sportowe_wizualizacja/PassClusters.ipynb

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  "metadata": {
    "colab": {
      "name": "PassClusters.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPfMQt+YGiFxDzZsdceN8ls",
      "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/PassClusters.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HJxpCQF07Lo",
        "colab_type": "code",
        "outputId": "71b81ca6-8e11-41ae-bf43-fece6a8d4244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "!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: 760, done.\u001b[K\n",
            "remote: Counting objects: 100% (760/760), done.\u001b[K\n",
            "remote: Compressing objects: 100% (367/367), done.\u001b[K\n",
            "remote: Total 9482 (delta 640), reused 504 (delta 384), pack-reused 8722\u001b[K\n",
            "Receiving objects: 100% (9482/9482), 991.55 MiB | 26.66 MiB/s, done.\n",
            "Resolving deltas: 100% (8387/8387), done.\n",
            "Checking out files: 100% (1648/1648), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4gtYmw71qLx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#5503\n",
        "\n",
        "#change this cell to change season and player name\n",
        "season = '2009/2010'\n",
        "ssn = '5503'\n",
        "length = int(20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2aofsrG1VvE",
        "colab_type": "code",
        "outputId": "e142d619-15ae-4a10-f68e-3bbc45dc567e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os\n",
        "import json\n",
        "from pandas.io.json import json_normalize\n",
        "import codecs\n",
        "import pandas as pd\n",
        "import codecs\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(r'/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 = 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'] == 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",
        "                    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 = json_normalize(df_match,sep=\"_\")    \n",
        "             \n",
        "                    main_df = main_df.append(df_match2,ignore_index=True,sort=False)                    \n",
        "print('Done')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDmUsMN74fmX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messi_df = main_df.query('type_id == 30 & player_id == 5503')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwcFd8lx5dza",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messi_df = messi_df[['location','pass_end_location','pass_height_name','pass_type_name','pass_body_part_name']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_jbQy5Q5uvW",
        "colab_type": "code",
        "outputId": "b6361e15-f95a-4b2a-fffc-fc35f3d83549",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "messi_df.head()"
      ],
      "execution_count": 6,
      "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>pass_end_location</th>\n",
              "      <th>pass_height_name</th>\n",
              "      <th>pass_type_name</th>\n",
              "      <th>pass_body_part_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>[29.5, 55.8]</td>\n",
              "      <td>[33.1, 48.1]</td>\n",
              "      <td>Low Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>[64.7, 72.8]</td>\n",
              "      <td>[57.2, 74.5]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>[64.4, 73.7]</td>\n",
              "      <td>[54.1, 42.1]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>[80.6, 33.0]</td>\n",
              "      <td>[73.3, 43.8]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right Foot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>[97.9, 46.4]</td>\n",
              "      <td>[115.5, 53.8]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         location pass_end_location  ... pass_type_name pass_body_part_name\n",
              "29   [29.5, 55.8]      [33.1, 48.1]  ...            NaN           Left Foot\n",
              "99   [64.7, 72.8]      [57.2, 74.5]  ...            NaN           Left Foot\n",
              "111  [64.4, 73.7]      [54.1, 42.1]  ...            NaN           Left Foot\n",
              "140  [80.6, 33.0]      [73.3, 43.8]  ...            NaN          Right Foot\n",
              "155  [97.9, 46.4]     [115.5, 53.8]  ...            NaN           Left Foot\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UI4LKrw61Jf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def draw_pitch(ax):\n",
        "    # focus on only half of the pitch\n",
        "    #Pitch Outline & Centre Line\n",
        "    Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)\n",
        "    #Left, Right Penalty Area and midline\n",
        "    LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)\n",
        "    RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)\n",
        "    midline = ConnectionPatch([60,0], [60,80], \"data\", \"data\")\n",
        "\n",
        "    #Left, Right 6-yard Box\n",
        "    LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)\n",
        "    RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)\n",
        "\n",
        "\n",
        "    #Prepare Circles\n",
        "    centreCircle = plt.Circle((60,40),8.1, fill = False)\n",
        "    centreSpot = plt.Circle((60,40),0.71)\n",
        "    #Penalty spots and Arcs around penalty boxes\n",
        "    leftPenSpot = plt.Circle((9.7,40),0.71)\n",
        "    rightPenSpot = plt.Circle((110.3,40),0.71)\n",
        "    leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50)\n",
