5a. Model Dev_W_L.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a06c1ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "import time\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, classification_report,\\\n",
    "    precision_score, recall_score, roc_auc_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import  GridSearchCV, cross_val_predict\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import lightgbm as lgb\n",
    "from xgboost import XGBClassifier\n",
    "import time\n",
    "from collections import defaultdict\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "294b4bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "games = pd.read_csv('data/full_data.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "424ca3b3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>Week</th>\n",
       "      <th>home_pts</th>\n",
       "      <th>away_pts</th>\n",
       "      <th>total_pts</th>\n",
       "      <th>home_win</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>home_abbr</th>\n",
       "      <th>away_abbr</th>\n",
       "      <th>PointSpread</th>\n",
       "      <th>OverUnder</th>\n",
       "      <th>AwayTeamMoneyLine</th>\n",
       "      <th>HomeTeamMoneyLine</th>\n",
       "      <th>home_fav</th>\n",
       "      <th>date_match</th>\n",
       "      <th>DST1_away</th>\n",
       "      <th>DST1_home</th>\n",
       "      <th>QB1_away</th>\n",
       "      <th>QB1_home</th>\n",
       "      <th>RB1_away</th>\n",
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       "      <th>RB2_away</th>\n",
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       "      <td>41.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2016</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sun</td>\n",
       "      <td>1:04PM</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BUF</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>130</td>\n",
       "      <td>-150</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2900.0</td>\n",
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       "      <td>6400.0</td>\n",
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       "      <td>2500.0</td>\n",
       "      <td>3000.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
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       "      <td>50.0</td>\n",
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       "      <td>1:04PM</td>\n",
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       "      <td>-6.0</td>\n",
       "      <td>48.0</td>\n",
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       "      <td>175</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-09-11</td>\n",
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       "      <td>7200.0</td>\n",
       "      <td>6700.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2016</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>1:05PM</td>\n",
       "      <td>ATL</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>7400.0</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2500.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1:05PM</td>\n",
       "      <td>HOU</td>\n",
       "      <td>CHI</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>220</td>\n",
       "      <td>-260</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
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       "      <th>1742</th>\n",
       "      <td>2022</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>4:05PM</td>\n",
       "      <td>SF</td>\n",
       "      <td>MIA</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>45.5</td>\n",
       "      <td>180</td>\n",
       "      <td>-217</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2600.0</td>\n",
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       "      <td>2022</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>True</td>\n",
       "      <td>2022-12-04</td>\n",
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       "      <td>2500.0</td>\n",
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       "      <td>8500.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>4200.0</td>\n",
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       "      <td>6500.0</td>\n",
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       "      <td>4600.0</td>\n",
       "      <td>5600.0</td>\n",
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       "      <td>113</td>\n",
       "      <td>False</td>\n",
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       "      <td>7100.0</td>\n",
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       "      <td>6000.0</td>\n",
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       "      <td>4900.0</td>\n",
       "      <td>7200.0</td>\n",
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       "      <td>3000.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>2500.0</td>\n",
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       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>2022</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Sun</td>\n",
       "      <td>8:20PM</td>\n",
       "      <td>DAL</td>\n",
       "      <td>IND</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>394</td>\n",
       "      <td>-513</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>2300.0</td>\n",
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       "      <td>5200.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>6100.0</td>\n",
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       "      <td>4600.0</td>\n",
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       "      <td>3400.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>2600.0</td>\n",
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       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>2022</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Mon</td>\n",
       "      <td>8:15PM</td>\n",
       "      <td>TB</td>\n",
       "      <td>NO</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>40.5</td>\n",
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       "      <td>True</td>\n",
       "      <td>2022-12-05</td>\n",
       "      <td>2500.0</td>\n",
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       "      <td>6400.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>6300.0</td>\n",
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       "      <td>3900.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>2700.0</td>\n",
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       "</table>\n",
       "<p>1747 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  Week  home_pts  away_pts  total_pts  home_win  Day    Time  \\\n",
       "0       2016     1      21.0      20.0       41.0         1  Thu  8:40PM   \n",
       "1       2016     1      13.0       7.0       20.0         1  Sun  1:04PM   \n",
       "2       2016     1      23.0      27.0       50.0         0  Sun  1:04PM   \n",
       "3       2016     1      24.0      31.0       55.0         0  Sun  1:05PM   \n",
       "4       2016     1      23.0      14.0       37.0         1  Sun  1:05PM   \n",
       "...      ...   ...       ...       ...        ...       ...  ...     ...   \n",
       "1742    2022    13       NaN       NaN        NaN         0  Sun  4:05PM   \n",
       "1743    2022    13       NaN       NaN        NaN         0  Sun  4:25PM   \n",
       "1744    2022    13       NaN       NaN        NaN         0  Sun  4:25PM   \n",
       "1745    2022    13       NaN       NaN        NaN         0  Sun  8:20PM   \n",
       "1746    2022    13       NaN       NaN        NaN         0  Mon  8:15PM   \n",
       "\n",
       "     home_abbr away_abbr  PointSpread  OverUnder  AwayTeamMoneyLine  \\\n",
       "0          DEN       CAR         -3.0       41.0               -165   \n",
       "1          BAL       BUF         -3.0       45.0                130   \n",
       "2          JAX        GB         -6.0       48.0               -210   \n",
       "3          ATL        TB         -3.0       47.0                115   \n",
       "4          HOU       CHI         -6.0       42.5                220   \n",
       "...        ...       ...          ...        ...                ...   \n",
       "1742        SF       MIA         -4.5       45.5                180   \n",
