{
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  "metadata": {
    "colab": {
      "name": "xGStatsbomb.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP4BsBlm9b0/iB3JUbOMLVZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/koushikkirugulige/Football-Analytics/blob/master/xGStatsbomb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g37QpaaPZHA5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "c8ee2fc2-3a61-41a1-dc0a-c5b738da11d3"
      },
      "source": [
        "%%time\n",
        "!git clone https://github.com/statsbomb/open-data.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'open-data'...\n",
            "remote: Enumerating objects: 1088, done.\u001b[K\n",
            "remote: Counting objects: 100% (1088/1088), done.\u001b[K\n",
            "remote: Compressing objects: 100% (591/591), done.\u001b[K\n",
            "remote: Total 9810 (delta 893), reused 674 (delta 479), pack-reused 8722\u001b[K\n",
            "Receiving objects: 100% (9810/9810), 995.57 MiB | 14.28 MiB/s, done.\n",
            "Resolving deltas: 100% (8640/8640), done.\n",
            "Checking out files: 100% (1648/1648), done.\n",
            "CPU times: user 548 ms, sys: 115 ms, total: 663 ms\n",
            "Wall time: 2min 44s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd6vcG3uZNJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import all modules\n",
        "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",
        "from matplotlib.patches import Ellipse\n",
        "from functools import reduce\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NMxa9NNZR5m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "133de3d7-2515-4ec6-f479-d4c69cee4f8c"
      },
      "source": [
        "%%time\n",
        "comp = ['FIFA World Cup','La Liga']\n",
        "main_df = pd.DataFrame(data=None)\n",
        "path_match = \"/content/open-data/data/events/\" #location for play by play events\n",
        "for root, dirs, files in os.walk('/content/open-data/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",
        "            #print(df['competition_competition_name'])\n",
        "            for i in range(len(df)):\n",
        "                if df.iloc[i]['competition_competition_name'] in comp  :\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\")]\n",
        "                    main_df = main_df.append(df_match2,ignore_index=True,sort=False)                    \n",
        "#print('total matches ',len(match_no)) \n",
        "print('Done')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n",
            "CPU times: user 6min 3s, sys: 1.3 s, total: 6min 4s\n",
            "Wall time: 6min 4s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waQI6t6OVM33",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "outputId": "59fa3bca-64b8-4260-b33a-b34cbfdd9248"
      },
      "source": [
        "main_df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>4</th>\n",
              "      <td>09c2667a-4827-4871-a70a-96adb1d73243</td>\n",
              "      <td>1381</td>\n",
              "      <td>1</td>\n",
              "      <td>00:33:19.875</td>\n",
              "      <td>33</td>\n",
              "      <td>19</td>\n",
              "      <td>63</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>16</td>\n",
              "      <td>Shot</td>\n",
              "      <td>217</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>4</td>\n",
              "      <td>From Throw In</td>\n",
              "      <td>217</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[19491e5f-dd7c-47a8-994d-b6aae0630b55, a81b342...</td>\n",
              "      <td>[97.3, 28.8]</td>\n",
              "      <td>6998.0</td>\n",
              "      <td>Rafael Alcântara do Nascimento</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Left Center Midfield</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",
              "      <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>...</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",
              "      <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",
              "      <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",
              "      <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 × 147 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     id  ...  half_end_early_video_end\n",
              "0  2f046b33-685c-4122-8af2-8ceadf56c83d  ...                       NaN\n",
              "1  855d47fc-9017-4508-8b41-0275dfb4d755  ...                       NaN\n",
              "2  7c69fb86-c77d-463b-8f00-503e447492a4  ...                       NaN\n",
              "3  77ea8775-f9f4-4bf7-b3f9-7635ab861ab5  ...                       NaN\n",
              "4  09c2667a-4827-4871-a70a-96adb1d73243  ...                       NaN\n",
              "\n",
              "[5 rows x 147 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPZPi5M7ZI5o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"Distance of shot location to centre of goal\"\"\"\n",
        "def distFormula(coordinate):\n",
        "  a =(math.sqrt(((coordinate.location[0] - 120)**2) + ((coordinate.location[1] - 36)**2))) \n",
        "  b =(math.sqrt(((coordinate.location[0] - 120)**2) + ((coordinate.location[1] - 44)**2))) \n",
        "  return ((a+b)/2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRX43XViZshf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\" near x y (nx,ny) (120,44)and far x y (fx,fy) (120,36)\"\"\" \n",
