match_class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ideas\n",
    "* Transformation of data using transAxes / transFigure instead of calculating the positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_columns = [col for col in match.df.columns if 'location' in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4        60.0\n",
       "5        69.0\n",
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       "16       33.0\n",
       "17       21.0\n",
       "18       95.0\n",
       "19       21.0\n",
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       "28       40.0\n",
       "29       56.0\n",
       "30       64.0\n",
       "31       62.0\n",
       "32       46.0\n",
       "33       46.0\n",
       "        ...  \n",
       "2644    107.0\n",
       "2645    106.0\n",
       "2646      2.0\n",
       "2647     10.0\n",
       "2648     53.0\n",
       "2649     54.0\n",
       "2650     42.0\n",
       "2651     45.0\n",
       "2652     56.0\n",
       "2653     75.0\n",
       "2654     88.0\n",
       "2655     18.0\n",
       "2656     10.0\n",
       "2657    111.0\n",
       "2658    113.0\n",
       "2659    109.0\n",
       "2660    110.0\n",
       "2661    111.0\n",
       "2662    111.0\n",
       "2663      1.0\n",
       "2664     29.0\n",
       "2665    109.0\n",
       "2666     17.0\n",
       "2667     25.0\n",
       "2668     98.0\n",
       "2669     97.0\n",
       "2670     24.0\n",
       "2671     93.0\n",
       "2672     28.0\n",
       "2673     33.0\n",
       "Name: location, Length: 2610, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "99      2.0\n",
       "138     3.0\n",
       "555     3.0\n",
       "585     3.0\n",
       "620     3.0\n",
       "1342    4.0\n",
       "1496    3.0\n",
       "1519    2.0\n",
       "1885    3.0\n",
       "1954    2.0\n",
       "2194    3.0\n",
       "2209    3.0\n",
       "2360    3.0\n",
       "2604    2.0\n",
       "Name: goalkeeper_end_location, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4        38.0\n",
       "6        93.0\n",
       "10       41.0\n",
       "13       36.0\n",
       "16       21.0\n",
       "19       15.0\n",
       "21       16.0\n",
       "23       28.0\n",
       "25       27.0\n",
       "27       40.0\n",
       "29       64.0\n",
       "31       46.0\n",
       "33       56.0\n",
       "35       87.0\n",
       "37       86.0\n",
       "39      101.0\n",
       "42       89.0\n",
       "44       98.0\n",
       "46       85.0\n",
       "48      101.0\n",
       "51      111.0\n",
       "53       37.0\n",
       "55       82.0\n",
       "57       89.0\n",
       "59       81.0\n",
       "62       70.0\n",
       "65       62.0\n",
       "67       61.0\n",
       "69       81.0\n",
       "72       65.0\n",
       "        ...  \n",
       "2588     97.0\n",
       "2591    115.0\n",
       "2594    118.0\n",
       "2596    105.0\n",
       "2598    100.0\n",
       "2601     99.0\n",
       "2607     67.0\n",
       "2609     92.0\n",
       "2613     53.0\n",
       "2615     49.0\n",
       "2617     54.0\n",
       "2619     57.0\n",
       "2621     53.0\n",
       "2623     87.0\n",
       "2625     93.0\n",
       "2628    100.0\n",
       "2630    100.0\n",
       "2634     80.0\n",
       "2636     85.0\n",
       "2638    117.0\n",
       "2640    120.0\n",
       "2642    106.0\n",
       "2647     53.0\n",
       "2649     42.0\n",
       "2651     56.0\n",
       "2653     88.0\n",
       "2658    109.0\n",
       "2660    111.0\n",
       "2665     98.0\n",
       "2673     93.0\n",
       "Name: pass_end_location, Length: 948, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "96      118.0\n",
       "136     105.0\n",
       "436     110.0\n",
       "553     101.0\n",
       "583     107.0\n",
       "619     106.0\n",
       "1001    120.0\n",
       "1147    118.0\n",
       "1341    119.0\n",
       "1373    118.0\n",
       "1485    120.0\n",
