BPassnetworks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3d31de4-1b42-4601-9fcf-a55979647d1f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import re\n",
    "import pandas as pd\n",
    "from datascience import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "50b3d344-fdb2-4320-a4b8-9ff921c9c9c2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def extract_json_from_html(html_path, save_output=False):\n",
    "    html_file = open(html_path, 'r', encoding='utf-8')\n",
    "    html = html_file.read()\n",
    "    html_file.close()\n",
    "    regex_pattern = r'(?<=require\\.config\\.params\\[\"args\"\\].=.)[\\s\\S]*?;'\n",
    "    data_txt = re.findall(regex_pattern, html)[0]\n",
    "\n",
    "    # add quotations for json parser\n",
    "    data_txt = data_txt.replace('matchId', '\"matchId\"')\n",
    "    data_txt = data_txt.replace('matchCentreData', '\"matchCentreData\"')\n",
    "    data_txt = data_txt.replace('matchCentreEventTypeJson', '\"matchCentreEventTypeJson\"')\n",
    "    data_txt = data_txt.replace('formationIdNameMappings', '\"formationIdNameMappings\"')\n",
    "    data_txt = data_txt.replace('};', '}')\n",
    "\n",
    "    if save_output:\n",
    "        # save json data to txt\n",
    "        output_file = open(f\"{html_path}.txt\", \"wt\")\n",
    "        n = output_file.write(data_txt)\n",
    "        output_file.close()\n",
    "\n",
    "    return data_txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "daf4e061-17f8-433b-b8b3-43d9ff09226e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def extract_data_from_dict(data):\n",
    "    # load data from json\n",
    "    event_types_json = data[\"matchCentreEventTypeJson\"]\n",
    "    formation_mappings = data[\"formationIdNameMappings\"]\n",
    "    events_dict = data[\"matchCentreData\"][\"events\"]\n",
    "    teams_dict = {data[\"matchCentreData\"]['home']['teamId']: data[\"matchCentreData\"]['home']['name'],\n",
    "                  data[\"matchCentreData\"]['away']['teamId']: data[\"matchCentreData\"]['away']['name']}\n",
    "    players_dict = data[\"matchCentreData\"][\"playerIdNameDictionary\"]\n",
    "    # create players dataframe\n",
    "    players_home_df = pd.DataFrame(data[\"matchCentreData\"]['home']['players'])\n",
    "    players_home_df[\"teamId\"] = data[\"matchCentreData\"]['home']['teamId']\n",
    "    players_away_df = pd.DataFrame(data[\"matchCentreData\"]['away']['players'])\n",
    "    players_away_df[\"teamId\"] = data[\"matchCentreData\"]['away']['teamId']\n",
    "    players_df = pd.concat([players_home_df, players_away_df])\n",
    "    players_ids = data[\"matchCentreData\"][\"playerIdNameDictionary\"]\n",
    "    return events_dict, players_df, teams_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "6beb2176-da5f-4745-ba39-783b25178b98",
   "metadata": {
    "tags": []
   },
   "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>playerId</th>\n",
       "      <th>shirtNo</th>\n",
       "      <th>name</th>\n",
       "      <th>position</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>isFirstEleven</th>\n",
       "      <th>isManOfTheMatch</th>\n",
       "      <th>field</th>\n",
       "      <th>stats</th>\n",
       "      <th>subbedInPlayerId</th>\n",
       "      <th>subbedOutPeriod</th>\n",
       "      <th>subbedOutExpandedMinute</th>\n",
       "      <th>subbedInPeriod</th>\n",
       "      <th>subbedInExpandedMinute</th>\n",
       "      <th>subbedOutPlayerId</th>\n",
       "      <th>teamId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>260843</td>\n",
       "      <td>24</td>\n",
       "      <td>André Onana</td>\n",
       "      <td>GK</td>\n",
       "      <td>190</td>\n",
       "      <td>93</td>\n",
       "      <td>28</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>home</td>\n",
       "      <td>{'totalSaves': {'0': 1.0, '26': 1.0, '34': 1.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>331924</td>\n",
       "      <td>29</td>\n",
       "      <td>Aaron Wan-Bissaka</td>\n",
       "      <td>DR</td>\n",
       "      <td>183</td>\n",
       "      <td>72</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>home</td>\n",
       "      <td>{'possession': {'0': 2.0, '5': 1.0, '13': 2.0,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88526</td>\n",
       "      <td>18</td>\n",
       "      <td>Casemiro</td>\n",
       "      <td>DC</td>\n",
       "      <td>185</td>\n",
       "      <td>84</td>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>home</td>\n",
       "      <td>{'possession': {'0': 2.0, '1': 3.0, '2': 1.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99487</td>\n",
