Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0798f236-e376-4c27-865e-728f495cf7b5",
   "metadata": {},
   "source": [
    "# Tackle analysis\n",
    "\n",
    "In this notebook, we analyse games from Super Rugby Pacific to identify trends and prepare for the opposition.\n",
    "\n",
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3793b60d-a171-4b0a-ba98-485926999ed0",
   "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>Game ID</th>\n",
       "      <th>Competition</th>\n",
       "      <th>Season</th>\n",
       "      <th>Date</th>\n",
       "      <th>Home team</th>\n",
       "      <th>Away team</th>\n",
       "      <th>Tackling team</th>\n",
       "      <th>Carrying team</th>\n",
       "      <th>Time</th>\n",
       "      <th>Phase</th>\n",
       "      <th>...</th>\n",
       "      <th>Tackle outcome</th>\n",
       "      <th>Tackle qualifier</th>\n",
       "      <th>Tackle dominance</th>\n",
       "      <th>Tackler number</th>\n",
       "      <th>Tackle area</th>\n",
       "      <th>Tackler name</th>\n",
       "      <th>Tackler position</th>\n",
       "      <th>Attacking player name</th>\n",
       "      <th>Missed tackle category</th>\n",
       "      <th>Missed tackle outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>945268</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>15/02/2025</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>1_00005</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Passive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Sione Havili Talitui</td>\n",
       "      <td>7</td>\n",
       "      <td>Vaiolini Ekuasi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>945268</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>15/02/2025</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>1_00007</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Passive</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Lalomilo Lalomilo</td>\n",
       "      <td>12</td>\n",
       "      <td>Vaiolini Ekuasi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>945268</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>15/02/2025</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>1_00028</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Missed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tom Robertson</td>\n",
       "      <td>3</td>\n",
       "      <td>Ardie Savea</td>\n",
       "      <td>Stepped</td>\n",
       "      <td>Clean Break</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>945268</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>15/02/2025</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>1_00029</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Missed</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nic Dolly</td>\n",
       "      <td>2</td>\n",
       "      <td>Ardie Savea</td>\n",
       "      <td>Positional</td>\n",
       "      <td>Clean Break</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>945268</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>15/02/2025</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Western Force</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>1_00033</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Ben Donaldson</td>\n",
       "      <td>10</td>\n",
       "      <td>Lalomilo Lalomilo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7829</th>\n",
       "      <td>945271</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>22/02/2025</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>2_07913</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Pouri Rakete-Stones</td>\n",
       "      <td>1</td>\n",
       "      <td>Isoa Nasilasila</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7830</th>\n",
       "      <td>945271</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>22/02/2025</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>2_07913</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Lower Torso</td>\n",
       "      <td>Hugo Plummer</td>\n",
       "      <td>5</td>\n",
       "      <td>Isoa Nasilasila</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7831</th>\n",
       "      <td>945271</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>22/02/2025</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>2_07922</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Lower Torso</td>\n",
       "      <td>Raymond Tuputupu</td>\n",
       "      <td>2</td>\n",
       "      <td>Inia Tabuavou</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7832</th>\n",
       "      <td>945271</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>22/02/2025</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>2_07938</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>Missed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Lower Torso</td>\n",
       "      <td>Bailyn Sullivan</td>\n",
       "      <td>13</td>\n",
       "      <td>Iosefo Masi</td>\n",
       "      <td>Bumped Off</td>\n",
       "      <td>Tackled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7833</th>\n",
       "      <td>945271</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>22/02/2025</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>Fijian Drua</td>\n",
       "      <td>2_07940</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>Passive</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Legs</td>\n",
       "      <td>Peter Lakai</td>\n",
       "      <td>8</td>\n",
       "      <td>Iosefo Masi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7834 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Game ID          Competition  Season        Date      Home team  \\\n",
       "0      945268  Super Rugby Pacific    2025  15/02/2025  Western Force   \n",
       "1      945268  Super Rugby Pacific    2025  15/02/2025  Western Force   \n",
       "2      945268  Super Rugby Pacific    2025  15/02/2025  Western Force   \n",
       "3      945268  Super Rugby Pacific    2025  15/02/2025  Western Force   \n",
       "4      945268  Super Rugby Pacific    2025  15/02/2025  Western Force   \n",
       "...       ...                  ...     ...         ...            ...   \n",
       "7829   945271  Super Rugby Pacific    2025  22/02/2025     Hurricanes   \n",
       "7830   945271  Super Rugby Pacific    2025  22/02/2025     Hurricanes   \n",
       "7831   945271  Super Rugby Pacific    2025  22/02/2025     Hurricanes   \n",
       "7832   945271  Super Rugby Pacific    2025  22/02/2025     Hurricanes   \n",
       "7833   945271  Super Rugby Pacific    2025  22/02/2025     Hurricanes   \n",
       "\n",
       "           Away team   Tackling team   Carrying team     Time  Phase  ...  \\\n",
       "0     Moana Pasifika  Moana Pasifika   Western Force  1_00005      1  ...   \n",
       "1     Moana Pasifika  Moana Pasifika   Western Force  1_00007      1  ...   \n",
       "2     Moana Pasifika   Western Force  Moana Pasifika  1_00028      1  ...   \n",
       "3     Moana Pasifika   Western Force  Moana Pasifika  1_00029      1  ...   \n",
       "4     Moana Pasifika   Western Force  Moana Pasifika  1_00033      1  ...   \n",
       "...              ...             ...             ...      ...    ...  ...   \n",
       "7829     Fijian Drua      Hurricanes     Fijian Drua  2_07913     17  ...   \n",
       "7830     Fijian Drua      Hurricanes     Fijian Drua  2_07913     17  ...   \n",
       "7831     Fijian Drua      Hurricanes     Fijian Drua  2_07922     18  ...   \n",
       "7832     Fijian Drua      Hurricanes     Fijian Drua  2_07938     19  ...   \n",
       "7833     Fijian Drua      Hurricanes     Fijian Drua  2_07940     19  ...   \n",
       "\n",
       "      Tackle outcome  Tackle qualifier    Tackle dominance Tackler number  \\\n",
       "0            Passive               NaN  Ineffective Tackle    1st Tackler   \n",
       "1            Passive            Assist  Ineffective Tackle    2nd Tackler   \n",
       "2             Missed               NaN  Ineffective Tackle    1st Tackler   \n",
       "3             Missed            Assist  Ineffective Tackle    2nd Tackler   \n",
       "4           Complete               NaN      Neutral Tackle    1st Tackler   \n",
       "...              ...               ...                 ...            ...   \n",
       "7829        Complete               NaN      Neutral Tackle    1st Tackler   \n",
       "7830        Complete            Assist      Neutral Tackle    2nd Tackler   \n",
       "7831        Complete               NaN      Neutral Tackle    1st Tackler   \n",
       "7832          Missed               NaN  Ineffective Tackle    1st Tackler   \n",