       "1494    107.0\n",
       "1517    120.0\n",
       "1665    119.0\n",
       "1883    107.0\n",
       "1952     97.0\n",
       "2192    107.0\n",
       "2208    106.0\n",
       "2246    119.0\n",
       "2358    120.0\n",
       "2379    118.0\n",
       "2502    119.0\n",
       "2603    119.0\n",
       "2644    119.0\n",
       "2662    120.0\n",
       "Name: shot_end_location, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in location_columns:\n",
    "    display(match.df[col].dropna().apply(lambda x: x[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99      [2.0, 40.0]\n",
       "138     [3.0, 43.0]\n",
       "555     [3.0, 40.0]\n",
       "585     [3.0, 38.0]\n",
       "620     [3.0, 41.0]\n",
       "1342    [4.0, 38.0]\n",
       "1496    [3.0, 41.0]\n",
       "1519    [2.0, 38.0]\n",
       "1885    [3.0, 41.0]\n",
       "1954    [2.0, 40.0]\n",
       "2194    [3.0, 42.0]\n",
       "2209    [3.0, 40.0]\n",
       "2360    [3.0, 38.0]\n",
       "2604    [2.0, 41.0]\n",
       "Name: goalkeeper_end_location, dtype: object"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.df.goalkeeper_end_location.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Ellipse, Rectangle\n",
    "from sklearn.preprocessing import minmax_scale, MinMaxScaler\n",
    "\n",
    "from pybomb.data import df_matches\n",
    "from pybomb.data import unstack_dict_column, unstack_dict_column_repeated\n",
    "\n",
    "class Match:\n",
    "    def __init__(self, match_id, pitch_x=105.0, pitch_y=68.0, x_min=0.04338, x_max=0.95652, y_min=0.0641, y_max=0.9359,\n",
    "                 pitch_color='#78AB46', home_color='red', away_color='white', \n",
    "                 event_data_path='data/events'):\n",
    "        self.event_data_path = event_data_path\n",
    "\n",
    "        # TODO: Get pitch_x and pitch_y from data?\n",
    "        self.pitch_x = pitch_x\n",
    "        self.pitch_y = pitch_y\n",
    "        self.x_min = x_min\n",
    "        self.x_max = x_max\n",
    "        self.y_min = y_min\n",
    "        self.y_max = y_max\n",
    "        self.pitch_color = pitch_color\n",
    "        self.team_colors = {\n",
    "            'H': home_color,\n",
    "            'A': away_color\n",
    "        }\n",
    "        self.outcome_colors = {\n",
    "            'Incomplete': 'C3'\n",
    "        }\n",
    "        \n",
    "        self.match_id = match_id\n",
    "        self.df = self._get_match_df()\n",
    "        \n",
    "        x_loc_range, y_loc_range = self._get_locations_range()\n",
    "        self.x_loc_range = x_loc_range\n",
    "        self.y_loc_range = y_loc_range\n",
    "        \n",
    "\n",
    "        meta_info = self._get_meta_info()\n",
    "        self.home_score = meta_info['home_score'].values[0]\n",
    "        self.away_score = meta_info['away_score'].values[0]\n",
    "        self.competition = meta_info['competition'].values[0]\n",
    "        self.date = meta_info['match_date'].values[0]\n",
    "        self.kickoff_time = meta_info['kick_off'].values[0]\n",
    "        self.stadium = meta_info['stadium_name'].values[0]\n",
    "        self.home_team_name = meta_info['home_team'].values[0]['home_team_name']\n",
    "        self.away_team_name = meta_info['away_team'].values[0]['away_team_name']\n",
    "        self.home_team_id = meta_info['home_team'].values[0]['home_team_id']\n",
    "        self.away_team_id = meta_info['away_team'].values[0]['away_team_id']\n",
    "\n",
    "    \n",
    "    def _get_meta_info(self):\n",
    "        return df_matches[df_matches['match_id'] == self.match_id]\n",
    "    \n",
    "    def _get_match_df(self):\n",
    "        df = pd.read_json(f'{self.event_data_path}/{self.match_id}.json', typ='frame')\n",
    "\n",
    "        dict_columns = [c for c in df.columns if isinstance(df[c].dropna().iloc[0], dict)]\n",
    "        for column in dict_columns:\n",
    "            df = unstack_dict_column_repeated(df, column)\n",
