372549</td>\n",
       "      <td>20.529412</td>\n",
       "      <td>-7.235294</td>\n",
       "      <td>47.196078</td>\n",
       "      <td>7.215686</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>17.117647</td>\n",
       "      <td>25.431373</td>\n",
       "      <td>6.039216</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>49.392157</td>\n",
       "      <td>27.490196</td>\n",
       "      <td>54.980392</td>\n",
       "      <td>39.215686</td>\n",
       "      <td>14.313725</td>\n",
       "      <td>0.368412</td>\n",
       "      <td>42.941176</td>\n",
       "      <td>88.607843</td>\n",
       "      <td>41.803922</td>\n",
       "      <td>0.474333</td>\n",
       "      <td>0.557548</td>\n",
       "      <td>0.442452</td>\n",
       "      <td>0.655555</td>\n",
       "      <td>0.344445</td>\n",
       "      <td>0.470840</td>\n",
       "      <td>0.366661</td>\n",
       "      <td>0.162499</td>\n",
       "      <td>24.078431</td>\n",
       "      <td>18.941176</td>\n",
       "      <td>0.782118</td>\n",
       "      <td>240.980392</td>\n",
       "      <td>8.215686</td>\n",
       "      <td>109.627451</td>\n",
       "      <td>10.921569</td>\n",
       "      <td>21.333333</td>\n",
       "      <td>7.235294</td>\n",
       "      <td>41.921569</td>\n",
       "      <td>8.980392</td>\n",
       "      <td>13.549020</td>\n",
       "      <td>51</td>\n",
       "      <td>12290</td>\n",
       "      <td>0.472</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.071</td>\n",
       "      <td>278</td>\n",
       "      <td>0.247</td>\n",
       "      <td>658</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DET</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>EAST</td>\n",
       "      <td>52</td>\n",
       "      <td>13</td>\n",
       "      <td>39</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>112.634615</td>\n",
       "      <td>22.634615</td>\n",
       "      <td>3.653846</td>\n",
       "      <td>31.384615</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>28.019231</td>\n",
       "      <td>56.038462</td>\n",
       "      <td>33.096154</td>\n",
       "      <td>11.826923</td>\n",
       "      <td>0.353808</td>\n",
       "      <td>35.480769</td>\n",
       "      <td>87.596154</td>\n",
       "      <td>39.846154</td>\n",
       "      <td>0.455058</td>\n",
       "      <td>0.621369</td>\n",
       "      <td>0.378631</td>\n",
       "      <td>0.702638</td>\n",
       "      <td>0.297362</td>\n",
       "      <td>0.499325</td>\n",
       "      <td>0.313542</td>\n",
       "      <td>0.187133</td>\n",
       "      <td>27.288462</td>\n",
       "      <td>21.115385</td>\n",
       "      <td>0.776423</td>\n",
       "      <td>241.442308</td>\n",
       "      <td>120.057692</td>\n",
       "      <td>11.038462</td>\n",
       "      <td>22.269231</td>\n",
       "      <td>-7.423077</td>\n",
       "      <td>42.423077</td>\n",
       "      <td>7.307692</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>14.826923</td>\n",
       "      <td>25.576923</td>\n",
       "      <td>5.673077</td>\n",
       "      <td>33.884615</td>\n",
       "      <td>54.980769</td>\n",
       "      <td>31.711538</td>\n",
       "      <td>63.423077</td>\n",
       "      <td>33.692308</td>\n",
       "      <td>12.115385</td>\n",
       "      <td>0.356269</td>\n",
       "      <td>36.346154</td>\n",
       "      <td>88.673077</td>\n",
       "      <td>43.826923</td>\n",
       "      <td>0.494885</td>\n",
       "      <td>0.621075</td>\n",
       "      <td>0.378925</td>\n",
       "      <td>0.724066</td>\n",
       "      <td>0.275934</td>\n",
       "      <td>0.529293</td>\n",
       "      <td>0.300326</td>\n",
       "      <td>0.170381</td>\n",
       "      <td>25.576923</td>\n",
       "      <td>20.288462</td>\n",
       "      <td>0.794692</td>\n",
       "      <td>241.442308</td>\n",
       "      <td>8.653846</td>\n",
       "      <td>112.634615</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>21.634615</td>\n",
       "      <td>7.423077</td>\n",
       "      <td>44.884615</td>\n",
       "      <td>7.192308</td>\n",
       "      <td>14.057692</td>\n",
       "      <td>52</td>\n",
       "      <td>12555</td>\n",
       "      <td>0.494</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.068</td>\n",
       "      <td>283</td>\n",
       "      <td>0.287</td>\n",
       "      <td>806</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TEAM_ABBR               TEAM_NAME CONFERENCE  GAMES  WINS  LOSES   WIN_PER  \\\n",
       "0        BOS          Boston Celtics       EAST     52    37     15  0.711538   \n",
       "1        DEN          Denver Nuggets       WEST     52    36     16  0.692308   \n",
       "2        MIL         Milwaukee Bucks       EAST     52    35     17  0.673077   \n",
       "3        PHI      Philadelphia 76ers       EAST     50    33     17  0.660000   \n",
       "4        CLE     Cleveland Cavaliers       EAST     54    32     22  0.592593   \n",
       "5        MEM       Memphis Grizzlies       WEST     52    32     20  0.615385   \n",
       "6        BKN           Brooklyn Nets       EAST     51    31     20  0.607843   \n",
       "7        MIA              Miami Heat       EAST     53    29     24  0.547170   \n",
       "8        SAC        Sacramento Kings       WEST     50    29     21  0.580000   \n",
       "9        LAC    Los Angeles Clippers       WEST     55    29     26  0.527273   \n",
       "10       NYK         New York Knicks       EAST     53    28     25  0.528302   \n",
       "11       MIN  Minnesota Timberwolves       WEST     54    28     26  0.518519   \n",
       "12       DAL        Dallas Mavericks       WEST     53    28     25  0.528302   \n",
       "13       PHX            Phoenix Suns       WEST     53    27     26  0.509434   \n",
       "14       UTA               Utah Jazz       WEST     53    27     26  0.509434   \n",
       "15       NOP    New Orleans Pelicans       EAST     53    26     27  0.490566   \n",
       "16       ATL           Atlanta Hawks       EAST     52    26     26  0.500000   \n",
       "17       GSW   Golden State Warriors       WEST     52    26     26  0.500000   \n",
       "18       LAL      Los Angeles Lakers       WEST     53    25     28  0.471698   \n",
       "19       POR  Portland Trail Blazers       WEST     51    25     26  0.490196   \n",
       "20       IND          Indiana Pacers       EAST     53    24     29  0.452830   \n",
       "21       OKC   Oklahoma City Thunder       WEST     51    24     27  0.470588   \n",
       "22       CHI           Chicago Bulls       EAST     51    24     27  0.470588   \n",
       "23       WAS      Washington Wizards       EAST     50    24     26  0.480000   \n",
       "24       TOR         Toronto Raptors       EAST     53    23     30  0.433962   \n",
       "25       ORL           Orlando Magic       EAST     52    20     32  0.384615   \n",
       "26       CHA       Charlotte Hornets       EAST     53    15     38  0.283019   \n",
       "27       SAS       San Antonio Spurs       WEST     52    14     38  0.269231   \n",
       "28       HOU         Houston Rockets       WEST     51    13     38  0.254902   \n",
       "29       DET         Detroit Pistons       EAST     52    13     39  0.250000   \n",
       "\n",
       "    LAST_10_WINS  LAST_10_LOSES  HOME_WINS  HOME_LOSES  AWAY_WINS  AWAY_LOSES  \\\n",
       "0              7              3         20           6         17           9   \n",
       "1              7              3         24           4         12          12   \n",
       "2              8              2         22           5         13          12   \n",
       "3              8              2         20           8         13           9   \n",
       "4              5              5         22           6         10          16   \n",
       "5              3              7         21           4         11          16   \n",
       "6              4              6         15           8         16          12   \n",
       "7              6              4         17           9         12          15   \n",
       "8              7              3         16          11         13          10   \n",
       "9              6              4         14          11         15          15   \n",
       "10             4              6         13          14         15          11   \n",
       "11             6              4         19          11          9          15   \n",
       "12             4              6         19           9          9          16   \n",
       "13             6              4         19           9          8          17   \n",
       "14             7              3         18           9          9          17   \n",
       "15             0             10         17           9          9          18   \n",
       "16             6              4         13          11         13          15   \n",
       "17             5              5         19           6          7          20   \n",
       "18             6              4         13          12         12          16   \n",
       "19             6              4         14          11         11          15   \n",
       "20             1              9         16          11          8          18   \n",
       "21             6              4         15          11          9          16   \n",
       "22             5              5         14          11         10          16   \n",
       "23             7              3         12          10         12          16   \n",
       "24             4              6         15          12          8          18   \n",
       "25             4              6         13          13          7          19   \n",
       "26             4              6          7          16          8          22   \n",
       "27             1              9          9          20          5          18   \n",
       "28             3              7          8          17          5          21   \n",
       "29             2              8          6          19          7          20   \n",
       "\n",
       "    WEST_WINS  WEST_LOSES  EAST_WINS  EAST_LOSES  OT_WINS  OT_LOSES  WI5_WINS  \\\n",
       "0          14           4         23          11        3         4         8   \n",
       "1          25           9         11           7        2         0        13   \n",
       "2          13           4         22          13        2         1         7   \n",
       "3          13           5         20          12        3         1         9   \n",
       "4          13          12         19          10        6         0         4   \n",
       "5          13          15         19           5        2         0         6   \n",
       "6           9           8         22          12        0         1        10   \n",
       "7          14           9         15          15        2         1        17   \n",
       "8          17          10         12          11        2         0         8   \n",
       "9          17          14         12          12        1         1         7   \n",
       "10          9          10         19          15        3         5        10   \n",
       "11         18          16         10          10        1         2         7   \n",
       "12         18          11         10          14        3         3        15   \n",
       "13         18          12          9          14        1         3         7   \n",
       "14         16          15         11          11        3         0        10   \n",
       "15         16          14         10          13        2         4         6   \n",
       "16          8           9         18          17        3         1         9   \n",
       "17         15           9         11          17        1         3         7   \n",
       "18         11          17         14          11        2         4         8   \n",
       "19         17          15          8          11        2         0         7   \n",
       "20          6          14         18          15        0         1         7   \n",
       "21         11          13         13          14        2         3         5   \n",
       "22          4          10         20          17        1         4         4   \n",
       "23         10          10         14          16        1         1         8   \n",
       "24          8          10         15          20        1         3         4   \n",
       "25          9          10         11          22        1         1         6   \n",
       "26          7          12          8          26        1         4         6   \n",
       "27          5          27          9          11        0         2         5   \n",
       "28          7          25          6          13        1         0         4   \n",
       "29          8          14          5          25        2         1         3   \n",
       "\n",
       "    WI5_LOSES         PTS        AST       BLK       DREB       FG2A  \\\n",
       "0           6  118.173077  26.346154  5.403846  35.230769  46.519231   \n",
       "1           5  116.980769  28.769231  4.384615  32.653846  55.173077   \n",
       "2           3  114.096154  24.596154  5.038462  37.115385  50.442308   \n",
       "3           4  114.480000  25.300000  4.820000  32.020000  51.220000   \n",
       "4           9  111.555556  24.685185  4.314815  32.351852  52.833333   \n",
       "5           6  116.500000  25.384615  5.942308  35.057692  59.923077   \n",
       "6           4  114.196078  26.196078  6.764706  32.647059  52.607843   \n",
       "7          11  108.377358  23.566038  2.924528  31.320755  51.320755   \n",
       "8           8  119.500000  26.940000  3.260000  33.060000  51.160000   \n",
       "9           7  110.545455  22.727273  4.636364  33.727273  52.072727   \n",