       "7833         Passive            Assist  Ineffective Tackle    2nd Tackler   \n",
       "\n",
       "      Tackle area          Tackler name Tackler position  \\\n",
       "0     Upper Torso  Sione Havili Talitui                7   \n",
       "1     Upper Torso     Lalomilo Lalomilo               12   \n",
       "2             NaN         Tom Robertson                3   \n",
       "3             NaN             Nic Dolly                2   \n",
       "4     Upper Torso         Ben Donaldson               10   \n",
       "...           ...                   ...              ...   \n",
       "7829  Upper Torso   Pouri Rakete-Stones                1   \n",
       "7830  Lower Torso          Hugo Plummer                5   \n",
       "7831  Lower Torso      Raymond Tuputupu                2   \n",
       "7832  Lower Torso       Bailyn Sullivan               13   \n",
       "7833         Legs           Peter Lakai                8   \n",
       "\n",
       "     Attacking player name Missed tackle category  Missed tackle outcome  \n",
       "0          Vaiolini Ekuasi                    NaN                    NaN  \n",
       "1          Vaiolini Ekuasi                    NaN                    NaN  \n",
       "2              Ardie Savea                Stepped            Clean Break  \n",
       "3              Ardie Savea             Positional            Clean Break  \n",
       "4        Lalomilo Lalomilo                    NaN                    NaN  \n",
       "...                    ...                    ...                    ...  \n",
       "7829       Isoa Nasilasila                    NaN                    NaN  \n",
       "7830       Isoa Nasilasila                    NaN                    NaN  \n",
       "7831         Inia Tabuavou                    NaN                    NaN  \n",
       "7832           Iosefo Masi             Bumped Off                Tackled  \n",
       "7833           Iosefo Masi                    NaN                    NaN  \n",
       "\n",
       "[7834 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# We load the CSV file\n",
    "df = pd.read_csv(\"./data/tackles_SuperRugbyPacific_2024-25.csv\") \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63a7ac8-aa53-4dd3-9aad-393aa069d405",
   "metadata": {},
   "source": [
    "## Team summaries\n",
    "\n",
    "### When the team is tackling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97a77e36-f2e2-4f0d-920e-4cbe4d40bc94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: ACT Brumbies\n",
      "Complete\t358\t51.6%\n",
      "Passive\t158\t22.8%\n",
      "Missed\t89\t12.8%\n",
      "Offload Allowed\t49\t7.1%\n",
      "Sack\t16\t2.3%\n",
      "Turnover Won\t10\t1.4%\n",
      "Pen Conceded\t6\t0.9%\n",
      "Ineffective\t5\t0.7%\n",
      "Try Saver\t2\t0.3%\n",
      "Forced in Touch\t1\t0.1%\n",
      "------------------------------\n",
      "Team: Blues\n",
      "Complete\t504\t52.2%\n",
      "Passive\t254\t26.3%\n",
      "Missed\t101\t10.5%\n",
      "Sack\t43\t4.5%\n",
      "Offload Allowed\t36\t3.7%\n",
      "Turnover Won\t11\t1.1%\n",
      "Pen Conceded\t7\t0.7%\n",
      "Ineffective\t4\t0.4%\n",
      "Forced in Touch\t3\t0.3%\n",
      "Try Saver\t2\t0.2%\n",
      "------------------------------\n",
      "Team: Chiefs\n",
      "Complete\t354\t48.0%\n",
      "Passive\t192\t26.0%\n",
      "Missed\t83\t11.2%\n",
      "Sack\t42\t5.7%\n",
      "Offload Allowed\t38\t5.1%\n",
      "Turnover Won\t8\t1.1%\n",
      "Ineffective\t7\t0.9%\n",
      "Pen Conceded\t7\t0.9%\n",
      "Forced in Touch\t4\t0.5%\n",
      "Try Saver\t3\t0.4%\n",
      "------------------------------\n",
      "Team: Crusaders\n",
      "Complete\t306\t47.2%\n",
      "Passive\t140\t21.6%\n",
      "Missed\t82\t12.7%\n",
      "Offload Allowed\t63\t9.7%\n",
      "Sack\t27\t4.2%\n",
      "Ineffective\t13\t2.0%\n",
      "Pen Conceded\t7\t1.1%\n",
      "Turnover Won\t5\t0.8%\n",
      "Forced in Touch\t4\t0.6%\n",
      "Try Saver\t1\t0.2%\n",
      "------------------------------\n",
      "Team: Fijian Drua\n",
      "Complete\t237\t38.0%\n",
      "Passive\t188\t30.2%\n",
      "Missed\t101\t16.2%\n",
      "Offload Allowed\t39\t6.3%\n",
      "Ineffective\t20\t3.2%\n",
      "Sack\t20\t3.2%\n",
      "Turnover Won\t12\t1.9%\n",
      "Pen Conceded\t5\t0.8%\n",
      "Forced in Touch\t1\t0.2%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Highlanders\n",
      "Complete\t266\t45.4%\n",
      "Passive\t143\t24.4%\n",
      "Missed\t64\t10.9%\n",
      "Offload Allowed\t43\t7.3%\n",
      "Sack\t43\t7.3%\n",
      "Ineffective\t13\t2.2%\n",
      "Pen Conceded\t6\t1.0%\n",
      "Turnover Won\t6\t1.0%\n",
      "Forced in Touch\t2\t0.3%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Hurricanes\n",
      "Complete\t293\t39.4%\n",
      "Passive\t242\t32.5%\n",
      "Missed\t99\t13.3%\n",
      "Offload Allowed\t44\t5.9%\n",
      "Sack\t26\t3.5%\n",
      "Ineffective\t23\t3.1%\n",
      "Pen Conceded\t9\t1.2%\n",
      "Turnover Won\t5\t0.7%\n",
      "Try Saver\t2\t0.3%\n",
      "Forced in Touch\t1\t0.1%\n",
      "------------------------------\n",
      "Team: Moana Pasifika\n",
      "Complete\t293\t43.7%\n",
      "Passive\t175\t26.1%\n",
      "Missed\t79\t11.8%\n",
      "Offload Allowed\t56\t8.3%\n",
      "Sack\t31\t4.6%\n",
      "Ineffective\t18\t2.7%\n",
      "Turnover Won\t10\t1.5%\n",
      "Pen Conceded\t5\t0.7%\n",
      "Forced in Touch\t4\t0.6%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: NSW Waratahs\n",
      "Complete\t184\t37.8%\n",
      "Passive\t152\t31.2%\n",
      "Missed\t59\t12.1%\n",
      "Offload Allowed\t30\t6.2%\n",
      "Sack\t27\t5.5%\n",
      "Ineffective\t14\t2.9%\n",
      "Turnover Won\t11\t2.3%\n",
      "Pen Conceded\t5\t1.0%\n",
      "Forced in Touch\t3\t0.6%\n",
      "Try Saver\t2\t0.4%\n",
      "------------------------------\n",
      "Team: Queensland Reds\n",
      "Complete\t418\t56.1%\n",
      "Passive\t164\t22.0%\n",
      "Missed\t65\t8.7%\n",
      "Offload Allowed\t46\t6.2%\n",
      "Sack\t18\t2.4%\n",
      "Ineffective\t15\t2.0%\n",
      "Turnover Won\t8\t1.1%\n",
      "Try Saver\t6\t0.8%\n",
      "Pen Conceded\t3\t0.4%\n",
      "Forced in Touch\t2\t0.3%\n",
      "------------------------------\n",
      "Team: Western Force\n",
      "Complete\t429\t46.0%\n",
      "Passive\t270\t28.9%\n",
      "Missed\t114\t12.2%\n",
      "Offload Allowed\t59\t6.3%\n",
      "Sack\t27\t2.9%\n",
      "Ineffective\t14\t1.5%\n",
      "Pen Conceded\t7\t0.8%\n",
      "Try Saver\t7\t0.8%\n",
      "Turnover Won\t4\t0.4%\n",
      "Forced in Touch\t2\t0.2%\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each outcome per team\n",
    "outcome_counts = df.groupby(['Tackling team', 'Tackle outcome']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages\n",
    "outcome_percentages = outcome_counts.div(outcome_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Show results team by team\n",
    "for team in outcome_counts.index:\n",
    "    print(f\"Team: {team}\")\n",
    "    team_data = pd.DataFrame({\n",
    "        'Count': outcome_counts.loc[team].astype(int),  # Ensure count is integer\n",
    "        'Percentage': outcome_percentages.loc[team]\n",
    "    }).sort_values(by='Count', ascending=False)\n",
    "\n",
    "    for outcome, row in team_data.iterrows():\n",
    "        print(f\"{outcome}\\t{int(row['Count'])}\\t{row['Percentage']:.1f}%\")\n",
    "\n",
    "    print(\"-\" * 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f903a2c3-2920-49d8-aac6-35f1f21c0b15",
   "metadata": {},
   "source": [
    "### When the team is carrying (i.e., being tackled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7509fc41-514f-44f7-bdb3-c2fa31d8c34f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: ACT Brumbies\n",
      "Complete\t457\t52.8%\n",
      "Passive\t226\t26.1%\n",
      "Missed\t79\t9.1%\n",
      "Offload Allowed\t38\t4.4%\n",
      "Sack\t27\t3.1%\n",
      "Ineffective\t14\t1.6%\n",
      "Turnover Won\t11\t1.3%\n",
      "Try Saver\t7\t0.8%\n",
      "Pen Conceded\t4\t0.5%\n",
      "Forced in Touch\t2\t0.2%\n",
      "------------------------------\n",
      "Team: Blues\n",
      "Complete\t320\t45.9%\n",
      "Passive\t195\t28.0%\n",
      "Missed\t77\t11.0%\n",
      "Offload Allowed\t41\t5.9%\n",
      "Sack\t37\t5.3%\n",
      "Ineffective\t12\t1.7%\n",
      "Pen Conceded\t7\t1.0%\n",
      "Turnover Won\t7\t1.0%\n",
      "Forced in Touch\t1\t0.1%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Chiefs\n",
      "Complete\t378\t44.4%\n",
      "Passive\t219\t25.7%\n",
      "Missed\t120\t14.1%\n",
      "Offload Allowed\t52\t6.1%\n",
      "Sack\t35\t4.1%\n",
      "Ineffective\t20\t2.4%\n",
      "Turnover Won\t11\t1.3%\n",
