{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import StatsbombPitch as sb\n",
    "#import squarify\n",
    "from functools import reduce\n",
    "path = \"/home/kirugulige/Documents/Football-Analytics/open-data-master/data/events/\"\n",
    "Xg_req = pd.DataFrame(data=None)\n",
    "filename = '69275.json'    \n",
    "with open(\"%s\" % path + filename,encoding=\"utf8\") as data_file:    \n",
    "    data = json.load(data_file)\n",
    "    df = pd.DataFrame(data=None)\n",
    "        \n",
    "    df = pd.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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "through_ball = df.query('pass_through_ball == True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "assist = df.query('pass_goal_assist == True')\n",
    "print(len(assist.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>id</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th>pass_end_location</th>\n",
       "      <th>pass_recipient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>1f6ef40d-cd32-4566-add6-f1558833d0c1</td>\n",
       "      <td>1</td>\n",
       "      <td>00:05:57.827</td>\n",
       "      <td>[81.5, 37.8]</td>\n",
       "      <td>[103.1, 33.4]</td>\n",
       "      <td>Pablo Daniel Osvaldo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>4040c0ab-d934-469b-84f5-c595213b851b</td>\n",
       "      <td>1</td>\n",
       "      <td>00:13:56.420</td>\n",
       "      <td>[48.2, 57.4]</td>\n",
       "      <td>[85.5, 38.3]</td>\n",
       "      <td>Pablo Daniel Osvaldo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>7dbe04a8-41c5-4ea8-bfa1-892fe23a72e6</td>\n",
       "      <td>1</td>\n",
       "      <td>00:18:30.829</td>\n",
       "      <td>[66.8, 46.7]</td>\n",
       "      <td>[93.5, 38.5]</td>\n",
       "      <td>Pedro Eliezer Rodríguez Ledesma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>3a7da0a1-e9c4-40a1-9702-e99c5ace03c2</td>\n",
       "      <td>1</td>\n",
       "      <td>00:20:46.623</td>\n",
       "      <td>[66.0, 56.5]</td>\n",
       "      <td>[105.5, 34.7]</td>\n",
       "      <td>José María Callejón Bueno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>2bc53e64-664a-4f83-9d0c-6b046d91a7d9</td>\n",
       "      <td>1</td>\n",
       "      <td>00:23:55.494</td>\n",
       "      <td>[81.5, 28.1]</td>\n",
       "      <td>[94.2, 31.7]</td>\n",
       "      <td>David Villa Sánchez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id  period     timestamp      location  \\\n",
       "280  1f6ef40d-cd32-4566-add6-f1558833d0c1       1  00:05:57.827  [81.5, 37.8]   \n",
       "604  4040c0ab-d934-469b-84f5-c595213b851b       1  00:13:56.420  [48.2, 57.4]   \n",
       "794  7dbe04a8-41c5-4ea8-bfa1-892fe23a72e6       1  00:18:30.829  [66.8, 46.7]   \n",
       "840  3a7da0a1-e9c4-40a1-9702-e99c5ace03c2       1  00:20:46.623  [66.0, 56.5]   \n",
       "962  2bc53e64-664a-4f83-9d0c-6b046d91a7d9       1  00:23:55.494  [81.5, 28.1]   \n",
       "\n",
       "    pass_end_location              pass_recipient_name  \n",
       "280     [103.1, 33.4]             Pablo Daniel Osvaldo  \n",
       "604      [85.5, 38.3]             Pablo Daniel Osvaldo  \n",
       "794      [93.5, 38.5]  Pedro Eliezer Rodríguez Ledesma  \n",
       "840     [105.5, 34.7]        José María Callejón Bueno  \n",
       "962      [94.2, 31.7]              David Villa Sánchez  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "through_ball= through_ball[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
    "through_ball.head()"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th>pass_end_location</th>\n",
       "      <th>pass_recipient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>7dbe04a8-41c5-4ea8-bfa1-892fe23a72e6</td>\n",
       "      <td>1</td>\n",
       "      <td>00:18:30.829</td>\n",