       "10          7  114.264151  22.528302  4.113208  34.000000  55.283019   \n",
       "11          7  115.148148  25.296296  5.351852  32.425926  53.444444   \n",
       "12          7  112.320755  21.886792  3.867925  30.811321  42.509434   \n",
       "13         11  112.679245  26.924528  5.037736  31.905660  56.754717   \n",
       "14         11  117.754717  25.924528  4.905660  32.716981  49.377358   \n",
       "15          8  114.792453  25.830189  4.339623  33.150943  57.679245   \n",
       "16          7  116.480769  24.384615  4.846154  33.230769  61.134615   \n",
       "17          8  118.000000  29.384615  3.826923  34.307692  46.903846   \n",
       "18          8  116.943396  25.094340  4.622642  35.584906  58.698113   \n",
       "19          8  114.274510  24.215686  4.352941  32.156863  50.215686   \n",
       "20          9  115.056604  26.358491  5.943396  31.981132  51.396226   \n",
       "21         11  116.647059  24.411765  5.039216  32.647059  59.333333   \n",
       "22         11  114.274510  24.313725  4.647059  33.960784  58.176471   \n",
       "23          6  113.000000  25.200000  5.420000  34.860000  55.260000   \n",
       "24         13  113.075472  23.226415  5.169811  29.943396  58.584906   \n",
       "25          7  110.923077  22.576923  4.653846  32.923077  53.750000   \n",
       "26          8  112.000000  25.075472  4.924528  33.150943  59.320755   \n",
       "27          6  112.519231  27.153846  4.000000  30.903846  60.615385   \n",
       "28          5  109.627451  22.274510  4.941176  33.823529  54.490196   \n",
       "29          6  112.634615  22.634615  3.653846  31.384615  54.500000   \n",
       "\n",
       "         FG2M    FG2_PTS       FG3A       FG3M   FG3_PCT    FG3_PTS  \\\n",
       "0   26.346154  52.692308  41.942308  15.615385  0.370519  46.846154   \n",
       "1   31.500000  63.000000  30.961538  12.326923  0.395462  36.980769   \n",
       "2   27.269231  54.538462  39.673077  14.211538  0.359154  42.634615   \n",
       "3   27.940000  55.880000  33.420000  12.860000  0.381340  38.580000   \n",
       "4   29.351852  58.703704  31.629630  11.611111  0.369296  34.833333   \n",
       "5   32.211538  64.423077  32.865385  11.288462  0.344769  33.865385   \n",
       "6   30.372549  60.745098  31.745098  12.392157  0.390275  37.176471   \n",
       "7   27.339623  54.679245  35.169811  11.849057  0.335830  35.547170   \n",
       "8   29.880000  59.760000  36.400000  13.380000  0.367200  40.140000   \n",
       "9   27.381818  54.763636  33.490909  12.600000  0.374236  37.800000   \n",
       "10  29.396226  58.792453  34.735849  11.886792  0.341453  35.660377   \n",
       "11  30.796296  61.592593  33.296296  11.777778  0.353870  35.333333   \n",
       "12  24.415094  48.830189  40.509434  14.622642  0.361547  43.867925   \n",
       "13  28.811321  57.622642  33.037736  12.679245  0.381943  38.037736   \n",
       "14  27.641509  55.283019  39.603774  14.566038  0.369170  43.698113   \n",
       "15  31.264151  62.528302  30.584906  10.849057  0.351019  32.547170   \n",
       "16  33.269231  66.538462  30.615385  10.730769  0.352365  32.192308   \n",
       "17  26.192308  52.384615  43.134615  16.442308  0.381173  49.326923   \n",
       "18  32.716981  65.433962  31.113208  10.452830  0.338509  31.358491   \n",
       "19  27.803922  55.607843  34.176471  12.882353  0.376608  38.647059   \n",
       "20  27.283019  54.566038  38.264151  13.849057  0.360868  41.547170   \n",
       "21  31.235294  62.470588  33.411765  12.058824  0.362529  36.176471   \n",
       "22  31.960784  63.921569  29.019608  10.705882  0.363784  32.117647   \n",
       "23  30.600000  61.200000  31.920000  11.220000  0.349800  33.660000   \n",
       "24  30.415094  60.830189  32.377358  10.830189  0.336057  32.490566   \n",
       "25  29.096154  58.192308  31.173077  10.865385  0.349577  32.596154   \n",
       "26  31.056604  62.113208  33.301887  10.716981  0.319321  32.150943   \n",
       "27  32.519231  65.038462  30.923077  10.615385  0.345346  31.846154   \n",
       "28  28.666667  57.333333  33.980392  11.098039  0.320255  33.294118   \n",
       "29  28.019231  56.038462  33.096154  11.826923  0.353808  35.480769   \n",
       "\n",
       "          FGA        FGM    FG_PCT  FRAC_ATT_2PT  FRAC_ATT_3PT  FRAC_MK_2PT  \\\n",
       "0   88.461538  41.961538  0.475558      0.525228      0.474772     0.628537   \n",
       "1   86.134615  43.826923  0.510288      0.640573      0.359427     0.717031   \n",
       "2   90.115385  41.480769  0.461442      0.559447      0.440553     0.655579   \n",
       "3   84.640000  40.800000  0.483040      0.604168      0.395832     0.682270   \n",
       "4   84.462963  40.962963  0.486926      0.624903      0.375097     0.715238   \n",
       "5   92.788462  43.500000  0.469250      0.645591      0.354409     0.740985   \n",
       "6   84.352941  42.764706  0.508039      0.622814      0.377186     0.709872   \n",
       "7   86.490566  39.188679  0.453264      0.592078      0.407922     0.694910   \n",
       "8   87.560000  43.260000  0.494540      0.583667      0.416333     0.689504   \n",
       "9   85.563636  39.981818  0.469455      0.607243      0.392757     0.684903   \n",
       "10  90.018868  41.283019  0.459509      0.612658      0.387342     0.708618   \n",
       "11  86.740741  42.574074  0.493167      0.616193      0.383807     0.724332   \n",
       "12  83.018868  39.037736  0.471245      0.512014      0.487986     0.621425   \n",
       "13  89.792453  41.490566  0.462094      0.631944      0.368056     0.694513   \n",
       "14  88.981132  42.207547  0.475038      0.554202      0.445798     0.653671   \n",
       "15  88.264151  42.113208  0.478547      0.652209      0.347791     0.741168   \n",
       "16  91.750000  44.000000  0.481231      0.665119      0.334881     0.754055   \n",
       "17  90.038462  42.634615  0.474269      0.520180      0.479820     0.613448   \n",
       "18  89.811321  43.169811  0.481925      0.654295      0.345705     0.758058   \n",
       "19  84.392157  40.686275  0.482353      0.594308      0.405692     0.684468   \n",
       "20  89.660377  41.132075  0.460396      0.571578      0.428422     0.662800   \n",
       "21  92.745098  43.294118  0.468157      0.640582      0.359418     0.720123   \n",
       "22  87.196078  42.666667  0.490137      0.666153      0.333847     0.749482   \n",
       "23  87.180000  41.820000  0.481380      0.634831      0.365169     0.730168   \n",
       "24  90.962264  41.245283  0.454792      0.643790      0.356210     0.735826   \n",
       "25  84.923077  39.961538  0.472308      0.631927      0.368073     0.727762   \n",
       "26  92.622642  41.773585  0.452547      0.640660      0.359340     0.744143   \n",
       "27  91.538462  43.134615  0.474038      0.661607      0.338393     0.753239   \n",
       "28  88.470588  39.764706  0.449941      0.615189      0.384811     0.720699   \n",
       "29  87.596154  39.846154  0.455058      0.621369      0.378631     0.702638   \n",
       "\n",
       "    FRAC_MK_3PT  FRAC_PTS_2PT  FRAC_PTS_3PT  FRAC_PTS_FT        FTA  \\\n",
       "0      0.371463      0.446293      0.394699     0.159008  22.480769   \n",
       "1      0.282969      0.539641      0.315466     0.144893  22.769231   \n",
       "2      0.344421      0.476679      0.374308     0.149013  22.923077   \n",
       "3      0.317730      0.489813      0.335288     0.174899  24.220000   \n",
       "4      0.284762      0.527849      0.310901     0.161250  23.296296   \n",
       "5      0.259015      0.554699      0.288633     0.156668  25.326923   \n",
       "6      0.290128      0.531926      0.324461     0.143613  20.686275   \n",
       "7      0.305090      0.503971      0.327034     0.168995  22.075472   \n",
       "8      0.310496      0.500991      0.334624     0.164385  24.760000   \n",
       "9      0.315097      0.497907      0.338691     0.163402  23.345455   \n",
       "10     0.291382      0.514318      0.311514     0.174167  25.547170   \n",
       "11     0.275668      0.536826      0.304462     0.158712  23.740741   \n",
       "12     0.378575      0.434297      0.390617     0.175086  26.415094   \n",
       "13     0.305487      0.513426      0.335626     0.150949  21.396226   \n",
       "14     0.346329      0.470416      0.370682     0.158902  23.886792   \n",
       "15     0.258832      0.545310      0.283283     0.171407  25.283019   \n",
       "16     0.245945      0.570771      0.276424     0.152805  21.692308   \n",
       "17     0.386552      0.443728      0.416943     0.139329  20.596154   \n",
       "18     0.241942      0.560727      0.266983     0.172290  25.830189   \n",
       "19     0.315532      0.488947      0.335742     0.175311  25.313725   \n",
       "20     0.337200      0.475253      0.360424     0.164323  23.660377   \n",
       "21     0.279877      0.536307      0.308888     0.154805  22.627451   \n",
       "22     0.250518      0.561058      0.279402     0.159540  22.411765   \n",
       "23     0.269832      0.542624      0.297409     0.159966  23.340000   \n",
       "24     0.264174      0.538359      0.286486     0.175154  25.000000   \n",
       "25     0.272238      0.526337      0.292208     0.181455  25.480769   \n",
       "26     0.255857      0.556294      0.284446     0.159261  23.547170   \n",
       "27     0.246761      0.580151      0.281485     0.138364  21.153846   \n",
       "28     0.279301      0.526388      0.300636     0.172976  25.313725   \n",
       "29     0.297362      0.499325      0.313542     0.187133  27.288462   \n",
       "\n",
       "          FTM    FT_PCT         MIN     OPP_PTS       OREB         PF  \\\n",
       "0   18.634615  0.831481  243.365385  111.980769   9.711538  18.826923   \n",
       "1   17.000000  0.748519  240.961538  112.807692  10.038462  19.288462   \n",
       "2   16.923077  0.742481  241.923077  111.788462  11.807692  18.326923   \n",
       "3   20.020000  0.825140  242.500000  110.700000   8.660000  20.720000   \n",
       "4   18.018519  0.774519  243.240741  106.888889   9.611111  18.814815   \n",
       "5   18.211538  0.730615  240.961538  112.769231  13.288462  20.173077   \n",
       "6   16.274510  0.787039  240.490196  112.058824   7.686275  21.764706   \n",
       "7   18.150943  0.822245  241.415094  108.113208   9.943396  18.660377   \n",
       "8   19.600000  0.799500  241.000000  116.360000   8.960000  19.940000   \n",
       "9   17.981818  0.766600  240.909091  110.454545  10.036364  19.600000   \n",
       "10  19.811321  0.771585  243.773585  112.547170  13.188679  20.471698   \n",
       "11  18.222222  0.763481  241.388889  115.000000   9.018519  21.944444   \n",
       "12  19.622642  0.733170  243.301887  111.849057   7.735849  20.924528   \n",
       "13  17.018868  0.793981  241.886792  111.849057  11.962264  20.867925   \n",
       "14  18.773585  0.777038  241.415094  116.528302  11.641509  20.735849   \n",
       "15  19.716981  0.778245  242.830189  113.603774  11.075472  20.660377   \n",
       "16  17.750000  0.819019  242.403846  116.480769  10.307692  19.230769   \n",
       "17  16.288462  0.789942  242.403846  118.346154  10.000000  22.153846   \n",
       "18  20.150943  0.775962  243.301887  118.132075   9.566038  18.490566   \n",
       "19  20.019608  0.792725  240.980392  113.705882  10.058824  20.784314   \n",
       "20  18.943396  0.797943  240.471698  117.094340  10.132075  21.547170   \n",
       "21  18.000000  0.789706  242.941176  115.803922  11.588235  21.705882   \n",
       "22  18.235294  0.808490  242.450980  114.078431   8.529412  19.745098   \n",
       "23  18.140000  0.778040  241.000000  113.040000   9.520000  19.460000   \n",
       "24  19.754717  0.788245  241.886792  112.641509  12.830189  20.283019   \n",
       "25  20.134615  0.788365  240.961538  114.346154   9.673077  20.365385   \n",
       "26  17.735849  0.750019  242.830189  118.603774  12.169811  20.981132   \n",
       "27  15.634615  0.734135  240.961538  122.250000  12.057692  19.826923   \n",
       "28  19.000000  0.750745  240.980392  116.862745  13.372549  20.529412   \n",
       "29  21.115385  0.776423  241.442308  120.057692  11.038462  22.269231   \n",
       "\n",
       "    PLUS_MINUS        REB       STL       TEAM_ID        TOV    OPP_AST  \\\n",
       "0     6.192308  44.942308  6.384615  1.610613e+09  13.500000  22.615385   \n",
       "1     4.173077  42.692308  7.519231  1.610613e+09  14.980769  25.653846   \n",
       "2     2.307692  48.923077  6.692308  1.610613e+09  15.230769  22.903846   \n",
       "3     3.780000  40.680000  8.020000  1.610613e+09  14.140000  23.880000   \n",
       "4     4.666667  41.962963  6.870370  1.610613e+09  13.962963  22.574074   \n",
       "5     3.730769  48.346154  8.269231  1.610613e+09  14.403846  26.653846   \n",
       "6     2.137255  40.333333  7.000000  1.610613e+09  14.352941  23.235294   \n",
       "7     0.264151  41.264151  8.528302  1.610613e+09  13.452830  25.169811   \n",
       "8     3.140000  42.020000  6.880000  1.610613e+09  14.160000  25.460000   \n",
       "9     0.090909  43.763636  6.690909  1.610613e+09  14.363636  24.181818   \n",
       "10    1.716981  47.188679  6.320755  1.610613e+09  13.113208  24.830189   \n",
       "11    0.148148  41.444444  8.277778  1.610613e+09  15.777778  25.425926   \n",
       "12    0.471698  38.547170  6.547170  1.610613e+09  12.207547  24.094340   \n",
       "13    0.830189  43.867925  6.924528  1.610613e+09  14.207547  23.566038   \n",
       "14    1.226415  44.358491  6.773585  1.610613e+09  15.207547  23.754717   \n",
       "15    1.188679  44.226415  8.547170  1.610613e+09  15.207547  25.056604   \n",
       "16    0.000000  43.538462  6.961538  1.610613e+09  12.846154  25.615385   \n",
       "17   -0.346154  44.307692  6.980769  1.610613e+09  16.653846  25.769231   \n",
       "18   -1.188679  45.150943  6.566038  1.610613e+09  14.056604  25.679245   \n",