        "    rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230)\n",
        "    \n",
        "    element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, \n",
        "               centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]\n",
        "    for i in element:\n",
        "        ax.add_patch(i)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75LxtmrS8MU6",
        "colab_type": "code",
        "outputId": "bbb6053b-41fb-4824-fbde-41591025b4aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
        "from matplotlib.offsetbox import  OffsetImage\n",
        "\n",
        "fig=plt.figure() #set up the figures\n",
        "fig.set_size_inches(7, 5)\n",
        "ax=fig.add_subplot(1,1,1)\n",
        "draw_pitch(ax) #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",
        "\n",
        "for i in range(len(messi_df)):\n",
        "    # annotate draw an arrow from a current position to pass_end_location\n",
        "    ax.annotate(\"\", xy = (messi_df.iloc[i]['pass_end_location'][0], messi_df.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
        "               xytext = (messi_df.iloc[i]['location'][0], messi_df.iloc[i]['location'][1]), textcoords = 'data',\n",
        "               arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"blue\"),)\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()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JhU0xgYH4v7QU/rAzZ/Bu9nk4nbjvwgWYcDduxHm7HeYPux39duAAzL3sNBZC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\nVoqiKErIo8pKURRFCXlUWSmKoighz/8DMyPvXkimHHwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQsuobid9gd5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messi_df[['X','Y']] = pd.DataFrame(messi_df.location.values.tolist(), index= messi_df.index)\n",
        "messi_df[['endX','endY']] = pd.DataFrame(messi_df.pass_end_location.values.tolist(), index= messi_df.index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxiCT0hn-W4b",
        "colab_type": "code",
        "outputId": "376ac331-3689-437c-af90-d553c4086510",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "messi_df"
      ],
      "execution_count": 10,
      "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>pass_end_location</th>\n",
              "      <th>pass_height_name</th>\n",
              "      <th>pass_type_name</th>\n",
              "      <th>pass_body_part_name</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>endX</th>\n",
              "      <th>endY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>[29.5, 55.8]</td>\n",
              "      <td>[33.1, 48.1]</td>\n",
              "      <td>Low Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>29.5</td>\n",
              "      <td>55.8</td>\n",
              "      <td>33.1</td>\n",
              "      <td>48.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>[64.7, 72.8]</td>\n",
              "      <td>[57.2, 74.5]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>64.7</td>\n",
              "      <td>72.8</td>\n",
              "      <td>57.2</td>\n",
              "      <td>74.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>[64.4, 73.7]</td>\n",
              "      <td>[54.1, 42.1]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>64.4</td>\n",
              "      <td>73.7</td>\n",
              "      <td>54.1</td>\n",
              "      <td>42.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>[80.6, 33.0]</td>\n",
              "      <td>[73.3, 43.8]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right Foot</td>\n",
              "      <td>80.6</td>\n",
              "      <td>33.0</td>\n",
              "      <td>73.3</td>\n",
              "      <td>43.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>[97.9, 46.4]</td>\n",
              "      <td>[115.5, 53.8]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>97.9</td>\n",
              "      <td>46.4</td>\n",
              "      <td>115.5</td>\n",
              "      <td>53.8</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127962</th>\n",
              "      <td>[69.9, 56.5]</td>\n",
              "      <td>[75.1, 73.7]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>69.9</td>\n",
              "      <td>56.5</td>\n",
              "      <td>75.1</td>\n",
              "      <td>73.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127969</th>\n",
              "      <td>[85.4, 60.7]</td>\n",
              "      <td>[90.3, 62.4]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right Foot</td>\n",
              "      <td>85.4</td>\n",
              "      <td>60.7</td>\n",
              "      <td>90.3</td>\n",
              "      <td>62.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128096</th>\n",
              "      <td>[75.3, 47.8]</td>\n",
              "      <td>[93.0, 45.7]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right Foot</td>\n",
              "      <td>75.3</td>\n",
              "      <td>47.8</td>\n",
              "      <td>93.0</td>\n",
              "      <td>45.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128221</th>\n",
              "      <td>[87.9, 26.6]</td>\n",
              "      <td>[81.4, 37.8]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>87.9</td>\n",
              "      <td>26.6</td>\n",
              "      <td>81.4</td>\n",
              "      <td>37.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128301</th>\n",
              "      <td>[83.7, 43.2]</td>\n",
              "      <td>[91.3, 64.7]</td>\n",