       "      <td>5</td>\n",
       "      <td>Harry Maguire</td>\n",
       "      <td>DC</td>\n",
       "      <td>194</td>\n",
       "      <td>90</td>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>home</td>\n",
       "      <td>{'possession': {'1': 1.0, '5': 3.0, '8': 1.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>353418</td>\n",
       "      <td>20</td>\n",
       "      <td>Diogo Dalot</td>\n",
       "      <td>DL</td>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>home</td>\n",
       "      <td>{'possession': {'4': 1.0, '9': 2.0, '11': 1.0,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   playerId  shirtNo               name position  height  weight  age  \\\n",
       "0    260843       24        André Onana       GK     190      93   28   \n",
       "1    331924       29  Aaron Wan-Bissaka       DR     183      72   26   \n",
       "2     88526       18           Casemiro       DC     185      84   32   \n",
       "3     99487        5      Harry Maguire       DC     194      90   31   \n",
       "4    353418       20        Diogo Dalot       DL     183      76   25   \n",
       "\n",
       "  isFirstEleven  isManOfTheMatch field  \\\n",
       "0          True            False  home   \n",
       "1          True            False  home   \n",
       "2          True            False  home   \n",
       "3          True            False  home   \n",
       "4          True            False  home   \n",
       "\n",
       "                                               stats  subbedInPlayerId  \\\n",
       "0  {'totalSaves': {'0': 1.0, '26': 1.0, '34': 1.0...               NaN   \n",
       "1  {'possession': {'0': 2.0, '5': 1.0, '13': 2.0,...               NaN   \n",
       "2  {'possession': {'0': 2.0, '1': 3.0, '2': 1.0, ...               NaN   \n",
       "3  {'possession': {'1': 1.0, '5': 3.0, '8': 1.0, ...               NaN   \n",
       "4  {'possession': {'4': 1.0, '9': 2.0, '11': 1.0,...               NaN   \n",
       "\n",
       "  subbedOutPeriod  subbedOutExpandedMinute subbedInPeriod  \\\n",
       "0             NaN                      NaN            NaN   \n",
       "1             NaN                      NaN            NaN   \n",
       "2             NaN                      NaN            NaN   \n",
       "3             NaN                      NaN            NaN   \n",
       "4             NaN                      NaN            NaN   \n",
       "\n",
       "   subbedInExpandedMinute  subbedOutPlayerId  teamId  \n",
       "0                     NaN                NaN      32  \n",
       "1                     NaN                NaN      32  \n",
       "2                     NaN                NaN      32  \n",
       "3                     NaN                NaN      32  \n",
       "4                     NaN                NaN      32  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_html_path = './Manchester United 1-1 Burnley - Premier League 2023_2024 Live.html'\n",
    "json_data_txt = extract_json_from_html(match_html_path)\n",
    "data = json.loads(json_data_txt)\n",
    "events_dict, players_df, teams_dict = extract_data_from_dict(data)\n",
    "players_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "bea996a6-fe11-41dd-9610-9f8308b66009",
   "metadata": {
    "tags": []
   },
   "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>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>endX</th>\n",
       "      <th>endY</th>\n",
       "      <th>teamId</th>\n",
       "      <th>playerId</th>\n",
       "      <th>receiver</th>\n",
       "      <th>eventType</th>\n",
       "      <th>outcomeType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.678794e+09</td>\n",
       "      <td>50.1</td>\n",
       "      <td>49.7</td>\n",
       "      <td>26.4</td>\n",
       "      <td>40.7</td>\n",
       "      <td>184</td>\n",
       "      <td>371447.0</td>\n",
       "      <td>362426.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.678794e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.4</td>\n",
       "      <td>75.2</td>\n",
       "      <td>77.9</td>\n",
       "      <td>184</td>\n",
       "      <td>362426.0</td>\n",
       "      <td>88526.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Unsuccessful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.678795e+09</td>\n",
       "      <td>20.2</td>\n",
       "      <td>17.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>32</td>\n",
       "      <td>88526.0</td>\n",
       "      <td>364315.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.678795e+09</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>20.9</td>\n",