        "nx = 120\n",
        "ny = 44\n",
        "fx = 120\n",
        "fy = 36\n",
        "\n",
        "goalpostLength = 8\n",
        "def shot_angle(points):\n",
        "  len1 = (math.sqrt(((points.location[0] - nx)**2) + ((points.location[1] - ny)**2))) \n",
        "  len2 = (math.sqrt(((points.location[0] - fx)**2) + ((points.location[1] - fy)**2)))\n",
        "  ang = (len1**2 + len2**2 - goalpostLength**2)/(2 * len1 * len2)\n",
        "  if ang > 1:\n",
        "    ang = 1\n",
        "  elif ang < -1:\n",
        "    ang = -1   \n",
        "  angRad = math.acos(ang)\n",
        "  return( (angRad * 180)/math.pi) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Skzv7m2GcOek",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"If shot was taken under Pressure?\"\"\"\n",
        "def under_pressure(coordinate):\n",
        "  if coordinate['under_pressure'] == True:\n",
        "    return 1\n",
        "  return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRBmlZBtdbCf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"The Shot type Id\"\"\"\n",
        "def shot_type(coordinate):\n",
        "  if coordinate['shot_type_id'] == 61:\n",
        "    return 1\n",
        "  if coordinate['shot_type_id'] == 62:\n",
        "    return 2\n",
        "  if coordinate['shot_type_id'] == 87:\n",
        "    return 3\n",
        "  if coordinate['shot_type_id'] == 88:\n",
        "    return 4\n",
        "  return 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfSB2laheYY4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"The Shot Body part\"\"\"\n",
        "def shot_body_part(coordinate):\n",
        "  if coordinate['shot_body_part_id'] == 37:\n",
        "    return 1\n",
        "  if coordinate['shot_body_part_id'] == 38:\n",
        "    return 2\n",
        "  if coordinate['shot_body_part_id'] == 70:\n",
        "    return 3\n",
        "  return 4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XshqSKelezC2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"The Shot Technique Id\"\"\"\n",
        "def shot_technique(coordinate):\n",
        "  if coordinate['shot_technique_id'] == 89:\n",
        "    return 1\n",
        "  if coordinate['shot_technique_id'] == 90:\n",
        "    return 2\n",
        "  if coordinate['shot_technique_id'] == 91:\n",
        "    return 3\n",
        "  if coordinate['shot_technique_id'] == 92:\n",
        "    return 4\n",
        "  if coordinate['shot_technique_id'] == 93:\n",
        "    return 5\n",
        "  if coordinate['shot_technique_id'] == 94:\n",
        "    return 6\n",
        "  return 7"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DLY4vXtffsL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"If shot was taken first time?\"\"\"\n",
        "def shot_first_time(coordinate):\n",
        "  if coordinate['shot_first_time'] == True:\n",
        "    return 1\n",
        "  return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0pQQZPDf4oV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"If shot was taken first time?\"\"\"\n",
        "def shot_one_on_one(coordinate):\n",
        "  if coordinate['shot_one_on_one'] == True:\n",
        "    return 1\n",
        "  return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2Qd_aEHZaja",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "main_df['Distance'] = main_df.apply(distFormula,axis = 1)\n",
        "main_df['Angle'] = main_df.apply(shot_angle,axis = 1)\n",
        "main_df['UnderPressure'] = main_df.apply(under_pressure,axis = 1)\n",
        "main_df['ShotType'] = main_df.apply(shot_type,axis = 1)\n",
        "main_df['ShotBodyPart'] = main_df.apply(shot_body_part,axis = 1)\n",
        "main_df['ShotTechnique'] = main_df.apply(shot_technique,axis = 1)\n",
        "main_df['ShotFirstTime'] = main_df.apply(shot_first_time,axis = 1)\n",
        "main_df['ShotOneonOne']= main_df.apply(shot_one_on_one,axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbP_YO_tg694",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goals_lst = main_df[main_df['shot_outcome_id'] == 97].index.tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4X-RpdOguqH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#if shot is a goal \n",
        "main_df['isGoal'] = False\n",
        "goals_lst\n",
        "main_df.loc[main_df.index.isin(goals_lst),'isGoal'] = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddhlIVCHbZ-_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "aa8e71b7-9573-4a33-e875-0542680136ef"
      },
      "source": [
        "main_df[['location','Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','isGoal']]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Distance</th>\n",
              "      <th>Angle</th>\n",
              "      <th>UnderPressure</th>\n",
              "      <th>ShotType</th>\n",
              "      <th>ShotBodyPart</th>\n",
              "      <th>ShotTechnique</th>\n",
              "      <th>ShotFirstTime</th>\n",
              "      <th>ShotOneonOne</th>\n",
              "      <th>isGoal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[104.4, 41.8]</td>\n",
              "      <td>16.198841</td>\n",
              "      <td>28.422114</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[110.8, 35.8]</td>\n",
              "      <td>10.763067</td>\n",