       "1743        LV       LAC         -2.5       49.5                115   \n",
       "1744       CIN        KC         -2.0       52.5               -134   \n",
       "1745       DAL       IND        -10.5       44.0                394   \n",
       "1746        TB        NO         -3.5       40.5                162   \n",
       "\n",
       "      HomeTeamMoneyLine  home_fav  date_match  DST1_away  DST1_home  QB1_away  \\\n",
       "0                   145     False  2016-09-08     3800.0     3700.0    7700.0   \n",
       "1                  -150      True  2016-09-11     2900.0     2700.0    6900.0   \n",
       "2                   175     False  2016-09-11     2800.0     2400.0    8500.0   \n",
       "3                  -135      True  2016-09-11     2600.0     2800.0    6600.0   \n",
       "4                  -260      True  2016-09-11     2500.0     3200.0    6300.0   \n",
       "...                 ...       ...         ...        ...        ...       ...   \n",
       "1742               -217      True  2022-12-04     2500.0     3100.0    6700.0   \n",
       "1743               -136      True  2022-12-04     3400.0     2500.0    7200.0   \n",
       "1744                113     False  2022-12-04     3500.0     2400.0    8300.0   \n",
       "1745               -513      True  2022-12-04     2300.0     3900.0    5200.0   \n",
       "1746               -193      True  2022-12-05     2500.0     3600.0    5200.0   \n",
       "\n",
       "      QB1_home  RB1_away  RB1_home  RB2_away  RB2_home  WR1_away  WR1_home  \\\n",
       "0       5200.0    5100.0    5900.0    3000.0    3300.0    5700.0    7000.0   \n",
       "1       6700.0    6400.0    4600.0    3800.0    4100.0    6900.0    4900.0   \n",
       "2       7200.0    6700.0    4300.0    4800.0    4100.0    7700.0    8300.0   \n",
       "3       7100.0    6200.0    6900.0    4400.0    4200.0    7400.0    9400.0   \n",
       "4       6300.0    4700.0    7000.0    3700.0    3600.0    7900.0    8800.0   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1742    5700.0    6100.0    8600.0    5500.0    4800.0    8800.0    6600.0   \n",
       "1743    5600.0    8500.0    7900.0    4200.0    4600.0    6500.0    8700.0   \n",
       "1744    6900.0    5700.0    7100.0    4500.0    6000.0    5700.0    7900.0   \n",
       "1745    6200.0    7700.0    6500.0    5300.0    5800.0    6100.0    7300.0   \n",
       "1746    5600.0    7000.0    5700.0    4800.0    5600.0    6400.0    6500.0   \n",
       "\n",
       "      WR2_away  WR2_home  WR3_away  WR3_home  WR4_away  WR4_home  TE1_away  \\\n",
       "0       4100.0    5700.0    3000.0    3000.0    3000.0    3000.0    5100.0   \n",
       "1       4600.0    4700.0    3300.0    4200.0    3000.0    3700.0    3400.0   \n",
       "2       7600.0    5800.0    4300.0    3500.0    3700.0    3100.0    3100.0   \n",
       "3       4400.0    5100.0    3400.0    3100.0    3300.0    3000.0    2900.0   \n",
       "4       5200.0    3800.0    3700.0    3700.0    3200.0    3000.0    3700.0   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1742    7400.0    6100.0    3300.0    3200.0    3000.0    3000.0    3300.0   \n",
       "1743    5900.0    4600.0    5600.0    4100.0    3900.0    3000.0    4400.0   \n",
       "1744    4900.0    7200.0    4400.0    5300.0    4100.0    3000.0    7900.0   \n",
       "1745    4600.0    4600.0    3700.0    3400.0    3000.0    3000.0    2800.0   \n",
       "1746    4000.0    6300.0    3200.0    3900.0    3000.0    3600.0    4700.0   \n",
       "\n",
       "      TE1_home  TE2_away  TE2_home  \n",
       "0       2900.0    4800.0    2500.0  \n",
       "1       3500.0    2500.0    3000.0  \n",
       "2       3800.0    2900.0    2700.0  \n",
       "3       2800.0    2800.0    2500.0  \n",
       "4       2600.0    2500.0    2500.0  \n",
       "...        ...       ...       ...  \n",
       "1742    5000.0    2600.0    2600.0  \n",
       "1743    4500.0    2500.0    3600.0  \n",
       "1744    3500.0    2700.0    2500.0  \n",
       "1745    4100.0    2600.0    2700.0  \n",
       "1746    3100.0    3400.0    2700.0  \n",
       "\n",
       "[1747 rows x 36 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3af70ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\binga\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return func(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "games_2022 = games.loc[(games['season'] == 2022) & (games['Week'] != 13)]\n",
    "games_dev = games.loc[games['season'] != 2022]\n",
    "games_dev.dropna(subset=['QB1_home'], axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc26ee38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create aggregations of offensive salaries for home, away, and total\n",
    "away_offense = ['QB1_away','RB1_away','RB2_away', 'WR1_away', 'WR2_away','WR3_away', 'WR4_away', 'TE1_away','TE2_away']\n",
    "home_offense = ['QB1_home','RB1_home', 'RB2_home', 'WR1_home','WR2_home', 'WR3_home', 'WR4_home','TE1_home', 'TE2_home']\n",
    "games['away_off_total'] = games.filter(away_offense).sum(axis = 1)\n",
    "games['home_off_total'] = games.filter(away_offense).sum(axis = 1)\n",
    "games['total_offense_sal'] = games['away_off_total'] + games['home_off_total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cea606f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cols = ['DST1_away', 'DST1_home', 'QB1_away', 'QB1_home', 'RB1_away',\n",
    "       'RB1_home', 'RB2_away', 'RB2_home', 'WR1_away', 'WR1_home', 'WR2_away',\n",
    "       'WR2_home', 'WR3_away', 'WR3_home', 'WR4_away', 'WR4_home', 'TE1_away','away_off_total','home_off_total',\n",
    "       'total_offense_sal','TE1_home', 'TE2_away', 'TE2_home','PointSpread','AwayTeamMoneyLine','HomeTeamMoneyLine','OverUnder']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6afcaeb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>Week</th>\n",
       "      <th>home_pts</th>\n",
       "      <th>away_pts</th>\n",
       "      <th>total_pts</th>\n",
       "      <th>home_win</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>home_abbr</th>\n",
       "      <th>away_abbr</th>\n",
       "      <th>PointSpread</th>\n",
       "      <th>OverUnder</th>\n",
       "      <th>AwayTeamMoneyLine</th>\n",
       "      <th>HomeTeamMoneyLine</th>\n",
       "      <th>home_fav</th>\n",
       "      <th>date_match</th>\n",
       "      <th>DST1_away</th>\n",
       "      <th>DST1_home</th>\n",
       "      <th>QB1_away</th>\n",
       "      <th>QB1_home</th>\n",
       "      <th>RB1_away</th>\n",
       "      <th>RB1_home</th>\n",
       "      <th>RB2_away</th>\n",
       "      <th>RB2_home</th>\n",
       "      <th>WR1_away</th>\n",
       "      <th>WR1_home</th>\n",
       "      <th>WR2_away</th>\n",
       "      <th>WR2_home</th>\n",
       "      <th>WR3_away</th>\n",
       "      <th>WR3_home</th>\n",
       "      <th>WR4_away</th>\n",
       "      <th>WR4_home</th>\n",
       "      <th>TE1_away</th>\n",
       "      <th>TE1_home</th>\n",
       "      <th>TE2_away</th>\n",
       "      <th>TE2_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Thu</td>\n",
       "      <td>8:20PM</td>\n",
       "      <td>LAR</td>\n",
       "      <td>BUF</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-130</td>\n",
       "      <td>109</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-09-08</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>1:00PM</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NO</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-240</td>\n",
       "      <td>199</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-09-11</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>1:00PM</td>\n",
       "      <td>CAR</td>\n",
       "      <td>CLE</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>41.5</td>\n",
       "      <td>105</td>\n",
       "      <td>-124</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-09-11</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sun</td>\n",
       "      <td>1:00PM</td>\n",
       "      <td>CHI</td>\n",
       "      <td>SF</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>-281</td>\n",
       "      <td>229</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-09-11</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>1:00PM</td>\n",
       "      <td>CIN</td>\n",