       "      <td>15.1</td>\n",
       "      <td>32</td>\n",
       "      <td>364315.0</td>\n",
       "      <td>331924.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.678795e+09</td>\n",
       "      <td>21.3</td>\n",
       "      <td>15.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>32</td>\n",
       "      <td>331924.0</td>\n",
       "      <td>362826.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Unsuccessful</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>2.679007e+09</td>\n",
       "      <td>53.0</td>\n",
       "      <td>78.4</td>\n",
       "      <td>85.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>184</td>\n",
       "      <td>362426.0</td>\n",
       "      <td>99487.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Unsuccessful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>2.679007e+09</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>184</td>\n",
       "      <td>402874.0</td>\n",
       "      <td>362426.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>2.679007e+09</td>\n",
       "      <td>19.2</td>\n",
       "      <td>37.1</td>\n",
       "      <td>34.9</td>\n",
       "      <td>30.6</td>\n",
       "      <td>184</td>\n",
       "      <td>362426.0</td>\n",
       "      <td>273292.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>2.679007e+09</td>\n",
       "      <td>43.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>184</td>\n",
       "      <td>273292.0</td>\n",
       "      <td>123761.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Unsuccessful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>2.679007e+09</td>\n",
       "      <td>61.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>70.6</td>\n",
       "      <td>26.6</td>\n",
       "      <td>32</td>\n",
       "      <td>343346.0</td>\n",
       "      <td>386634.0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id     x     y  endX  endY  teamId  playerId  receiver  \\\n",
       "2     2.678794e+09  50.1  49.7  26.4  40.7     184  371447.0  362426.0   \n",
       "3     2.678794e+09  28.0  40.4  75.2  77.9     184  362426.0   88526.0   \n",
       "4     2.678795e+09  20.2  17.6  26.0  14.9      32   88526.0  364315.0   \n",
       "5     2.678795e+09  26.0  14.9  20.9  15.1      32  364315.0  331924.0   \n",
       "7     2.678795e+09  21.3  15.1  53.0  39.4      32  331924.0  362826.0   \n",
       "...            ...   ...   ...   ...   ...     ...       ...       ...   \n",
       "1478  2.679007e+09  53.0  78.4  85.9  11.9     184  362426.0   99487.0   \n",
       "1484  2.679007e+09  45.0   5.9  22.7  37.0     184  402874.0  362426.0   \n",
       "1485  2.679007e+09  19.2  37.1  34.9  30.6     184  362426.0  273292.0   \n",
       "1486  2.679007e+09  43.0  28.0  46.0  33.3     184  273292.0  123761.0   \n",
       "1489  2.679007e+09  61.0  40.8  70.6  26.6      32  343346.0  386634.0   \n",
       "\n",
       "     eventType   outcomeType  \n",
       "2         Pass    Successful  \n",
       "3         Pass  Unsuccessful  \n",
       "4         Pass    Successful  \n",
       "5         Pass    Successful  \n",
       "7         Pass  Unsuccessful  \n",
       "...        ...           ...  \n",
       "1478      Pass  Unsuccessful  \n",
       "1484      Pass    Successful  \n",
       "1485      Pass    Successful  \n",
       "1486      Pass  Unsuccessful  \n",
       "1489      Pass    Successful  \n",
       "\n",
       "[918 rows x 10 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_passes_df(events_dict):\n",
    "    df = pd.DataFrame(events_dict)\n",
    "    df['eventType'] = df.apply(lambda row: row['type']['displayName'], axis=1)\n",
    "    df['outcomeType'] = df.apply(lambda row: row['outcomeType']['displayName'], axis=1)\n",
    "\n",
    "    # create receiver column based on the next event\n",
    "    # this will be correct only for successfull passes\n",
    "    df[\"receiver\"] = df[\"playerId\"].shift(-1)\n",
    "\n",
    "    # filter only passes\n",
    "    passes_ids = df.index[df['eventType'] == 'Pass']\n",
    "    df_passes = df.loc[\n",
    "        passes_ids, [\"id\", \"x\", \"y\", \"endX\", \"endY\", \"teamId\", \"playerId\", \"receiver\", \"eventType\", \"outcomeType\"]]\n",
    "\n",
    "    return df_passes\n",
    "passes_df = get_passes_df(events_dict)\n",
    "#passes_df.head()\n",
    "passes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0cce7af8-a9b0-49bc-907e-e9794255cd19",
   "metadata": {
    "tags": []
   },
