Pass Map.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be040eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from mplsoccer.pitch import Pitch,VerticalPitch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a0c7061",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f89b195a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>startX</th>\n",
       "      <th>startY</th>\n",
       "      <th>endX</th>\n",
       "      <th>endY</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>89</td>\n",
       "      <td>19</td>\n",
       "      <td>97</td>\n",
       "      <td>40</td>\n",
       "      <td>un</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>94</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>68</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>88</td>\n",
       "      <td>18</td>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>75</td>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>59</td>\n",
       "      <td>19</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>60</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>93</td>\n",
       "      <td>7</td>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>70</td>\n",
       "      <td>9</td>\n",
       "      <td>61</td>\n",
       "      <td>11</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>97</td>\n",
       "      <td>27</td>\n",
       "      <td>98</td>\n",
       "      <td>47</td>\n",
       "      <td>un</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>79</td>\n",
       "      <td>30</td>\n",
       "      <td>93</td>\n",
       "      <td>33</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>91</td>\n",
       "      <td>12</td>\n",
       "      <td>73</td>\n",
       "      <td>29</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>76</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>38</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>81</td>\n",
       "      <td>14</td>\n",
       "      <td>72</td>\n",
       "      <td>21</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>69</td>\n",
       "      <td>39</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>59</td>\n",
       "      <td>83</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Home</td>\n",
       "      <td>Doku</td>\n",
       "      <td>Pass</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>un</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team Player Event  startX  startY  endX  endY outcome\n",
       "0   Home   Doku  Pass      89      19    97    40      un\n",
       "1   Home   Doku  Pass      94      26    82    68      su\n",
       "2   Home   Doku  Pass      88      18    75    31      su\n",
       "3   Home   Doku  Pass      75      19    65    22      su\n",
       "4   Home   Doku  Pass      59      19    41    20      su\n",
       "5   Home   Doku  Pass      60      17    55     7      su\n",
       "6   Home   Doku  Pass      93       7    78    12      su\n",
       "7   Home   Doku  Pass      73      12    74     4      su\n",
       "8   Home   Doku  Pass      67      14    79     5      su\n",
       "9   Home   Doku  Pass      67       6    63    20      su\n",
       "10  Home   Doku  Pass      70       9    61    11      su\n",
       "11  Home   Doku  Pass      59       4    68     5      su\n",
       "12  Home   Doku  Pass      97      27    98    47      un\n",
       "13  Home   Doku  Pass      79      30    93    33      su\n",
       "14  Home   Doku  Pass      79      28    81    67      su\n",
       "15  Home   Doku  Pass      91      12    73    29      su\n",
       "16  Home   Doku  Pass      76      23    75    38      su\n",
       "17  Home   Doku  Pass      81      14    72    21      su\n",
       "18  Home   Doku  Pass      73      11    69    39      su\n",
       "19  Home   Doku  Pass      39      33    59    83      su\n",
       "20  Home   Doku  Pass      27       6    23    14      un"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb195175",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['startX'] = df['startX']*1.2\n",
    "df['startY'] = df['startY']*0.8\n",
    "df['endX'] = df['endX']*1.2\n",
    "df['endY'] = df['endY']*0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcb2e93a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Doku vs Italy')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(13.5,8))\n",
    "fig.set_facecolor('#000')\n",
    "ax.patch.set_facecolor('#000')\n",
    "pitch = Pitch(pitch_color='#000', line_color='#fff',positional=True, positional_color='#708090',positional_linewidth=0.5,\n",
    "              stripe_color='#c2d59d', stripe=False)  # optional stripes\n",
    "pitch.draw(ax=ax)\n",
    "\n",
    "for i in range(len(df['startX'])):\n",
    "    if df['outcome'][i] == 'su':\n",
    "        plt.plot((df['startX'][i],df['endX'][i]),(df['startY'][i],df['endY'][i]),color='green',linewidth=2)\n",
    "        plt.scatter(df['startX'][i],df['startY'][i],color='green')\n",
    "    if df['outcome'][i] == 'un':\n",
    "        plt.plot((df['startX'][i],df['endX'][i]),(df['startY'][i],df['endY'][i]),color='red',linewidth=3,alpha=0.75)\n",
    "        plt.scatter(df['startX'][i],df['startY'][i],color='red')\n",
    "        \n",
    "plt.title(\"Doku vs Italy\", color='white',size=20)\n",
    "# plt.savefig('dokupassmap.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "519f6e6e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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