      "Pen Conceded\t10\t1.2%\n",
      "Try Saver\t5\t0.6%\n",
      "Forced in Touch\t1\t0.1%\n",
      "------------------------------\n",
      "Team: Crusaders\n",
      "Complete\t355\t51.0%\n",
      "Passive\t138\t19.8%\n",
      "Missed\t89\t12.8%\n",
      "Offload Allowed\t55\t7.9%\n",
      "Sack\t29\t4.2%\n",
      "Ineffective\t12\t1.7%\n",
      "Try Saver\t6\t0.9%\n",
      "Turnover Won\t6\t0.9%\n",
      "Pen Conceded\t4\t0.6%\n",
      "Forced in Touch\t2\t0.3%\n",
      "------------------------------\n",
      "Team: Fijian Drua\n",
      "Complete\t276\t42.8%\n",
      "Passive\t200\t31.0%\n",
      "Missed\t75\t11.6%\n",
      "Offload Allowed\t34\t5.3%\n",
      "Sack\t25\t3.9%\n",
      "Turnover Won\t13\t2.0%\n",
      "Ineffective\t10\t1.6%\n",
      "Pen Conceded\t7\t1.1%\n",
      "Forced in Touch\t3\t0.5%\n",
      "Try Saver\t2\t0.3%\n",
      "------------------------------\n",
      "Team: Highlanders\n",
      "Complete\t245\t50.6%\n",
      "Passive\t93\t19.2%\n",
      "Missed\t58\t12.0%\n",
      "Offload Allowed\t41\t8.5%\n",
      "Sack\t26\t5.4%\n",
      "Ineffective\t10\t2.1%\n",
      "Turnover Won\t6\t1.2%\n",
      "Pen Conceded\t3\t0.6%\n",
      "Forced in Touch\t2\t0.4%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Hurricanes\n",
      "Passive\t314\t38.4%\n",
      "Complete\t272\t33.3%\n",
      "Missed\t117\t14.3%\n",
      "Offload Allowed\t57\t7.0%\n",
      "Sack\t29\t3.5%\n",
      "Ineffective\t10\t1.2%\n",
      "Turnover Won\t8\t1.0%\n",
      "Pen Conceded\t6\t0.7%\n",
      "Forced in Touch\t4\t0.5%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Moana Pasifika\n",
      "Complete\t412\t51.4%\n",
      "Passive\t191\t23.8%\n",
      "Missed\t85\t10.6%\n",
      "Offload Allowed\t49\t6.1%\n",
      "Sack\t21\t2.6%\n",
      "Ineffective\t19\t2.4%\n",
      "Pen Conceded\t9\t1.1%\n",
      "Turnover Won\t7\t0.9%\n",
      "Forced in Touch\t5\t0.6%\n",
      "Try Saver\t3\t0.4%\n",
      "------------------------------\n",
      "Team: NSW Waratahs\n",
      "Complete\t242\t40.1%\n",
      "Passive\t177\t29.3%\n",
      "Missed\t84\t13.9%\n",
      "Sack\t42\t7.0%\n",
      "Offload Allowed\t40\t6.6%\n",
      "Ineffective\t10\t1.7%\n",
      "Pen Conceded\t4\t0.7%\n",
      "Turnover Won\t4\t0.7%\n",
      "Forced in Touch\t1\t0.2%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Queensland Reds\n",
      "Complete\t302\t52.1%\n",
      "Passive\t105\t18.1%\n",
      "Missed\t70\t12.1%\n",
      "Offload Allowed\t60\t10.3%\n",
      "Sack\t13\t2.2%\n",
      "Ineffective\t12\t2.1%\n",
      "Pen Conceded\t8\t1.4%\n",
      "Turnover Won\t8\t1.4%\n",
      "Forced in Touch\t2\t0.3%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Team: Western Force\n",
      "Complete\t383\t48.2%\n",
      "Passive\t220\t27.7%\n",
      "Missed\t82\t10.3%\n",
      "Offload Allowed\t36\t4.5%\n",
      "Sack\t36\t4.5%\n",
      "Ineffective\t17\t2.1%\n",
      "Turnover Won\t9\t1.1%\n",
      "Pen Conceded\t5\t0.6%\n",
      "Forced in Touch\t4\t0.5%\n",
      "Try Saver\t2\t0.3%\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each outcome per team\n",
    "outcome_counts = df.groupby(['Carrying team', 'Tackle outcome']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages\n",
    "outcome_percentages = outcome_counts.div(outcome_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Show results team by team\n",
    "for team in outcome_counts.index:\n",
    "    print(f\"Team: {team}\")\n",
    "    team_data = pd.DataFrame({\n",
    "        'Count': outcome_counts.loc[team].astype(int),  # Ensure count is integer\n",
    "        'Percentage': outcome_percentages.loc[team]\n",
    "    }).sort_values(by='Count', ascending=False)\n",
    "\n",
    "    for outcome, row in team_data.iterrows():\n",
    "        print(f\"{outcome}\\t{int(row['Count'])}\\t{row['Percentage']:.1f}%\")\n",
    "\n",
    "    print(\"-\" * 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a5e042-b386-477d-acb3-a18b07e806fd",
   "metadata": {},
   "source": [
    "## Team-specific defence analysis\n",
    "\n",
    "### Players missing tackles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb603601-82a2-46d5-bdfe-a88826120260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: NSW Waratahs\n",
      "Player: Andrew Kellaway\n",
      "Missed\t4\t40.0%\n",
      "Passive\t4\t40.0%\n",
      "Complete\t2\t20.0%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jamie Adamson\n",
      "Missed\t2\t40.0%\n",
      "Sack\t2\t40.0%\n",
      "Complete\t1\t20.0%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Passive\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Taniela Tupou\n",
      "Complete\t7\t53.8%\n",
      "Missed\t5\t38.5%\n",
      "Passive\t1\t7.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tane Edmed\n",
      "Missed\t3\t33.3%\n",
      "Passive\t2\t22.2%\n",
      "Complete\t1\t11.1%\n",
      "Offload Allowed\t1\t11.1%\n",
      "Sack\t1\t11.1%\n",
      "Turnover Won\t1\t11.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jack Grant\n",
      "Passive\t8\t61.5%\n",
      "Missed\t4\t30.8%\n",
      "Complete\t1\t7.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Triston Reilly\n",
      "Passive\t3\t42.9%\n",
      "Complete\t2\t28.6%\n",
      "Missed\t2\t28.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Darby Lancaster\n",
      "Passive\t8\t50.0%\n",
      "Missed\t4\t25.0%\n",
      "Offload Allowed\t2\t12.5%\n",
      "Forced in Touch\t1\t6.2%\n",
      "Sack\t1\t6.2%\n",
      "Complete\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Miles Amatosero\n",
      "Complete\t11\t42.3%\n",
      "Missed\t5\t19.2%\n",
      "Passive\t5\t19.2%\n",
      "Offload Allowed\t2\t7.7%\n",
      "Ineffective\t1\t3.8%\n",
      "Sack\t1\t3.8%\n",
      "Turnover Won\t1\t3.8%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Lalakai Foketi\n",
      "Complete\t7\t43.8%\n",
      "Missed\t3\t18.8%\n",
      "Passive\t3\t18.8%\n",
      "Ineffective\t1\t6.2%\n",
      "Offload Allowed\t1\t6.2%\n",
      "Turnover Won\t1\t6.2%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Henry O'Donnell\n",
      "Passive\t9\t32.1%\n",
      "Complete\t7\t25.0%\n",
      "Missed\t5\t17.9%\n",
      "Offload Allowed\t3\t10.7%\n",
      "Sack\t2\t7.1%\n",
      "Forced in Touch\t1\t3.6%\n",
      "Turnover Won\t1\t3.6%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joey Walton\n",
      "Passive\t15\t53.6%\n",
      "Complete\t5\t17.9%\n",
      "Missed\t4\t14.3%\n",
      "Sack\t2\t7.1%\n",
      "Ineffective\t1\t3.6%\n",
      "Offload Allowed\t1\t3.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jake Gordon\n",
      "Passive\t6\t37.5%\n",
      "Complete\t5\t31.2%\n",
      "Missed\t2\t12.5%\n",
      "Offload Allowed\t2\t12.5%\n",
      "Ineffective\t1\t6.2%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tom Lambert\n",
      "Complete\t4\t44.4%\n",
      "Passive\t2\t22.2%\n",
      "Missed\t1\t11.1%\n",
      "Offload Allowed\t1\t11.1%\n",
      "Pen Conceded\t1\t11.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Leafi Talataina\n",
      "Passive\t4\t44.4%\n",
      "Complete\t2\t22.2%\n",
      "Ineffective\t1\t11.1%\n",
      "Missed\t1\t11.1%\n",
      "Sack\t1\t11.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Rob Leota\n",
      "Complete\t10\t35.7%\n",
      "Passive\t7\t25.0%\n",
      "Offload Allowed\t4\t14.3%\n",
      "Missed\t3\t10.7%\n",
      "Pen Conceded\t2\t7.1%\n",
      "Ineffective\t1\t3.6%\n",
      "Sack\t1\t3.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Dave Porecki\n",
      "Complete\t14\t46.7%\n",
      "Passive\t7\t23.3%\n",
      "Missed\t3\t10.0%\n",
      "Turnover Won\t3\t10.0%\n",
      "Sack\t2\t6.7%\n",
      "Ineffective\t1\t3.3%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Siosifa Amone\n",
      "Complete\t6\t42.9%\n",
      "Passive\t4\t28.6%\n",
      "Ineffective\t2\t14.3%\n",
      "Missed\t1\t7.1%\n",
      "Try Saver\t1\t7.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Langi Gleeson\n",
      "Complete\t12\t40.0%\n",
      "Passive\t10\t33.3%\n",
      "Sack\t3\t10.0%\n",
      "Missed\t2\t6.7%\n",
      "Offload Allowed\t1\t3.3%\n",
      "Try Saver\t1\t3.3%\n",
      "Turnover Won\t1\t3.3%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Hugh Sinclair\n",
      "Complete\t21\t56.8%\n",
      "Passive\t8\t21.6%\n",
      "Offload Allowed\t3\t8.1%\n",
      "Missed\t2\t5.4%\n",
      "Pen Conceded\t1\t2.7%\n",
      "Sack\t1\t2.7%\n",
      "Turnover Won\t1\t2.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Ben Grant\n",
      "Complete\t10\t41.7%\n",
      "Passive\t6\t25.0%\n",
      "Sack\t4\t16.7%\n",
      "Ineffective\t2\t8.3%\n",