              "      <td>Ground Pass</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Left Foot</td>\n",
              "      <td>83.7</td>\n",
              "      <td>43.2</td>\n",
              "      <td>91.3</td>\n",
              "      <td>64.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1642 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            location pass_end_location pass_height_name  ...     Y   endX  endY\n",
              "29      [29.5, 55.8]      [33.1, 48.1]         Low Pass  ...  55.8   33.1  48.1\n",
              "99      [64.7, 72.8]      [57.2, 74.5]      Ground Pass  ...  72.8   57.2  74.5\n",
              "111     [64.4, 73.7]      [54.1, 42.1]      Ground Pass  ...  73.7   54.1  42.1\n",
              "140     [80.6, 33.0]      [73.3, 43.8]      Ground Pass  ...  33.0   73.3  43.8\n",
              "155     [97.9, 46.4]     [115.5, 53.8]      Ground Pass  ...  46.4  115.5  53.8\n",
              "...              ...               ...              ...  ...   ...    ...   ...\n",
              "127962  [69.9, 56.5]      [75.1, 73.7]      Ground Pass  ...  56.5   75.1  73.7\n",
              "127969  [85.4, 60.7]      [90.3, 62.4]      Ground Pass  ...  60.7   90.3  62.4\n",
              "128096  [75.3, 47.8]      [93.0, 45.7]      Ground Pass  ...  47.8   93.0  45.7\n",
              "128221  [87.9, 26.6]      [81.4, 37.8]      Ground Pass  ...  26.6   81.4  37.8\n",
              "128301  [83.7, 43.2]      [91.3, 64.7]      Ground Pass  ...  43.2   91.3  64.7\n",
              "\n",
              "[1642 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZw7T6aT85n0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YS7-VHD488l2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYO1QlOx8-4G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create training data for clustering\n",
        "training_data = []\n",
        "training_data = messi_df[['X','Y','endX','endY']].to_numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4D0bvtBY_QIs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cluster_model = KMeans(n_clusters=25).fit(training_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFlNwR-N_mXB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cluster_df = pd.DataFrame(data=cluster_model.cluster_centers_,columns= ['X','Y','endX','endY'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7eLnuOlB2lF",
        "colab_type": "code",
        "outputId": "a493fbfa-2e9f-41e0-dfd7-07d193deaa49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "cluster_df.head()"
      ],
      "execution_count": 16,
      "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>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>endX</th>\n",
              "      <th>endY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69.526437</td>\n",
              "      <td>73.794253</td>\n",
              "      <td>61.916092</td>\n",
              "      <td>70.040230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>91.892045</td>\n",
              "      <td>33.001136</td>\n",
              "      <td>104.770455</td>\n",
              "      <td>24.712500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>56.681333</td>\n",
              "      <td>40.832000</td>\n",
              "      <td>50.676000</td>\n",
              "      <td>40.288000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>86.620000</td>\n",
              "      <td>48.557778</td>\n",
              "      <td>96.874444</td>\n",
              "      <td>34.186667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>78.813235</td>\n",
              "      <td>36.172059</td>\n",
              "      <td>83.302941</td>\n",
              "      <td>49.801471</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           X          Y        endX       endY\n",
              "0  69.526437  73.794253   61.916092  70.040230\n",
              "1  91.892045  33.001136  104.770455  24.712500\n",
              "2  56.681333  40.832000   50.676000  40.288000\n",
              "3  86.620000  48.557778   96.874444  34.186667\n",
              "4  78.813235  36.172059   83.302941  49.801471"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S15d3IpX_uGW",
        "colab_type": "code",
        "outputId": "2d0127d8-373c-460b-e35b-3237c51c7f46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
        "from matplotlib.offsetbox import  OffsetImage\n",
        "plt.style.use('classic')\n",
        "fig=plt.figure() #set up the figures\n",
        "fig.set_size_inches(16, 9)\n",
        "ax=fig.add_subplot(1,1,1)\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",
        "y_cary_end = 0\n",
        "y_loc = 0\n",
        "ax.set_facecolor(\"grey\")\n",
        "for i in range(len(cluster_df)):\n",
        "    # annotate draw an arrow from a current position to pass_end_location\n",
        "    ax.annotate(\"\", xy = (cluster_df.iloc[i]['endX'], cluster_df.iloc[i]['endY']), xycoords = 'data',\n",
        "               xytext = (cluster_df.iloc[i].X, cluster_df.iloc[i].Y), textcoords = 'data',\n",
        "               arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"seagreen\"),)\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=\"fancy\",connectionstyle=\"arc3\", color = \"red\"),)\n",
        "\"\"\"\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p+QOGnq5/W5dlz1RZUq7VcTABGGZAT9SvVXZcqq7On2d1HOC0KPBnRoEHAGAc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\ngg==\n",
            "text/plain": [
              "<Figure size 1280x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}