              "      <td>40.465393</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[109.9, 40.5]</td>\n",
              "      <td>10.873186</td>\n",
              "      <td>43.128076</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[90.0, 36.2]</td>\n",
              "      <td>30.499043</td>\n",
              "      <td>14.956182</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[97.3, 28.8]</td>\n",
              "      <td>25.566766</td>\n",
              "      <td>16.208386</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12952</th>\n",
              "      <td>[111.0, 27.0]</td>\n",
              "      <td>15.981653</td>\n",
              "      <td>17.102729</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12953</th>\n",
              "      <td>[114.0, 33.0]</td>\n",
              "      <td>9.619084</td>\n",
              "      <td>34.824489</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12954</th>\n",
              "      <td>[107.0, 32.0]</td>\n",
              "      <td>15.646638</td>\n",
              "      <td>25.606661</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12955</th>\n",
              "      <td>[97.0, 22.0]</td>\n",
              "      <td>29.376742</td>\n",
              "      <td>12.398277</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12956</th>\n",
              "      <td>[109.0, 52.0]</td>\n",
              "      <td>16.508979</td>\n",
              "      <td>19.464104</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12957 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            location   Distance      Angle  ...  ShotFirstTime  ShotOneonOne  isGoal\n",
              "0      [104.4, 41.8]  16.198841  28.422114  ...              1             0   False\n",
              "1      [110.8, 35.8]  10.763067  40.465393  ...              0             0   False\n",
              "2      [109.9, 40.5]  10.873186  43.128076  ...              0             0   False\n",
              "3       [90.0, 36.2]  30.499043  14.956182  ...              0             0   False\n",
              "4       [97.3, 28.8]  25.566766  16.208386  ...              0             0   False\n",
              "...              ...        ...        ...  ...            ...           ...     ...\n",
              "12952  [111.0, 27.0]  15.981653  17.102729  ...              0             0   False\n",
              "12953  [114.0, 33.0]   9.619084  34.824489  ...              0             0    True\n",
              "12954  [107.0, 32.0]  15.646638  25.606661  ...              0             0   False\n",
              "12955   [97.0, 22.0]  29.376742  12.398277  ...              0             0   False\n",
              "12956  [109.0, 52.0]  16.508979  19.464104  ...              0             0   False\n",
              "\n",
              "[12957 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54NSbs9R1HQD",
        "colab_type": "text"
      },
      "source": [
        "#xG Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCl0-Opqhxx-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import xgboost as xgb\n",
        "from sklearn import svm\n",
        "from sklearn import linear_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5_VKZrBhyRi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xgModel = main_df[['location','Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','isGoal']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOLF5IqBh1Nx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(xgModel[['location','Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']],xgModel['isGoal'],test_size = 0.2,shuffle = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJrKwMAy1Pdl",
        "colab_type": "text"
      },
      "source": [
        "**Logistic Regression** Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwxMcSWQiCbw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = LogisticRegression(random_state=0,max_iter = 5000).fit(X_train[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']], y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2sAXpKaiS7d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e53dfd53-f6db-45f5-a800-1c7b3bea10e4"
      },
      "source": [
        "#model weights\n",
        "clf.coef_[0]"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.06605534,  0.02878253, -0.60546461,  1.03559372,  0.18762927,\n",
              "        0.01151648,  0.18877916,  0.57099517])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DBQ7Hrsm1WNv",
        "colab_type": "text"
      },
      "source": [
        "**SGD** Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02YCl85sicPO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xG = clf.predict_proba(X_test[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']])[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmD0Au5iv0Lh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "9f6610d6-0166-410b-a5df-10107647e772"
      },
      "source": [
        "#SGD\n",
        "sgdclf = linear_model.SGDClassifier(loss='log', alpha = 0.17)\n",
        "sgdclf.fit(X_train[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']], y_train)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier(alpha=0.17, average=False, class_weight=None,\n",
              "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
              "              l1_ratio=0.15, learning_rate='optimal', loss='log', max_iter=1000,\n",