    "        return df\n",
    "    \n",
    "    def _get_locations_range(self):\n",
    "        location_columns = [col for col in match.df.columns if 'location' in col]\n",
    "        x_locations = []\n",
    "        y_locations = []\n",
    "\n",
    "        for col in location_columns:\n",
    "            x_locations.extend(self.df[col].dropna().apply(lambda x: x[0]).values)\n",
    "            y_locations.extend(self.df[col].dropna().apply(lambda x: x[1]).values)\n",
    "        \n",
    "        x_loc_range = np.max(x_locations) - np.min(x_locations) \n",
    "        y_loc_range = np.max(y_locations) - np.min(y_locations)\n",
    "        print(x_loc_range, y_loc_range)\n",
    "\n",
    "        return x_loc_range, y_loc_range\n",
    "\n",
    "\n",
    "    def correct_positions(self, column='location'):\n",
    "        xs, ys = get_normalized_positions(self.df[column])\n",
    "        self.df[column] = [[x, y] for x, y in zip(xs, ys)]\n",
    "\n",
    "    def get_passes_df(self):\n",
    "        return self.df[self.df['type_name'] == 'Pass']\n",
    "\n",
    "    def get_shot_df(self):\n",
    "        return self.df[self.df['type_name'] == 'Shot']\n",
    "                \n",
    "    def draw_pitch(self):\n",
    "        #set up field    \n",
    "        fig = plt.figure(figsize=(self.pitch_x/10, self.pitch_y/10))\n",
    "\n",
    "        axes = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "        axes.xaxis.set_visible(False)\n",
    "        axes.yaxis.set_visible(False)\n",
    "\n",
    "        plt.xlim([-5,self.pitch_x+5])\n",
    "        plt.ylim([-5,self.pitch_y+5])\n",
    "\n",
    "        box_height = ((16.5*2 + 7.32)/self.pitch_y)/1.15\n",
    "        box_width = (16.5/self.pitch_x)/1.15 \n",
    "\n",
    "        # pitch field\n",
    "        field = plt.Rectangle(\n",
    "            (0, 0), 1, 1, edgecolor=self.pitch_color, facecolor=self.pitch_color, \n",
    "            alpha=1, transform=axes.transAxes, zorder=1\n",
    "        ) \n",
    "        \n",
    "        #pitch boundary lines\n",
    "        boundary = plt.Rectangle(\n",
    "            (self.x_min, self.y_min), (self.x_max - self.x_min), (self.y_max - self.y_min),\n",
    "            edgecolor=\"white\", facecolor=\"none\", alpha=1, transform=axes.transAxes, zorder=1\n",
    "        ) \n",
    "        \n",
    "        #half-way line\n",
    "        mid_line = plt.Line2D(\n",
    "            [0.5, 0.5], [self.y_max, self.y_min],\n",
    "            c='w', transform=axes.transAxes, zorder=1) \n",
    "        \n",
    "        #middle circle\n",
    "        mid_circle = Ellipse(\n",
    "            (0.5, 0.5), 9.15*2 / self.pitch_x, 9.15*2 / self.pitch_y,\n",
    "            ec='w', fc='none', transform=axes.transAxes, zorder=1\n",
    "        ) \n",
    "\n",
    "        #penalty area\n",
    "        penalty_left = plt.Rectangle(\n",
    "            (self.x_min, (1-box_height)/2), box_width, box_height,\n",
    "            ec='w', fc='none', transform=axes.transAxes, zorder=1\n",
    "        ) \n",
    "        \n",
    "        #penalty area\n",
    "        penalty_right = plt.Rectangle(\n",
    "            (self.x_max - box_width, (1 - box_height) / 2), box_width, box_height,\n",
    "            ec='w', fc='none', transform=axes.transAxes, zorder=1) \n",
    "\n",
    "        # extend lines to plot\n",
    "        fig.lines.extend([field, boundary, mid_line, mid_circle, penalty_left, penalty_right])\n",
    "    \n",
    "        return fig, axes\n",
    "    \n",
    "    \n",
    "    def get_normalized_positions(self, positions):\n",
    "        x = positions.apply(lambda x: x[0]).values\n",
    "        y = positions.apply(lambda x: x[1]).values\n",
    "        #x_range = (self.x_min, self.x_max)\n",
    "        #y_range = (self.y_min, self.y_max)\n",
    "        #x_scaled = minmax_scale(x, feature_range=x_range)\n",
    "        #y_scaled = minmax_scale(y, feature_range=y_range)\n",
