analiza-danych-sportowych-s440058/script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4045b0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\piotr\\anaconda3\\lib\\site-packages (1.3.5)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\piotr\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\piotr\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\piotr\\appdata\\roaming\\python\\python38\\site-packages (from pandas) (1.21.4)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\piotr\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12138336",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "571c44cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../open-data/data/events/3749068.json') as f:\n",
    "    match = json.load(f)\n",
    "\n",
    "df = pd.json_normalize(match, sep='_').assign(match_id=\"7567\")\n",
    "shots = df[df.type_name == 'Shot'].set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e96953e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from FCPython import createPitch\n",
    "\n",
    "pitch_width = 120\n",
    "pitch_height = 80\n",
    "\n",
    "fig, ax = createPitch(pitch_width, pitch_height, 'yards', 'gray')\n",
    "\n",
    "home_team = 'Tottenham Hotspur'\n",
    "away_team = 'Arsenal'\n",
    "\n",
    "for i, shot in shots.iterrows():\n",
    "    x = shot['location'][0]\n",
    "    y = shot['location'][1]\n",
    "    \n",
    "    goal = shot['shot_outcome_name']=='Goal'\n",
    "    team_name = shot['team_name']\n",
    "    \n",
    "    circle_size = np.sqrt(shot['shot_statsbomb_xg'] * 30)\n",
    "    \n",
    "    if team_name == home_team:\n",
    "        if goal:\n",
    "            shot_circle = plt.Circle((x, pitch_height-y), circle_size, color='grey')\n",
    "            plt.text((x+1), pitch_height-y+1, shot['player_name'])\n",
    "        else:\n",
    "            shot_circle = plt.Circle((x, pitch_height-y), circle_size, color='grey')\n",
    "            shot_circle.set_alpha(.1)\n",
    "    elif team_name == away_team:\n",
    "        if goal:\n",
    "            shot_circle = plt.Circle((pitch_width-x, y), circle_size, color='red')\n",
    "            plt.text((pitch_width-x+1), y+1, shot['player_name'])\n",
    "        else:\n",
    "            shot_circle = plt.Circle((pitch_width-x, y), circle_size, color='red')\n",
    "            shot_circle.set_alpha(.1)\n",
    "    \n",
    "    ax.add_patch(shot_circle)\n",
    "    \n",
    "plt.text(5, 75, away_team)\n",
    "plt.text(100, 75, home_team)\n",
    "\n",
    "plt.title('Arsenal vs Tottenham Hotspur shots with expected goals size')\n",
    "\n",
    "fig.set_size_inches(15, 11)\n",
    "\n",
    "plt.show()"
   ]
  },
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