       "19    0.568627  42.215686  6.372549  1.610613e+09  14.960784  25.705882   \n",
       "20   -2.037736  42.113208  8.018868  1.610613e+09  15.358491  26.339623   \n",
       "21    0.843137  44.235294  8.156863  1.610613e+09  13.725490  25.098039   \n",
       "22    0.196078  42.490196  7.588235  1.610613e+09  13.745098  26.117647   \n",
       "23   -0.040000  44.380000  6.800000  1.610613e+09  13.900000  23.560000   \n",
       "24    0.433962  42.773585  9.245283  1.610613e+09  11.716981  26.075472   \n",
       "25   -3.423077  42.596154  6.961538  1.610613e+09  15.192308  26.000000   \n",
       "26   -6.603774  45.320755  7.867925  1.610613e+09  13.792453  26.377358   \n",
       "27   -9.730769  42.961538  6.826923  1.610613e+09  15.307692  26.615385   \n",
       "28   -7.235294  47.196078  7.215686  1.610613e+09  17.117647  25.431373   \n",
       "29   -7.423077  42.423077  7.307692  1.610613e+09  14.826923  25.576923   \n",
       "\n",
       "     OPP_BLK   OPP_DREB   OPP_FG2A   OPP_FG2M  OPP_FG2_PTS   OPP_FG3A  \\\n",
       "0   3.750000  34.057692  57.519231  30.557692    61.115385  32.903846   \n",
       "1   4.346154  30.480769  52.865385  29.596154    59.192308  33.538462   \n",
       "2   4.288462  33.576923  58.596154  30.096154    60.192308  33.480769   \n",
       "3   5.040000  32.480000  52.860000  29.060000    58.120000  32.760000   \n",
       "4   4.185185  31.240741  52.314815  27.370370    54.740741  31.407407   \n",
       "5   5.403846  32.557692  53.788462  27.596154    55.192308  36.211538   \n",
       "6   3.784314  32.647059  55.392157  27.980392    55.960784  33.039216   \n",
       "7   3.811321  33.641509  46.830189  26.207547    52.415094  36.000000   \n",
       "8   4.200000  32.360000  57.160000  32.100000    64.200000  31.840000   \n",
       "9   4.327273  33.018182  53.836364  28.872727    57.745455  33.400000   \n",
       "10  4.584906  32.509434  51.301887  27.264151    54.528302  37.924528   \n",
       "11  3.981481  32.944444  53.277778  28.277778    56.555556  35.444444   \n",
       "12  4.056604  34.264151  53.396226  29.452830    58.905660  31.264151   \n",
       "13  4.188679  32.735849  54.886792  29.113208    58.226415  31.188679   \n",
       "14  4.754717  31.905660  58.358491  31.792453    63.584906  31.622642   \n",
       "15  5.075472  32.433962  51.320755  29.037736    58.075472  36.150943   \n",
       "16  5.019231  34.519231  56.653846  31.230769    62.461538  33.230769   \n",
       "17  4.211538  33.596154  54.826923  29.653846    59.307692  35.807692   \n",
       "18  5.433962  34.471698  57.566038  31.735849    63.471698  36.207547   \n",
       "19  4.392157  30.882353  54.901961  29.960784    59.921569  32.137255   \n",
       "20  5.207547  33.433962  54.830189  29.433962    58.867925  33.867925   \n",
       "21  5.470588  35.019608  53.921569  28.980392    57.960784  35.294118   \n",
       "22  5.058824  34.058824  51.607843  28.549020    57.098039  36.607843   \n",
       "23  4.740000  32.940000  57.860000  30.220000    60.440000  32.400000   \n",
       "24  4.584906  33.245283  48.698113  27.849057    55.698113  34.094340   \n",
       "25  4.942308  32.346154  49.807692  28.326923    56.653846  37.634615   \n",
       "26  5.886792  35.490566  54.000000  30.037736    60.075472  35.471698   \n",
       "27  4.750000  33.076923  58.269231  33.230769    66.461538  31.846154   \n",
       "28  6.039216  31.000000  49.392157  27.490196    54.980392  39.215686   \n",
       "29  5.673077  33.884615  54.980769  31.711538    63.423077  33.692308   \n",
       "\n",
       "     OPP_FG3M  OPP_FG3_PCT  OPP_FG3_PTS    OPP_FGA    OPP_FGM  OPP_FG_PCT  \\\n",
       "0   11.596154     0.353635    34.788462  90.423077  42.153846    0.467365   \n",
       "1   11.826923     0.349788    35.480769  86.403846  41.423077    0.480423   \n",
       "2   11.711538     0.347327    35.134615  92.076923  41.807692    0.455673   \n",
       "3   11.220000     0.343640    33.660000  85.620000  40.280000    0.472640   \n",
       "4   11.685185     0.370519    35.055556  83.722222  39.055556    0.467889   \n",
       "5   12.980769     0.358942    38.942308  90.000000  40.576923    0.452269   \n",
       "6   12.274510     0.369078    36.823529  88.431373  40.254902    0.455686   \n",
       "7   13.169811     0.364906    39.509434  82.830189  39.377358    0.476415   \n",
       "8   11.460000     0.362200    34.380000  89.000000  43.560000    0.490440   \n",
       "9   11.836364     0.353873    35.509091  87.236364  40.709091    0.467691   \n",
       "10  13.245283     0.347547    39.735849  89.226415  40.509434    0.454887   \n",
       "11  12.981481     0.369130    38.944444  88.722222  41.259259    0.466000   \n",
       "12  11.132075     0.355755    33.396226  84.660377  40.584906    0.480566   \n",
       "13  11.358491     0.360868    34.075472  86.075472  40.471698    0.470906   \n",
       "14  11.415094     0.354830    34.245283  89.981132  43.207547    0.482038   \n",
       "15  12.433962     0.341679    37.301887  87.471698  41.471698    0.475170   \n",
       "16  11.576923     0.345712    34.730769  89.884615  42.807692    0.478058   \n",
       "17  12.980769     0.366692    38.942308  90.634615  42.634615    0.472212   \n",
       "18  12.547170     0.342302    37.641509  93.773585  44.283019    0.473585   \n",
       "19  11.666667     0.362412    35.000000  87.039216  41.627451    0.479667   \n",
       "20  12.584906     0.370585    37.754717  88.698113  42.018868    0.475151   \n",
       "21  12.509804     0.353451    37.529412  89.215686  41.490196    0.465725   \n",
       "22  13.000000     0.354353    39.000000  88.215686  41.549020    0.473000   \n",
       "23  11.500000     0.350820    34.500000  90.260000  41.720000    0.463940   \n",
       "24  12.754717     0.372547    38.264151  82.792453  40.603774    0.491717   \n",
       "25  13.134615     0.346173    39.403846  87.442308  41.461538    0.474519   \n",
       "26  12.962264     0.361774    38.886792  89.471698  43.000000    0.482094   \n",
       "27  12.769231     0.399212    38.307692  90.115385  46.000000    0.512500   \n",
       "28  14.313725     0.368412    42.941176  88.607843  41.803922    0.474333   \n",
       "29  12.115385     0.356269    36.346154  88.673077  43.826923    0.494885   \n",
       "\n",
       "    OPP_FRAC_ATT_2PT  OPP_FRAC_ATT_3PT  OPP_FRAC_MK_2PT  OPP_FRAC_MK_3PT  \\\n",
       "0           0.636301          0.363699         0.724365         0.275635   \n",
       "1           0.611265          0.388735         0.715310         0.284690   \n",
       "2           0.635905          0.364095         0.718665         0.281335   \n",
       "3           0.617360          0.382640         0.721277         0.278723   \n",
       "4           0.623773          0.376227         0.700599         0.299401   \n",
       "5           0.596620          0.403380         0.678676         0.321324   \n",
       "6           0.624672          0.375328         0.693865         0.306135   \n",
       "7           0.565151          0.434849         0.661823         0.338177   \n",
       "8           0.642228          0.357772         0.734242         0.265758   \n",
       "9           0.615249          0.384751         0.709643         0.290357   \n",
       "10          0.573926          0.426074         0.672377         0.327623   \n",
       "11          0.599643          0.400357         0.682711         0.317289   \n",
       "12          0.631157          0.368843         0.725758         0.274242   \n",
       "13          0.636455          0.363545         0.716524         0.283476   \n",
       "14          0.646846          0.353154         0.733780         0.266220   \n",
       "15          0.584904          0.415096         0.697899         0.302101   \n",
       "16          0.629932          0.370068         0.731125         0.268875   \n",
       "17          0.604588          0.395412         0.695415         0.304585   \n",
       "18          0.613099          0.386901         0.718353         0.281647   \n",
       "19          0.630392          0.369608         0.719526         0.280474   \n",
       "20          0.617348          0.382652         0.699422         0.300578   \n",
       "21          0.603078          0.396922         0.695632         0.304368   \n",
       "22          0.585110          0.414890         0.686959         0.313041   \n",
       "23          0.640018          0.359982         0.720778         0.279222   \n",
       "24          0.586859          0.413141         0.684930         0.315070   \n",
       "25          0.569105          0.430895         0.680049         0.319951   \n",
       "26          0.603196          0.396804         0.698089         0.301911   \n",
       "27          0.644362          0.355638         0.723208         0.276792   \n",
       "28          0.557548          0.442452         0.655555         0.344445   \n",
       "29          0.621075          0.378925         0.724066         0.275934   \n",
       "\n",
       "    OPP_FRAC_PTS_2PT  OPP_FRAC_PTS_3PT  OPP_FRAC_PTS_FT    OPP_FTA    OPP_FTM  \\\n",
       "0           0.547110          0.309414         0.143477  20.576923  16.076923   \n",
       "1           0.528844          0.310650         0.160506  23.788462  18.134615   \n",
       "2           0.539440          0.312948         0.147612  21.192308  16.461538   \n",
       "3           0.526374          0.302984         0.170642  24.160000  18.920000   \n",
       "4           0.515696          0.324831         0.159474  22.203704  17.092593   \n",
       "5           0.489966          0.344254         0.165780  23.692308  18.634615   \n",
       "6           0.500887          0.326688         0.172426  25.098039  19.274510   \n",
       "7           0.485370          0.364750         0.149880  20.905660  16.188679   \n",
       "8           0.551262          0.296429         0.152309  22.460000  17.780000   \n",
       "9           0.524336          0.318495         0.157170  22.600000  17.200000   \n",
       "10          0.485248          0.352396         0.162356  23.773585  18.283019   \n",
       "11          0.492509          0.337456         0.170034  25.962963  19.500000   \n",
       "12          0.529194          0.296230         0.174576  25.056604  19.547170   \n",
       "13          0.519972          0.304134         0.175894  24.792453  19.547170   \n",
       "14          0.546459          0.292437         0.161103  24.094340  18.698113   \n",
       "15          0.512269          0.327484         0.160246  23.358491  18.226415   \n",
       "16          0.537699          0.295292         0.167010  23.942308  19.288462   \n",
       "17          0.502412          0.327382         0.170207  26.096154  20.096154   \n",
       "18          0.540470          0.315305         0.144225  22.245283  17.018868   \n",
       "19          0.528262          0.306181         0.165557  24.235294  18.784314   \n",
       "20          0.503953          0.320373         0.175674  25.584906  20.471698   \n",
       "21          0.500957          0.324118         0.174925  25.980392  20.313725   \n",
       "22          0.502493          0.340431         0.157076  23.137255  17.980392   \n",
       "23          0.536062          0.304825         0.159113  22.620000  18.100000   \n",
       "24          0.494429          0.338807         0.166764  23.339623  18.679245   \n",
       "25          0.494769          0.344064         0.161167  23.346154  18.288462   \n",
       "26          0.508698          0.325361         0.165941  24.886792  19.641509   \n",
       "27          0.546114          0.309356         0.144529  22.711538  17.480769   \n",
       "28          0.470840          0.366661         0.162499  24.078431  18.941176   \n",
       "29          0.529293          0.300326         0.170381  25.576923  20.288462   \n",
       "\n",
       "    OPP_FT_PCT     OPP_MIN  OPP_MONTH  OPP_OPP_PTS   OPP_OREB     OPP_PF  \\\n",
       "0     0.779942  243.365385   8.096154   118.173077   9.615385  19.480769   \n",
       "1     0.762481  240.961538   7.884615   116.980769   9.884615  20.250000   \n",
       "2     0.779269  241.923077   7.923077   114.096154  10.846154  19.000000   \n",
       "3     0.777440  242.500000   8.120000   114.480000   9.740000  19.700000   \n",
       "4     0.770944  243.240741   8.037037   111.555556   9.277778  21.037037   \n",
       "5     0.788000  240.961538   7.903846   116.500000  11.096154  21.384615   \n",
       "6     0.767373  240.490196   8.196078   114.196078  11.666667  18.156863   \n",
       "7     0.766151  241.415094   8.150943   108.377358   8.622642  19.716981   \n",
       "8     0.786740  241.000000   7.980000   119.500000   9.600000  21.240000   \n",
       "9     0.761945  240.909091   8.072727   110.545455  10.054545  19.145455   \n",
       "10    0.773679  243.773585   8.169811   114.264151  11.264151  21.150943   \n",
       "11    0.754000  241.388889   8.018519   115.148148  11.703704  20.203704   \n",
       "12    0.779981  243.301887   8.207547   112.320755   9.867925  22.943396   \n",
       "13    0.790717  241.886792   8.188679   112.679245  10.415094  20.245283   \n",
       "14    0.778189  241.415094   8.509434   117.754717  11.339623  20.981132   \n",
       "15    0.777377  242.830189   7.981132   114.792453   9.679245  20.773585   \n",
       "16    0.802135  242.403846   8.076923   116.480769  11.038462  19.903846   \n",
       "17    0.769577  242.403846   8.307692   118.000000  10.634615  18.826923   \n",
       "18    0.759491  243.301887   8.000000   116.943396  11.245283  21.283019   \n",
       "19    0.770922  240.980392   8.019608   114.274510  10.372549  21.392157   \n",
       "20    0.802208  240.471698   8.150943   115.056604  12.169811  20.188679   \n",
       "21    0.782216  242.941176   8.235294   116.647059  12.431373  19.784314   \n",
       "22    0.777471  242.450980   8.235294   114.274510   9.843137  18.843137   \n",