    "        \n",
    "        # TODO: change range to max value, since pitch positions should start at 0.\n",
    "        \n",
    "        print(self.x_loc_range, (self.x_max - self.x_min))\n",
    "        print(self.y_loc_range, (self.y_max - self.y_min))\n",
    "\n",
    "        x_scaled = x / self.x_loc_range * (self.x_max - self.x_min)\n",
    "        y_scaled = y / self.y_loc_range * (self.y_max - self.y_min)\n",
    "\n",
    "        return x_scaled, y_scaled\n",
    "\n",
    "    def filter_column(self, df, column, value):\n",
    "        if column in df.columns:\n",
    "            if value in df[column].unique():\n",
    "                df = df.loc[df[column] == value]\n",
    "            else:\n",
    "                raise ValueError(f'Value {value} is not in column {column} of the match data.')\n",
    "        else:\n",
    "            raise ValueError(f'Column {column} is not in the match data.') \n",
    "        \n",
    "        return df\n",
    "        \n",
    "\n",
    "    def get_passes(self, team_id=None, player_id=None, recipient_id=None, return_df=False):\n",
    "        '''\n",
    "        Returns start_x_scaled, start_y_scaled, end_x_scaled, end_y_scaled, outcomes\n",
    "        '''\n",
    "        df = self.get_passes_df()\n",
    "        \n",
    "        if team_id is not None:\n",
    "            df = self.filter_column(df, 'team_id', team_id)\n",
    "            \n",
    "        if player_id is not None:\n",
    "            df = self.filter_column(df, 'player_id', player_id)\n",
    "\n",
    "        if recipient_id is not None:\n",
    "            df = self.filter_column(df, 'pass_recipient_id', recipient_id)\n",
    "\n",
    "        start_x_scaled, start_y_scaled = self.get_normalized_positions(df['location'])\n",
    "        end_x_scaled, end_y_scaled = self.get_normalized_positions(df['pass_end_location'])\n",
    "        outcomes = df['pass_outcome_name']\n",
    "        if return_df:\n",
    "            return df\n",
    "\n",
    "        return start_x_scaled, start_y_scaled, end_x_scaled, end_y_scaled, outcomes\n",
    "    \n",
    "\n",
    "    def draw_passes(self, passes, ax):\n",
    "        for x1, y1, x2, y2, outcome in zip(*passes):\n",
    "            color = self.outcome_colors.get(outcome, 'C0')\n",
    "\n",
    "            ax.annotate(\n",
    "                '', xy=(x2, y2), xytext=(x1, y1), xycoords='axes fraction', textcoords='axes fraction', \n",
    "                arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color=color), zorder=100\n",
    "            )\n",
    "        return ax\n",
    "\n",
    "    \n",
    "    def plot_match_passes_home_away(self):\n",
    "        for team_id, team_name in zip(\n",
    "            [self.home_team_id, self.away_team_id],\n",
    "            [self.home_team_name, self.away_team_name]\n",
    "        ):\n",
    "            fig, ax = self.draw_pitch()\n",
    "            self.draw_passes(self.get_passes(team_id), ax)\n",
    "            ax.set_title(f'Passes for {team_name}', fontdict=dict(color='C5'))\n",
    "            ax.plot([0, 1], [0, 1])\n",
    "            while ax.texts:\n",
    "                fig.texts.append(ax.texts.pop())\n",
    "\n",
    "        return fig, ax\n",
    "    \n",
    "    def plot_passes(self, team_id=None, player_id=None, recipient_id=None):\n",
    "        fig, ax = self.draw_pitch()\n",
    "        passes = self.get_passes(team_id, player_id, recipient_id)\n",
    "        print(passes)\n",
    "        self.draw_passes(passes, ax)\n",
    "        ax.plot([0, 1], [0, 1])\n",
    "        while ax.texts:\n",
    "            fig.texts.append(ax.texts.pop())\n",
    "        return fig, ax\n",
    "    \n",
    "\n",
    "    def plot_shot(self):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_cup_matches = pd.read_json('data/matches/43.json').sort_values('match_id')\n",