      "Missed\t1\t4.2%\n",
      "Offload Allowed\t1\t4.2%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Charlie Gamble\n",
      "Complete\t21\t42.9%\n",
      "Passive\t21\t42.9%\n",
      "Offload Allowed\t3\t6.1%\n",
      "Missed\t2\t4.1%\n",
      "Sack\t2\t4.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Felix Kalapu\n",
      "Complete\t2\t66.7%\n",
      "Passive\t1\t33.3%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Teddy Wilson\n",
      "Complete\t4\t66.7%\n",
      "Ineffective\t1\t16.7%\n",
      "Passive\t1\t16.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Lawson Creighton\n",
      "Passive\t3\t50.0%\n",
      "Complete\t1\t16.7%\n",
      "Forced in Touch\t1\t16.7%\n",
      "Turnover Won\t1\t16.7%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Max Jorgensen\n",
      "Passive\t6\t42.9%\n",
      "Complete\t2\t14.3%\n",
      "Ineffective\t2\t14.3%\n",
      "Offload Allowed\t2\t14.3%\n",
      "Sack\t1\t7.1%\n",
      "Turnover Won\t1\t7.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Mahe Vailanu\n",
      "Complete\t6\t46.2%\n",
      "Passive\t4\t30.8%\n",
      "Sack\t2\t15.4%\n",
      "Offload Allowed\t1\t7.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Angus Bell\n",
      "Complete\t13\t72.2%\n",
      "Passive\t2\t11.1%\n",
      "Offload Allowed\t1\t5.6%\n",
      "Pen Conceded\t1\t5.6%\n",
      "Sack\t1\t5.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Isaac Kailea\n",
      "Complete\t7\t87.5%\n",
      "Offload Allowed\t1\t12.5%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Passive\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joseph-Aukuso Suaalii\n",
      "Passive\t2\t100.0%\n",
      "Complete\t0\t0.0%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "selected_team = 'NSW Waratahs'\n",
    "\n",
    "# Filter for the selected team\n",
    "df_team = df[df['Tackling team'] == selected_team]\n",
    "\n",
    "# Count occurrences of each outcome per player\n",
    "outcome_counts = df_team.groupby(['Tackler name', 'Tackle outcome']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages\n",
    "outcome_percentages = outcome_counts.div(outcome_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Sort players by percentage of 'Missed' tackles (descending), defaulting to 0% if 'Missed' is not present\n",
    "sorted_players = outcome_percentages.get('Missed', pd.Series(0, index=outcome_percentages.index)).sort_values(ascending=False).index\n",
    "\n",
    "# Show results player by player\n",
    "print(f\"Team: {selected_team}\")\n",
    "for player in sorted_players:\n",
    "    print(f\"Player: {player}\")\n",
    "    player_data = pd.DataFrame({\n",
    "        'Count': outcome_counts.loc[player].astype(int),  # Ensure count is integer\n",
    "        'Percentage': outcome_percentages.loc[player]\n",
    "    }).sort_values(by='Count', ascending=False)\n",
    "\n",
    "    for outcome, row in player_data.iterrows():\n",
    "        print(f\"{outcome}\\t{int(row['Count'])}\\t{row['Percentage']:.1f}%\")\n",
    "\n",
    "    print(\"-\" * 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6447e70-a391-4caa-85be-3297aa88769e",
   "metadata": {},
   "source": [
    "### Players with dominant tackles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a840fb6d-1a80-4162-8751-01ab86bf52d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: NSW Waratahs\n",
      "Player: Jamie Adamson\n",
      "Missed\t2\t40.0%\n",
      "Sack\t2\t40.0%\n",
      "Complete\t1\t20.0%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Passive\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Ben Grant\n",
      "Complete\t10\t41.7%\n",
      "Passive\t6\t25.0%\n",
      "Sack\t4\t16.7%\n",
      "Ineffective\t2\t8.3%\n",
      "Missed\t1\t4.2%\n",
      "Offload Allowed\t1\t4.2%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Mahe Vailanu\n",
      "Complete\t6\t46.2%\n",
      "Passive\t4\t30.8%\n",
      "Sack\t2\t15.4%\n",
      "Offload Allowed\t1\t7.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Leafi Talataina\n",
      "Passive\t4\t44.4%\n",
      "Complete\t2\t22.2%\n",
      "Ineffective\t1\t11.1%\n",
      "Missed\t1\t11.1%\n",
      "Sack\t1\t11.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tane Edmed\n",
      "Missed\t3\t33.3%\n",
      "Passive\t2\t22.2%\n",
      "Complete\t1\t11.1%\n",
      "Offload Allowed\t1\t11.1%\n",
      "Sack\t1\t11.1%\n",
      "Turnover Won\t1\t11.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Langi Gleeson\n",
      "Complete\t12\t40.0%\n",
      "Passive\t10\t33.3%\n",
      "Sack\t3\t10.0%\n",
      "Missed\t2\t6.7%\n",
      "Offload Allowed\t1\t3.3%\n",
      "Try Saver\t1\t3.3%\n",
      "Turnover Won\t1\t3.3%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Max Jorgensen\n",
      "Passive\t6\t42.9%\n",
      "Complete\t2\t14.3%\n",
      "Ineffective\t2\t14.3%\n",
      "Offload Allowed\t2\t14.3%\n",
      "Sack\t1\t7.1%\n",
      "Turnover Won\t1\t7.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Henry O'Donnell\n",
      "Passive\t9\t32.1%\n",
      "Complete\t7\t25.0%\n",
      "Missed\t5\t17.9%\n",
      "Offload Allowed\t3\t10.7%\n",
      "Sack\t2\t7.1%\n",
      "Forced in Touch\t1\t3.6%\n",
      "Turnover Won\t1\t3.6%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joey Walton\n",
      "Passive\t15\t53.6%\n",
      "Complete\t5\t17.9%\n",
      "Missed\t4\t14.3%\n",
      "Sack\t2\t7.1%\n",
      "Ineffective\t1\t3.6%\n",
      "Offload Allowed\t1\t3.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Dave Porecki\n",
      "Complete\t14\t46.7%\n",
      "Passive\t7\t23.3%\n",
      "Missed\t3\t10.0%\n",
      "Turnover Won\t3\t10.0%\n",
      "Sack\t2\t6.7%\n",
      "Ineffective\t1\t3.3%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Darby Lancaster\n",
      "Passive\t8\t50.0%\n",
      "Missed\t4\t25.0%\n",
      "Offload Allowed\t2\t12.5%\n",
      "Forced in Touch\t1\t6.2%\n",
      "Sack\t1\t6.2%\n",
      "Complete\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Angus Bell\n",
      "Complete\t13\t72.2%\n",
      "Passive\t2\t11.1%\n",
      "Offload Allowed\t1\t5.6%\n",
      "Pen Conceded\t1\t5.6%\n",
      "Sack\t1\t5.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Charlie Gamble\n",
      "Complete\t21\t42.9%\n",
      "Passive\t21\t42.9%\n",
      "Offload Allowed\t3\t6.1%\n",
      "Missed\t2\t4.1%\n",
      "Sack\t2\t4.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Miles Amatosero\n",
      "Complete\t11\t42.3%\n",
      "Missed\t5\t19.2%\n",
      "Passive\t5\t19.2%\n",
      "Offload Allowed\t2\t7.7%\n",
      "Ineffective\t1\t3.8%\n",
      "Sack\t1\t3.8%\n",
      "Turnover Won\t1\t3.8%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Rob Leota\n",
      "Complete\t10\t35.7%\n",
      "Passive\t7\t25.0%\n",
      "Offload Allowed\t4\t14.3%\n",
      "Missed\t3\t10.7%\n",
      "Pen Conceded\t2\t7.1%\n",
      "Ineffective\t1\t3.6%\n",
      "Sack\t1\t3.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Hugh Sinclair\n",
      "Complete\t21\t56.8%\n",
      "Passive\t8\t21.6%\n",
      "Offload Allowed\t3\t8.1%\n",
      "Missed\t2\t5.4%\n",
      "Pen Conceded\t1\t2.7%\n",
      "Sack\t1\t2.7%\n",
      "Turnover Won\t1\t2.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Andrew Kellaway\n",
      "Missed\t4\t40.0%\n",
      "Passive\t4\t40.0%\n",
      "Complete\t2\t20.0%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Siosifa Amone\n",
      "Complete\t6\t42.9%\n",
      "Passive\t4\t28.6%\n",
      "Ineffective\t2\t14.3%\n",
      "Missed\t1\t7.1%\n",
      "Try Saver\t1\t7.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Teddy Wilson\n",
      "Complete\t4\t66.7%\n",
      "Ineffective\t1\t16.7%\n",
      "Passive\t1\t16.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tom Lambert\n",
      "Complete\t4\t44.4%\n",
      "Passive\t2\t22.2%\n",
      "Missed\t1\t11.1%\n",
      "Offload Allowed\t1\t11.1%\n",
      "Pen Conceded\t1\t11.1%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Taniela Tupou\n",
      "Complete\t7\t53.8%\n",
      "Missed\t5\t38.5%\n",
      "Passive\t1\t7.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joseph-Aukuso Suaalii\n",
      "Passive\t2\t100.0%\n",
      "Complete\t0\t0.0%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Lawson Creighton\n",
      "Passive\t3\t50.0%\n",
      "Complete\t1\t16.7%\n",
      "Forced in Touch\t1\t16.7%\n",
      "Turnover Won\t1\t16.7%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Lalakai Foketi\n",
      "Complete\t7\t43.8%\n",
      "Missed\t3\t18.8%\n",
      "Passive\t3\t18.8%\n",
      "Ineffective\t1\t6.2%\n",
      "Offload Allowed\t1\t6.2%\n",