       "23    0.799200  241.000000   8.560000   113.000000  10.620000  19.800000   \n",
       "24    0.802811  241.886792   7.962264   113.075472   9.509434  20.735849   \n",
       "25    0.784423  240.961538   8.288462   110.923077   9.769231  20.288462   \n",
       "26    0.795094  242.830189   8.169811   112.000000  10.735849  20.660377   \n",
       "27    0.766423  240.961538   8.076923   112.519231  10.442308  18.846154   \n",
       "28    0.782118  240.980392   8.215686   109.627451  10.921569  21.333333   \n",
       "29    0.794692  241.442308   8.653846   112.634615  11.000000  21.634615   \n",
       "\n",
       "    OPP_PLUS_MINUS    OPP_REB   OPP_STL    OPP_TOV   G     MP    FG%  Dist  \\\n",
       "0        -6.192308  43.673077  6.500000  12.807692  52  12655  0.466  14.3   \n",
       "1        -4.173077  40.365385  7.596154  13.961538  52  12530  0.479  13.8   \n",
       "2        -2.307692  44.423077  7.326923  12.326923  52  12580  0.454  14.2   \n",
       "3        -3.780000  42.220000  6.840000  15.260000  50  12125  0.470  14.1   \n",
       "4        -4.666667  40.518519  7.333333  15.092593  54  13135  0.466  13.8   \n",
       "5        -3.730769  43.653846  7.230769  15.250000  52  12530  0.451  14.0   \n",
       "6        -2.137255  44.313725  7.313725  13.960784  51  12265  0.455  13.5   \n",
       "7        -0.264151  42.264151  6.886792  16.528302  53  12795  0.475  15.0   \n",
       "8        -3.140000  41.960000  7.380000  14.320000  50  12050  0.489  13.4   \n",
       "9        -0.090909  43.072727  7.709091  12.909091  55  13250  0.467  14.0   \n",
       "10       -1.716981  43.773585  6.018868  12.905660  53  12920  0.454  14.7   \n",
       "11       -0.148148  44.648148  8.092593  16.037037  54  13035  0.465  13.9   \n",
       "12       -0.471698  44.132075  6.226415  13.679245  53  12895  0.479  13.6   \n",
       "13       -0.830189  43.150943  7.471698  14.811321  53  12820  0.470  13.6   \n",
       "14       -1.226415  43.245283  7.660377  13.641509  53  12795  0.480  13.5   \n",
       "15       -1.188679  42.113208  7.471698  15.377358  53  12870  0.474  14.4   \n",
       "16        0.000000  45.557692  7.307692  14.634615  52  12605  0.476  13.3   \n",
       "17        0.346154  44.230769  7.865385  14.769231  52  12605  0.470  14.6   \n",
       "18        1.188679  45.716981  7.603774  12.849057  53  12895  0.472  14.2   \n",
       "19       -0.568627  41.254902  7.686275  13.392157  51  12290  0.478  13.3   \n",
       "20        2.037736  45.603774  7.905660  15.584906  53  12745  0.474  13.6   \n",
       "21       -0.843137  47.450980  7.470588  16.686275  51  12390  0.465  13.7   \n",
       "22       -0.196078  43.901961  6.725490  14.843137  51  12365  0.471  14.2   \n",
       "23        0.040000  43.560000  6.660000  12.300000  50  12050  0.462  13.9   \n",
       "24       -0.433962  42.754717  5.943396  16.566038  53  12820  0.490  14.1   \n",
       "25        3.423077  42.115385  7.884615  14.211538  52  12530  0.474  14.8   \n",
       "26        6.603774  46.226415  6.622642  14.962264  53  12870  0.481  14.1   \n",
       "27        9.730769  43.519231  8.250000  14.019231  52  12530  0.510  13.1   \n",
       "28        7.235294  41.921569  8.980392  13.549020  51  12290  0.472  15.0   \n",
       "29        7.423077  44.884615  7.192308  14.057692  52  12555  0.494  13.6   \n",
       "\n",
       "    % of FGA by Distance 2P  % of FGA by Distance 0-3  \\\n",
       "0                     0.636                     0.207   \n",
       "1                     0.612                     0.235   \n",
       "2                     0.636                     0.233   \n",
       "3                     0.617                     0.268   \n",
       "4                     0.625                     0.273   \n",
       "5                     0.598                     0.265   \n",
       "6                     0.626                     0.265   \n",
       "7                     0.565                     0.218   \n",
       "8                     0.642                     0.245   \n",
       "9                     0.617                     0.216   \n",
       "10                    0.575                     0.252   \n",
       "11                    0.601                     0.266   \n",
       "12                    0.631                     0.252   \n",
       "13                    0.638                     0.257   \n",
       "14                    0.649                     0.235   \n",
       "15                    0.587                     0.242   \n",
       "16                    0.630                     0.291   \n",
       "17                    0.605                     0.157   \n",
       "18                    0.614                     0.227   \n",
       "19                    0.631                     0.306   \n",
       "20                    0.618                     0.276   \n",
       "21                    0.604                     0.264   \n",
       "22                    0.585                     0.273   \n",
       "23                    0.641                     0.210   \n",
       "24                    0.588                     0.263   \n",
       "25                    0.570                     0.236   \n",
       "26                    0.604                     0.259   \n",
       "27                    0.647                     0.288   \n",
       "28                    0.557                     0.209   \n",
       "29                    0.620                     0.258   \n",
       "\n",
       "    % of FGA by Distance 3-10  % of FGA by Distance 10-16  \\\n",
       "0                       0.221                       0.113   \n",
       "1                       0.230                       0.090   \n",
       "2                       0.182                       0.141   \n",
       "3                       0.173                       0.093   \n",
       "4                       0.179                       0.106   \n",
       "5                       0.183                       0.090   \n",
       "6                       0.211                       0.087   \n",
       "7                       0.189                       0.098   \n",
       "8                       0.225                       0.104   \n",
       "9                       0.241                       0.100   \n",
       "10                      0.154                       0.100   \n",
       "11                      0.179                       0.099   \n",
       "12                      0.201                       0.106   \n",
       "13                      0.208                       0.102   \n",
       "14                      0.258                       0.091   \n",
       "15                      0.190                       0.090   \n",
       "16                      0.171                       0.101   \n",
       "17                      0.289                       0.090   \n",
       "18                      0.207                       0.107   \n",
       "19                      0.173                       0.088   \n",
       "20                      0.191                       0.094   \n",
       "21                      0.210                       0.077   \n",
       "22                      0.169                       0.082   \n",
       "23                      0.267                       0.097   \n",
       "24                      0.187                       0.086   \n",
       "25                      0.184                       0.088   \n",
       "26                      0.187                       0.093   \n",
       "27                      0.194                       0.097   \n",
       "28                      0.204                       0.083   \n",
       "29                      0.220                       0.082   \n",
       "\n",
       "    % of FGA by Distance 16-3P  % of FGA by Distance 3P  FG% by Distance 2P  \\\n",
       "0                        0.095                    0.364               0.531   \n",
       "1                        0.057                    0.388               0.560   \n",
       "2                        0.080                    0.364               0.514   \n",
       "3                        0.084                    0.383               0.550   \n",
       "4                        0.067                    0.375               0.523   \n",
       "5                        0.059                    0.402               0.513   \n",
       "6                        0.063                    0.374               0.505   \n",
       "7                        0.060                    0.435               0.560   \n",
       "8                        0.069                    0.358               0.562   \n",
       "9                        0.060                    0.383               0.536   \n",
       "10                       0.069                    0.425               0.531   \n",
       "11                       0.056                    0.399               0.531   \n",
       "12                       0.072                    0.369               0.552   \n",
       "13                       0.071                    0.362               0.530   \n",
       "14                       0.065                    0.351               0.545   \n",
       "15                       0.065                    0.413               0.566   \n",
       "16                       0.067                    0.370               0.551   \n",
       "17                       0.069                    0.395               0.541   \n",
       "18                       0.072                    0.386               0.551   \n",
       "19                       0.063                    0.369               0.546   \n",
       "20                       0.057                    0.382               0.537   \n",
       "21                       0.054                    0.396               0.537   \n",
       "22                       0.061                    0.415               0.553   \n",
       "23                       0.068                    0.359               0.522   \n",
       "24                       0.052                    0.412               0.572   \n",
       "25                       0.062                    0.430               0.569   \n",
       "26                       0.065                    0.396               0.556   \n",
       "27                       0.067                    0.353               0.570   \n",
       "28                       0.061                    0.443               0.557   \n",
       "29                       0.060                    0.380               0.577   \n",
       "\n",
       "    FG% by Distance 0-3  FG% by Distance 3-10  FG% by Distance 10-16  \\\n",
       "0                 0.687                 0.456                  0.442   \n",
       "1                 0.751                 0.459                  0.430   \n",
       "2                 0.674                 0.420                  0.446   \n",
       "3                 0.736                 0.397                  0.407   \n",
       "4                 0.667                 0.412                  0.404   \n",
       "5                 0.646                 0.408                  0.389   \n",
       "6                 0.668                 0.391                  0.390   \n",
       "7                 0.721                 0.492                  0.426   \n",
       "8                 0.736                 0.481                  0.446   \n",
       "9                 0.721                 0.440                  0.465   \n",
       "10                0.662                 0.426                  0.445   \n",
       "11                0.692                 0.407                  0.424   \n",
       "12                0.703                 0.449                  0.467   \n",
       "13                0.694                 0.415                  0.446   \n",
       "14                0.692                 0.455                  0.484   \n",
       "15                0.751                 0.451                  0.416   \n",
       "16                0.691                 0.437                  0.434   \n",
       "17                0.701                 0.518                  0.419   \n",
       "18                0.715                 0.452                  0.473   \n",
       "19                0.689                 0.430                  0.411   \n",
       "20                0.660                 0.452                  0.437   \n",
       "21                0.681                 0.429                  0.458   \n",
       "22                0.691                 0.456                  0.431   \n",
       "23                0.702                 0.448                  0.439   \n",
       "24                0.714                 0.449                  0.508   \n",
       "25                0.737                 0.465                  0.451   \n",
       "26                0.684                 0.455                  0.467   \n",
       "27                0.692                 0.472                  0.466   \n",
       "28                0.735                 0.476                  0.435   \n",
       "29                0.731                 0.490                  0.395   \n",
       "\n",
       "    FG% by Distance 16-3P  FG% by Distance 3P  % of FG Ast'd 2P  \\\n",
       "0                   0.472               0.352             0.449   \n",
       "1                   0.387               0.353             0.528   \n",
       "2                   0.379               0.350             0.466   \n",
       "3                   0.429               0.342             0.496   \n",
       "4                   0.421               0.372             0.470   \n",
       "5                   0.432               0.358             0.561   \n",
       "6                   0.364               0.372             0.474   \n",
       "7                   0.403               0.366             0.533   \n",
       "8                   0.380               0.360             0.502   \n",