              "              n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,\n",
              "              random_state=None, shuffle=True, tol=0.001,\n",
              "              validation_fraction=0.1, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVID9Zsn1atX",
        "colab_type": "text"
      },
      "source": [
        "**Predict** Shot Probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8yo8gQev-q8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# change model here sgcclf(SGD) or clf(LR)\n",
        "xG = sgdclf.predict_proba(X_test[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']])[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xL2vLcVfihcb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2e44cbbb-3847-46f3-e5b1-ed8fc46f4620"
      },
      "source": [
        "X_test['xG'] = xG\n",
        "#X_test.head()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Distance</th>\n",
              "      <th>Angle</th>\n",
              "      <th>UnderPressure</th>\n",
              "      <th>ShotType</th>\n",
              "      <th>ShotBodyPart</th>\n",
              "      <th>ShotTechnique</th>\n",
              "      <th>ShotFirstTime</th>\n",
              "      <th>ShotOneonOne</th>\n",
              "      <th>xG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2961</th>\n",
              "      <td>[92.2, 58.9]</td>\n",
              "      <td>33.779300</td>\n",
              "      <td>11.289656</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10990</th>\n",
              "      <td>[115.2, 45.8]</td>\n",
              "      <td>8.019390</td>\n",
              "      <td>43.348531</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.340789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12649</th>\n",
              "      <td>[115.0, 41.0]</td>\n",
              "      <td>6.451010</td>\n",
              "      <td>75.963757</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.453033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5264</th>\n",
              "      <td>[111.9, 32.5]</td>\n",
              "      <td>11.445052</td>\n",
              "      <td>31.472019</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.082841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9283</th>\n",
              "      <td>[111.6, 37.0]</td>\n",
              "      <td>9.696832</td>\n",
              "      <td>46.594546</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.135449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            location   Distance  ...  ShotOneonOne        xG\n",
              "2961    [92.2, 58.9]  33.779300  ...             0  0.035840\n",
              "10990  [115.2, 45.8]   8.019390  ...             1  0.340789\n",
              "12649  [115.0, 41.0]   6.451010  ...             0  0.453033\n",
              "5264   [111.9, 32.5]  11.445052  ...             0  0.082841\n",
              "9283   [111.6, 37.0]   9.696832  ...             0  0.135449\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2BO-NLSijd5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "241cbd08-910e-4ae5-f255-5038663cc0b3"
      },
      "source": [
        "sortxg = X_test.sort_values(by = ['xG'],ascending=False)\n",
        "sortxg"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Distance</th>\n",
              "      <th>Angle</th>\n",
              "      <th>UnderPressure</th>\n",
              "      <th>ShotType</th>\n",
              "      <th>ShotBodyPart</th>\n",
              "      <th>ShotTechnique</th>\n",
              "      <th>ShotFirstTime</th>\n",
              "      <th>ShotOneonOne</th>\n",
              "      <th>xG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4624</th>\n",
              "      <td>[119.3, 41.4]</td>\n",
              "      <td>4.068882</td>\n",
              "      <td>157.545469</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.955584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3818</th>\n",
              "      <td>[119.1, 42.6]</td>\n",
              "      <td>4.162706</td>\n",
              "      <td>139.499608</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.927110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6952</th>\n",
              "      <td>[118.4, 39.4]</td>\n",
              "      <td>4.313989</td>\n",
              "      <td>135.619868</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.918449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8311</th>\n",
              "      <td>[119.2, 37.0]</td>\n",
              "      <td>4.163095</td>\n",
              "      <td>134.820390</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.902713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12822</th>\n",
              "      <td>[119.0, 43.0]</td>\n",
              "      <td>4.242641</td>\n",
              "      <td>126.869898</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.897920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3937</th>\n",
              "      <td>[69.2, 77.6]</td>\n",
              "      <td>63.283107</td>\n",
              "      <td>5.832625</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.004453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2834</th>\n",
              "      <td>[81.1, 5.9]</td>\n",
              "      <td>51.817864</td>\n",
              "      <td>6.672792</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.003497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6385</th>\n",