      "Turnover Won\t1\t6.2%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jake Gordon\n",
      "Passive\t6\t37.5%\n",
      "Complete\t5\t31.2%\n",
      "Missed\t2\t12.5%\n",
      "Offload Allowed\t2\t12.5%\n",
      "Ineffective\t1\t6.2%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jack Grant\n",
      "Passive\t8\t61.5%\n",
      "Missed\t4\t30.8%\n",
      "Complete\t1\t7.7%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Isaac Kailea\n",
      "Complete\t7\t87.5%\n",
      "Offload Allowed\t1\t12.5%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Passive\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Felix Kalapu\n",
      "Complete\t2\t66.7%\n",
      "Passive\t1\t33.3%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Missed\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Triston Reilly\n",
      "Passive\t3\t42.9%\n",
      "Complete\t2\t28.6%\n",
      "Missed\t2\t28.6%\n",
      "Forced in Touch\t0\t0.0%\n",
      "Ineffective\t0\t0.0%\n",
      "Offload Allowed\t0\t0.0%\n",
      "Pen Conceded\t0\t0.0%\n",
      "Sack\t0\t0.0%\n",
      "Try Saver\t0\t0.0%\n",
      "Turnover Won\t0\t0.0%\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Filter for the selected team\n",
    "df_team = df[df['Tackling team'] == selected_team]\n",
    "\n",
    "# Count occurrences of each outcome per player\n",
    "outcome_counts = df_team.groupby(['Tackler name', 'Tackle outcome']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages\n",
    "outcome_percentages = outcome_counts.div(outcome_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Sort players by percentage of 'Missed' tackles (descending), defaulting to 0% if 'Missed' is not present\n",
    "sorted_players = outcome_percentages.get('Sack', pd.Series(0, index=outcome_percentages.index)).sort_values(ascending=False).index\n",
    "\n",
    "# Show results player by player\n",
    "print(f\"Team: {selected_team}\")\n",
    "for player in sorted_players:\n",
    "    print(f\"Player: {player}\")\n",
    "    player_data = pd.DataFrame({\n",
    "        'Count': outcome_counts.loc[player].astype(int),  # Ensure count is integer\n",
    "        'Percentage': outcome_percentages.loc[player]\n",
    "    }).sort_values(by='Count', ascending=False)\n",
    "\n",
    "    for outcome, row in player_data.iterrows():\n",
    "        print(f\"{outcome}\\t{int(row['Count'])}\\t{row['Percentage']:.1f}%\")\n",
    "\n",
    "    print(\"-\" * 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8331ff51-70e0-411f-a29b-bd4b845f0238",
   "metadata": {},
   "source": [
    "### Type of missed tackles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f3f9544-0218-473c-ad21-2c8914b57216",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter for the selected team\n",
    "df_team = df[df['Tackling team'] == selected_team]\n",
    "\n",
    "# Filter for missed tackles\n",
    "df_missed = df_team.dropna(subset=['Missed tackle category'], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70f0ecff-9ee4-469c-ac1d-8f4c24cf8248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: NSW Waratahs\n",
      "Player: Andrew Kellaway - Missed tackles: 4 (40.0%)\n",
      "Bumped Off\t2\t50.0%\n",
      "Stepped\t2\t50.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jamie Adamson - Missed tackles: 2 (40.0%)\n",
      "Bumped Off\t2\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Taniela Tupou - Missed tackles: 5 (38.5%)\n",
      "Bumped Off\t3\t60.0%\n",
      "Stepped\t2\t40.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tane Edmed - Missed tackles: 3 (33.3%)\n",
      "Bumped Off\t2\t66.7%\n",
      "Outpaced\t1\t33.3%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jack Grant - Missed tackles: 4 (30.8%)\n",
      "Stepped\t3\t75.0%\n",
      "Bumped Off\t1\t25.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Triston Reilly - Missed tackles: 2 (28.6%)\n",
      "Outpaced\t1\t50.0%\n",
      "Positional\t1\t50.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Darby Lancaster - Missed tackles: 4 (25.0%)\n",
      "Bumped Off\t2\t50.0%\n",
      "Positional\t1\t25.0%\n",
      "Stepped\t1\t25.0%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Miles Amatosero - Missed tackles: 5 (19.2%)\n",
      "Bumped Off\t3\t60.0%\n",
      "Positional\t1\t20.0%\n",
      "Stepped\t1\t20.0%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Lalakai Foketi - Missed tackles: 3 (18.8%)\n",
      "Bumped Off\t3\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Henry O'Donnell - Missed tackles: 5 (17.9%)\n",
      "Bumped Off\t3\t60.0%\n",
      "Stepped\t2\t40.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joey Walton - Missed tackles: 4 (14.3%)\n",
      "Bumped Off\t2\t50.0%\n",
      "Positional\t1\t25.0%\n",
      "Stepped\t1\t25.0%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jake Gordon - Missed tackles: 2 (12.5%)\n",
      "Stepped\t2\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tom Lambert - Missed tackles: 1 (11.1%)\n",
      "Stepped\t1\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Leafi Talataina - Missed tackles: 1 (11.1%)\n",
      "Outpaced\t1\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Rob Leota - Missed tackles: 3 (10.7%)\n",
      "Positional\t2\t66.7%\n",
      "Bumped Off\t1\t33.3%\n",
      "Outpaced\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Dave Porecki - Missed tackles: 3 (10.0%)\n",
      "Stepped\t3\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Siosifa Amone - Missed tackles: 1 (7.1%)\n",
      "Stepped\t1\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Langi Gleeson - Missed tackles: 2 (6.7%)\n",
      "Bumped Off\t1\t50.0%\n",
      "Positional\t1\t50.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Hugh Sinclair - Missed tackles: 2 (5.4%)\n",
      "Bumped Off\t2\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Ben Grant - Missed tackles: 1 (4.2%)\n",
      "Bumped Off\t1\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Charlie Gamble - Missed tackles: 2 (4.1%)\n",
      "Bumped Off\t1\t50.0%\n",
      "Stepped\t1\t50.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each tackle outcome per player\n",
    "outcome_counts = df_team.groupby(['Tackler name', 'Tackle outcome']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages\n",
    "outcome_percentages = outcome_counts.div(outcome_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Extract only missed tackles for analysis\n",
    "df_missed = df_team[df_team['Tackle outcome'] == 'Missed']\n",
    "\n",
    "# Count occurrences of each missed tackle category per player\n",
    "missed_category_counts = df_missed.groupby(['Tackler name', 'Missed tackle category']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages for missed tackle categories\n",
    "missed_category_percentages = missed_category_counts.div(missed_category_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Sort players by percentage of missed tackles (descending), defaulting to 0% if no missed tackles\n",
    "sorted_players = outcome_percentages.get('Missed', pd.Series(0, index=outcome_percentages.index)).sort_values(ascending=False).index\n",
    "\n",
    "# Show results player by player in sorted order\n",
    "print(f\"Team: {selected_team}\")\n",
    "for player in sorted_players:\n",
    "    total_missed = outcome_counts.get('Missed', pd.Series(0, index=outcome_counts.index)).loc[player]  # Total missed tackles\n",
    "    missed_percentage = outcome_percentages.get('Missed', pd.Series(0, index=outcome_percentages.index)).loc[player]  # Percentage of missed tackles\n",
    "\n",
    "    if total_missed > 0:\n",
    "        print(f\"Player: {player} - Missed tackles: {total_missed} ({missed_percentage:.1f}%)\")\n",
    "        player_missed_data = pd.DataFrame({\n",
    "            'Count': missed_category_counts.loc[player].astype(int),\n",
    "            'Percentage': missed_category_percentages.loc[player]\n",
    "        }).sort_values(by='Count', ascending=False)\n",
    "\n",
    "        for category, row in player_missed_data.iterrows():\n",
    "            print(f\"{category}\\t{int(row['Count'])}\\t{row['Percentage']:.1f}%\")\n",
    "\n",
    "        print(\"-\" * 30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf86fe2c-ea68-4d4b-a534-12e0b75c02f1",
   "metadata": {},
   "source": [
    "## Team-specific attack analysis\n",
    "\n",
    "### Players evading tackles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "728cddd5-55eb-44e7-bdc6-290ca97fa97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_team = 'NSW Waratahs'\n",
    "\n",
    "# Filter for the selected team\n",
    "df_team = df[df['Carrying team'] == selected_team]\n",
    "\n",
    "# Filter for missed tackles\n",