       "      <td>PIT</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>255</td>\n",
       "      <td>-321</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-09-11</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>4:05PM</td>\n",
       "      <td>ARI</td>\n",
       "      <td>LAC</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>-146</td>\n",
       "      <td>123</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-11-27</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sun</td>\n",
       "      <td>4:25PM</td>\n",
       "      <td>KC</td>\n",
       "      <td>LAR</td>\n",
       "      <td>-16.5</td>\n",
       "      <td>41.5</td>\n",
       "      <td>785</td>\n",
       "      <td>-1299</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-11-27</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>8200.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sun</td>\n",
       "      <td>4:25PM</td>\n",
       "      <td>SF</td>\n",
       "      <td>NO</td>\n",
       "      <td>-8.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>320</td>\n",
       "      <td>-409</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-11-27</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sun</td>\n",
       "      <td>8:20PM</td>\n",
       "      <td>PHI</td>\n",
       "      <td>GB</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>233</td>\n",
       "      <td>-288</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-11-27</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mon</td>\n",
       "      <td>8:15PM</td>\n",
       "      <td>IND</td>\n",
       "      <td>PIT</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>117</td>\n",
       "      <td>-139</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-11-28</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  Week  home_pts  away_pts  total_pts  home_win  Day    Time  \\\n",
       "1552    2022     1      10.0      31.0       41.0         0  Thu  8:20PM   \n",
       "1553    2022     1      26.0      27.0       53.0         0  Sun  1:00PM   \n",
       "1554    2022     1      24.0      26.0       50.0         0  Sun  1:00PM   \n",
       "1555    2022     1      19.0      10.0       29.0         1  Sun  1:00PM   \n",
       "1556    2022     1      20.0      23.0       43.0         0  Sun  1:00PM   \n",
       "...      ...   ...       ...       ...        ...       ...  ...     ...   \n",
       "1727    2022    12      24.0      25.0       49.0         0  Sun  4:05PM   \n",
       "1728    2022    12      26.0      10.0       36.0         1  Sun  4:25PM   \n",
       "1729    2022    12      13.0       0.0       13.0         1  Sun  4:25PM   \n",
       "1730    2022    12      40.0      33.0       73.0         1  Sun  8:20PM   \n",
       "1731    2022    12      17.0      24.0       41.0         0  Mon  8:15PM   \n",
       "\n",
       "     home_abbr away_abbr  PointSpread  OverUnder  AwayTeamMoneyLine  \\\n",
       "1552       LAR       BUF         -1.5       52.0               -130   \n",
       "1553       ATL        NO         -5.5       44.0               -240   \n",
       "1554       CAR       CLE         -1.5       41.5                105   \n",
       "1555       CHI        SF         -6.5       37.5               -281   \n",
       "1556       CIN       PIT         -7.0       44.5                255   \n",
       "...        ...       ...          ...        ...                ...   \n",
       "1727       ARI       LAC         -2.5       48.5               -146   \n",
       "1728        KC       LAR        -16.5       41.5                785   \n",
       "1729        SF        NO         -8.5       43.5                320   \n",
       "1730       PHI        GB         -6.5       46.0                233   \n",
       "1731       IND       PIT         -2.5       39.5                117   \n",
       "\n",
       "      HomeTeamMoneyLine  home_fav  date_match  DST1_away  DST1_home  QB1_away  \\\n",
       "1552                109     False  2022-09-08     3200.0     3100.0    7800.0   \n",
       "1553                199     False  2022-09-11     3700.0     2800.0    5300.0   \n",
       "1554               -124      True  2022-09-11     3800.0     3400.0    6600.0   \n",
       "1555                229     False  2022-09-11     4100.0     2200.0    6000.0   \n",
       "1556               -321      True  2022-09-11     2400.0     3600.0    5000.0   \n",
       "...                 ...       ...         ...        ...        ...       ...   \n",
       "1727                123     False  2022-11-27     3300.0     2700.0    7000.0   \n",
       "1728              -1299      True  2022-11-27     2300.0     2800.0    5300.0   \n",
       "1729               -409      True  2022-11-27     2400.0     4000.0    5300.0   \n",
       "1730               -288      True  2022-11-27     2200.0     3400.0    5800.0   \n",
       "1731               -139      True  2022-11-28     2900.0     3100.0    5100.0   \n",
       "\n",
       "      QB1_home  RB1_away  RB1_home  RB2_away  RB2_home  WR1_away  WR1_home  \\\n",
       "1552    6200.0    5800.0    5900.0    4400.0    5300.0    7200.0    8500.0   \n",
       "1553    5200.0    7600.0    5800.0    5900.0    4700.0    5700.0    5000.0   \n",
       "1554    5300.0    6500.0    8500.0    5700.0    5400.0    5900.0    6000.0   \n",
       "1555    5600.0    5400.0    6000.0    5100.0    5200.0    7400.0    5700.0   \n",
       "1556    6400.0    6400.0    7100.0    4800.0    5000.0    6000.0    7100.0   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1727    6800.0    8500.0    6600.0    4200.0    4800.0    6300.0    7700.0   \n",
       "1728    8200.0    4900.0    5500.0    4900.0    4700.0    8000.0    5700.0   \n",
       "1729    5600.0    7300.0    8800.0    5000.0    4900.0    6600.0    6800.0   \n",
       "1730    8100.0    7100.0    6300.0    5200.0    5000.0    5800.0    7500.0   \n",
       "1731    5200.0    5800.0    7900.0    4300.0    5000.0    5200.0    6000.0   \n",
       "\n",
       "      WR2_away  WR2_home  WR3_away  WR3_home  WR4_away  WR4_home  TE1_away  \\\n",
       "1552    5800.0    6100.0    4400.0    3500.0    4400.0    3000.0    4400.0   \n",
       "1553    5000.0    4200.0    4500.0    4000.0    4300.0    3500.0    4600.0   \n",
       "1554    4400.0    4800.0    3600.0    4300.0    3300.0    3900.0    3900.0   \n",
       "1555    5200.0    4400.0    3200.0    3600.0    3100.0    3000.0    5900.0   \n",
       "1556    5000.0    6100.0    4100.0    4800.0    3200.0    3000.0    4200.0   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1727    6100.0    5300.0    5400.0    5100.0    3700.0    3400.0    4400.0   \n",
       "1728    5500.0    4900.0    4500.0    4500.0    3400.0    4000.0    4200.0   \n",
       "1729    4400.0    6400.0    3300.0    3100.0    3000.0    3000.0    4800.0   \n",
       "1730    5000.0    6100.0    4800.0    3500.0    3900.0    3200.0    3300.0   \n",
       "1731    5000.0    4300.0    3000.0    3500.0    3000.0    3000.0    4000.0   \n",
       "\n",
       "      TE1_home  TE2_away  TE2_home  \n",
       "1552    3800.0    2500.0    2500.0  \n",
       "1553    5700.0    3100.0    4000.0  \n",
       "1554    4000.0    2900.0    3100.0  \n",
       "1555    3700.0    2700.0    2700.0  \n",
       "1556    3300.0    2800.0    3000.0  \n",
       "...        ...       ...       ...  \n",
       "1727    3100.0    2800.0    2500.0  \n",
       "1728    7700.0    2500.0    2800.0  \n",
       "1729    5300.0    3400.0    2700.0  \n",
       "1730    4900.0    2600.0    2500.0  \n",
       "1731    2800.0    2500.0    2600.0  \n",
       "\n",
       "[180 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b61d0fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(games_dev.filter(pred_cols).fillna(0),games_dev['home_win'], test_size=0.25, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2cbe842",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)\n",
    "x_train_scaled = scaler.transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6cc3a56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47b28003",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_and_get_metrics(clf):\n",
    "    preds_pp = cross_val_predict(clf, x_train, y_train, cv=5, method='predict_proba')[:,1]\n",
    "    preds = preds_pp.round()\n",
    "    print(classification_report(y_train, preds))\n",
