sportowe_wizualizacja/11tegen11 position map.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#edit only this tab\n",
    "#give the folder path of the match\n",
    "path = \"/home/kirugulige/Documents/Football-Analytics/open-data-master/data/events/\"\n",
    "home_team = 'Espanyol'\n",
    "away_team = 'Barcelona'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import codecs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
    "from matplotlib.offsetbox import  OffsetImage\n",
    "import StatsbombPitch as sb\n",
    "from functools import reduce\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "Xg_req = pd.DataFrame(data=None)\n",
    "\n",
    "filename = '69275.json' # remove the comment line to work for this match\n",
    "with codecs.open(\"%s\" % path + filename,encoding='utf-8') as data_file:   \n",
    "    data = json.load(data_file)\n",
    "    df = pd.DataFrame(data=None)\n",
    "        \n",
    "    df = json_normalize(data, sep = \"_\")\n",
    "        \n",
    "        #df =  df[(df['type_name'] == \"Shot\")]\n",
    "        #df = df.loc[:,['location','shot_body_part_id','shot_end_location','shot_one_on_one','shot_technique_id','shot_type_id','under_pressure','shot_outcome_id']]\n",
    "        #print(df.shape)\n",
    "Xg_req = Xg_req.append(df,ignore_index=True,sort=False)\n",
    "        #df.drop(df.index, inplace=True)\n",
    "        \n",
    "print(\"done\")\n",
    "df = Xg_req"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type_id=30 is pass event AND type_id=19 is substitution event\n",
    "\n",
    "pass_m = df.query('type_id == 30')\n",
    "substitution = df.query('type_id == 19')\n",
    "#pass_m = df.query('type_name == pass')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Javier Alejandro Mascherano' 'Seydou Kéita' 'Bojan Krkíc Pérez']\n",
      "['Jesús Alberto Dátolo' 'Jordi Amat Maas' 'David García De La Cruz']\n"
     ]
    }
   ],
   "source": [
    "#this cell is WIP\n",
    "substitution_home = set()\n",
    "substitution_home = substitution[substitution.team_name == home_team]\n",
    "substitution_home = substitution_home['substitution_replacement_name'].unique()\n",
    "substitution_away = set()\n",
    "substitution_away = substitution[substitution.team_name == away_team]\n",
    "substitution_away = substitution_away['substitution_replacement_name'].unique()\n",
    "print(substitution_away)\n",
    "print(substitution_home)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to get \n",
    "\n",
    "home_player = pass_m[(pass_m.team_name == home_team)] \n",
    "home_team_list = set()\n",
    "home_team_list = home_player['player_name'].unique()\n",
    "#print(substitution_belgium)\n",
    "#belgium_list = [player for player in belgium_list if player not in substitution_belgium]\n",
    "#belgium_list.remove([x for x in substitution_belgium])#belgium_list - substitution_belgium\n",
    "home_player =pass_m['player_name'].isin(home_team_list)\n",
    "pass_home = pass_m[home_player] #contains 11 players of home team\n",
    "\n",
    "away_player = pass_m[(pass_m.team_name == away_team)] \n",
    "away_list = away_player['player_name'].unique()\n",
    "#away_list = away_list - substitution_brazil\n",
    "away_player = pass_m['player_name'].isin(away_list)\n",
    "pass_away = pass_m[away_player]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>index</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>possession</th>\n",
       "      <th>duration</th>\n",
       "      <th>type_id</th>\n",
       "      <th>type_name</th>\n",
       "      <th>...</th>\n",
       "      <th>pass_cut_back</th>\n",
       "      <th>substitution_outcome_id</th>\n",
       "      <th>substitution_outcome_name</th>\n",
       "      <th>substitution_replacement_id</th>\n",
       "      <th>substitution_replacement_name</th>\n",
       "      <th>injury_stoppage_in_chain</th>\n",
       "      <th>foul_committed_offensive</th>\n",
       "      <th>pass_deflected</th>\n",
       "      <th>block_deflection</th>\n",
       "      <th>goalkeeper_punched_out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6406089-778b-49e0-a70a-ade4788cf911</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:00.738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.606100</td>\n",
       "      <td>30</td>\n",
       "      <td>Pass</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>73d8e43f-8e84-4b5d-88cc-9423278134d5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:02.511</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.776163</td>\n",
       "      <td>30</td>\n",
       "      <td>Pass</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8e47d35d-ab38-4419-a991-efe7105dec56</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:09.581</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1.242900</td>\n",
       "      <td>30</td>\n",
       "      <td>Pass</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>a449bfa2-7bd9-4f94-8e13-595620c9bcee</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:18.784</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1.298500</td>\n",
       "      <td>30</td>\n",
       "      <td>Pass</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77f45a53-2eab-4ee5-bfbd-ad39d20a36cb</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:20.122</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950262</td>\n",
       "      <td>30</td>\n",
       "      <td>Pass</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id  index  period     timestamp  minute  \\\n",
       "4   d6406089-778b-49e0-a70a-ade4788cf911      5       1  00:00:00.738       0   \n",
       "8   73d8e43f-8e84-4b5d-88cc-9423278134d5      9       1  00:00:02.511       0   \n",
       "16  8e47d35d-ab38-4419-a991-efe7105dec56     17       1  00:00:09.581       0   \n",