              "      <td>[57.2, 34.0]</td>\n",
              "      <td>63.211517</td>\n",
              "      <td>7.223482</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.003241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11776</th>\n",
              "      <td>[62.0, 36.0]</td>\n",
              "      <td>58.274562</td>\n",
              "      <td>7.853313</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.002498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6659</th>\n",
              "      <td>[51.9, 43.4]</td>\n",
              "      <td>68.301760</td>\n",
              "      <td>6.706436</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.002257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2592 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            location   Distance  ...  ShotOneonOne        xG\n",
              "4624   [119.3, 41.4]   4.068882  ...             0  0.955584\n",
              "3818   [119.1, 42.6]   4.162706  ...             0  0.927110\n",
              "6952   [118.4, 39.4]   4.313989  ...             0  0.918449\n",
              "8311   [119.2, 37.0]   4.163095  ...             1  0.902713\n",
              "12822  [119.0, 43.0]   4.242641  ...             0  0.897920\n",
              "...              ...        ...  ...           ...       ...\n",
              "3937    [69.2, 77.6]  63.283107  ...             0  0.004453\n",
              "2834     [81.1, 5.9]  51.817864  ...             0  0.003497\n",
              "6385    [57.2, 34.0]  63.211517  ...             0  0.003241\n",
              "11776   [62.0, 36.0]  58.274562  ...             0  0.002498\n",
              "6659    [51.9, 43.4]  68.301760  ...             0  0.002257\n",
              "\n",
              "[2592 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrUnxsmxpvPM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "outputId": "45ab7647-b57e-4eab-fa14-1cf8120e5851"
      },
      "source": [
        "import StatsbombPitch as sb\n",
        "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
        "plt.gca().invert_yaxis()\n",
        "for i in range(len(sortxg)):\n",
        "    xe = sortxg.iloc[i]['location'][0]\n",
        "    ye = sortxg.iloc[i]['location'][1]\n",
        "    \n",
        "    if sortxg.iloc[i]['xG'] >= 0.75:\n",
        "      g = plt.scatter(xe,ye,color=\"#ee3e32\",edgecolors=\"none\",zorder=10,alpha=1,s = 40 )\n",
        "    elif sortxg.iloc[i]['xG'] < 0.75 and sortxg.iloc[i]['xG'] >=0.5:\n",
        "      o = plt.scatter(xe,ye,color=\"#f68838\",edgecolors=\"none\",zorder=8,alpha=0.75,s = 30 )\n",
        "    elif sortxg.iloc[i]['xG'] < 0.5 and sortxg.iloc[i]['xG'] >=0.25:\n",
        "      a = plt.scatter(xe,ye,color=\"#fbb021\",edgecolors=\"none\",zorder=6,alpha=0.5,s = 20 )  \n",
        "    else:\n",
        "      b = plt.scatter(xe,ye,color=\"#1b8a5a\",edgecolors=\"none\",zorder=4,alpha=0.25,s = 10 )    \n",
        "plt.axis('off')\n",
        "plt.legend((g,o,a,b),('>=0.75','>=0.5','>=0.25','<0.25'),scatterpoints=1,loc=2,title = 'xG Value',fontsize='small', fancybox=True)\n",
        "#plt.title('xG SGD model')\n",
        "#plt.savefig('xgSGDmodel.png')\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 748.8x489.6 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzQLtbmByqBa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "outputId": "85b007f9-71ec-402d-88ae-543690ab4585"
      },
      "source": [
        "import StatsbombPitch as sb\n",
        "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"vertical\",\"half\")\n",
        "#plt.gca().invert_xaxis()\n",
        "for i in range(len(sortxg)):\n",
        "    xe = sortxg.iloc[i]['location'][0]\n",
        "    ye = sortxg.iloc[i]['location'][1]\n",
        "    \n",
        "    if sortxg.iloc[i]['xG'] >= 0.75:\n",
        "      g = plt.scatter(ye,xe,color=\"#ee3e32\",edgecolors=\"none\",zorder=10,alpha=1,s = 40 )\n",
        "    elif sortxg.iloc[i]['xG'] < 0.75 and sortxg.iloc[i]['xG'] >=0.5:\n",
        "      o = plt.scatter(ye,xe,color=\"#f68838\",edgecolors=\"none\",zorder=8,alpha=0.75,s = 30 )\n",
        "    elif sortxg.iloc[i]['xG'] < 0.5 and sortxg.iloc[i]['xG'] >=0.25:\n",
        "      a = plt.scatter(ye,xe,color=\"#fbb021\",edgecolors=\"none\",zorder=6,alpha=0.5,s = 20 )  \n",
        "    else:\n",
        "      b = plt.scatter(ye,xe,color=\"#1b8a5a\",edgecolors=\"none\",zorder=4,alpha=0.25,s = 10 )    \n",
        "plt.axis('off')\n",
        "plt.legend((g,o,a,b),('>=0.75','>=0.5','>=0.25','<0.25'),scatterpoints=1,loc=3,title = 'xG Value',fontsize='small', fancybox=True,edgecolor = 'black',framealpha = 2\n",
        "           )\n",
        "\n",
        "\n",
        "#ax = plt.subplot()\n",
        "\n",
        "#plt.savefig('MessiValverdeEraScatter.png')\n",
        "plt.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 748.8x489.6 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHBsENu7wRim",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c0f194be-e285-4381-9485-cd5a7f2e7a7c"
      },
      "source": [
        "#[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']]\n",
        "clf.predict_proba(np.array([12.55,37.156,0,4,2,5,0,0]).reshape(1, -1))[:,1]"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.38105829])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    }
  ]
}