   "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>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>endX</th>\n",
       "      <th>endY</th>\n",
       "      <th>teamId</th>\n",
       "      <th>playerId</th>\n",
       "      <th>receiver</th>\n",
       "      <th>eventType</th>\n",
       "      <th>outcomeType</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, x, y, endX, endY, teamId, playerId, receiver, eventType, outcomeType, diff]\n",
       "Index: []"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming 'passes_df' is your DataFrame containing pass data\n",
    "\n",
    "# Create the 'diff' column\n",
    "passes_df['diff'] = passes_df['endX'] - passes_df['x']\n",
    "\n",
    "# Filter the DataFrame for backward passes\n",
    "backwardpasses_df = passes_df[passes_df['diff'] < 0]\n",
    "backwardpasses_df2 = backwardpasses_df[backwardpasses_df['receiver'] == 13754.0]\n",
    "backwardpasses_df2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "e16aa902-d17c-4041-bb88-ba784eaea9db",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_passes_between_df(team_id, passes_df, players_df):\n",
    "    # filter for only team\n",
    "    passes_df = passes_df[passes_df[\"teamId\"] == team_id]\n",
    "\n",
    "    # add column with first eleven players only\n",
    "    passes_df = passes_df.merge(players_df[[\"playerId\", \"isFirstEleven\"]], on='playerId', how='left')\n",
    "    # filter on first eleven column\n",
    "    passes_df = passes_df[passes_df['isFirstEleven'] == True]\n",
    "\n",
    "    # calculate mean positions for players\n",
    "    average_locs_and_count_df = (passes_df.groupby('playerId')\n",
    "                                 .agg({'x': ['mean'], 'y': ['mean', 'count']}))\n",
    "    average_locs_and_count_df.columns = ['x', 'y', 'count']\n",
    "    average_locs_and_count_df = average_locs_and_count_df.merge(players_df[['playerId', 'name', 'shirtNo', 'position']],\n",
    "                                                                on='playerId', how='left')\n",
    "    average_locs_and_count_df = average_locs_and_count_df.set_index('playerId')\n",
    "\n",
    "    # calculate the number of passes between each position (using min/ max so we get passes both ways)\n",
    "    passes_player_ids_df = passes_df.loc[:, ['id', 'playerId', 'receiver', 'teamId']]\n",
    "    passes_player_ids_df['pos_max'] = (passes_player_ids_df[['playerId', 'receiver']].max(axis='columns'))\n",
    "    passes_player_ids_df['pos_min'] = (passes_player_ids_df[['playerId', 'receiver']].min(axis='columns'))\n",
    "\n",
    "    # get passes between each player\n",
    "    passes_between_df = passes_player_ids_df.groupby(['pos_min', 'pos_max']).id.count().reset_index()\n",
    "    passes_between_df.rename({'id': 'pass_count'}, axis='columns', inplace=True)\n",
    "\n",
    "    # Filter to include only players who have made 2 or more passes between themselves\n",
    "    passes_between_df = passes_between_df[passes_between_df['pass_count'] >= 4]\n",
    "\n",
    "    # add on the location of each player so we have the start and end positions of the lines\n",
    "    passes_between_df = passes_between_df.merge(average_locs_and_count_df, left_on='pos_min', right_index=True)\n",
    "    passes_between_df = passes_between_df.merge(average_locs_and_count_df, left_on='pos_max', right_index=True,\n",
    "                                                suffixes=['', '_end'])\n",
    "    return passes_between_df, average_locs_and_count_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "2d6a6e11-10cf-430b-bd18-8d432f240a40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "home_team_id = list(teams_dict.keys())[0]  # selected home team\n",
    "home_passes_between_df, home_average_locs_and_count_df = get_passes_between_df(home_team_id, backwardpasses_df, players_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "45b13ae8-d4d4-459f-8d94-7bc1e8d9489d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "away_team_id = list(teams_dict.keys())[1]  # selected home team\n",
    "away_passes_between_df, away_average_locs_and_count_df = get_passes_between_df(away_team_id, backwardpasses_df, players_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "fcce30c9-f4e2-458a-9bc5-4870a9c4bb2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import to_rgba\n",
    "from mplsoccer import Pitch, FontManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "8ae89f8d-4c9c-4e55-837d-bce41305d45a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def pass_network_visualization(ax, passes_between_df, average_locs_and_count_df, flipped=False):\n",
    "    MAX_LINE_WIDTH = 10\n",
    "    MAX_MARKER_SIZE = 3000\n",