       "20  a449bfa2-7bd9-4f94-8e13-595620c9bcee     21       1  00:00:18.784       0   \n",
       "24  77f45a53-2eab-4ee5-bfbd-ad39d20a36cb     25       1  00:00:20.122       0   \n",
       "\n",
       "    second  possession  duration  type_id type_name  ...  pass_cut_back  \\\n",
       "4        0           2  0.606100       30      Pass  ...            NaN   \n",
       "8        2           2  1.776163       30      Pass  ...            NaN   \n",
       "16       9           2  1.242900       30      Pass  ...            NaN   \n",
       "20      18           3  1.298500       30      Pass  ...            NaN   \n",
       "24      20           3  0.950262       30      Pass  ...            NaN   \n",
       "\n",
       "   substitution_outcome_id  substitution_outcome_name  \\\n",
       "4                      NaN                        NaN   \n",
       "8                      NaN                        NaN   \n",
       "16                     NaN                        NaN   \n",
       "20                     NaN                        NaN   \n",
       "24                     NaN                        NaN   \n",
       "\n",
       "   substitution_replacement_id  substitution_replacement_name  \\\n",
       "4                          NaN                            NaN   \n",
       "8                          NaN                            NaN   \n",
       "16                         NaN                            NaN   \n",
       "20                         NaN                            NaN   \n",
       "24                         NaN                            NaN   \n",
       "\n",
       "   injury_stoppage_in_chain  foul_committed_offensive pass_deflected  \\\n",
       "4                       NaN                       NaN            NaN   \n",
       "8                       NaN                       NaN            NaN   \n",
       "16                      NaN                       NaN            NaN   \n",
       "20                      NaN                       NaN            NaN   \n",
       "24                      NaN                       NaN            NaN   \n",
       "\n",
       "   block_deflection goalkeeper_punched_out  \n",
       "4               NaN                    NaN  \n",
       "8               NaN                    NaN  \n",
       "16              NaN                    NaN  \n",
       "20              NaN                    NaN  \n",
       "24              NaN                    NaN  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_home.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 748.8x489.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "players = home_team_list\n",
    "#fig=plt.figure() #set up the figures\n",
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
    "plt.axis('off')\n",
    "\n",
    "for player in players:\n",
    "    x_avg = 0\n",
    "    y_avg = 0\n",
    "    touches = 0\n",
    "    if player not in substitution_home: \n",
    "        play_temp = pass_home[(pass_home.player_name == player)]\n",
    "    #print(play_temp.location, players[player])\n",
    "        for i in range(len(play_temp)):\n",
    "            touches+=1\n",
    "            \n",
    "            #https://math.stackexchange.com/questions/1013230/how-to-find-coordinates-of-reflected-point\n",
    "            x_avg = x_avg + play_temp.iloc[i]['location'][0]\n",
    "            y_avg = y_avg + play_temp.iloc[i]['location'][1]\n",
    "        x_avg = x_avg/len(play_temp)\n",
    "        y_avg = y_avg/len(play_temp)\n",
    "    #print(x_avg,y_avg,players[player])\n",
    "        plt.scatter(x_avg, y_avg, s= (3 *touches ),color=\"red\",edgecolors=\"black\",zorder = 15)  \n",
    "        plt.annotate(player, (x_avg, y_avg),zorder = 20,color = 'white')\n",
    "        plt.gca().invert_yaxis()\n",
    "        plt.annotate(\"\", xy=(25, 75), xytext=(5, 75),\n",
    "        arrowprops=dict(arrowstyle=\"->\", linewidth=2))\n",
    "        plt.text(7,73,'Attack',fontsize=20,color = 'white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 748.8x489.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "players = away_list #['Alisson Ramsés Becker','Fágner Conserva Lemos','Fernando Luiz Rosa','Gabriel Fernando de Jesus','João Miranda de Souza Filho','José Paulo Bezzera Maciel Júnior','Marcelo Vieira da Silva Júnior','Neymar da Silva Santos Junior','Philippe Coutinho Correia','Thiago Emiliano da Silva','Willian Borges da Silva']\n",
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
    "plt.axis('off')\n",
    "for player in players:\n",
    "    x_avg = 0\n",
    "    y_avg = 0\n",
    "    touches = 0\n",
    "    if player not in substitution_away:\n",
    "    #print(players[player])\n",
    "        play_temp = pass_away[(pass_away.player_name == player)]\n",
    "    #print(play_temp.location, players[player])\n",
    "        for i in range(len(play_temp)):\n",
    "            touches+=1\n",
    "            x_avg = x_avg + play_temp.iloc[i]['location'][0]\n",
    "            y_avg = y_avg + play_temp.iloc[i]['location'][1]\n",
    "        x_avg = x_avg/len(play_temp)\n",
    "        y_avg = y_avg/len(play_temp)\n",
    "    #print(x_avg,y_avg,players[player])\n",
    "        plt.scatter(x_avg, y_avg, s= (3 *touches ),color=\"red\",edgecolors=\"black\",zorder = 15)  \n",
    "        plt.annotate(player, (x_avg, y_avg),zorder = 20,color = 'white')\n",
    "        plt.annotate(\"\", xy=(25, 75), xytext=(5, 75),\n",
    "        arrowprops=dict(arrowstyle=\"->\", linewidth=2))\n",
    "        plt.text(7,73,'Attack',fontsize=20,color = 'white')\n",
    "        plt.gca().invert_yaxis()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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