       "      <td>[66.8, 46.7]</td>\n",
       "      <td>[93.5, 38.5]</td>\n",
       "      <td>Pedro Eliezer Rodríguez Ledesma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2436</th>\n",
       "      <td>9810ac65-15b8-4736-a09e-08f7faaff01d</td>\n",
       "      <td>2</td>\n",
       "      <td>00:17:04.830</td>\n",
       "      <td>[52.7, 63.4]</td>\n",
       "      <td>[84.2, 57.1]</td>\n",
       "      <td>Pablo Daniel Osvaldo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>7269b3a6-43d7-45bc-8823-bcfd1542ad45</td>\n",
       "      <td>2</td>\n",
       "      <td>00:30:03.492</td>\n",
       "      <td>[64.4, 33.8]</td>\n",
       "      <td>[79.4, 25.5]</td>\n",
       "      <td>David Villa Sánchez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>dc1b453d-6e7e-49b1-ad88-79525e8223f9</td>\n",
       "      <td>2</td>\n",
       "      <td>00:38:47.689</td>\n",
       "      <td>[70.1, 30.6]</td>\n",
       "      <td>[98.0, 23.3]</td>\n",
       "      <td>David Villa Sánchez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  period     timestamp  \\\n",
       "794   7dbe04a8-41c5-4ea8-bfa1-892fe23a72e6       1  00:18:30.829   \n",
       "2436  9810ac65-15b8-4736-a09e-08f7faaff01d       2  00:17:04.830   \n",
       "2935  7269b3a6-43d7-45bc-8823-bcfd1542ad45       2  00:30:03.492   \n",
       "3344  dc1b453d-6e7e-49b1-ad88-79525e8223f9       2  00:38:47.689   \n",
       "\n",
       "          location pass_end_location              pass_recipient_name  \n",
       "794   [66.8, 46.7]      [93.5, 38.5]  Pedro Eliezer Rodríguez Ledesma  \n",
       "2436  [52.7, 63.4]      [84.2, 57.1]             Pablo Daniel Osvaldo  \n",
       "2935  [64.4, 33.8]      [79.4, 25.5]              David Villa Sánchez  \n",
       "3344  [70.1, 30.6]      [98.0, 23.3]              David Villa Sánchez  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assist= assist[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
    "assist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
    "plt.gca().invert_yaxis() #overlay our different objects on the pitch\n",
    "#plt.ylim(-2, 82)\n",
    "#plt.xlim(-2, 122)\n",
    "#plt.plot(x_axis,y_axis,'ro')\n",
    "#plt.plot(x,y,'bo')\n",
    "plt.axis('off')\n",
    "for i in range(len(through_ball)):\n",
    "    x = through_ball.iloc[i]['pass_end_location'][0]\n",
    "    y = through_ball.iloc[i]['pass_end_location'][1]\n",
    "    xe = through_ball.iloc[i]['location'][0]\n",
    "    ye = through_ball.iloc[i]['location'][0]\n",
    "    plt.scatter(x,y,color=\"red\",edgecolors=\"black\",zorder=10,alpha=1)\n",
    "    plt.plot([x,xe],[y,ye],zorder=11,alpha=1,color=\"black\")\n",
    "\n",
    "\"\"\"\n",
    "for i in range(len(assist)):\n",
    "    # annotate draw an arrow from a current position to pass_end_location\n",
    "    ax.annotate(\"\", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
    "               xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',\n",
    "               arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"red\"),)\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
    "plt.gca().invert_yaxis() #overlay our different objects on the pitch\n",
    "#plt.ylim(-2, 82)\n",
    "#plt.xlim(-2, 122)\n",
    "#plt.plot(x_axis,y_axis,'ro')\n",
    "#plt.plot(x,y,'bo')\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "for i in range(len(assist)):\n",
    "    x = assist.iloc[i]['pass_end_location'][0]\n",
    "    y = assist.iloc[i]['pass_end_location'][1]\n",
    "    xe = assist.iloc[i]['location'][0]\n",
    "    ye = assist.iloc[i]['location'][0]\n",
    "    plt.scatter(x,y,color=\"red\",edgecolors=\"black\",zorder=10,alpha=1)\n",
    "    plt.plot([x,xe],[y,ye],zorder=11,alpha=1,color=\"black\")\n",
    "\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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}