    "\n",
    "    clf_cv_acc = accuracy_score(y_train, preds)\n",
    "    clf_cv_prec = precision_score(y_train, preds)\n",
    "    clf_cv_recall = recall_score(y_train, preds)\n",
    "    clf_cv_auc = roc_auc_score(y_train, preds)\n",
    "\n",
    "    cm = confusion_matrix(y_train, preds, labels=clf.classes_)\n",
    "    disp = ConfusionMatrixDisplay(cm, display_labels=clf.classes_)\n",
    "    disp.plot()\n",
    "    plt.show()\n",
    "\n",
    "    return preds_pp, preds, clf_cv_acc, clf_cv_prec, clf_cv_recall, clf_cv_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3998910d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_and_get_metrics_scaled(clf):\n",
    "    preds_pp = cross_val_predict(clf, x_train_scaled, y_train, cv=5, method='predict_proba')[:,1]\n",
    "    preds = preds_pp.round()\n",
    "    print(classification_report(y_train, preds))\n",
    "\n",
    "    clf_cv_acc = accuracy_score(y_train, preds)\n",
    "    clf_cv_prec = precision_score(y_train, preds)\n",
    "    clf_cv_recall = recall_score(y_train, preds)\n",
    "    clf_cv_auc = roc_auc_score(y_train, preds)\n",
    "\n",
    "    cm = confusion_matrix(y_train, preds, labels=clf.classes_)\n",
    "    disp = ConfusionMatrixDisplay(cm, display_labels=clf.classes_)\n",
    "    disp.plot()\n",
    "    plt.show()\n",
    "\n",
    "    return preds_pp, preds, clf_cv_acc, clf_cv_prec, clf_cv_recall, clf_cv_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5d458b",
   "metadata": {},
   "source": [
    "### KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fdb14b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.354233503341675\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=-1, n_neighbors=18, p=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "start_time = time.time()\n",
    "param_grid = {\n",
    "    'n_neighbors' : [num for num in range(2,34)],\n",
    "    'weights' : ['uniform','distance'],\n",
    "    'p': [1,2],\n",
    "    'n_jobs': [-1]\n",
    "}\n",
    "\n",
    "knn_grid = GridSearchCV(knn, param_grid, scoring='balanced_accuracy', n_jobs=-1)\n",
    "knn_grid.fit(x_train, y_train)\n",
    "best_knn = knn_grid.best_estimator_\n",
    "print(time.time() - start_time)\n",
    "best_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02dd70b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.65      0.63       537\n",
      "           1       0.68      0.65      0.66       622\n",
      "\n",
      "    accuracy                           0.65      1159\n",
      "   macro avg       0.65      0.65      0.65      1159\n",
      "weighted avg       0.65      0.65      0.65      1159\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_run = KNeighborsClassifier(n_jobs=-1, n_neighbors=18, p=1)\n",
    "best_knn = knn_run.fit(x_train,y_train)\n",
    "knn_preds_pp, knn_preds, knn_cv_acc, knn_cv_prec, knn_cv_recall, knn_cv_auc = plot_and_get_metrics(best_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87374506",
   "metadata": {},
   "source": [
    "### One Class SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd5586d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_home_w = x_train[y_train == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19513b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneClassSVM(gamma=50, kernel='poly')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_cl_svm = svm.OneClassSVM(kernel = 'poly', gamma = 50)\n",
    "one_cl_svm.fit(x_train_home_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddf2f600",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_cl_svm_pred = np.where(one_cl_svm.predict(x_test) == -1,0,one_cl_svm.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f81eebe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5503875968992248"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-(np.equal(one_cl_svm_pred,y_test).sum()/len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01798f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma_vals = np.arange(0.1,5.0,0.1)\n",
    "svm_misclass_rates = []\n",
    "for x in range(len(gamma_vals)):\n",
    "    svm_clf = svm.OneClassSVM(kernel = 'poly', coef0 = gamma_vals[x])\n",
    "    svm_clf.fit(x_train_home_w)\n",
    "    svm_pred = svm_clf.predict(x_test)\n",
    "    svm_pred = np.where(svm_pred == -1,0,svm_pred)\n",
    "    svm_misclass_rates.append(1-(np.equal(svm_pred,y_test).sum()/len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "81648588",
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm_plot():\n",
    "    plt.figure(figsize=(15,5))\n",
    "    plt.plot(gamma_vals,svm_misclass_rates, label ='One Class SVM Misclassification Rate')\n",
    "    plt.ylabel(\"Misclassification rate\")\n",
    "    plt.title(\"Misclassification Rate for Gamma Values in One Class SVM\")\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"Gamma\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b712e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1740e356",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "77570ad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.64      0.64       537\n",
      "           1       0.69      0.68      0.68       622\n",
      "\n",
      "    accuracy                           0.66      1159\n",
      "   macro avg       0.66      0.66      0.66      1159\n",
      "weighted avg       0.66      0.66      0.66      1159\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_nb = GaussianNB()\n",
    "run_nb.fit(x_train,y_train)\n",
    "best_nb = run_nb\n",
    "nb_preds_pp, nb_preds, nb_cv_acc, nb_cv_prec, nb_cv_recall, nb_cv_auc = plot_and_get_metrics(run_nb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1763a0",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83ec1206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.64890480041504\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.5, degree=2, kernel='linear', probability=True, random_state=44)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(probability = True, random_state = random_state, max_iter = -1)\n",
    "start_time = time.time()\n",
    "param_grid = {\n",
    "    'C' : [1,1.2,1.3,1.5,1.65,1.8,2],\n",
    "    'kernel' : ['poly','rbf','linear'],\n",
    "    'degree': [2,3],\n",
    "}\n",
    "\n",
    "svm_grid = GridSearchCV(svm, param_grid, scoring='balanced_accuracy', n_jobs=-1)\n",
    "svm_grid.fit(x_train_scaled, y_train)\n",
    "best_svm = svm_grid.best_estimator_\n",
    "print(time.time() - start_time)\n",
    "best_svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a3e260c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.59      0.63       537\n",
      "           1       0.68      0.74      0.71       622\n",
      "\n",
      "    accuracy                           0.67      1159\n",
      "   macro avg       0.67      0.67      0.67      1159\n",
      "weighted avg       0.67      0.67      0.67      1159\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_svm = SVC(C=1.2, degree=2, kernel='linear', probability=True, random_state=44)\n",
    "svm_preds_pp, svm_preds, svm_cv_acc, svm_cv_prec, svm_cv_recall, svm_cv_auc = plot_and_get_metrics_scaled(best_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26d88cf",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9858d1ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.57\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=15, min_samples_leaf=2, n_estimators=150,\n",
       "                       random_state=44)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "rf = RandomForestClassifier(class_weight='balanced', random_state=random_state)\n",
    "param_grid = {\n",
    "    'n_estimators': [150,200,250],\n",
    "    'criterion': ['entropy', 'gini'],\n",
    "    'min_samples_leaf': [x for x in range(2,7)],\n",
    "    'max_depth': [5,10,15,20],\n",
    "    'bootstrap': [True]\n",
    "}\n",
    "rf_clf = GridSearchCV(estimator=rf, param_grid=param_grid, scoring='accuracy', n_jobs=-1)\n",
    "rf_clf.fit(x_train, y_train)\n",
    "best_rf = rf_clf.best_estimator_\n",
    "print(round((time.time() - start_time)/60, 2))\n",
    "best_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5c2dba97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.66      0.65       537\n",
      "           1       0.70      0.68      0.69       622\n",
      "\n",
      "    accuracy                           0.67      1159\n",
