{
 "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",
       "    .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>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",
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       "      <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",
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       "      <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",
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       "      <td>NaN</td>\n",
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       "      <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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\n",
      "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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\n",
      "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}