       "9                   0.383               0.354             0.506   \n",
       "10                  0.415               0.349             0.505   \n",
       "11                  0.348               0.366             0.517   \n",
       "12                  0.433               0.356             0.502   \n",
       "13                  0.399               0.364             0.494   \n",
       "14                  0.455               0.361             0.468   \n",
       "15                  0.421               0.344             0.513   \n",
       "16                  0.412               0.348             0.507   \n",
       "17                  0.434               0.363             0.504   \n",
       "18                  0.437               0.347             0.484   \n",
       "19                  0.357               0.363             0.533   \n",
       "20                  0.388               0.372             0.537   \n",
       "21                  0.371               0.354             0.486   \n",
       "22                  0.371               0.355             0.527   \n",
       "23                  0.379               0.355             0.471   \n",
       "24                  0.400               0.374             0.542   \n",
       "25                  0.400               0.349             0.538   \n",
       "26                  0.466               0.365             0.508   \n",
       "27                  0.484               0.401             0.492   \n",
       "28                  0.383               0.365             0.511   \n",
       "29                  0.478               0.360             0.514   \n",
       "\n",
       "    % of FG Ast'd 3P  Dunks %FGA  Dunks Md.  Layups %FGA  Layups Md.  \\\n",
       "0              0.766       0.052        223        0.273         702   \n",
       "1              0.849       0.055        232        0.290         794   \n",
       "2              0.757       0.053        214        0.245         640   \n",
       "3              0.843       0.052        211        0.274         696   \n",
       "4              0.832       0.058        230        0.264         673   \n",
       "5              0.861       0.063        263        0.261         657   \n",
       "6              0.812       0.053        221        0.297         705   \n",
       "7              0.850       0.046        190        0.238         633   \n",
       "8              0.815       0.054        220        0.279         745   \n",
       "9              0.810       0.057        246        0.259         706   \n",
       "10             0.835       0.047        206        0.251         663   \n",
       "11             0.833       0.059        252        0.281         731   \n",
       "12             0.837       0.053        216        0.269         701   \n",
       "13             0.809       0.054        221        0.285         734   \n",
       "14             0.777       0.069        285        0.304         789   \n",
       "15             0.818       0.061        264        0.262         736   \n",
       "16             0.846       0.070        297        0.285         760   \n",
       "17             0.834       0.053        230        0.264         701   \n",
       "18             0.823       0.052        228        0.252         733   \n",
       "19             0.835       0.074        280        0.283         728   \n",
       "20             0.838       0.074        291        0.288         740   \n",
       "21             0.879       0.062        244        0.300         733   \n",
       "22             0.852       0.065        267        0.264         691   \n",
       "23             0.812       0.058        222        0.300         723   \n",
       "24             0.861       0.070        272        0.271         698   \n",
       "25             0.820       0.068        280        0.251         692   \n",
       "26             0.857       0.063        250        0.268         702   \n",
       "27             0.803       0.069        283        0.297         814   \n",
       "28             0.795       0.071        278        0.247         658   \n",
       "29             0.767       0.068        283        0.287         806   \n",
       "\n",
       "    Corner %3PA  Corner 3P%  \n",
       "0         0.173       0.378  \n",
       "1         0.258       0.384  \n",
       "2         0.190       0.352  \n",
       "3         0.211       0.362  \n",
       "4         0.253       0.413  \n",
       "5         0.190       0.411  \n",
       "6         0.235       0.399  \n",
       "7         0.277       0.418  \n",
       "8         0.253       0.360  \n",
       "9         0.220       0.356  \n",
       "10        0.244       0.386  \n",
       "11        0.235       0.356  \n",
       "12        0.254       0.378  \n",
       "13        0.204       0.436  \n",
       "14        0.195       0.382  \n",
       "15        0.240       0.396  \n",
       "16        0.240       0.389  \n",
       "17        0.231       0.387  \n",
       "18        0.219       0.404  \n",
       "19        0.264       0.373  \n",
       "20        0.263       0.396  \n",
       "21        0.280       0.371  \n",
       "22        0.240       0.335  \n",
       "23        0.186       0.372  \n",
       "24        0.305       0.411  \n",
       "25        0.243       0.433  \n",
       "26        0.250       0.415  \n",
       "27        0.211       0.438  \n",
       "28        0.212       0.380  \n",
       "29        0.254       0.389  "
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_team_master = pd.merge(df_records, \n",
    "                       df_team_master,\n",
    "                       right_on='TEAM_ABBR',\n",
    "                       left_on='TEAM_ABBR',\n",
    "                       how='left')\n",
    "df_team_master = df_team_master.drop(['TEAM_NAME_y'],axis=1)\n",
    "df_team_master = df_team_master.rename(columns={'TEAM_NAME_x':'TEAM_NAME'})\n",
    "\n",
    "df_team_master"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38e626f",
   "metadata": {},
   "source": [
    "### Function to return DF with R2 for a specified stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "59008b34",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def r2_comparison_from_y(df, staty):\n",
    "    \n",
    "    pd.set_option('display.max_rows', None)\n",
    "    \n",
    "    df_new = df.copy()\n",
    "    \n",
    "    try: df_new = df_new.drop(columns=['TEAM_NAME'])\n",
    "    except: pass\n",
    "    try: df_new = df_new.drop(columns=['TEAM_ABBR'])\n",
    "    except: pass\n",
    "    \n",
    "    df_out = pd.DataFrame(columns=['x','y','r2'])\n",
    "    \n",
    "    y=df_new[staty]\n",
    "    y=y.fillna(y.mean())\n",
    "    \n",
    "    for i in df_new.columns:\n",
    "        x=df_new[i]\n",
    "        try: x=x.fillna(x.mean())\n",
    "        except: x=x.fillna(0)\n",
    "        if is_numeric_dtype(df_new[i]):\n",
    "            model = LinearRegression()\n",
    "            model = model.fit(x.values.reshape(-1, 1), y)\n",
    "            r2= model.score(x.values.reshape(-1, 1), y)\n",
    "            df_temp = pd.DataFrame([[i, staty, r2]], columns=['x','y','r2'])\n",
    "            df_out = df_out.append(df_temp)\n",
    "        \n",
    "    df_out = df_out.sort_values(by=['r2'],ascending=False).reset_index(drop=True)\n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a57b2d",
   "metadata": {},
   "source": [
    "### What stats are most correlated with points?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "f49509c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OPP_OPP_PTS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PTS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FGM</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.566433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AST</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.376762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OPP_FG2A</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.316170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OPP_FGA</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.268374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FG_PCT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.228167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OPP_FG2M</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.220696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OPP_FG2_PTS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.220696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OPP_PLUS_MINUS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.214117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PLUS_MINUS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.214117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OPP_FRAC_MK_2PT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.207081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OPP_FRAC_MK_3PT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.207081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LOSES</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.205118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OPP_FRAC_PTS_3PT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.199953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>WIN_PER</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.188662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>OPP_FRAC_PTS_2PT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.178978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OPP_FRAC_ATT_3PT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.167503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OPP_FRAC_ATT_2PT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.167503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>% of FGA by Distance 3P</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.166329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>% of FGA by Distance 2P</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.166329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FG3_PTS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.163742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FG3M</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.163742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>WINS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.162464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>FG3_PCT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.159912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AWAY_LOSES</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.156525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Layups %FGA</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.153963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>HOME_LOSES</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.150824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Layups Md.</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.137777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HOME_WINS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.134815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>OPP_FGM</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.129792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>EAST_LOSES</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.127864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>FRAC_PTS_FT</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.115589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Dist</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.113017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>DREB</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.107203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Corner 3P%</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.099794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OPP_OREB</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.099609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OPP_FG3M</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.096230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>OPP_FG3_PTS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.096230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>FGA</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.088441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>FG3A</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.085685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>OPP_PTS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.081226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AWAY_WINS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.080456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>LAST_10_LOSES</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.076494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>LAST_10_WINS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.076494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>% of FGA by Distance 3-10</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.076045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>G</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.075664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>GAMES</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.075664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WEST_WINS</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.073281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>FG% by Distance 3P</td>\n",
       "      <td>PTS</td>\n",
       "      <td>0.067621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            x    y        r2\n",
       "0                 OPP_OPP_PTS  PTS  1.000000\n",
       "1                         PTS  PTS  1.000000\n",
       "2                         FGM  PTS  0.566433\n",
       "3                         AST  PTS  0.376762\n",