    "match_ids = world_cup_matches['match_id']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'home_team_id': 796, 'home_team_name': 'Russia'}"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_cup_matches['home_team'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7525: Russia vs. Saudi Arabia\n",
      "7529: Croatia vs. Nigeria\n",
      "7530: France vs. Australia\n",
      "7531: Argentina vs. Iceland\n",
      "7532: Peru vs. Denmark\n",
      "7533: Brazil vs. Switzerland\n",
      "7534: Germany vs. Mexico\n",
      "7535: Costa Rica vs. Serbia\n",
      "7536: Belgium vs. Panama\n",
      "7537: Tunisia vs. England\n",
      "7538: Sweden vs. South Korea\n",
      "7539: Poland vs. Senegal\n",
      "7540: Russia vs. Egypt\n",
      "7541: Colombia vs. Japan\n",
      "7542: Portugal vs. Morocco\n",
      "7543: Iran vs. Spain\n",
      "7544: Uruguay vs. Saudi Arabia\n",
      "7545: Argentina vs. Croatia\n",
      "7546: France vs. Peru\n",
      "7547: Denmark vs. Australia\n",
      "7548: Brazil vs. Costa Rica\n",
      "7549: Nigeria vs. Iceland\n",
      "7550: Serbia vs. Switzerland\n",
      "7551: Germany vs. Sweden\n",
      "7552: Belgium vs. Tunisia\n",
      "7553: South Korea vs. Mexico\n",
      "7554: England vs. Panama\n",
      "7555: Poland vs. Colombia\n",
      "7556: Japan vs. Senegal\n",
      "7557: Iran vs. Portugal\n",
      "7558: Uruguay vs. Russia\n",
      "7559: Saudi Arabia vs. Egypt\n",
      "7560: Spain vs. Morocco\n",
      "7561: Iceland vs. Croatia\n",
      "7562: Australia vs. Peru\n",
      "7563: Denmark vs. France\n",
      "7564: Nigeria vs. Argentina\n",
      "7565: Serbia vs. Brazil\n",
      "7566: Mexico vs. Sweden\n",
      "7567: South Korea vs. Germany\n",
      "7568: Switzerland vs. Costa Rica\n",
      "7569: Panama vs. Tunisia\n",
      "7570: England vs. Belgium\n",
      "7571: Senegal vs. Colombia\n",
      "7572: Japan vs. Poland\n",
      "7576: Portugal vs. Spain\n",
      "7577: Morocco vs. Iran\n",
      "7578: Egypt vs. Uruguay\n",
      "7579: Uruguay vs. Portugal\n",
      "7580: France vs. Argentina\n",
      "7581: Croatia vs. Denmark\n",
      "7582: Spain vs. Russia\n",
      "7583: Brazil vs. Mexico\n",
      "7584: Belgium vs. Japan\n",
      "7585: Colombia vs. England\n",
      "7586: Sweden vs. Switzerland\n",
      "8649: Uruguay vs. France\n",
      "8650: Brazil vs. Belgium\n",
      "8651: Sweden vs. England\n",
      "8652: Russia vs. Croatia\n",
      "8655: France vs. Belgium\n",
      "8656: Croatia vs. England\n",
      "8657: Belgium vs. England\n",
      "8658: France vs. Croatia\n"
     ]
    }
   ],
   "source": [
    "for i, row in world_cup_matches[['match_id', 'home_team', 'away_team']].iterrows():\n",
    "    home = row[1]['home_team_name']\n",
    "    away = row[2]['away_team_name']\n",
    "    print(f'{row[0]}: {home} vs. {away}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119.0 79.0\n"
     ]
    }
   ],
   "source": [
    "match = Match(7551)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_pass_plot(match, team_id=None, player_id=None, recipient_id=None):\n",
    "    print(team_id, player_id, recipient_id)\n",
    "    match.plot_passes(team_id=team_id, player_id=player_id, recipient_id=recipient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dropdown(df, id_column, value_column):\n",
    "    options = [('None', None)] + [tuple(option) for option in df[[value_column, id_column]].drop_duplicates().values]\n",
    "    return widgets.Dropdown(options=options, value=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_dropdown = get_dropdown(match.df, 'team_id', 'team_name')\n",
    "player_dropdown = get_dropdown(match.df, 'player_id', 'player_name')\n",
    "recipient_dropdown = get_dropdown(match.df, 'pass_recipient_id', 'pass_recipient_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b3322206e204085bc7bdc22f19a73b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='team_id', options=(('None', None), ('Germany', 770), ('Sweden', 79…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.show_pass_plot(match, team_id=None, player_id=None, recipient_id=None)>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact_manual(\n",