      "   macro avg       0.67      0.67      0.67      1159\n",
      "weighted avg       0.67      0.67      0.67      1159\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_run = RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
    "                       max_depth=5, min_samples_leaf=6, n_estimators=250,\n",
    "                       random_state=44)\n",
    "rf_preds_pp, rf_preds, rf_cv_acc, rf_cv_prec, rf_cv_recall, rf_cv_auc = plot_and_get_metrics(best_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6637ac2",
   "metadata": {},
   "source": [
    "### Light GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aff428a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.46\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(class_weight='balanced', learning_rate=0.025, max_depth=5,\n",
       "               min_child_samples=4, n_estimators=250, random_state=44,\n",
       "               reg_alpha=0.5, reg_lambda=0.5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "lgbmc = lgb.LGBMClassifier(class_weight='balanced', random_state=random_state)\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [5,10,15,20],\n",
    "    # 'num_leaves': [5, 10],\n",
    "    'learning_rate': [0.001, 0.01, 0.025],\n",
    "    'n_estimators': [150, 200, 250],\n",
    "    # 'feature_fraction': [0.5],\n",
    "    'min_child_samples': [x for x in range(2,5)],\n",
    "    'reg_alpha': [0.1, 0.5,1,1.5],\n",
    "    'reg_lambda': [0.1, 0.5,1,1.5]\n",
    "}\n",
    "\n",
    "lgbmc_clf = GridSearchCV(estimator=lgbmc,\n",
    "                         param_grid=param_grid,\n",
    "                         scoring='accuracy',\n",
    "                         cv=5,\n",
    "                         verbose=False,\n",
    "                         n_jobs=-1)\n",
    "\n",
    "lgbmc_clf.fit(x_train, y_train)\n",
    "best_lgb = lgbmc_clf.best_estimator_\n",
    "print(round((time.time() - start_time)/60, 2))\n",
    "best_lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4e4ff2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_run = lgb.LGBMClassifier(class_weight='balanced', learning_rate=0.025, max_depth=10,\n",
    "               min_child_samples=2, n_estimators=200, random_state=44,\n",
    "               reg_alpha=0.1, reg_lambda=0.5)\n",
    "best_lgb = lgbm_run.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f1b511d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.62      0.62       537\n",
      "           1       0.67      0.66      0.67       622\n",
      "\n",
      "    accuracy                           0.64      1159\n",
      "   macro avg       0.64      0.64      0.64      1159\n",
      "weighted avg       0.64      0.64      0.64      1159\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb_preds_pp, lgb_preds, lgb_cv_acc, lgb_cv_prec, lgb_cv_recall, lgb_cv_auc = plot_and_get_metrics(best_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5c3f99dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.barplot(data=pd.DataFrame({'Feature': x_train.columns, 'Importance': best_lgb.feature_importances_}).\\\n",
    "    sort_values('Importance', ascending=False), x='Importance', y='Feature', color='g').set_title('LGBM')\n",
    "plt.savefig('feat_import_LGBM.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1378d6",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "634a4999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22:06:33] WARNING: D:\\bld\\xgboost-split_1645118015404\\work\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "23.867789208889008\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(alpha=0.1, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', lambda=1, learning_rate=0.1,\n",
       "              max_delta_step=0, max_depth=15, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=150, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=44,\n",
       "              reg_alpha=0.100000001, reg_lambda=1, scale_pos_weight=4,\n",
       "              subsample=0.5, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "xgb = XGBClassifier(scale_pos_weight=4, random_state=random_state)\n",
    "start_time = time.time()\n",
    "param_grid = {\n",
    "    'n_estimators': [150,250,500],\n",
    "    'learning_rate': [0.025,0.05,0.1],\n",
    "    'max_depth': [15,20,25],\n",
    "    'subsample': [0.5, 0.75],\n",
    "    'alpha': [0.1,1,1.5],\n",
    "    'lambda': [0.5,1,5]\n",
    "}\n",
    "\n",
    "xgb_clf = GridSearchCV(estimator=xgb, param_grid=param_grid, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "xgb_clf.fit(x_train, y_train)\n",
    "best_xgb = xgb_clf.best_estimator_\n",
    "print((time.time() - start_time)/60)\n",
    "best_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1216ff7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_run = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.8,\n",
    "              enable_categorical=False, eval_metric='error', gamma=0.5,\n",
    "              gpu_id=-1, importance_type=None, interaction_constraints='',\n",
    "              learning_rate=0.025, max_delta_step=0, max_depth=25,\n",
    "              min_child_weight=1, monotone_constraints='()',\n",
    "              n_estimators=200, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
    "              random_state=44, reg_alpha=0, reg_lambda=1, scale_pos_weight=4,\n",
    "              subsample=0.75, tree_method='exact', validate_parameters=1,\n",
    "              verbosity=None)\n",
    "best_xgb = xgb_run.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0465d64e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.42      0.52       537\n",
      "           1       0.62      0.82      0.71       622\n",
      "\n",
      "    accuracy                           0.63      1159\n",
      "   macro avg       0.64      0.62      0.61      1159\n",
      "weighted avg       0.64      0.63      0.62      1159\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_preds_pp, xgb_preds, xgb_cv_acc, xgb_cv_prec, xgb_cv_recall, xgb_cv_auc = plot_and_get_metrics(best_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cabf6abf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.barplot(data=pd.DataFrame({'Feature': x_train.columns, 'Importance': best_xgb.feature_importances_}).\\\n",
    "    sort_values('Importance', ascending=False), x='Importance', y='Feature', color='g').set_title('XGB')\n",
    "plt.savefig('feat_import_xgb.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2d5db234",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['XGB', 'LGBM', 'RF',  'KNN', 'SVM','NB']\n",
    "cv_accuracy = pd.Series([xgb_cv_acc, lgb_cv_acc, rf_cv_acc, knn_cv_acc, svm_cv_acc, nb_cv_acc], name='Accuracy')\n",
    "cv_recall = pd.Series([xgb_cv_recall, lgb_cv_recall, rf_cv_recall, knn_cv_recall, svm_cv_recall, nb_cv_recall], name='Recall')\n",
    "cv_precision = pd.Series([xgb_cv_prec, lgb_cv_prec, rf_cv_prec, knn_cv_prec, svm_cv_prec, nb_cv_prec],name='Precision')\n",
    "cv_auc = pd.Series([xgb_cv_auc, lgb_cv_auc, rf_cv_auc,  knn_cv_auc, svm_cv_auc, nb_cv_auc], name='AUC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "24e8fbeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_51670_row0_col1, #T_51670_row2_col2, #T_51670_row2_col3, #T_51670_row4_col0 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_51670_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col2\" >Precision</th>\n",
       "      <th class=\"col_heading level0 col3\" >AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_51670_level0_row0\" class=\"row_heading level0 row0\" >XGB</th>\n",
       "      <td id=\"T_51670_row0_col0\" class=\"data row0 col0\" >0.634167</td>\n",
       "      <td id=\"T_51670_row0_col1\" class=\"data row0 col1\" >0.815113</td>\n",
       "      <td id=\"T_51670_row0_col2\" class=\"data row0 col2\" >0.621324</td>\n",
       "      <td id=\"T_51670_row0_col3\" class=\"data row0 col3\" >0.619847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51670_level0_row1\" class=\"row_heading level0 row1\" >LGBM</th>\n",
       "      <td id=\"T_51670_row1_col0\" class=\"data row1 col0\" >0.642796</td>\n",
       "      <td id=\"T_51670_row1_col1\" class=\"data row1 col1\" >0.660772</td>\n",
       "      <td id=\"T_51670_row1_col2\" class=\"data row1 col2\" >0.669381</td>\n",