       "4                    OPP_FG2A  PTS  0.316170\n",
       "5                     OPP_FGA  PTS  0.268374\n",
       "6                      FG_PCT  PTS  0.228167\n",
       "7                    OPP_FG2M  PTS  0.220696\n",
       "8                 OPP_FG2_PTS  PTS  0.220696\n",
       "9              OPP_PLUS_MINUS  PTS  0.214117\n",
       "10                 PLUS_MINUS  PTS  0.214117\n",
       "11            OPP_FRAC_MK_2PT  PTS  0.207081\n",
       "12            OPP_FRAC_MK_3PT  PTS  0.207081\n",
       "13                      LOSES  PTS  0.205118\n",
       "14           OPP_FRAC_PTS_3PT  PTS  0.199953\n",
       "15                    WIN_PER  PTS  0.188662\n",
       "16           OPP_FRAC_PTS_2PT  PTS  0.178978\n",
       "17           OPP_FRAC_ATT_3PT  PTS  0.167503\n",
       "18           OPP_FRAC_ATT_2PT  PTS  0.167503\n",
       "19    % of FGA by Distance 3P  PTS  0.166329\n",
       "20    % of FGA by Distance 2P  PTS  0.166329\n",
       "21                    FG3_PTS  PTS  0.163742\n",
       "22                       FG3M  PTS  0.163742\n",
       "23                       WINS  PTS  0.162464\n",
       "24                    FG3_PCT  PTS  0.159912\n",
       "25                 AWAY_LOSES  PTS  0.156525\n",
       "26                Layups %FGA  PTS  0.153963\n",
       "27                 HOME_LOSES  PTS  0.150824\n",
       "28                 Layups Md.  PTS  0.137777\n",
       "29                  HOME_WINS  PTS  0.134815\n",
       "30                    OPP_FGM  PTS  0.129792\n",
       "31                 EAST_LOSES  PTS  0.127864\n",
       "32                FRAC_PTS_FT  PTS  0.115589\n",
       "33                       Dist  PTS  0.113017\n",
       "34                       DREB  PTS  0.107203\n",
       "35                 Corner 3P%  PTS  0.099794\n",
       "36                   OPP_OREB  PTS  0.099609\n",
       "37                   OPP_FG3M  PTS  0.096230\n",
       "38                OPP_FG3_PTS  PTS  0.096230\n",
       "39                        FGA  PTS  0.088441\n",
       "40                       FG3A  PTS  0.085685\n",
       "41                    OPP_PTS  PTS  0.081226\n",
       "42                  AWAY_WINS  PTS  0.080456\n",
       "43              LAST_10_LOSES  PTS  0.076494\n",
       "44               LAST_10_WINS  PTS  0.076494\n",
       "45  % of FGA by Distance 3-10  PTS  0.076045\n",
       "46                          G  PTS  0.075664\n",
       "47                      GAMES  PTS  0.075664\n",
       "48                  WEST_WINS  PTS  0.073281\n",
       "49         FG% by Distance 3P  PTS  0.067621"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_comparison_from_y(df_team_master, 'PTS').head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "29f2edaf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WIN_PER</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.989516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LOSES</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.963574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OPP_PLUS_MINUS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.886343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLUS_MINUS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.886343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HOME_LOSES</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.802169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HOME_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.785106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AWAY_LOSES</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.630076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AWAY_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.547350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OPP_PTS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.491976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WEST_LOSES</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.437361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OPP_BLK</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.431421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EAST_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.431200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EAST_LOSES</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.390521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>WEST_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.375762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dunks %FGA</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.369104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FG3_PCT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.358190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dunks Md.</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.335430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LAST_10_LOSES</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.330599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LAST_10_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.330599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FG%</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.317129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FG% by Distance 2P</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OPP_AST</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.311763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>OPP_FG_PCT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.311278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>OPP_FGM</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.260534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>OPP_FG3M</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.247276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>OPP_FG3_PTS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.247276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>% of FGA by Distance 10-16</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.240013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>WI5_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.231935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>FG_PCT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.230137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>OPP_MONTH</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.217928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>FG3M</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.212263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>FG3_PTS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.212263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>FG% by Distance 3-10</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.204160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>OT_WINS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.188817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>FRAC_PTS_3PT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.179065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>TEAM_ID</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.167087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OPP_FTM</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.166741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>OPP_OPP_PTS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.162464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PTS</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.162464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>% of FGA by Distance 16-3P</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.161093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>FG2A</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.154665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>FG% by Distance 3P</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.153128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>FRAC_MK_3PT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.152576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>FRAC_MK_2PT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.152576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>PF</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.145168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>OPP_FG3_PCT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.125284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>OREB</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.124655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>FRAC_ATT_3PT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.118022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>FRAC_ATT_2PT</td>\n",
       "      <td>WINS</td>\n",
       "      <td>0.118022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             x     y        r2\n",
       "0                         WINS  WINS  1.000000\n",
       "1                      WIN_PER  WINS  0.989516\n",
       "2                        LOSES  WINS  0.963574\n",
       "3               OPP_PLUS_MINUS  WINS  0.886343\n",
       "4                   PLUS_MINUS  WINS  0.886343\n",
       "5                   HOME_LOSES  WINS  0.802169\n",
       "6                    HOME_WINS  WINS  0.785106\n",
       "7                   AWAY_LOSES  WINS  0.630076\n",
       "8                    AWAY_WINS  WINS  0.547350\n",
       "9                      OPP_PTS  WINS  0.491976\n",
       "10                  WEST_LOSES  WINS  0.437361\n",
       "11                     OPP_BLK  WINS  0.431421\n",
       "12                   EAST_WINS  WINS  0.431200\n",
       "13                  EAST_LOSES  WINS  0.390521\n",
       "14                   WEST_WINS  WINS  0.375762\n",
       "15                  Dunks %FGA  WINS  0.369104\n",
       "16                     FG3_PCT  WINS  0.358190\n",
       "17                   Dunks Md.  WINS  0.335430\n",
       "18               LAST_10_LOSES  WINS  0.330599\n",
       "19                LAST_10_WINS  WINS  0.330599\n",
       "20                         FG%  WINS  0.317129\n",
       "21          FG% by Distance 2P  WINS  0.312500\n",
       "22                     OPP_AST  WINS  0.311763\n",
       "23                  OPP_FG_PCT  WINS  0.311278\n",
       "24                     OPP_FGM  WINS  0.260534\n",
       "25                    OPP_FG3M  WINS  0.247276\n",
       "26                 OPP_FG3_PTS  WINS  0.247276\n",
       "27  % of FGA by Distance 10-16  WINS  0.240013\n",
       "28                    WI5_WINS  WINS  0.231935\n",
       "29                      FG_PCT  WINS  0.230137\n",
       "30                   OPP_MONTH  WINS  0.217928\n",
       "31                        FG3M  WINS  0.212263\n",
       "32                     FG3_PTS  WINS  0.212263\n",
       "33        FG% by Distance 3-10  WINS  0.204160\n",
       "34                     OT_WINS  WINS  0.188817\n",
       "35                FRAC_PTS_3PT  WINS  0.179065\n",
       "36                     TEAM_ID  WINS  0.167087\n",
       "37                     OPP_FTM  WINS  0.166741\n",
       "38                 OPP_OPP_PTS  WINS  0.162464\n",
       "39                         PTS  WINS  0.162464\n",
       "40  % of FGA by Distance 16-3P  WINS  0.161093\n",
       "41                        FG2A  WINS  0.154665\n",
       "42          FG% by Distance 3P  WINS  0.153128\n",
       "43                 FRAC_MK_3PT  WINS  0.152576\n",
       "44                 FRAC_MK_2PT  WINS  0.152576\n",
       "45                          PF  WINS  0.145168\n",
       "46                 OPP_FG3_PCT  WINS  0.125284\n",
       "47                        OREB  WINS  0.124655\n",
       "48                FRAC_ATT_3PT  WINS  0.118022\n",
       "49                FRAC_ATT_2PT  WINS  0.118022"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_comparison_from_y(df_team_master, 'WINS').head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de11b2d",
   "metadata": {},
   "source": [
    "### Regression Analysis + Visualization Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "e1d4978c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_analysis_team_avg(df, statx, staty):\n",
    "    \n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 14))\n",
    "    \n",
    "    ax.set_facecolor('silver')\n",
    "    fig.set_facecolor('lightsteelblue')\n",
    "    fig.set_edgecolor('black')\n",
    "    \n",
    "    df_new = df.copy()\n",
    "\n",
    "    x=df_new[statx]\n",
    "    y=df_new[staty]\n",
    "\n",
    "    # Calc the season trendline\n",
    "    z = np.polyfit(x, y, 1)\n",
    "    p = np.poly1d(z)\n",
    "    plt.plot(x,p(x), label='TRENDLIINE', linestyle = 'solid', alpha=0.5, color='blue', linewidth=2)\n",
    "\n",
    "    plt.scatter(x,y, c=y, cmap='RdYlGn',edgecolor='black', s=70)\n",
    "    try:\n",
    "        plt.axvline(x.mean(), label=f\"{statx} AVG = {round(df_new[df_new['TEAM_ABBR']=='NBA'][statx].mean(),2)}\", linestyle='dashed', color='purple', linewidth=2)\n",
    "        plt.axhline(y.mean(), label=f\"{staty} AVG = {round(df_new[df_new['TEAM_ABBR']=='NBA'][staty].mean(),2)}\", linestyle='dashed', color='teal', linewidth=2)\n",
    "    except:\n",
    "        plt.axvline(x.mean(), label=f\"{statx} AVG = {round(df_new[df_new['TEAM_ABBREVIATION']=='NBA'][statx].mean(),2)}\", linestyle='dashed', color='purple', linewidth=2)\n",
    "        plt.axhline(y.mean(), label=f\"{staty} AVG = {round(df_new[df_new['TEAM_ABBREVIATION']=='NBA'][staty].mean(),2)}\", linestyle='dashed', color='teal', linewidth=2)\n",
    "    plt.xlabel(statx, fontsize=20, fontweight='bold')\n",
    "    plt.ylabel(staty, fontsize=20, fontweight='bold')\n",
    "\n",
    "    plt.title(f'{statx} vs {staty} ~ NBA 22-23', fontsize=30, fontweight='bold')\n",
    "    plt.xticks(fontsize=20, fontweight='bold')\n",
    "    plt.yticks(fontsize=20, fontweight='bold')\n",
    "    \n",
    "    cbar = plt.colorbar()\n",
    "    cbar.ax.tick_params(labelsize=16)\n",
    "    cbar.set_label(label=staty, size=18, weight='bold')\n",