    "    show_pass_plot,\n",
    "    match=widgets.fixed(match),\n",
    "    team_id=team_dropdown,\n",
    "    player_id=player_dropdown,\n",
    "    recipient_id=recipient_dropdown,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>player_id</th>\n",
       "      <th>player_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5625.0</td>\n",
       "      <td>Emil Forsberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5607.0</td>\n",
       "      <td>Andreas Granqvist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3167.0</td>\n",
       "      <td>Antonio Rüdiger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3186.0</td>\n",
       "      <td>Julian Draxler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5574.0</td>\n",
       "      <td>Toni Kroos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6040.0</td>\n",
       "      <td>Jonas Hector</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5578.0</td>\n",
       "      <td>Jérôme Boateng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5579.0</td>\n",
       "      <td>Joshua Kimmich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6039.0</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5559.0</td>\n",
       "      <td>Marco Reus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5562.0</td>\n",
       "      <td>Thomas Müller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5621.0</td>\n",
       "      <td>Ludwig Augustinsson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5557.0</td>\n",
       "      <td>Timo Werner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>5613.0</td>\n",
       "      <td>Viktor Claesson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5570.0</td>\n",
       "      <td>Manuel Neuer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>5612.0</td>\n",
       "      <td>Mikael Lustig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>5624.0</td>\n",
       "      <td>Albin Ekdal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>11098.0</td>\n",
       "      <td>Marcus Berg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>5622.0</td>\n",
       "      <td>Robin Olsen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>3702.0</td>\n",
       "      <td>Victor Nilsson Lindelöf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>4417.0</td>\n",
       "      <td>Ola Toivonen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>5617.0</td>\n",
       "      <td>Sebastian Larsson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>10287.0</td>\n",
       "      <td>İlkay Gündoğan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>5565.0</td>\n",
       "      <td>Mario Gómez García</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>19988.0</td>\n",
       "      <td>Jimmy Durmaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>6038.0</td>\n",
       "      <td>John Guidetti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2467</th>\n",
       "      <td>5555.0</td>\n",
       "      <td>Julian Brandt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      player_id              player_name\n",
       "4        5625.0            Emil Forsberg\n",
       "6        5607.0        Andreas Granqvist\n",
       "10       3167.0          Antonio Rüdiger\n",
       "13       3186.0           Julian Draxler\n",
       "16       5574.0               Toni Kroos\n",
       "19       6040.0             Jonas Hector\n",
       "23       5578.0           Jérôme Boateng\n",
       "25       5579.0           Joshua Kimmich\n",
       "31       6039.0           Sebastian Rudy\n",
       "46       5559.0               Marco Reus\n",
       "51       5562.0            Thomas Müller\n",
       "53       5621.0      Ludwig Augustinsson\n",
       "59       5557.0              Timo Werner\n",