    "df_missed = df_team.dropna(subset=['Missed tackle category'], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "27a7ef8e-a3d5-4fce-9008-b011eec519cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: NSW Waratahs\n",
      "Player: Darby Lancaster - Evaded tackles: 6 (54.5%)\n",
      "Bumped Off\t3\t50.0%\n",
      "Outpaced\t1\t16.7%\n",
      "Positional\t1\t16.7%\n",
      "Stepped\t1\t16.7%\n",
      "------------------------------\n",
      "Player: Max Jorgensen - Evaded tackles: 13 (48.1%)\n",
      "Stepped\t6\t46.2%\n",
      "Bumped Off\t3\t23.1%\n",
      "Outpaced\t2\t15.4%\n",
      "Positional\t2\t15.4%\n",
      "------------------------------\n",
      "Player: Andrew Kellaway - Evaded tackles: 9 (29.0%)\n",
      "Stepped\t4\t44.4%\n",
      "Bumped Off\t3\t33.3%\n",
      "Outpaced\t1\t11.1%\n",
      "Positional\t1\t11.1%\n",
      "------------------------------\n",
      "Player: Jack Grant - Evaded tackles: 2 (25.0%)\n",
      "Bumped Off\t2\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Lalakai Foketi - Evaded tackles: 5 (21.7%)\n",
      "Bumped Off\t2\t40.0%\n",
      "Positional\t2\t40.0%\n",
      "Stepped\t1\t20.0%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Angus Bell - Evaded tackles: 17 (20.7%)\n",
      "Bumped Off\t11\t64.7%\n",
      "Stepped\t5\t29.4%\n",
      "Positional\t1\t5.9%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Siosifa Amone - Evaded tackles: 3 (15.8%)\n",
      "Bumped Off\t2\t66.7%\n",
      "Positional\t1\t33.3%\n",
      "Outpaced\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joseph-Aukuso Suaalii - Evaded tackles: 3 (15.8%)\n",
      "Stepped\t2\t66.7%\n",
      "Bumped Off\t1\t33.3%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Tane Edmed - Evaded tackles: 2 (15.4%)\n",
      "Positional\t1\t50.0%\n",
      "Stepped\t1\t50.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Charlie Gamble - Evaded tackles: 3 (13.6%)\n",
      "Bumped Off\t2\t66.7%\n",
      "Stepped\t1\t33.3%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Dave Porecki - Evaded tackles: 3 (12.5%)\n",
      "Bumped Off\t2\t66.7%\n",
      "Stepped\t1\t33.3%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Rob Leota - Evaded tackles: 3 (12.0%)\n",
      "Bumped Off\t3\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Felix Kalapu - Evaded tackles: 1 (11.1%)\n",
      "Stepped\t1\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Langi Gleeson - Evaded tackles: 7 (10.0%)\n",
      "Stepped\t3\t42.9%\n",
      "Bumped Off\t2\t28.6%\n",
      "Positional\t2\t28.6%\n",
      "Outpaced\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Henry O'Donnell - Evaded tackles: 2 (8.0%)\n",
      "Bumped Off\t1\t50.0%\n",
      "Stepped\t1\t50.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Jamie Adamson - Evaded tackles: 1 (7.7%)\n",
      "Stepped\t1\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Ben Grant - Evaded tackles: 1 (5.9%)\n",
      "Bumped Off\t1\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Taniela Tupou - Evaded tackles: 1 (5.3%)\n",
      "Positional\t1\t100.0%\n",
      "Bumped Off\t0\t0.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Leafi Talataina - Evaded tackles: 1 (5.0%)\n",
      "Bumped Off\t1\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n",
      "Player: Joey Walton - Evaded tackles: 1 (4.2%)\n",
      "Bumped Off\t1\t100.0%\n",
      "Outpaced\t0\t0.0%\n",
      "Positional\t0\t0.0%\n",
      "Stepped\t0\t0.0%\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each tackle outcome per player\n",
    "outcome_counts = df_team.groupby(['Attacking player name', 'Tackle outcome']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages\n",
    "outcome_percentages = outcome_counts.div(outcome_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Extract only missed tackles for analysis\n",
    "df_missed = df_team[df_team['Tackle outcome'] == 'Missed']\n",
    "\n",
    "# Count occurrences of each missed tackle category per player\n",
    "missed_category_counts = df_missed.groupby(['Attacking player name', 'Missed tackle category']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate percentages for missed tackle categories\n",
    "missed_category_percentages = missed_category_counts.div(missed_category_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Sort players by percentage of missed tackles (descending), defaulting to 0% if no missed tackles\n",
    "sorted_players = outcome_percentages.get('Missed', pd.Series(0, index=outcome_percentages.index)).sort_values(ascending=False).index\n",
    "\n",
    "# Show results player by player in sorted order\n",
    "print(f\"Team: {selected_team}\")\n",
    "for player in sorted_players:\n",
    "    total_missed = outcome_counts.get('Missed', pd.Series(0, index=outcome_counts.index)).loc[player]  # Total missed tackles\n",
    "    missed_percentage = outcome_percentages.get('Missed', pd.Series(0, index=outcome_percentages.index)).loc[player]  # Percentage of missed tackles\n",
    "\n",
    "    if total_missed > 0:\n",
    "        print(f\"Player: {player} - Evaded tackles: {total_missed} ({missed_percentage:.1f}%)\")\n",
    "        player_missed_data = pd.DataFrame({\n",
    "            'Count': missed_category_counts.loc[player].astype(int),\n",
    "            'Percentage': missed_category_percentages.loc[player]\n",
    "        }).sort_values(by='Count', ascending=False)\n",
    "\n",
    "        for category, row in player_missed_data.iterrows():\n",
    "            print(f\"{category}\\t{int(row['Count'])}\\t{row['Percentage']:.1f}%\")\n",
    "\n",
    "        print(\"-\" * 30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac6e014-f2ba-4e26-9ef5-d382ecec9cd0",
   "metadata": {},
   "source": [
    "### Players offloading the ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "23ba5e1e-3f04-44ea-b076-09af95260983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Players sorted by 'Offload Allowed' (Team: NSW Waratahs)\n",
      "Lalakai Foketi\t4\t17.4%\n",
      "Max Jorgensen\t4\t14.8%\n",
      "Henry O'Donnell\t4\t16.0%\n",
      "Jake Gordon\t3\t37.5%\n",
      "Tane Edmed\t3\t23.1%\n",
      "Mahe Vailanu\t3\t10.3%\n",
      "Andrew Kellaway\t3\t9.7%\n",
      "Charlie Gamble\t3\t13.6%\n",
      "Angus Bell\t2\t2.4%\n",
      "Felix Kalapu\t2\t22.2%\n",
      "Rob Leota\t2\t8.0%\n",
      "Hugh Sinclair\t1\t3.8%\n",
      "Joseph-Aukuso Suaalii\t1\t5.3%\n",
      "Langi Gleeson\t1\t1.4%\n",
      "Leafi Talataina\t1\t5.0%\n",
      "Siosifa Amone\t1\t5.3%\n",
      "Ben Grant\t1\t5.9%\n",
      "Teddy Wilson\t1\t50.0%\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of \"Offload Allowed\" per player\n",
    "offload_counts = df_team[df_team['Tackle outcome'] == 'Offload Allowed'].groupby('Attacking player name').size()\n",
    "\n",
    "# Calculate total tackles per player to compute percentages\n",
    "total_tackles_per_player = df_team.groupby('Attacking player name').size()\n",
    "\n",
    "# Compute percentage of \"Offload Allowed\" tackles for each player\n",
    "offload_percentage = (offload_counts / total_tackles_per_player * 100).fillna(0)\n",
    "\n",
    "# Create a sorted list of players based on \"Offload Allowed\" count (descending)\n",
    "sorted_offload_players = offload_counts.sort_values(ascending=False).index\n",
    "\n",
    "# Print the results\n",
    "print(f\"Players sorted by 'Offload Allowed' (Team: {selected_team})\")\n",
    "for player in sorted_offload_players:\n",
    "    print(f\"{player}\\t{offload_counts[player]}\\t{offload_percentage[player]:.1f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb41d7a-7eb2-4351-b046-fd8da8722f7b",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1b0fb08-d9eb-4e87-a8cd-251b9b7a329c",
   "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>Game ID</th>\n",
       "      <th>Competition</th>\n",
       "      <th>Season</th>\n",
       "      <th>Date</th>\n",
       "      <th>Home team</th>\n",
       "      <th>Away team</th>\n",
       "      <th>Tackling team</th>\n",
       "      <th>Carrying team</th>\n",
       "      <th>Time</th>\n",
       "      <th>Phase</th>\n",
       "      <th>...</th>\n",
       "      <th>Tackle outcome</th>\n",
       "      <th>Tackle qualifier</th>\n",
       "      <th>Tackle dominance</th>\n",
       "      <th>Tackler number</th>\n",
       "      <th>Tackle area</th>\n",
       "      <th>Tackler name</th>\n",
       "      <th>Tackler position</th>\n",
       "      <th>Attacking player name</th>\n",
       "      <th>Missed tackle category</th>\n",