       "      <td id=\"T_51670_row1_col3\" class=\"data row1 col3\" >0.641373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51670_level0_row2\" class=\"row_heading level0 row2\" >RF</th>\n",
       "      <td id=\"T_51670_row2_col0\" class=\"data row2 col0\" >0.672994</td>\n",
       "      <td id=\"T_51670_row2_col1\" class=\"data row2 col1\" >0.681672</td>\n",
       "      <td id=\"T_51670_row2_col2\" class=\"data row2 col2\" >0.700826</td>\n",
       "      <td id=\"T_51670_row2_col3\" class=\"data row2 col3\" >0.672307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51670_level0_row3\" class=\"row_heading level0 row3\" >KNN</th>\n",
       "      <td id=\"T_51670_row3_col0\" class=\"data row3 col0\" >0.647972</td>\n",
       "      <td id=\"T_51670_row3_col1\" class=\"data row3 col1\" >0.646302</td>\n",
       "      <td id=\"T_51670_row3_col2\" class=\"data row3 col2\" >0.681356</td>\n",
       "      <td id=\"T_51670_row3_col3\" class=\"data row3 col3\" >0.648105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51670_level0_row4\" class=\"row_heading level0 row4\" >SVM</th>\n",
       "      <td id=\"T_51670_row4_col0\" class=\"data row4 col0\" >0.673857</td>\n",
       "      <td id=\"T_51670_row4_col1\" class=\"data row4 col1\" >0.742765</td>\n",
       "      <td id=\"T_51670_row4_col2\" class=\"data row4 col2\" >0.679412</td>\n",
       "      <td id=\"T_51670_row4_col3\" class=\"data row4 col3\" >0.668403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51670_level0_row5\" class=\"row_heading level0 row5\" >NB</th>\n",
       "      <td id=\"T_51670_row5_col0\" class=\"data row5 col0\" >0.663503</td>\n",
       "      <td id=\"T_51670_row5_col1\" class=\"data row5 col1\" >0.680064</td>\n",
       "      <td id=\"T_51670_row5_col2\" class=\"data row5 col2\" >0.688925</td>\n",
       "      <td id=\"T_51670_row5_col3\" class=\"data row5 col3\" >0.662192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x159a902a8b0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_summary = pd.concat([cv_accuracy, cv_recall, cv_precision, cv_auc], axis=1)\n",
    "scores_summary.columns = ['Accuracy', 'Recall', 'Precision', 'AUC']\n",
    "scores_summary.index = model_names\n",
    "scores_summary.style.highlight_max(color='green', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "798b1e8b",
   "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>true</th>\n",
       "      <th>knn_preds_pp</th>\n",
       "      <th>rf_preds_pp</th>\n",
       "      <th>lgb_preds_pp</th>\n",
       "      <th>xgb_preds_pp</th>\n",
       "      <th>nb_preds_pp</th>\n",
       "      <th>svm_preds_pp</th>\n",
       "      <th>home_ML</th>\n",
       "      <th>away_ML</th>\n",
       "      <th>vegas_home_prob</th>\n",
       "      <th>vegas_away_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.727100</td>\n",
       "      <td>0.782526</td>\n",
       "      <td>0.908619</td>\n",
       "      <td>0.653586</td>\n",
       "      <td>0.740587</td>\n",
       "      <td>-385</td>\n",
       "      <td>304</td>\n",
       "      <td>0.793814</td>\n",
       "      <td>0.247525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.780644</td>\n",
       "      <td>0.825327</td>\n",
       "      <td>0.935778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.824761</td>\n",
       "      <td>-1100</td>\n",
       "      <td>700</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1039</th>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.454908</td>\n",
       "      <td>0.586433</td>\n",
       "      <td>0.728541</td>\n",
       "      <td>0.308748</td>\n",
       "      <td>0.378285</td>\n",
       "      <td>149</td>\n",
       "      <td>-182</td>\n",
       "      <td>0.401606</td>\n",
       "      <td>0.645390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.517015</td>\n",
       "      <td>0.480662</td>\n",
       "      <td>0.743682</td>\n",
       "      <td>0.848523</td>\n",
       "      <td>0.610664</td>\n",
       "      <td>-150</td>\n",
       "      <td>127</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.440529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.549158</td>\n",
       "      <td>0.731565</td>\n",
       "      <td>0.521176</td>\n",
       "      <td>0.905527</td>\n",
       "      <td>0.572536</td>\n",
       "      <td>-180</td>\n",
       "      <td>150</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      true  knn_preds_pp  rf_preds_pp  lgb_preds_pp  xgb_preds_pp  \\\n",
       "1524     0      0.611111     0.727100      0.782526      0.908619   \n",
       "218      1      0.722222     0.780644      0.825327      0.935778   \n",
       "1039     0      0.500000     0.454908      0.586433      0.728541   \n",
       "1229     0      0.722222     0.517015      0.480662      0.743682   \n",
       "61       1      0.555556     0.549158      0.731565      0.521176   \n",
       "\n",
       "      nb_preds_pp  svm_preds_pp  home_ML  away_ML  vegas_home_prob  \\\n",
       "1524     0.653586      0.740587     -385      304         0.793814   \n",
       "218      1.000000      0.824761    -1100      700         0.916667   \n",
       "1039     0.308748      0.378285      149     -182         0.401606   \n",
       "1229     0.848523      0.610664     -150      127         0.600000   \n",
       "61       0.905527      0.572536     -180      150         0.642857   \n",
       "\n",
       "      vegas_away_prob  \n",
       "1524         0.247525  \n",
       "218          0.125000  \n",
       "1039         0.645390  \n",
       "1229         0.440529  \n",
       "61           0.400000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df = pd.DataFrame({'true': y_train, 'knn_preds_pp': knn_preds_pp, 'rf_preds_pp': rf_preds_pp,\\\n",
    "                        'lgb_preds_pp':lgb_preds_pp,'xgb_preds_pp':xgb_preds_pp ,'nb_preds_pp':nb_preds_pp,\\\n",
    "                        'svm_preds_pp': svm_preds_pp,'home_ML': x_train['HomeTeamMoneyLine'],\\\n",
    "                        'away_ML': x_train['AwayTeamMoneyLine']\n",
    "                        })\n",
    "comp_df['vegas_home_prob'] = np.where(comp_df['home_ML'] < 0, abs(comp_df['home_ML'])/(abs(comp_df['home_ML'])+100), 100/(comp_df['home_ML']+100))\n",
    "comp_df['vegas_away_prob'] = np.where(comp_df['away_ML'] < 0, abs(comp_df['away_ML'])/(abs(comp_df['away_ML'])+100), 100/(comp_df['away_ML']+100))\n",
    "comp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21b6fef",
   "metadata": {},
   "source": [
    "Only bet when our model probability is higher than Vegas' implied probability. If both higher than Vegas, take the one with higher margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "065438d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def away_return(df):\n",
    "    if (df['bet_away'] and (df['true'] == 1)):\n",
    "        return -100\n",
    "    elif (df['bet_away'] and (df['away_ML']>0) and (df['true'] == 0)):\n",
    "        return df['away_ML']\n",
    "    elif (df['bet_away'] and (df['away_ML']<0) & (df['true'] == 0)):\n",
    "        return round((100/df['away_ML'])*-100,2)\n",
    "    else: \n",
    "        return 0\n",
    "    \n",
    "def home_return(df):\n",
    "    if (df['bet_home'] and (df['true'] == 0)):\n",
    "        return -100\n",
    "    elif (df['bet_home'] and (df['home_ML']>0) and (df['true'] == 1)):\n",
    "        return df['home_ML']\n",
    "    elif (df['bet_home'] and (df['home_ML']<0) & (df['true'] == 1)):\n",
    "        return round((100/df['home_ML'])*-100,2)\n",
    "    else: \n",
    "        return 0\n",
    "def pred_vs_vegas(model_preds, comp, margin = 0):\n",
    "    '''\n",
    "    \n",
    "    :param margin: margin between our predictions and vegas predictions to trigger making a bet (example: 0.05)\n",
    "    :param comp: comparison dataframe to use for function (train or test predictions)\n",
    "    :param model_preds: model column name of prediction probability\n",
    "    :return:% return, $ risked, total $ returned\n",
    "    '''\n",
    "    #create working dataframe\n",
    "    df_w = comp[['true',model_preds,'home_ML','away_ML','vegas_home_prob','vegas_away_prob']]\n",
    "    \n",
    "    #calculate differential of our predictions vs vegas' implied probability\n",
    "    df_w['home_prob_adv'] = df_w[model_preds] - df_w['vegas_home_prob']\n",
    "    df_w['away_prob_adv'] = (1-df_w[model_preds]) - df_w['vegas_away_prob']\n",
    "    \n",