       "140      5613.0          Viktor Claesson\n",
       "142      5570.0             Manuel Neuer\n",
       "173      5612.0            Mikael Lustig\n",
       "221      5624.0              Albin Ekdal\n",
       "240     11098.0              Marcus Berg\n",
       "310      5622.0              Robin Olsen\n",
       "497      3702.0  Victor Nilsson Lindelöf\n",
       "498      4417.0             Ola Toivonen\n",
       "519      5617.0        Sebastian Larsson\n",
       "1006    10287.0           İlkay Gündoğan\n",
       "1533     5565.0       Mario Gómez García\n",
       "2240    19988.0             Jimmy Durmaz\n",
       "2334     6038.0            John Guidetti\n",
       "2467     5555.0            Julian Brandt"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passes_df[['player_id', 'player_name']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of passes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_passes = match.get_passes_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df_passes[df_passes['player_id']==6039.0][['location', 'pass_end_location', 'pass_outcome_name', 'player_name', 'minute']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.95652, 0.04338)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.x_max, match.x_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>index</th>\n",
       "      <th>location</th>\n",
       "      <th>pass_end_location</th>\n",
       "      <th>pass_outcome_name</th>\n",
       "      <th>player_name</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>[62.0, 21.0]</td>\n",
       "      <td>[46.0, 28.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>[84.0, 76.0]</td>\n",
       "      <td>[82.0, 65.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128</td>\n",
       "      <td>[87.0, 44.0]</td>\n",
       "      <td>[80.0, 51.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191</td>\n",
       "      <td>[47.0, 20.0]</td>\n",
       "      <td>[56.0, 53.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>263</td>\n",
       "      <td>[28.0, 32.0]</td>\n",
       "      <td>[20.0, 4.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>301</td>\n",
       "      <td>[70.0, 41.0]</td>\n",
       "      <td>[66.0, 18.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>362</td>\n",
       "      <td>[75.0, 43.0]</td>\n",
       "      <td>[67.0, 20.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>378</td>\n",
       "      <td>[28.0, 19.0]</td>\n",
       "      <td>[30.0, 3.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>399</td>\n",
       "      <td>[92.0, 43.0]</td>\n",
       "      <td>[103.0, 68.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>415</td>\n",
       "      <td>[91.0, 24.0]</td>\n",
       "      <td>[88.0, 17.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>458</td>\n",
       "      <td>[81.0, 30.0]</td>\n",
       "      <td>[110.0, 77.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>464</td>\n",
       "      <td>[95.0, 65.0]</td>\n",
       "      <td>[96.0, 78.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>559</td>\n",
       "      <td>[72.0, 58.0]</td>\n",
       "      <td>[72.0, 50.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>567</td>\n",
       "      <td>[58.0, 54.0]</td>\n",
       "      <td>[94.0, 76.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>604</td>\n",
       "      <td>[40.0, 24.0]</td>\n",
       "      <td>[46.0, 49.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>639</td>\n",
       "      <td>[40.0, 49.0]</td>\n",
       "      <td>[46.0, 75.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>703</td>\n",
       "      <td>[46.0, 43.0]</td>\n",
       "      <td>[47.0, 33.0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sebastian Rudy</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      location pass_end_location pass_outcome_name     player_name  \\\n",
       "0      31  [62.0, 21.0]      [46.0, 28.0]               NaN  Sebastian Rudy   \n",