       "      <th>Missed tackle outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>945283</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>09/03/2025</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>1_00121</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Missed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Lower Torso</td>\n",
       "      <td>Lachie Anderson</td>\n",
       "      <td>14</td>\n",
       "      <td>Will Jordan</td>\n",
       "      <td>Positional</td>\n",
       "      <td>Tackled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>945283</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>09/03/2025</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>1_00125</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Passive</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Legs</td>\n",
       "      <td>Tate McDermott</td>\n",
       "      <td>9</td>\n",
       "      <td>Will Jordan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>945283</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>09/03/2025</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>1_00135</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Passive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Tim Ryan</td>\n",
       "      <td>11</td>\n",
       "      <td>Christian Lio-Willie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>945283</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>09/03/2025</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>1_00144</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Ryan Smith</td>\n",
       "      <td>5</td>\n",
       "      <td>Taha Kemara</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>945283</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>09/03/2025</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Crusaders</td>\n",
       "      <td>1_00509</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Matt Faessler</td>\n",
       "      <td>2</td>\n",
       "      <td>Scott Barrett</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7459</th>\n",
       "      <td>945270</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>21/02/2025</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>2_07932</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Ryan Smith</td>\n",
       "      <td>5</td>\n",
       "      <td>Sama Malolo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7460</th>\n",
       "      <td>945270</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>21/02/2025</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>2_07936</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Zane Nonggorr</td>\n",
       "      <td>3</td>\n",
       "      <td>Semisi Tupou Ta'eiloa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7461</th>\n",
       "      <td>945270</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>21/02/2025</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>2_07936</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Complete</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Neutral Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Matt Faessler</td>\n",
       "      <td>2</td>\n",
       "      <td>Semisi Tupou Ta'eiloa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7462</th>\n",
       "      <td>945270</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>21/02/2025</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>2_07944</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>Ineffective</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>1st Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Harry Wilson</td>\n",
       "      <td>8</td>\n",
       "      <td>Allan Craig</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7463</th>\n",
       "      <td>945270</td>\n",
       "      <td>Super Rugby Pacific</td>\n",
       "      <td>2025</td>\n",
       "      <td>21/02/2025</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>Queensland Reds</td>\n",
       "      <td>Moana Pasifika</td>\n",
       "      <td>2_07944</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>Ineffective</td>\n",
       "      <td>Assist</td>\n",
       "      <td>Ineffective Tackle</td>\n",
       "      <td>2nd Tackler</td>\n",
       "      <td>Upper Torso</td>\n",
       "      <td>Ryan Smith</td>\n",
       "      <td>5</td>\n",
       "      <td>Allan Craig</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>745 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Game ID          Competition  Season        Date        Home team  \\\n",
       "1990   945283  Super Rugby Pacific    2025  09/03/2025        Crusaders   \n",
       "1991   945283  Super Rugby Pacific    2025  09/03/2025        Crusaders   \n",
       "1992   945283  Super Rugby Pacific    2025  09/03/2025        Crusaders   \n",
       "1993   945283  Super Rugby Pacific    2025  09/03/2025        Crusaders   \n",
       "2018   945283  Super Rugby Pacific    2025  09/03/2025        Crusaders   \n",
       "...       ...                  ...     ...         ...              ...   \n",
       "7459   945270  Super Rugby Pacific    2025  21/02/2025  Queensland Reds   \n",
       "7460   945270  Super Rugby Pacific    2025  21/02/2025  Queensland Reds   \n",
       "7461   945270  Super Rugby Pacific    2025  21/02/2025  Queensland Reds   \n",
       "7462   945270  Super Rugby Pacific    2025  21/02/2025  Queensland Reds   \n",
       "7463   945270  Super Rugby Pacific    2025  21/02/2025  Queensland Reds   \n",
       "\n",
       "            Away team    Tackling team   Carrying team     Time  Phase  ...  \\\n",
       "1990  Queensland Reds  Queensland Reds       Crusaders  1_00121      1  ...   \n",
       "1991  Queensland Reds  Queensland Reds       Crusaders  1_00125      1  ...   \n",
       "1992  Queensland Reds  Queensland Reds       Crusaders  1_00135      2  ...   \n",
       "1993  Queensland Reds  Queensland Reds       Crusaders  1_00144      3  ...   \n",
       "2018  Queensland Reds  Queensland Reds       Crusaders  1_00509      1  ...   \n",
       "...               ...              ...             ...      ...    ...  ...   \n",
       "7459   Moana Pasifika  Queensland Reds  Moana Pasifika  2_07932      1  ...   \n",
       "7460   Moana Pasifika  Queensland Reds  Moana Pasifika  2_07936      2  ...   \n",
       "7461   Moana Pasifika  Queensland Reds  Moana Pasifika  2_07936      2  ...   \n",
       "7462   Moana Pasifika  Queensland Reds  Moana Pasifika  2_07944      3  ...   \n",
       "7463   Moana Pasifika  Queensland Reds  Moana Pasifika  2_07944      3  ...   \n",
       "\n",
       "      Tackle outcome  Tackle qualifier    Tackle dominance Tackler number  \\\n",
       "1990          Missed               NaN  Ineffective Tackle    1st Tackler   \n",
       "1991         Passive            Assist  Ineffective Tackle    2nd Tackler   \n",
       "1992         Passive               NaN  Ineffective Tackle    1st Tackler   \n",
       "1993        Complete               NaN      Neutral Tackle    1st Tackler   \n",
       "2018        Complete               NaN      Neutral Tackle    1st Tackler   \n",
       "...              ...               ...                 ...            ...   \n",
       "7459        Complete            Assist      Neutral Tackle    2nd Tackler   \n",
       "7460        Complete               NaN      Neutral Tackle    1st Tackler   \n",
       "7461        Complete            Assist      Neutral Tackle    2nd Tackler   \n",
       "7462     Ineffective               NaN  Ineffective Tackle    1st Tackler   \n",
       "7463     Ineffective            Assist  Ineffective Tackle    2nd Tackler   \n",
       "\n",
       "      Tackle area     Tackler name Tackler position  Attacking player name  \\\n",
       "1990  Lower Torso  Lachie Anderson               14            Will Jordan   \n",
       "1991         Legs   Tate McDermott                9            Will Jordan   \n",
       "1992  Upper Torso         Tim Ryan               11   Christian Lio-Willie   \n",
       "1993  Upper Torso       Ryan Smith                5            Taha Kemara   \n",
       "2018  Upper Torso    Matt Faessler                2          Scott Barrett   \n",
       "...           ...              ...              ...                    ...   \n",
       "7459  Upper Torso       Ryan Smith                5            Sama Malolo   \n",
       "7460  Upper Torso    Zane Nonggorr                3  Semisi Tupou Ta'eiloa   \n",
       "7461  Upper Torso    Matt Faessler                2  Semisi Tupou Ta'eiloa   \n",
       "7462  Upper Torso     Harry Wilson                8            Allan Craig   \n",
       "7463  Upper Torso       Ryan Smith                5            Allan Craig   \n",
       "\n",
       "     Missed tackle category  Missed tackle outcome  \n",