    "    #flag whether the home differential is higher than the away differential\n",
    "    df_w['home_adv_higher'] = df_w['home_prob_adv'] > df_w['away_prob_adv']\n",
    "    \n",
    "    #flag whether to bet on the home or away team\n",
    "    df_w['bet_home'] = np.where((df_w['home_adv_higher'] & (df_w['home_prob_adv']>margin)), True, False)\n",
    "    df_w['bet_away'] = np.where(((df_w['bet_home'] == False) & (df_w['away_prob_adv']>margin)), True, False)\n",
    "    \n",
    "    #generate bet returns\n",
    "    df_w['home_risk'] = np.where(df_w['bet_home'], 100, 0)\n",
    "    df_w['away_risk'] = np.where(df_w['bet_away'], 100, 0)\n",
    "    df_w['away_return'] = df_w.apply(away_return, axis = 1)\n",
    "    df_w['home_return'] = df_w.apply(home_return, axis = 1)\n",
    "    \n",
    "    #aggregate risks and returns\n",
    "    df_w['risk'] = df_w['home_risk'] + df_w['away_risk']\n",
    "    df_w['return'] = df_w['home_return'] + df_w['away_return']\n",
    "    \n",
    "    return df_w\n",
    "\n",
    "def aggregate_returns(df_w):\n",
    "    return df_w['risk'].sum(),df_w['return'].sum(),(df_w['return'].sum()/df_w['risk'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e651cc63",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_models = ['knn_preds_pp','svm_preds_pp','nb_preds_pp','rf_preds_pp','xgb_preds_pp','lgb_preds_pp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ee5ff2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_risk = []\n",
    "mod_ret = []\n",
    "mod_pct_ret = []\n",
    "models = []\n",
    "margins = []\n",
    "for model in final_models:\n",
    "    for margin in np.arange(0,.2,0.01):\n",
    "        df = pred_vs_vegas(model,comp_df,margin)\n",
    "        risk, ret, pct = aggregate_returns(df)\n",
    "        mod_risk.append(risk)\n",
    "        mod_ret.append(ret)\n",
    "        mod_pct_ret.append(pct)\n",
    "        models.append(model)\n",
    "        margins.append(margin)\n",
    "\n",
    "higher_exp_val_df = pd.DataFrame({'model':models,'margin': margins, 'pct_ret': mod_pct_ret, 'risked':mod_risk, 'dollar_ret':mod_ret})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "160c09de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.lineplot(x='margin', y='pct_ret', hue='model', data=higher_exp_val_df)\n",
    "plt.suptitle('% Return on ML Bets per Model by Margin', size=16)\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('strat_3_ret.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b679dc93",
   "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>model</th>\n",
       "      <th>margin</th>\n",
       "      <th>pct_ret</th>\n",
       "      <th>risked</th>\n",
       "      <th>dollar_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.010822</td>\n",
       "      <td>98800</td>\n",
       "      <td>1069.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.003114</td>\n",
       "      <td>88900</td>\n",
       "      <td>-276.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.003199</td>\n",
       "      <td>78700</td>\n",
       "      <td>-251.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.045611</td>\n",
       "      <td>69800</td>\n",
       "      <td>-3183.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.023956</td>\n",
       "      <td>61600</td>\n",
       "      <td>-1475.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.030273</td>\n",
       "      <td>53200</td>\n",
       "      <td>-1610.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.040603</td>\n",
       "      <td>46300</td>\n",
       "      <td>-1879.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.052671</td>\n",
       "      <td>38300</td>\n",
       "      <td>-2017.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.033030</td>\n",
       "      <td>32600</td>\n",
       "      <td>-1076.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.021130</td>\n",
       "      <td>26000</td>\n",
       "      <td>-549.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.049703</td>\n",
       "      <td>22200</td>\n",
       "      <td>-1103.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.011994</td>\n",
       "      <td>17400</td>\n",
       "      <td>-208.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.039351</td>\n",
       "      <td>13600</td>\n",
       "      <td>535.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.055265</td>\n",
       "      <td>11800</td>\n",
       "      <td>652.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.009576</td>\n",
       "      <td>9000</td>\n",
       "      <td>86.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.051962</td>\n",
       "      <td>7700</td>\n",
       "      <td>400.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.023422</td>\n",
       "      <td>4900</td>\n",
       "      <td>-114.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.112772</td>\n",
       "      <td>3900</td>\n",
       "      <td>-439.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.041927</td>\n",
       "      <td>3000</td>\n",
       "      <td>125.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.102914</td>\n",
       "      <td>2100</td>\n",
       "      <td>-216.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model  margin   pct_ret  risked  dollar_ret\n",
       "60  rf_preds_pp    0.00  0.010822   98800     1069.19\n",
       "61  rf_preds_pp    0.01 -0.003114   88900     -276.85\n",
       "62  rf_preds_pp    0.02 -0.003199   78700     -251.76\n",
       "63  rf_preds_pp    0.03 -0.045611   69800    -3183.68\n",
       "64  rf_preds_pp    0.04 -0.023956   61600    -1475.66\n",
       "65  rf_preds_pp    0.05 -0.030273   53200    -1610.51\n",
       "66  rf_preds_pp    0.06 -0.040603   46300    -1879.90\n",
       "67  rf_preds_pp    0.07 -0.052671   38300    -2017.29\n",
       "68  rf_preds_pp    0.08 -0.033030   32600    -1076.78\n",
       "69  rf_preds_pp    0.09 -0.021130   26000     -549.38\n",
       "70  rf_preds_pp    0.10 -0.049703   22200    -1103.41\n",
       "71  rf_preds_pp    0.11 -0.011994   17400     -208.69\n",
       "72  rf_preds_pp    0.12  0.039351   13600      535.17\n",
       "73  rf_preds_pp    0.13  0.055265   11800      652.13\n",
       "74  rf_preds_pp    0.14  0.009576    9000       86.18\n",
       "75  rf_preds_pp    0.15  0.051962    7700      400.11\n",
       "76  rf_preds_pp    0.16 -0.023422    4900     -114.77\n",
       "77  rf_preds_pp    0.17 -0.112772    3900     -439.81\n",
       "78  rf_preds_pp    0.18  0.041927    3000      125.78\n",
       "79  rf_preds_pp    0.19 -0.102914    2100     -216.12"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_exp_val_df.loc[higher_exp_val_df['model'] == 'rf_preds_pp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4c675d6c",
   "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>true</th>\n",
       "      <th>knn_preds_pp</th>\n",
       "      <th>rf_preds_pp</th>\n",
       "      <th>lgb_preds_pp</th>\n",
       "      <th>xgb_preds_pp</th>\n",
       "      <th>nb_preds_pp</th>\n",
       "      <th>svm_preds_pp</th>\n",
       "      <th>home_ML</th>\n",
       "      <th>away_ML</th>\n",
       "      <th>vegas_home_prob</th>\n",
       "      <th>vegas_away_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.448983</td>\n",
       "      <td>0.554664</td>\n",
       "      <td>0.808429</td>\n",
       "      <td>0.113006</td>\n",
       "      <td>0.425005</td>\n",
       "      <td>109</td>\n",
       "      <td>-130</td>\n",
       "      <td>0.478469</td>\n",
       "      <td>0.565217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.326760</td>\n",
       "      <td>0.331682</td>\n",
       "      <td>0.274078</td>\n",
       "      <td>0.048327</td>\n",
       "      <td>0.320614</td>\n",
       "      <td>199</td>\n",
       "      <td>-240</td>\n",
       "      <td>0.334448</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.430881</td>\n",
       "      <td>0.496473</td>\n",
       "      <td>0.624065</td>\n",
       "      <td>0.236285</td>\n",
       "      <td>0.570964</td>\n",
       "      <td>-124</td>\n",
       "      <td>105</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>1</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.196620</td>\n",
       "      <td>0.103749</td>\n",
       "      <td>0.083245</td>\n",
       "      <td>0.004784</td>\n",
