sportowe_wizualizacja/BarcaGoalLocByManagers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change this cell to change season and player name\n",
    "season = ['2017/2018','2018/2019','2019/2020']\n",
    "ssn = '0506'\n",
    "length = int(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import StatsbombPitch as sb\n",
    "#from pandas.io.json import json_normalize\n",
    "import codecs\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import codecs\n",
    "import StatsbombPitch as sb\n",
    "\n",
    "main_df = pd.DataFrame(data=None)\n",
    "path_match = \"/home/kirugulige/Documents/Football-Analytics/open-data-master/data/events/\" #location for play by play events\n",
    "for root, dirs, files in os.walk('/home/kirugulige/Documents/Football-Analytics/open-data-master/data/matches'):\n",
    "     for file in files:\n",
    "        with open(os.path.join(root, file), \"r\") as auto:\n",
    "            with codecs.open(root + str('/') + file,encoding='utf-8') as data_file:\n",
    "                data = json.load(data_file)\n",
    "                df = pd.DataFrame(data=None)\n",
    "                df = pd.json_normalize(data, sep = \"_\")\n",
    "            #for x in df.competition_country_name:\n",
    "            #    if x == 'Spain':\n",
    "            #        print(df.match_id)\n",
    "            for i in range(len(df)):\n",
    "                if df.iloc[i]['competition_country_name'] == 'Spain'  and df.iloc[i]['season_season_name'] in season :\n",
    "                    match_no = df.iloc[i]['match_id'] #gets match with Spain as country\n",
    "                    match_no = str(match_no) # from int to str \n",
    "                    #print('match list \\n',match_no)\n",
    "                    with codecs.open(path_match + match_no + str(r'.json'),encoding=\"utf8\") as event_file: #open the respective file\n",
    "                        df_match = json.load(event_file)\n",
    "                        df_match2 = pd.DataFrame(data=None)\n",
    "                        df_match2 = pd.json_normalize(df_match,sep=\"_\")    \n",
    "                        df_match2 =  df_match2[(df_match2['type_name'] == \"Shot\") & (df_match2['team_name'] == 'Barcelona')]\n",
    "                    main_df = main_df.append(df_match2,ignore_index=True,sort=False)                    \n",
    "#print('total matches ',len(match_no)) \n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#main_df['player_name'].value_counts()\n",
    "df = main_df.loc[:,['location','shot_body_part_id','shot_end_location','shot_outcome_id','player_name','player_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['player_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['shot_outcome_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>location</th>\n",
       "      <th>shot_body_part_id</th>\n",
       "      <th>shot_end_location</th>\n",
       "      <th>shot_outcome_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[99.1, 44.1]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 36.6, 2.2]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>[94.7, 51.6]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 42.4, 0.4]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>[106.7, 36.8]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 37.9, 0.1]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>[114.1, 41.1]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 39.0, 1.8]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>[109.3, 32.0]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 40.9, 0.2]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location  shot_body_part_id   shot_end_location  shot_outcome_id  \\\n",
       "3    [99.1, 44.1]               38.0  [120.0, 36.6, 2.2]             97.0   \n",
       "48   [94.7, 51.6]               38.0  [120.0, 42.4, 0.4]             97.0   \n",
       "49  [106.7, 36.8]               38.0  [120.0, 37.9, 0.1]             97.0   \n",
       "54  [114.1, 41.1]               38.0  [120.0, 39.0, 1.8]             97.0   \n",
       "62  [109.3, 32.0]               38.0  [120.0, 40.9, 0.2]             97.0   \n",
       "\n",
       "                       player_name  player_id  \n",
       "3   Lionel Andrés Messi Cuccittini     5503.0  \n",
       "48  Lionel Andrés Messi Cuccittini     5503.0  \n",
       "49  Lionel Andrés Messi Cuccittini     5503.0  \n",
       "54  Lionel Andrés Messi Cuccittini     5503.0  \n",
       "62  Lionel Andrés Messi Cuccittini     5503.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_goal = df[(df['shot_outcome_id'] == 97) ]\n",
    "#df_shot = df[(df['shot_outcome_id'] != 97) ]\n",
    "\n",
    "df_goal = df[(df['shot_outcome_id'] == 97) & (df['player_id'] == 5503.0)] #for messi\n",
    "df_shot = df[(df['shot_outcome_id'] != 97) & (df['player_id'] == 5503.0)] #for messi\n",
    "df_goal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "import StatsbombPitch as sb\n",
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
    "\n",
    "for i in range(len(df_goal)):\n",
    "    xe = df_goal.iloc[i]['location'][0]\n",
    "    ye = df_goal.iloc[i]['location'][1]\n",
    "    g = plt.scatter(xe,ye,color=\"red\",edgecolors=\"black\",zorder=10,alpha=1)\n",
    "for i in range(len(df_shot)):\n",
    "    xe = df_shot.iloc[i]['location'][0]\n",
    "    ye = df_shot.iloc[i]['location'][1]\n",
    "    s = plt.scatter(xe,ye,color=\"blue\",edgecolors=\"black\",zorder=5,alpha=0.6) \n",
    "plt.legend((g,s),('goal','shot'),scatterpoints=1,loc=0)    \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": [
    "import StatsbombPitch as sb\n",
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"vertical\",\"half\")\n",
    "\n",
    "for i in range(len(df_goal)):\n",
    "    xe = df_goal.iloc[i]['location'][0]\n",
    "    ye = df_goal.iloc[i]['location'][1]\n",
    "    g = plt.scatter(ye,xe,color=\"red\",edgecolors=\"black\",zorder=10,alpha=1)\n",
    "for i in range(len(df_shot)):\n",
    "    xe = df_shot.iloc[i]['location'][0]\n",
    "    ye = df_shot.iloc[i]['location'][1]\n",
    "    s = plt.scatter(ye,xe,color=\"blue\",edgecolors=\"black\",zorder=5,alpha=0.6) \n",
    "plt.title('Messi in Valverde era') \n",
    "\n",
    "#ax = plt.subplot()\n",
    "plt.legend((g,s),('goal','shot'),scatterpoints=1,loc=0)\n",
    "plt.savefig('MessiValverdeEraScatter.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(df_goal.iloc[0]['location'])\n",
    "dfj = pd.DataFrame(df_goal['location'].values.tolist(), columns=['x','y'])\n",
    "dfj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "hmap = dfj.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdraw_pitch(\"#195905\",\"#faf0e6\",\"vertical\",\"half\")\n",
    "#plt.gca().invert_xaxis()\n",
    "sns.kdeplot(dfj.y,dfj.x, cmap=\"YlOrRd\", shade=True,shade_lowest = False,zorder = 20)\n",
    "plt.title('Barca Valverde era')  \n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('BarcaEnriqueeraheatmap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "fig=plt.figure() #set up the figures\n",
    "fig.set_size_inches(12, 8)\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "\n",
    "ax.set_facecolor('grey')\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",
    "plt.gca().invert_yaxis()\n",
    "sns.kdeplot(dfj.x,dfj.y, cmap=\"YlOrRd\", shade=True,shade_lowest = False,n_levels=40)\n",
    "#plt.hexbin(dfj.x, dfj.y,gridsize=(100,100))\n",
    "#sns.jointplot(x=dfj['x'], y=dfj['y'],  kind=\"kde\", space=0, color=\"r\",dropna='True');\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": "3.6.9"
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 },
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}