       "1990             Positional                Tackled  \n",
       "1991                    NaN                    NaN  \n",
       "1992                    NaN                    NaN  \n",
       "1993                    NaN                    NaN  \n",
       "2018                    NaN                    NaN  \n",
       "...                     ...                    ...  \n",
       "7459                    NaN                    NaN  \n",
       "7460                    NaN                    NaN  \n",
       "7461                    NaN                    NaN  \n",
       "7462                    NaN                    NaN  \n",
       "7463                    NaN                    NaN  \n",
       "\n",
       "[745 rows x 23 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz_team = \"Queensland Reds\"\n",
    "viz_season = 2025\n",
    "\n",
    "df_viz = df[(df['Season'] == viz_season) & (df['Tackling team'] == viz_team)]\n",
    "df_viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "455a1a75-7308-46ce-947e-22adf8b42c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class field():\n",
    "    def __init__(self):\n",
    "        #import the libraries and then create self variables so that all necessary imports are initialized only once\n",
    "        from matplotlib.patches import Rectangle as rect\n",
    "        self.plt = plt\n",
    "        self.rect = rect\n",
    "        \n",
    "    \n",
    "    def union_pitch(self, pitch_color = 'white', line_color = 'Black', poles = False, linestyle = '-', line_alpha=1):\n",
    "        \n",
    "        #draw the rectangle that makes up the pitch and lies behind the field lines\n",
    "        ax.add_patch(self.rect((0, 0), 100, 68, fc = pitch_color, zorder=0))\n",
    "\n",
    "        #top & bottom lines\n",
    "        plt.hlines(0, 0, 100, line_color), plt.hlines(68, 0, 100, line_color)\n",
    "\n",
    "        #ends, half, 22s\n",
    "        for x in range(5):\n",
    "            c = [0, 22, 50, 78, 100]\n",
    "            plt.vlines(c[x], 0, 68, line_color)\n",
    "\n",
    "        #vertical and horizontal coordinates\n",
    "        vertical_lines = [(2, 8), (12, 18), (66, 60), (56, 50), (22, 29), (39, 46)]\n",
    "        horizontal_lines = [(47, 53), (37, 43), (57, 63), (19, 25), (75, 81), (5, 11), (95, 89)]\n",
    "        vertical_y, horizontal_y = [5, 40, 60, 95], [5, 15, 53, 63]\n",
    "\n",
    "        #lines\n",
    "        for x in range(6):\n",
    "            for y in range(4):\n",
    "                plt.vlines(vertical_y[y], vertical_lines[x][0], vertical_lines[x][1], line_color, linestyle, line_alpha, zorder=0)\n",
    "        for x in range(7):\n",
    "            for y in range(4):\n",
    "                plt.hlines(horizontal_y[y], horizontal_lines[x][0], horizontal_lines[x][1], line_color, linestyle, line_alpha, zorder=0)\n",
    "        \n",
    "        #if poles is true draw two thick rectangles at either end of the field\n",
    "        if poles is True:\n",
    "                plt.vlines(0, 29, 39, line_color, '-', alpha=1, linewidth=5, zorder=0)\n",
    "                plt.vlines(100, 29, 39, line_color, '-', alpha=1, linewidth=5, zorder=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb12d4b6-616c-4347-867a-4a182bf06d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "plot=plt.figure(figsize=(12, 7))\n",
    "ax=plot.add_subplot()\n",
    "\n",
    "# Generate the pitch background\n",
    "x = field()\n",
    "x.union_pitch(poles=True, pitch_color='white', line_color='black')  \n",
    "\n",
    "# Define colors and markers for different kick types\n",
    "styles = {\n",
    "    'Missed':      {'color': 'red',  'marker': 'o'},\n",
    "    # 'Kick Line Break': {'color': 'blue',  'marker': 's'}\n",
    "}\n",
    "\n",
    "type_counts = df_viz['Tackle outcome'].value_counts()\n",
    "\n",
    "# Plot kicks and store scatter handles for legend\n",
    "scatter_handles = {}\n",
    "for _, row in df_viz.iterrows():\n",
    "    if row['Tackle outcome'] != 'Missed':\n",
    "        continue\n",
    "    style = styles.get(row['Tackle outcome'], {'color': 'white', 'marker': 'o'})  # Default style\n",
    "    scatter = ax.scatter(row['x_start'], row['y_start'], color=style['color'], marker=style['marker'], s=100, edgecolors='black', zorder=2)\n",
    "    if row['Tackle outcome'] not in scatter_handles:\n",
    "        scatter_handles[row['Tackle outcome']] = scatter\n",
    "\n",
    "# Create legend in the order of break_styles\n",
    "legend_handles = [scatter_handles[key] for key in styles.keys() if key in scatter_handles]\n",
    "legend_labels = [f'{key} ({type_counts[key]})' for key in styles.keys() if key in scatter_handles]\n",
    "plt.legend(legend_handles, legend_labels, bbox_to_anchor=(1.02, 0.98), loc='upper left', title='Tackle outcome', fontsize=10, title_fontsize=12)\n",
    "\n",
    "\n",
    "# Remove axis labels and ticks\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# Set limits\n",
    "ax.set_xlim(-10, 105)\n",
    "ax.set_ylim(-1, 70)\n",
    "\n",
    "# Figure title\n",
    "ax.set_title(f\"Missed tackles - {viz_team} - {viz_season}  (opp. attacking right to left)\")\n",
    "\n",
    "# Adjust subplot to give more space on the right side for the legend\n",
    "plt.subplots_adjust(right=0.8)  # Adjust the right margin to fit the legend\n",
    "\n",
    "# Show and save plot\n",
    "plt.savefig(f\"./figures/{viz_team} - Defence - {viz_season} - Missed Tackle Map.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "406d2dc6-bda5-4dde-a30d-96dead80b753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "plot=plt.figure(figsize=(12, 7))\n",
    "ax=plot.add_subplot()\n",
    "\n",
    "# Generate the pitch background\n",
    "x = field()\n",
    "x.union_pitch(poles=True, pitch_color='white', line_color='black')  \n",
    "\n",
    "# Define a list of colors and markers for combinations\n",
    "colors = ['blue', 'red', 'green', 'purple', 'yellow', 'cyan']\n",
    "markers = ['o', 's', 'd', '^', 'v', '<', '>']\n",
    "\n",
    "# Generate all combinations of colors and markers\n",
    "color_marker_combinations = list(itertools.product(markers, colors))\n",
    "\n",
    "# Generate a unique color-marker combination for each 'Kicking player'\n",
    "players = df_viz['Tackler name'].unique()\n",
    "styles = {\n",
    "    player: {'color': color_marker_combinations[i % len(color_marker_combinations)][1], \n",
    "             'marker': color_marker_combinations[i % len(color_marker_combinations)][0]}\n",
    "    for i, player in enumerate(players)\n",
    "}\n",
    "\n",
    "# Count the frequency of each tackler\n",
    "player_counts = df_viz[df_viz['Tackle outcome']=='Missed']['Tackler name'].value_counts()\n",
    "\n",
    "# Sort players by frequency (descending)\n",
    "sorted_players = player_counts.index\n",
    "\n",
    "# Plot kicks and store scatter handles for legend\n",
    "scatter_handles = {}\n",
    "for _, row in df_viz.iterrows():\n",
    "    if row['Tackle outcome']!='Missed':\n",
    "        continue\n",
    "    style = styles.get(row['Tackler name'], {'color': 'white', 'marker': 'o'})  # Default style\n",
    "    scatter = ax.scatter(row['x_start'], row['y_start'], color=style['color'], marker=style['marker'], s=100, edgecolors='black', zorder=2)\n",
    "    if row['Tackler name'] not in scatter_handles:\n",
    "        scatter_handles[row['Tackler name']] = scatter\n",
    "\n",
    "# Create legend in the order of break_styles\n",
    "legend_handles = [scatter_handles[player] for player in sorted_players if player in scatter_handles]\n",
    "legend_labels = [f'{player} ({player_counts[player]})' for player in sorted_players if player in scatter_handles]\n",
    "\n",
    "# Plot the legend\n",
    "plt.legend(legend_handles, legend_labels, bbox_to_anchor=(1.02, 0.98), loc='upper left', title='Tacklers', fontsize=10, title_fontsize=12)\n",
    "\n",
    "\n",
    "# Remove axis labels and ticks\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# Set limits\n",
    "ax.set_xlim(-10, 105)\n",
    "ax.set_ylim(-1, 70)\n",
    "\n",
    "# Figure title\n",
    "ax.set_title(f\"Missed tackles - {viz_team} - {viz_season}  (opp. attacking right to left)\")\n",
    "\n",
    "# Adjust subplot to give more space on the right side for the legend\n",
    "plt.subplots_adjust(right=0.8)  # Adjust the right margin to fit the legend\n",
    "\n",
    "# Show and save plot\n",
    "plt.savefig(f\"./figures/{viz_team} - Defence - {viz_season} - Missed Tackle Map (by players).png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab8ef803-76c8-4bf5-81d7-f41abbf542ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}