       "      <td>0.274847</td>\n",
       "      <td>229</td>\n",
       "      <td>-281</td>\n",
       "      <td>0.303951</td>\n",
       "      <td>0.737533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.841345</td>\n",
       "      <td>0.916689</td>\n",
       "      <td>0.981228</td>\n",
       "      <td>0.988300</td>\n",
       "      <td>0.650365</td>\n",
       "      <td>-321</td>\n",
       "      <td>255</td>\n",
       "      <td>0.762470</td>\n",
       "      <td>0.281690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      true  knn_preds_pp  rf_preds_pp  lgb_preds_pp  xgb_preds_pp  \\\n",
       "1552     0      0.611111     0.448983      0.554664      0.808429   \n",
       "1553     0      0.444444     0.326760      0.331682      0.274078   \n",
       "1554     0      0.444444     0.430881      0.496473      0.624065   \n",
       "1555     1      0.388889     0.196620      0.103749      0.083245   \n",
       "1556     0      0.722222     0.841345      0.916689      0.981228   \n",
       "\n",
       "      nb_preds_pp  svm_preds_pp  home_ML  away_ML  vegas_home_prob  \\\n",
       "1552     0.113006      0.425005      109     -130         0.478469   \n",
       "1553     0.048327      0.320614      199     -240         0.334448   \n",
       "1554     0.236285      0.570964     -124      105         0.553571   \n",
       "1555     0.004784      0.274847      229     -281         0.303951   \n",
       "1556     0.988300      0.650365     -321      255         0.762470   \n",
       "\n",
       "      vegas_away_prob  \n",
       "1552         0.565217  \n",
       "1553         0.705882  \n",
       "1554         0.487805  \n",
       "1555         0.737533  \n",
       "1556         0.281690  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df2 = pd.DataFrame({'true': games_2022['home_win'], 'knn_preds_pp': best_knn.predict_proba(games_2022.filter(pred_cols).fillna(0))[:,1],\\\n",
    "                        'rf_preds_pp': best_rf.predict_proba(games_2022.filter(pred_cols).fillna(0))[:,1],\\\n",
    "                        'lgb_preds_pp':best_lgb.predict_proba(games_2022.filter(pred_cols).fillna(0))[:,1],\\\n",
    "                        'xgb_preds_pp':best_xgb.predict_proba(games_2022.filter(pred_cols).fillna(0))[:,1],\\\n",
    "                        'nb_preds_pp':best_nb.predict_proba(games_2022.filter(pred_cols).fillna(0))[:,1],\\\n",
    "                        'svm_preds_pp':best_svm.predict_proba(scaler.transform(games_2022.filter(pred_cols).fillna(0)))[:,1],\\\n",
    "                        'home_ML': games_2022['HomeTeamMoneyLine'], 'away_ML': games_2022['AwayTeamMoneyLine']\n",
    "                        })\n",
    "comp_df2['vegas_home_prob'] = np.where(comp_df2['home_ML'] < 0, abs(comp_df2['home_ML'])/(abs(comp_df2['home_ML'])+100), 100/(comp_df2['home_ML']+100))\n",
    "comp_df2['vegas_away_prob'] = np.where(comp_df2['away_ML'] < 0, abs(comp_df2['away_ML'])/(abs(comp_df2['away_ML'])+100), 100/(comp_df2['away_ML']+100))\n",
    "comp_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "60107aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_risk = []\n",
    "mod_ret = []\n",
    "mod_pct_ret = []\n",
    "models = []\n",
    "margins = []\n",
    "for model in final_models:\n",
    "    for margin in np.arange(0,.2,0.01):\n",
    "        df = pred_vs_vegas(model,comp_df2,margin)\n",
    "        risk, ret, pct = aggregate_returns(df)\n",
    "        mod_risk.append(risk)\n",
    "        mod_ret.append(ret)\n",
    "        mod_pct_ret.append(pct)\n",
    "        models.append(model)\n",
    "        margins.append(margin)\n",
    "\n",
    "higher_exp_val_df2 = pd.DataFrame({'model':models,'margin': margins, 'pct_ret': mod_pct_ret, 'risked':mod_risk, 'dollar_ret':mod_ret})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc3aeaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.lineplot(x='margin', y='pct_ret', hue='model', data=higher_exp_val_df2)\n",
    "plt.suptitle('% Return on ML Bets per Model by Margin in 2022 Games', size=16)\n",
    "plt.legend(loc='lower left')\n",
    "plt.savefig('strat_3_ret.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8bb132a0",
   "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>model</th>\n",
       "      <th>margin</th>\n",
       "      <th>pct_ret</th>\n",
       "      <th>risked</th>\n",
       "      <th>dollar_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.065520</td>\n",
       "      <td>14100</td>\n",
       "      <td>-923.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.067021</td>\n",
       "      <td>12400</td>\n",
       "      <td>-831.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.051487</td>\n",
       "      <td>11200</td>\n",
       "      <td>-576.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.060362</td>\n",
       "      <td>9200</td>\n",
       "      <td>-555.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.078785</td>\n",
       "      <td>8000</td>\n",
       "      <td>-630.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.059597</td>\n",
       "      <td>7400</td>\n",
       "      <td>-441.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.016380</td>\n",
       "      <td>5900</td>\n",
       "      <td>-96.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.034357</td>\n",
       "      <td>4900</td>\n",
       "      <td>168.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.086324</td>\n",
       "      <td>3700</td>\n",
       "      <td>-319.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.241038</td>\n",
       "      <td>2400</td>\n",
       "      <td>-578.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.244453</td>\n",
       "      <td>1700</td>\n",
       "      <td>-415.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.298190</td>\n",
       "      <td>1000</td>\n",
       "      <td>-298.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.365722</td>\n",
       "      <td>900</td>\n",
       "      <td>-329.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.365722</td>\n",
       "      <td>900</td>\n",
       "      <td>-329.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.356500</td>\n",
       "      <td>600</td>\n",
       "      <td>-213.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.546333</td>\n",
       "      <td>300</td>\n",
       "      <td>-163.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.546333</td>\n",
       "      <td>300</td>\n",
       "      <td>-163.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.546333</td>\n",
       "      <td>300</td>\n",
       "      <td>-163.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>-200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>rf_preds_pp</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>-200.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model  margin   pct_ret  risked  dollar_ret\n",
       "60  rf_preds_pp    0.00 -0.065520   14100     -923.83\n",
       "61  rf_preds_pp    0.01 -0.067021   12400     -831.06\n",
       "62  rf_preds_pp    0.02 -0.051487   11200     -576.65\n",
       "63  rf_preds_pp    0.03 -0.060362    9200     -555.33\n",
       "64  rf_preds_pp    0.04 -0.078785    8000     -630.28\n",
       "65  rf_preds_pp    0.05 -0.059597    7400     -441.02\n",
       "66  rf_preds_pp    0.06 -0.016380    5900      -96.64\n",
       "67  rf_preds_pp    0.07  0.034357    4900      168.35\n",
       "68  rf_preds_pp    0.08 -0.086324    3700     -319.40\n",
       "69  rf_preds_pp    0.09 -0.241038    2400     -578.49\n",
       "70  rf_preds_pp    0.10 -0.244453    1700     -415.57\n",
       "71  rf_preds_pp    0.11 -0.298190    1000     -298.19\n",
       "72  rf_preds_pp    0.12 -0.365722     900     -329.15\n",
       "73  rf_preds_pp    0.13 -0.365722     900     -329.15\n",
       "74  rf_preds_pp    0.14 -0.356500     600     -213.90\n",
       "75  rf_preds_pp    0.15 -0.546333     300     -163.90\n",
       "76  rf_preds_pp    0.16 -0.546333     300     -163.90\n",
       "77  rf_preds_pp    0.17 -0.546333     300     -163.90\n",
       "78  rf_preds_pp    0.18 -1.000000     200     -200.00\n",
       "79  rf_preds_pp    0.19 -1.000000     200     -200.00"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_exp_val_df2.loc[higher_exp_val_df2['model'] == 'rf_preds_pp']"
   ]
  }
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