       "1      55  [84.0, 76.0]      [82.0, 65.0]               NaN  Sebastian Rudy   \n",
       "2     128  [87.0, 44.0]      [80.0, 51.0]               NaN  Sebastian Rudy   \n",
       "3     191  [47.0, 20.0]      [56.0, 53.0]               NaN  Sebastian Rudy   \n",
       "4     263  [28.0, 32.0]       [20.0, 4.0]               NaN  Sebastian Rudy   \n",
       "5     301  [70.0, 41.0]      [66.0, 18.0]               NaN  Sebastian Rudy   \n",
       "6     362  [75.0, 43.0]      [67.0, 20.0]               NaN  Sebastian Rudy   \n",
       "7     378  [28.0, 19.0]       [30.0, 3.0]               NaN  Sebastian Rudy   \n",
       "8     399  [92.0, 43.0]     [103.0, 68.0]               NaN  Sebastian Rudy   \n",
       "9     415  [91.0, 24.0]      [88.0, 17.0]               NaN  Sebastian Rudy   \n",
       "10    458  [81.0, 30.0]     [110.0, 77.0]               NaN  Sebastian Rudy   \n",
       "11    464  [95.0, 65.0]      [96.0, 78.0]               NaN  Sebastian Rudy   \n",
       "12    559  [72.0, 58.0]      [72.0, 50.0]               NaN  Sebastian Rudy   \n",
       "13    567  [58.0, 54.0]      [94.0, 76.0]               NaN  Sebastian Rudy   \n",
       "14    604  [40.0, 24.0]      [46.0, 49.0]               NaN  Sebastian Rudy   \n",
       "15    639  [40.0, 49.0]      [46.0, 75.0]               NaN  Sebastian Rudy   \n",
       "16    703  [46.0, 43.0]      [47.0, 33.0]               NaN  Sebastian Rudy   \n",
       "\n",
       "    minute  \n",
       "0        0  \n",
       "1        1  \n",
       "2        2  \n",
       "3        4  \n",
       "4        6  \n",
       "5        6  \n",
       "6        9  \n",
       "7       10  \n",
       "8       11  \n",
       "9       11  \n",
       "10      12  \n",
       "11      12  \n",
       "12      15  \n",
       "13      16  \n",
       "14      17  \n",
       "15      19  \n",
       "16      21  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "3   0.302330  0.079395\n",
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       "5   0.615796  0.400584\n",
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       "15  0.206927  0.522942\n",
       "16  0.288701  0.431174"
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     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.DataFrame(match.get_normalized_positions(df_filtered['location'])).transpose()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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       "           0         1\n",
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       "14  0.307176  0.598804\n",
       "15  0.307176  0.901028\n",
       "16  0.317322  0.412820"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(match.get_normalized_positions(df_filtered['pass_end_location'])).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.50676448, 0.80660149, 0.84748836, 0.30233015, 0.04338   ,\n",
       "        0.61579612, 0.6839409 , 0.04338   , 0.91563313, 0.90200418,\n",
       "        0.76571463, 0.95652   , 0.64305403, 0.45224866, 0.20692746,\n",
       "        0.20692746, 0.28870119]),\n",
       " array([0.09468947, 0.9359    , 0.44646842, 0.07939474, 0.26293158,\n",
       "        0.40058421, 0.43117368, 0.0641    , 0.43117368, 0.14057368,\n",
       "        0.23234211, 0.76765789, 0.66059474, 0.59941579, 0.14057368,\n",
       "        0.52294211, 0.43117368]))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.get_normalized_positions(df_filtered['location'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes what to check:\n",
    "* Passes of players Brandt, Rudy, etc. look strange as well as coordinate transformations for recipient passes.\n",
    "* Look into that if something goes wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = a.get_passes_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['player_id'] == 5002]['player_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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