Action Map.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efe8ed77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from mplsoccer.pitch import Pitch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9051eb5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('IscoEvents1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04d3b870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>57</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Interception</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Shot</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>21</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player         Event  Mins  Secs   X   Y\n",
       "0    Isco        Tackle     0    11  56  57\n",
       "1    Isco        Tackle     0    16  54  12\n",
       "2    Isco        Tackle     0    32  83  85\n",
       "3    Isco       Take On     1    37  57  70\n",
       "4    Isco        Tackle     1    39  36  65\n",
       "5    Isco  Interception     2     5  32  57\n",
       "6    Isco       Take On     2    13  44  65\n",
       "7    Isco       Take On     2    18  34  44\n",
       "8    Isco          Shot     2    26  13  37\n",
       "9    Isco       Take On     2    41  48  47\n",
       "10   Isco       Take On     3    16  40  65\n",
       "11   Isco       Take On     3    23  54  78\n",
       "12   Isco       Take On     3    55  21  87"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3407b03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['X'] = df1['X']*1.2\n",
    "df1['Y'] = df1['Y']*0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f68e7955",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>67.2</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>64.8</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>99.6</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>68.4</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Tackle</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>43.2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Interception</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>38.4</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>52.8</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>40.8</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Shot</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>15.6</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>57.6</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>64.8</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Isco</td>\n",
       "      <td>Take On</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>25.2</td>\n",
       "      <td>69.6</td>\n",
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      ],
      "text/plain": [
       "   Player         Event  Mins  Secs     X     Y\n",
       "0    Isco        Tackle     0    11  67.2  45.6\n",
       "1    Isco        Tackle     0    16  64.8   9.6\n",
       "2    Isco        Tackle     0    32  99.6  68.0\n",
       "3    Isco       Take On     1    37  68.4  56.0\n",
       "4    Isco        Tackle     1    39  43.2  52.0\n",
       "5    Isco  Interception     2     5  38.4  45.6\n",
       "6    Isco       Take On     2    13  52.8  52.0\n",
       "7    Isco       Take On     2    18  40.8  35.2\n",
       "8    Isco          Shot     2    26  15.6  29.6\n",
       "9    Isco       Take On     2    41  57.6  37.6\n",
       "10   Isco       Take On     3    16  48.0  52.0\n",
       "11   Isco       Take On     3    23  64.8  62.4\n",
       "12   Isco       Take On     3    55  25.2  69.6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e6cc2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Isco Action Map vs Levante 21/22')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 744.115x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Action Map\n",
    "DIFF_LINEWIDTH = 1.2 # amount the glow linewidth increases each loop\n",
    "NUM_GLOW_LINES = 10  # the amount of loops, if you increase the glow will be wider\n",
    "ALPHA_PITCH_LINE = 0.3\n",
    "ALPHA_PASS_LINE = 0.15\n",
    "BACKGROUND_COLOR = '#000000'\n",
    "Tackle = '#FEBE10' #yellow\n",
    "TakeOn = '#EE324E' #red\n",
    "Shot = '#FFF'\n",
    "Interception = '#00529F'#blue\n",
    "LINE_COLOR ='#FFF'\n",
    "pitch = Pitch(line_color=LINE_COLOR, pitch_color=BACKGROUND_COLOR, linewidth=3,\n",
    "              line_alpha=1, goal_alpha=1, goal_type='box')\n",
    "fig, ax = pitch.grid(grid_height=0.75, title_height=0, axis=False,\n",
    "                     endnote_height=0, title_space=0, endnote_space=0)\n",
    "fig.set_facecolor(BACKGROUND_COLOR)\n",
    "for i in range(1, NUM_GLOW_LINES + 1):\n",
    "    pitch = Pitch(line_color=LINE_COLOR, pitch_color=BACKGROUND_COLOR,\n",
    "                  linewidth=3 + (DIFF_LINEWIDTH * i),\n",
    "                  line_alpha=ALPHA_PITCH_LINE / NUM_GLOW_LINES,\n",
    "                  goal_alpha=ALPHA_PITCH_LINE / NUM_GLOW_LINES,\n",
    "                  goal_type='box')\n",
    "    pitch.draw(ax=ax['pitch'])  # we plot on-top of our previous axis from pitch.grid\n",
    "    \n",
    "for i in range(len(df1['Player'])):\n",
    "    if df1['Event'][i] == 'Tackle':\n",
    "        pitch.scatter(df1['X'][i], df1['Y'][i],\n",
    "        # size varies between 100 and 1000 (points squared)\n",
    "        s=300,\n",
    "        c=Tackle,  # color for scatter in hex format\n",
    "        marker='X',\n",
    "        alpha=1,\n",
    "        ax=ax['pitch'])\n",
    "    if df1['Event'][i] == 'Shot':\n",
    "        pitch.scatter(df1['X'][i], df1['Y'][i],\n",
    "        # size varies between 100 and 1000 (points squared)\n",
    "        s=300,\n",
    "        c=Shot,  # color for scatter in hex format\n",
    "        marker='o',\n",
    "        alpha=1,\n",
    "        ax=ax['pitch'])\n",
    "    if df1['Event'][i] == 'Take On':\n",
    "        pitch.scatter(df1['X'][i], df1['Y'][i],\n",
    "        # size varies between 100 and 1000 (points squared)\n",
    "        s=300,\n",
    "        c=TakeOn,  # color for scatter in hex format\n",
    "        marker='o',\n",
    "        alpha=1,\n",
    "        ax=ax['pitch'])\n",
    "    if df1['Event'][i] == 'Interception':\n",
    "        pitch.scatter(df1['X'][i], df1['Y'][i],\n",
    "        # size varies between 100 and 1000 (points squared)\n",
    "        s=300,\n",
    "        c=Interception,  # color for scatter in hex format\n",
    "        marker='<',\n",
    "        alpha=1,\n",
    "        ax=ax['pitch'])\n",
    "        \n",
    "plt.title(\"Isco Action Map vs Levante 21/22\", color='white',size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b2332dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}