    "    \n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    print()\n",
    "    print(color.BOLD +'Season Trend Line Equation: ' + color.END + \"y = %.6fx + (%.6f)\"%(z[0],z[1]))\n",
    "\n",
    "    # Calc R-squared\n",
    "    model = LinearRegression()\n",
    "    model.fit(x.values.reshape(-1, 1), y)\n",
    "    r_squared = model.score(x.values.reshape(-1, 1), y)\n",
    "    print()\n",
    "    print(color.BOLD + \"R-squared: \"+ color.END + str(r_squared))\n",
    "    print()\n",
    "\n",
    "    lm = smf.ols(formula = f'{staty} ~ {statx}', data=df_new).fit()\n",
    "    print()\n",
    "    print(lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52d4f0f",
   "metadata": {},
   "source": [
    "### TOV vs PTS Regression Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "07671aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mSeason Trend Line Equation: \u001b[0my = -0.362929x + (119.429633)\n",
      "\n",
      "\u001b[1mR-squared: \u001b[0m0.015619970462863342\n",
      "\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    PTS   R-squared:                       0.016\n",
      "Model:                            OLS   Adj. R-squared:                  0.015\n",
      "Method:                 Least Squares   F-statistic:                     24.82\n",
      "Date:                Fri, 03 Feb 2023   Prob (F-statistic):           7.00e-07\n",
      "Time:                        13:39:16   Log-Likelihood:                -6036.5\n",
      "No. Observations:                1566   AIC:                         1.208e+04\n",
      "Df Residuals:                    1564   BIC:                         1.209e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    119.4296      1.087    109.900      0.000     117.298     121.561\n",
      "TOV           -0.3629      0.073     -4.982      0.000      -0.506      -0.220\n",
      "==============================================================================\n",
      "Omnibus:                        4.467   Durbin-Watson:                   1.857\n",
      "Prob(Omnibus):                  0.107   Jarque-Bera (JB):                4.506\n",
      "Skew:                           0.130   Prob(JB):                        0.105\n",
      "Kurtosis:                       2.962   Cond. No.                         56.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "regression_analysis_team_avg(df, 'TOV', 'PTS')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7608e64e",
   "metadata": {},
   "source": [
    "### Function that webscrapes and displays todays NBA matchups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "ef75a38d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NBA Basketball Scores - NBA Scoreboard - ESPN\n",
      "Friday, February 3, 2023\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>away_tm</th>\n",
       "      <th>home_tm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>7:00 PM</td>\n",
       "      <td>Hornets</td>\n",
       "      <td>Pistons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>7:00 PM</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Pacers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>7:00 PM</td>\n",
       "      <td>Trail Blazers</td>\n",
       "      <td>Wizards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>7:30 PM</td>\n",
       "      <td>Suns</td>\n",
       "      <td>Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>8:00 PM</td>\n",
       "      <td>Raptors</td>\n",
       "      <td>Rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>8:00 PM</td>\n",
       "      <td>Magic</td>\n",
       "      <td>Timberwolves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>8:00 PM</td>\n",
       "      <td>76ers</td>\n",
       "      <td>Spurs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Friday, February 3, 2023</td>\n",
       "      <td>9:00 PM</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Date     Time        away_tm       home_tm\n",
       "0  Friday, February 3, 2023  7:00 PM        Hornets       Pistons\n",
       "1  Friday, February 3, 2023  7:00 PM          Kings        Pacers\n",
       "2  Friday, February 3, 2023  7:00 PM  Trail Blazers       Wizards\n",
       "3  Friday, February 3, 2023  7:30 PM           Suns       Celtics\n",
       "4  Friday, February 3, 2023  8:00 PM        Raptors       Rockets\n",
       "5  Friday, February 3, 2023  8:00 PM          Magic  Timberwolves\n",
       "6  Friday, February 3, 2023  8:00 PM          76ers         Spurs\n",
       "7  Friday, February 3, 2023  9:00 PM          Hawks          Jazz"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def today_matchups():\n",
    "    opts = Options()\n",
    "    opts.add_argument(\"--headless\")\n",
    "    \n",
    "    url = \"https://www.espn.com/nba/scoreboard\"\n",
    "    \n",
    "    chrome_driver = \"C:\\\\Users\\\\Peter\\\\Downloads\\\\chromedriver_win32\\\\chromedriver.exe\"\n",
    "    \n",
    "    driver = webdriver.Chrome(options=opts, service=Service(chrome_driver))\n",
    "    \n",
    "    driver.set_page_load_timeout(5)\n",
    "    time.sleep(1)\n",
    "    driver.set_window_size(2100, 9000)\n",
    "    \n",
    "    driver.get(url)\n",
    "\n",
    "    html = driver.page_source\n",
    "    soup = BeautifulSoup(html, \"html.parser\")\n",
    "\n",
    "    date = soup.find('h3', class_=\"Card__Header__Title Card__Header__Title--no-theme\").text\n",
    "\n",
    "    games = soup.find_all(\"div\", class_='Scoreboard__Row flex w-100 Scoreboard__Row__Main')\n",
    "    games_data = []\n",
    "\n",
    "    df = pd.DataFrame()\n",
    "\n",
    "    for game in games:\n",
    "        game_data = {}\n",
    "        away_tm = game.find_all(\"div\", class_=\"ScoreCell__TeamName ScoreCell__TeamName--shortDisplayName truncate db\")[0]\n",
    "        home_tm = game.find_all(\"div\", class_=\"ScoreCell__TeamName ScoreCell__TeamName--shortDisplayName truncate db\")[1]\n",
    "        game_data['Date'] = date\n",
    "        game_data['Time'] = game.find(\"div\", class_=\"ScoreCell__Time ScoreboardScoreCell__Time h9 clr-gray-03\").text\n",
    "        game_data[\"away_tm\"] = away_tm.text\n",
    "        #game_data[\"away_tm_abbr\"] = team_names_nocity_dict[away_tm.text]\n",
    "        \n",
    "        game_data[\"home_tm\"] = home_tm.text\n",
    "        #game_data[\"home_tm_abbr\"] = team_names_nocity_dict[home_tm.text]\n",
    "        \n",
    "        \n",
    "        games_data.append(game_data)\n",
    "\n",
    "    df = pd.DataFrame(games_data)\n",
    "    \n",
    "    print(soup.title.text)\n",
    "    print(date)\n",
    "    display(df)\n",
    "    \n",
    "    driver.quit()\n",
    "\n",
    "today_matchups()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89dc427a",
   "metadata": {},
   "source": [
    "### Function that simulates a matchup a specified number of times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "9ef266eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Team 1 - Away\n",
    "# Team 2 - Home\n",
    "\n",
    "def multi_game_simulations(n, team1abbr, team2abbr):\n",
    "    \n",
    "    def gauss_game_sim(team1abbr, team2abbr):\n",
    "        \n",
    "        df1 = df[df.TEAM_ABBREVIATION==team1abbr]\n",
    "        df1_A = df1[df1.HOME_AWAY == 'AWAY']\n",
    "        df1_L1 = df1.head(1)\n",
    "        df1_L3 = df1.head(3)\n",
    "        df1_L7 = df1.head(7)\n",
    "        df1_L10 = df1.head(10)\n",
    "        df1_master = df_team_master[df_team_master.TEAM_ABBR==team1abbr]\n",
    "        \n",
    "        df2 = df[df.TEAM_ABBREVIATION==team2abbr]\n",
    "        df2_H = df2[df2.HOME_AWAY == 'HOME']\n",
    "        df2_L1 = df2.head(1)\n",
    "        df2_L3 = df2.head(3)\n",
    "        df2_L7 = df2.head(7)\n",
    "        df2_L10 = df2.head(10)\n",
    "        df2_master = df_team_master[df_team_master.TEAM_ABBR==team2abbr]\n",
    "        \n",
    "        # 30 Percent Weight - Overall\n",
    "        team1Score_all = (rnd.gauss(df1.PTS.mean(),df1.PTS.std())+rnd.gauss(df2.OPP_PTS.mean(),df2.OPP_PTS.std()))/2\n",
    "        team2Score_all = (rnd.gauss(df2.PTS.mean(),df2.PTS.std())+rnd.gauss(df1.OPP_PTS.mean(),df1.OPP_PTS.std()))/2\n",
    "        \n",
    "        # 30 Percent Weight - Away vs Home\n",
    "        team1AScore = (rnd.gauss(df1_A.PTS.mean(),df1_A.PTS.std())+rnd.gauss(df2_H.OPP_PTS.mean(),df2_H.OPP_PTS.std()))/2\n",
    "        team2HScore = (rnd.gauss(df2_H.PTS.mean(),df2_H.PTS.std())+rnd.gauss(df1_A.OPP_PTS.mean(),df1_A.OPP_PTS.std()))/2\n",
    "        \n",
    "        # 40 Percent - Trends/Rolling Means - 10% each \n",
    "        team1_L1Score = df1_L1.PTS\n",
    "        team2_L1Score = df2_L1.PTS\n",
    "        \n",
    "        team1_L3Score = (rnd.gauss(df1_L3.PTS.mean(),df1_L3.PTS.std())+rnd.gauss(df2_L3.OPP_PTS.mean(),df2_L3.OPP_PTS.std()))/2\n",
    "        team2_L3Score = (rnd.gauss(df2_L3.PTS.mean(),df2_L3.PTS.std())+rnd.gauss(df1_L3.OPP_PTS.mean(),df1_L3.OPP_PTS.std()))/2\n",
    "        \n",
    "        team1_L7Score = (rnd.gauss(df1_L7.PTS.mean(),df1_L7.PTS.std())+rnd.gauss(df2_L7.OPP_PTS.mean(),df2_L7.OPP_PTS.std()))/2\n",
    "        team2_L7Score = (rnd.gauss(df2_L7.PTS.mean(),df2_L7.PTS.std())+rnd.gauss(df1_L7.OPP_PTS.mean(),df1_L7.OPP_PTS.std()))/2\n",
    "        \n",
    "        team1_L10Score = (rnd.gauss(df1_L10.PTS.mean(),df1_L10.PTS.std())+rnd.gauss(df2_L10.OPP_PTS.mean(),df2_L10.OPP_PTS.std()))/2\n",
    "        team2_L10Score = (rnd.gauss(df2_L10.PTS.mean(),df2_L10.PTS.std())+rnd.gauss(df1_L10.OPP_PTS.mean(),df1_L10.OPP_PTS.std()))/2\n",
    "        \n",
    "        \n",
    "        # Weigh in previous matchup? 20 percent\n",
    "        try:\n",
    "            df1_past = df1[df1.OPP_TEAM_ABBR == team2abbr]\n",
    "            team1_pastScore = rnd.gauss(df1_past.PTS.mean(),df1_past.PTS.std())\n",
    "            team2_pastScore = rnd.gauss(df1_past.OPP_PTS.mean(),df1_past.OPP_PTS.std())\n",
    "            \n",
    "            team1Score = int(round(((team1Score_all*3) + (team1AScore*3) + team1_L1Score + team1_L3Score +\n",
    "                          team1_L7Score + team1_L10Score + (team1_pastScore*2))/12))\n",
    "            \n",
    "            team2Score = int(round(((team2Score_all*3) + (team2HScore*3) + team2_L1Score + team2_L3Score +\n",
    "                          team2_L7Score + team2_L10Score + (team2_pastScore*2))/12))\n",
    "        except:\n",
    "            team1Score = int(round(((team1Score_all*3) + (team1AScore*3) + team1_L1Score +\n",
    "                          team1_L3Score + team1_L7Score + team1_L10Score)/10))\n",
    "            \n",
    "            team2Score = int(round(((team2Score_all*3) + (team2HScore*3) + team2_L1Score +\n",
    "                          team2_L3Score + team2_L7Score + team2_L10Score)/10))\n",
    "        \n",
    "        \n",
    "        # Factor in Turnovers \n",
    "        team1TOV = (rnd.gauss(df1.TOV.mean(),df1.TOV.std()) + rnd.gauss(df[df.OPP_TEAM_ABBR==team1abbr].TOV.mean(),df[df.OPP_TEAM_ABBR==team1abbr].TOV.std()))/2\n",
    "        team2TOV = (rnd.gauss(df2.TOV.mean(),df2.TOV.std()) + rnd.gauss(df[df.OPP_TEAM_ABBR==team2abbr].TOV.mean(),df[df.OPP_TEAM_ABBR==team2abbr].TOV.std()))/2\n",
    "        \n",
    "        TOV_diff = team2TOV-team1TOV\n",
    "        \n",
    "        #Pts off turnover - assuming each turnover affects score by 0.369 points\n",
    "        team1Score = team1Score + (TOV_diff*0.369/2)\n",
    "        team2Score = team2Score - (TOV_diff*0.369/2)\n",
    "        \n",
    "        # Factor in w/i 5 Games -> award 1 point for every 20 percent W% differential -> 100% diff = 5 points\n",
    "        team1_wi5_Wper = df1_master.WI5_WINS.item() / (df1_master.WI5_WINS.item() + df1_master.WI5_LOSES.item())\n",
    "        team2_wi5_Wper = df2_master.WI5_WINS.item() / (df2_master.WI5_WINS.item() + df2_master.WI5_LOSES.item())\n",
    "        wi5_diff = team1_wi5_Wper - team2_wi5_Wper\n",
    "        \n",
    "        team1Score = int(round(team1Score + int(wi5_diff*2.5)))\n",
    "        team2Score = int(round(team2Score - int(wi5_diff*2.5)))\n",
    "        \n",
    "        # ***Later additions?***\n",
    "        # Factor in Offensive Rebs Differential?\n",
    "        # Factor in Blocks?\n",
    "            \n",
    "        # For Ties, simulate overtime based on OT - 10 points in overtime\n",
    "        while team1Score == team2Score:\n",
    "            team1Score = team1Score + int(round((((rnd.gauss(df1.PTS.mean(),df1.PTS.std())+rnd.gauss(df2.OPP_PTS.mean(),df2.OPP_PTS.std()))/2)/9.6)*((df1_master.OT_WINS / (df1_master.OT_WINS + df1_master.OT_LOSES))*2)))\n",
    "            team2Score = team2Score + int(round((((rnd.gauss(df2.PTS.mean(),df2.PTS.std())+rnd.gauss(df1.OPP_PTS.mean(),df1.OPP_PTS.std()))/2)/9.6)*((df2_master.OT_WINS / (df2_master.OT_WINS + df2_master.OT_LOSES))*2)))\n",
    "        \n",
    "        if team1Score > team2Score:\n",
    "            return 1, team1Score, team2Score\n",
    "        elif team1Score < team2Score:\n",
    "            return -1, team1Score, team2Score\n",
    "        else: return 0, team1Score, team2Score\n",
    "    \n",
    "    gameResults = []\n",
    "    team1Wins = 0\n",
    "    team2Wins = 0\n",
    "    Ties = 0\n",
    "    team1scores=[]\n",
    "    team2scores=[]\n",
    "    for i in range(n):\n",
    "        gm, tm1sc, tm2sc = gauss_game_sim(team1abbr, team2abbr)\n",
    "        team1scores.append(tm1sc)\n",
    "        team2scores.append(tm2sc)\n",
    "        gameResults.append(gm)\n",
    "        if gm == 1:\n",
    "            team1Wins += 1\n",
    "        elif gm == -1:\n",
    "            team2Wins += 1\n",
    "        else: Ties += 1\n",
    "            \n",
    "    team1WinPer = team1Wins/(n)\n",
    "    team2WinPer = team2Wins/(n)\n",
    "    TiePer = Ties/(n)\n",
    "    \n",
    "    team1MeanSc = statistics.mean(team1scores)\n",
    "    team2MeanSc = statistics.mean(team2scores)\n",
