sportowe_wizualizacja/11tegen11 position map.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#edit only this tab\n",
    "#give the folder path of the match\n",
    "path = \"\"\"C:\\\\Users\\\\Koushik\\\\Downloads\\\\open-data-master\\\\open-data-master\\\\data\\\\passmap\\\\ARGNIG\\\\\"\"\"\n",
    "home_team = 'Argentina'\n",
    "away_team = 'Nigeria'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import codecs\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "#import seaborn as sns\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 squarify\n",
    "from functools import reduce\n",
    "\n",
    "Xg_req = pd.DataFrame(data=None)\n",
    "for filename in (os.listdir(path)):\n",
    "    #print(filename)\n",
    "    \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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Odion Jude Ighalo' 'Alex Iwobi' 'Simeon Tochukwu Nwankwo']\n",
      "['Cristian David Pavón' 'Maximiliano Eduardo Meza'\n",
      " 'Sergio Leonel Agüero del Castillo']\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": 5,
   "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": [],
   "source": [
    "#print(home_team_list)\n",
    "\n",
    "#home_team_list = np.delete(home_team_list,substitution_home)\n",
    "#print(home_team_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_pitch(ax):\n",
    "    # focus on only half of the pitch\n",
    "    #Pitch Outline & Centre Line\n",
    "    Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)\n",
    "    #Left, Right Penalty Area and midline\n",
    "    LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)\n",
    "    RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)\n",
    "    midline = ConnectionPatch([60,0], [60,80], \"data\", \"data\")\n",
    "\n",
    "    #Left, Right 6-yard Box\n",
    "    LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)\n",
    "    RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)\n",
    "\n",
    "\n",
    "    #Prepare Circles\n",
    "    centreCircle = plt.Circle((60,40),8.1,color=\"black\", fill = False)\n",
    "    centreSpot = plt.Circle((60,40),0.71,color=\"black\")\n",
    "    #Penalty spots and Arcs around penalty boxes\n",
    "    leftPenSpot = plt.Circle((9.7,40),0.71,color=\"black\")\n",
    "    rightPenSpot = plt.Circle((110.3,40),0.71,color=\"black\")\n",
    "    leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=\"black\")\n",
    "    rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=\"black\")\n",
    "    \n",
    "    element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, \n",
    "               centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]\n",
    "    for i in element:\n",
    "        ax.add_patch(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "players = home_team_list\n",
    "#['Thibaut Courtois','Jan Vertonghen','Vincent Kompany','Toby Albertine Maurits Alderweireld','Thomas Meunier','Nacer Chadli','Axel Witsel','Marouane Fellaini-Bakkioui','Eden Hazard','Kevin De Bruyne','Romelu Lukaku Menama']\n",
    "fig=plt.figure() #set up the figures\n",
    "\n",
    "fig.set_size_inches(7, 5)\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "draw_pitch(ax) #overlay our different objects on the pitch\n",
    "plt.ylim(-2, 82)\n",
    "plt.xlim(-2, 122)\n",
    "plt.axis('off')\n",
    "for player in players:\n",
    "    x_avg = 0\n",
    "    y_avg = 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",
    "            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",
    "        ax.scatter(x_avg, y_avg, s=100)  \n",
    "        ax.annotate(player.split()[0] +str(' ') + player.split()[-1], (x_avg, y_avg))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 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",
    "fig=plt.figure() #set up the figures\n",
    "\n",
    "fig.set_size_inches(7, 5)\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "draw_pitch(ax) #overlay our different objects on the pitch\n",
    "plt.ylim(-2, 82)\n",
    "plt.xlim(-2, 122)\n",
    "plt.axis('off')\n",
    "\n",
    "for player in players:\n",
    "    x_avg = 0\n",
    "    y_avg = 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",
    "            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",
    "        ax.scatter(x_avg, y_avg, s=150)  \n",
    "        ax.annotate(player.split()[0] +str(' ') + player.split()[-1], (x_avg, y_avg))\n",
    "plt.show()"
   ]
  }
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