    "    MIN_FONT_SIZE = 8\n",
    "    \n",
    "    # Calculate line width and marker size\n",
    "    passes_between_df['width'] = (passes_between_df.pass_count / passes_between_df.pass_count.max() * MAX_LINE_WIDTH)\n",
    "    average_locs_and_count_df['marker_size'] = (average_locs_and_count_df['count'] / average_locs_and_count_df['count'].max() * MAX_MARKER_SIZE)\n",
    "    \n",
    "    MIN_TRANSPARENCY = 0.3\n",
    "    color = np.array(to_rgba('#507293'))\n",
    "    color = np.tile(color, (len(passes_between_df), 1))\n",
    "    c_transparency = passes_between_df.pass_count / passes_between_df.pass_count.max()\n",
    "    c_transparency = (c_transparency * (1 - MIN_TRANSPARENCY)) + MIN_TRANSPARENCY\n",
    "    color[:, 3] = c_transparency\n",
    "    \n",
    "    pitch = Pitch(pitch_type='opta', pitch_color='WHITE', line_color='#507293')\n",
    "    pitch.draw(ax=ax)\n",
    "\n",
    "    if flipped:\n",
    "        passes_between_df['x'] = pitch.dim.right - passes_between_df['x']\n",
    "        passes_between_df['y'] = pitch.dim.right - passes_between_df['y']\n",
    "        passes_between_df['x_end'] = pitch.dim.right - passes_between_df['x_end']\n",
    "        passes_between_df['y_end'] = pitch.dim.right - passes_between_df['y_end']\n",
    "        average_locs_and_count_df['x'] = pitch.dim.right - average_locs_and_count_df['x']\n",
    "        average_locs_and_count_df['y'] = pitch.dim.right - average_locs_and_count_df['y']\n",
    "\n",
    "    pass_lines = pitch.lines(passes_between_df.x, passes_between_df.y,\n",
    "                             passes_between_df.x_end, passes_between_df.y_end, lw=passes_between_df.width,\n",
    "                             color=color, zorder=1, ax=ax)\n",
    "    pass_nodes = pitch.scatter(average_locs_and_count_df.x, average_locs_and_count_df.y,\n",
    "                               s=average_locs_and_count_df.marker_size,\n",
    "                               color='RED', edgecolor='#507293', linewidth=1, alpha=1, ax=ax)\n",
    "    \n",
    "    # Calculate font size for player initials based on marker size\n",
    "    max_marker_size = average_locs_and_count_df['marker_size'].max()\n",
    "    font_size = 14 * (average_locs_and_count_df['marker_size'] / max_marker_size)\n",
    "    \n",
    "    # Apply minimum font size\n",
    "    font_size = np.maximum(font_size, MIN_FONT_SIZE)\n",
    "    \n",
    "    # Annotate player initials\n",
    "    for index, row in average_locs_and_count_df.iterrows():\n",
    "        player_name = row[\"name\"].split()\n",
    "        player_initials = \"\".join(word[0] for word in player_name).upper()\n",
    "        pitch.annotate(player_initials, xy=(row.x, row.y), c='BLACK', va='center',\n",
    "                       ha='center', size=font_size[index], ax=ax)\n",
    "\n",
    "    return pitch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b72a61ea-8f6e-4688-9819-8f6e610f4c58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))  # Use a single subplot for the home team\n",
    "\n",
    "# plot variables\n",
    "main_color = '#FBFAF5'\n",
    "\n",
    "# home team viz\n",
    "pass_network_visualization(ax, home_passes_between_df, home_average_locs_and_count_df)\n",
    "\n",
    "# Set title for the home team\n",
    "ax.set_title(teams_dict[home_team_id], color=main_color, fontsize=14)\n",
    "\n",
    "# Set suptitle and subtitle\n",
    "\n",
    "# Save the image with the home team name as filename\n",
    "plt.savefig(f\"{teams_dict[home_team_id]}_vs_{teams_dict[away_team_id]}_{teams_dict[home_team_id]}_back_passing_network.png\", bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "4a5ba3d5-12ac-4658-aa25-a9b1927aacbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))  # Use a single subplot for the home team\n",
    "\n",
    "# plot variables\n",
    "main_color = '#FBFAF5'\n",
    "\n",
    "# home team viz\n",
    "pass_network_visualization(ax, away_passes_between_df, away_average_locs_and_count_df)\n",
    "\n",
    "# Set title for the home team\n",
    "ax.set_title(teams_dict[away_team_id], color=main_color, fontsize=14)\n",
    "\n",
    "# Set suptitle and subtitle\n",
    "\n",
    "# Save the image with the home team name as filename\n",
    "plt.savefig(f\"{teams_dict[home_team_id]}_vs_{teams_dict[away_team_id]}_{teams_dict[away_team_id]}_back_passing_network.png\", bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5effc450-8ce5-41e1-983f-c7cb9cb5710b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e41f3757-cbbb-48d1-b09c-49a0ccda616c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}