    "\n",
    "    return team1WinPer, team2WinPer, team1MeanSc, team2MeanSc, team1scores, team2scores\n",
    "\n",
    "#multi_game_simulations(10, 'CLE', 'BOS')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac73212",
   "metadata": {},
   "source": [
    "### Todays matchups with with n-simulations win% and mean score for each matchup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "2a83d5a6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NBA Score Predictions\n",
      "Date:  Friday, February 3, 2023\n",
      "Time:  02:20 PM\n",
      "Through 10,000 simulations\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Date</th>\n",
       "      <th class=\"col_heading level0 col1\" >Time</th>\n",
       "      <th class=\"col_heading level0 col2\" >O/U</th>\n",
       "      <th class=\"col_heading level0 col3\" >Line</th>\n",
       "      <th class=\"col_heading level0 col4\" >away_tm</th>\n",
       "      <th class=\"col_heading level0 col5\" >away_sim_W%</th>\n",
       "      <th class=\"col_heading level0 col6\" >away_mean_sc</th>\n",
       "      <th class=\"col_heading level0 col7\" >home_tm</th>\n",
       "      <th class=\"col_heading level0 col8\" >home_sim_W%</th>\n",
       "      <th class=\"col_heading level0 col9\" >home_mean_sc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4fd32_row0_col0\" class=\"data row0 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row0_col1\" class=\"data row0 col1\" >7:00 PM</td>\n",
       "      <td id=\"T_4fd32_row0_col2\" class=\"data row0 col2\" >239.5</td>\n",
       "      <td id=\"T_4fd32_row0_col3\" class=\"data row0 col3\" >DET -1.0</td>\n",
       "      <td id=\"T_4fd32_row0_col4\" class=\"data row0 col4\" >Hornets</td>\n",
       "      <td id=\"T_4fd32_row0_col5\" class=\"data row0 col5\" >0.403</td>\n",
       "      <td id=\"T_4fd32_row0_col6\" class=\"data row0 col6\" >114.97</td>\n",
       "      <td id=\"T_4fd32_row0_col7\" class=\"data row0 col7\" >Pistons</td>\n",
       "      <td id=\"T_4fd32_row0_col8\" class=\"data row0 col8\" >0.597</td>\n",
       "      <td id=\"T_4fd32_row0_col9\" class=\"data row0 col9\" >116.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_4fd32_row1_col0\" class=\"data row1 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row1_col1\" class=\"data row1 col1\" >7:00 PM</td>\n",
       "      <td id=\"T_4fd32_row1_col2\" class=\"data row1 col2\" >234.0</td>\n",
       "      <td id=\"T_4fd32_row1_col3\" class=\"data row1 col3\" >SAC -3.5</td>\n",
       "      <td id=\"T_4fd32_row1_col4\" class=\"data row1 col4\" >Kings</td>\n",
       "      <td id=\"T_4fd32_row1_col5\" class=\"data row1 col5\" >0.781</td>\n",
       "      <td id=\"T_4fd32_row1_col6\" class=\"data row1 col6\" >119.56</td>\n",
       "      <td id=\"T_4fd32_row1_col7\" class=\"data row1 col7\" >Pacers</td>\n",
       "      <td id=\"T_4fd32_row1_col8\" class=\"data row1 col8\" >0.219</td>\n",
       "      <td id=\"T_4fd32_row1_col9\" class=\"data row1 col9\" >114.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_4fd32_row2_col0\" class=\"data row2 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row2_col1\" class=\"data row2 col1\" >7:00 PM</td>\n",
       "      <td id=\"T_4fd32_row2_col2\" class=\"data row2 col2\" >236.5</td>\n",
       "      <td id=\"T_4fd32_row2_col3\" class=\"data row2 col3\" >WSH -3.5</td>\n",
       "      <td id=\"T_4fd32_row2_col4\" class=\"data row2 col4\" >Trail Blazers</td>\n",
       "      <td id=\"T_4fd32_row2_col5\" class=\"data row2 col5\" >0.366</td>\n",
       "      <td id=\"T_4fd32_row2_col6\" class=\"data row2 col6\" >115.66</td>\n",
       "      <td id=\"T_4fd32_row2_col7\" class=\"data row2 col7\" >Wizards</td>\n",
       "      <td id=\"T_4fd32_row2_col8\" class=\"data row2 col8\" >0.634</td>\n",
       "      <td id=\"T_4fd32_row2_col9\" class=\"data row2 col9\" >117.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_4fd32_row3_col0\" class=\"data row3 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row3_col1\" class=\"data row3 col1\" >7:30 PM</td>\n",
       "      <td id=\"T_4fd32_row3_col2\" class=\"data row3 col2\" >220.0</td>\n",
       "      <td id=\"T_4fd32_row3_col3\" class=\"data row3 col3\" >BOS -9.0</td>\n",
       "      <td id=\"T_4fd32_row3_col4\" class=\"data row3 col4\" >Suns</td>\n",
       "      <td id=\"T_4fd32_row3_col5\" class=\"data row3 col5\" >0.057</td>\n",
       "      <td id=\"T_4fd32_row3_col6\" class=\"data row3 col6\" >110.78</td>\n",
       "      <td id=\"T_4fd32_row3_col7\" class=\"data row3 col7\" >Celtics</td>\n",
       "      <td id=\"T_4fd32_row3_col8\" class=\"data row3 col8\" >0.944</td>\n",
       "      <td id=\"T_4fd32_row3_col9\" class=\"data row3 col9\" >119.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_4fd32_row4_col0\" class=\"data row4 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row4_col1\" class=\"data row4 col1\" >8:00 PM</td>\n",
       "      <td id=\"T_4fd32_row4_col2\" class=\"data row4 col2\" >225.0</td>\n",
       "      <td id=\"T_4fd32_row4_col3\" class=\"data row4 col3\" >TOR -8.0</td>\n",
       "      <td id=\"T_4fd32_row4_col4\" class=\"data row4 col4\" >Raptors</td>\n",
       "      <td id=\"T_4fd32_row4_col5\" class=\"data row4 col5\" >0.784</td>\n",
       "      <td id=\"T_4fd32_row4_col6\" class=\"data row4 col6\" >116.56</td>\n",
       "      <td id=\"T_4fd32_row4_col7\" class=\"data row4 col7\" >Rockets</td>\n",
       "      <td id=\"T_4fd32_row4_col8\" class=\"data row4 col8\" >0.216</td>\n",
       "      <td id=\"T_4fd32_row4_col9\" class=\"data row4 col9\" >112.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_4fd32_row5_col0\" class=\"data row5 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row5_col1\" class=\"data row5 col1\" >8:00 PM</td>\n",
       "      <td id=\"T_4fd32_row5_col2\" class=\"data row5 col2\" >229.5</td>\n",
       "      <td id=\"T_4fd32_row5_col3\" class=\"data row5 col3\" >MIN -4.5</td>\n",
       "      <td id=\"T_4fd32_row5_col4\" class=\"data row5 col4\" >Magic</td>\n",
       "      <td id=\"T_4fd32_row5_col5\" class=\"data row5 col5\" >0.199</td>\n",
       "      <td id=\"T_4fd32_row5_col6\" class=\"data row5 col6\" >111.04</td>\n",
       "      <td id=\"T_4fd32_row5_col7\" class=\"data row5 col7\" >Timberwolves</td>\n",
       "      <td id=\"T_4fd32_row5_col8\" class=\"data row5 col8\" >0.801</td>\n",
       "      <td id=\"T_4fd32_row5_col9\" class=\"data row5 col9\" >115.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_4fd32_row6_col0\" class=\"data row6 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row6_col1\" class=\"data row6 col1\" >8:00 PM</td>\n",
       "      <td id=\"T_4fd32_row6_col2\" class=\"data row6 col2\" >234.5</td>\n",
       "      <td id=\"T_4fd32_row6_col3\" class=\"data row6 col3\" >PHI -10.5</td>\n",
       "      <td id=\"T_4fd32_row6_col4\" class=\"data row6 col4\" >76ers</td>\n",
       "      <td id=\"T_4fd32_row6_col5\" class=\"data row6 col5\" >0.871</td>\n",
       "      <td id=\"T_4fd32_row6_col6\" class=\"data row6 col6\" >118.40</td>\n",
       "      <td id=\"T_4fd32_row6_col7\" class=\"data row6 col7\" >Spurs</td>\n",
       "      <td id=\"T_4fd32_row6_col8\" class=\"data row6 col8\" >0.130</td>\n",
       "      <td id=\"T_4fd32_row6_col9\" class=\"data row6 col9\" >111.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4fd32_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_4fd32_row7_col0\" class=\"data row7 col0\" >Friday, February 3, 2023</td>\n",
       "      <td id=\"T_4fd32_row7_col1\" class=\"data row7 col1\" >9:00 PM</td>\n",
       "      <td id=\"T_4fd32_row7_col2\" class=\"data row7 col2\" >242.0</td>\n",
       "      <td id=\"T_4fd32_row7_col3\" class=\"data row7 col3\" >UTAH -1.5</td>\n",
       "      <td id=\"T_4fd32_row7_col4\" class=\"data row7 col4\" >Hawks</td>\n",
       "      <td id=\"T_4fd32_row7_col5\" class=\"data row7 col5\" >0.444</td>\n",
       "      <td id=\"T_4fd32_row7_col6\" class=\"data row7 col6\" >120.51</td>\n",
       "      <td id=\"T_4fd32_row7_col7\" class=\"data row7 col7\" >Jazz</td>\n",
       "      <td id=\"T_4fd32_row7_col8\" class=\"data row7 col8\" >0.556</td>\n",
       "      <td id=\"T_4fd32_row7_col9\" class=\"data row7 col9\" >121.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2bb9a19dca0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def today_multi_preds(n):\n",
    "    opts = Options()\n",
    "    opts.add_argument(\"--headless\")\n",
    "    \n",
    "    url = \"https://www.espn.com/nba/scoreboard\"\n",
    "    \n",
    "    chrome_driver = \"C:\\\\Users\\\\Peter\\\\Downloads\\\\chromedriver_win32\\\\chromedriver.exe\"\n",
    "    \n",
    "    driver = webdriver.Chrome(options=opts, service=Service(chrome_driver))\n",
    "    \n",
    "    driver.set_page_load_timeout(5)\n",
    "    time.sleep(1)\n",
    "    driver.set_window_size(2100, 9000)\n",
    "    \n",
    "    driver.get(url)\n",
    "\n",
    "    html = driver.page_source\n",
    "    soup = BeautifulSoup(html, \"html.parser\")\n",
    "\n",
    "    date = soup.find('h3', class_=\"Card__Header__Title Card__Header__Title--no-theme\").text\n",
    "\n",
    "    games = soup.find_all(\"div\", class_='Scoreboard__RowContainer flex flex-column flex-auto')\n",
    "    games_data = []\n",
    "\n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    i,j=0,0\n",
    "    PLOTS_PER_ROW = 3\n",
    "    \n",
    "    cdict = {'red': ((0.0, 0.0, 0.0),\n",
    "                (0.5, 1.0, 1.0),\n",
    "                (1.0, 1.0, 0.7)),\n",
    "\n",
    "        'green': ((0.0, 0.7, 0.7),\n",
    "                  (0.5, 1.0, 1.0),\n",
    "                  (1.0, 0.0, 0.0)),\n",
    "\n",
    "         'blue': ((0.0, 0.0, 0.0),\n",
    "                  (0.5, 1.0, 1.0),\n",
    "                  (1.0, 0.0, 0.0))\n",
    "         }\n",
    "    GnRd = colors.LinearSegmentedColormap('GnRd', cdict).reversed()\n",
    "    \n",
    "    fig, axs = plt.subplots(nrows=math.ceil(len(games)/PLOTS_PER_ROW),ncols=PLOTS_PER_ROW, figsize=(20, 20))\n",
    "    fig.set_facecolor('lightgrey')\n",
    "    \n",
    "    fig.suptitle(f'Score Predictions KDE Distribution through {n} simulations', y=0.93, fontsize=20, fontweight='bold')\n",
    "    \n",
    "    for game in games:\n",
    "        game_data = {}\n",
    "        \n",
    "        away_tm = game.find_all(\"div\", class_=\"ScoreCell__TeamName ScoreCell__TeamName--shortDisplayName truncate db\")[0]\n",
    "        home_tm = game.find_all(\"div\", class_=\"ScoreCell__TeamName ScoreCell__TeamName--shortDisplayName truncate db\")[1]\n",
    "        \n",
    "        away_sim_Wper, home_sim_Wper, away_mn_sc, home_mn_sc, away_scores, home_scores = multi_game_simulations(n, team_names_nocity_dict[away_tm.text], team_names_nocity_dict[home_tm.text])\n",
    "        \n",
    "        game_data['Date'] = date\n",
    "        game_data['Time'] = game.find(\"div\", class_=\"ScoreCell__Time ScoreboardScoreCell__Time h9 clr-gray-03\").text\n",
    "        \n",
    "        game_data['O/U'] = '{0:.1f}'.format(float(str(game.find(\"div\", class_=\"Odds__Message\").text).split(' ')[-1]))\n",
    "        game_data['Line'] = str(game.find(\"div\", class_=\"Odds__Message\").text).lstrip('Line : ').split('O/U')[0]\n",
    "        \n",
    "        game_data[\"away_tm\"] = away_tm.text\n",
    "        #game_data[\"away_tm_abbr\"] = team_names_nocity_dict[away_tm.text]\n",
    "        game_data[f\"away_sim_W%\"] = '{0:.3f}'.format(away_sim_Wper)\n",
    "        game_data[f\"away_mean_sc\"] = '{0:.2f}'.format(away_mn_sc)\n",
    "        \n",
    "        game_data[\"home_tm\"] = home_tm.text\n",
    "        #game_data[\"home_tm_abbr\"] = team_names_nocity_dict[home_tm.text]\n",
    "        game_data[f\"home_sim_W%\"] = '{0:.3f}'.format(home_sim_Wper)\n",
    "        game_data[f\"home_mean_sc\"] = '{0:.2f}'.format(home_mn_sc)\n",
    "        \n",
    "        # Add line\n",
    "        # Add O/U\n",
    "        # Add mean pred Total\n",
    "        \n",
    "        games_data.append(game_data)\n",
    "        \n",
    "        axs[i][j].set_facecolor('grey')\n",
    "        sns.kdeplot(np.array(home_scores), ax=axs[i][j], color=GnRd(home_sim_Wper), linewidth=2, label=home_tm.text)\n",
    "        sns.kdeplot(np.array(away_scores), ax=axs[i][j], color=GnRd(away_sim_Wper), linewidth=2, label=away_tm.text)\n",
    "        \n",
    "        axs[i][j].axvline(np.array(home_scores).mean(), color=GnRd(home_sim_Wper), linewidth=1, linestyle='dashed')\n",
    "        axs[i][j].axvline(np.array(away_scores).mean(), color=GnRd(away_sim_Wper), linewidth=1, linestyle='dashed')\n",
    "        \n",
    "        axs[i][j].set_ylabel('Density', fontsize=13, fontweight='bold')\n",
    "        axs[i][j].set_xlabel('PTS', fontsize=13, fontweight='bold')\n",
    "        \n",
    "        axs[i][j].margins(y=1)\n",
    "        \n",
    "        axs[i][j].tick_params(axis ='both', which ='major', labelsize = 13)\n",
    "        \n",
    "        axs[i][j].legend(loc='best', facecolor='lightgrey')\n",
    "        \n",
    "        axs[i][j].set_title(f'Home: {home_tm.text} vs. Away: {away_tm.text}', fontsize=14, fontweight='bold')\n",
    "        axs[i][j].grid(color='lightgrey',alpha=0.2)\n",
    "        \n",
    "        j+=1\n",
    "        if j%PLOTS_PER_ROW==0:\n",
    "            i+=1\n",
    "            j=0\n",
    "\n",
    "    df = pd.DataFrame(games_data)\n",
    "    \n",
    "    print('NBA Score Predictions')\n",
    "    print('Date: ',date)\n",
    "    print('Time: ',datetime.datetime.now().strftime('%I:%M %p'))\n",
    "    print(f'Through {\"{:,}\".format(n)} simulations')\n",
    "    \n",
    "    df = df.style.background_gradient(axis=None, subset=['away_sim_W%','home_sim_W%'], cmap=GnRd, vmin=0.1, vmax=0.9)\n",
    "    display(df)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "today_multi_preds(10000)"
   ]
  }
 ],
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