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amaral220x/spfcxffc.ipynb

Created May 31, 2021 01:21
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spfcxffc.ipynb
{
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"metadata": {
"colab": {
"name": "SPFCxFFC.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyNiS4DSmi0JJCM4WDTCbBtw",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/amaral220x/336f303ea0da06cab2c5917396d7cce7/spfcxffc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "C8UACy60rG_T"
},
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from mplsoccer.pitch import Pitch\n",
"import seaborn as sns \n",
"from highlight_text import fig_text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "meHdWBWqrTQ1"
},
"source": [
"df = pd.read_csv('FLUxSPFC2905.csv')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"id": "70fN4bhMrdVA",
"outputId": "d27de08e-deaa-42ba-b7b6-e7f1fe56bf94"
},
"source": [
"df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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"</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>Mins</th>\n",
" <th>Secs</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>X2</th>\n",
" <th>Y2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>FLU</td>\n",
" <td>Egídio</td>\n",
" <td>Pass</td>\n",
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" <td>11</td>\n",
" <td>37</td>\n",
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" <td>75</td>\n",
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" <th>1</th>\n",
" <td>FLU</td>\n",
" <td>Abelito</td>\n",
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" <td>13</td>\n",
" <td>83</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>FLU</td>\n",
" <td>CP</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>67</td>\n",
" <td>11</td>\n",
" <td>76</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>FLU</td>\n",
" <td>Biel</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>77</td>\n",
" <td>12</td>\n",
" <td>90</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>FLU</td>\n",
" <td>Egídio</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>90</td>\n",
" <td>8</td>\n",
" <td>90</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>FLU</td>\n",
" <td>CP</td>\n",
" <td>Shot</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
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" <td>-</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>FLU</td>\n",
" <td>CP</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>67</td>\n",
" <td>61</td>\n",
" <td>84</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>FLU</td>\n",
" <td>Nene</td>\n",
" <td>Shot</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>89</td>\n",
" <td>50</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>FLU</td>\n",
" <td>Abelito</td>\n",
" <td>Falta</td>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>88</td>\n",
" <td>39</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>FLU</td>\n",
" <td>Martinelli</td>\n",
" <td>Pass</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>26</td>\n",
" <td>22</td>\n",
" <td>10</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>FLU</td>\n",
" <td>Biel</td>\n",
" <td>Shot</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>60</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>FLU</td>\n",
" <td>Kayky</td>\n",
" <td>Pass</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>33</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>FLU</td>\n",
" <td>Bobadilla</td>\n",
" <td>Shot</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>13</td>\n",
" <td>39</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Team Player Event Mins Secs X Y X2 Y2\n",
"0 FLU Egídio Pass 0 11 37 8 75 28\n",
"1 FLU Abelito Shot 0 13 83 29 - -\n",
"2 FLU Biel Pass 0 21 61 5 67 12\n",
"3 FLU CP Pass 0 23 67 11 76 6\n",
"4 FLU Biel Pass 0 28 77 12 90 8\n",
"5 FLU Egídio Pass 0 38 90 8 90 38\n",
"6 FLU CP Shot 0 38 90 38 - -\n",
"7 FLU CP Pass 0 42 67 61 84 38\n",
"8 FLU Nene Shot 0 55 89 50 - -\n",
"9 FLU Abelito Falta 0 43 88 39 - -\n",
"10 FLU Martinelli Pass 1 6 26 22 10 60\n",
"11 FLU Biel Shot 1 6 10 60 - -\n",
"12 FLU Kayky Pass 1 19 33 23 15 45\n",
"13 FLU Bobadilla Shot 1 20 13 39 - -"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 966
},
"id": "hSTDFw9xsXKh",
"outputId": "9d198860-590f-4127-96a1-fcf15ec74963"
},
"source": [
"df['X'] = df['X']*1.2 \n",
"df['Y'] = df['Y']*0.8 \n",
"for x in range(len(df['Event'])):\n",
" if df['Event'][x] == 'Pass':\n",
" print(df['X2'][x])\n",
" df['X2'][x] = int(df['X2'][x]) * 1.2 \n",
" print(df['X2'][x])\n",
" df['Y2'][x] = int(df['Y2'][x]) * 0.8 \n",
"df"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"75\n",
"90.0\n",
"67\n",
"80.39999999999999\n",
"76\n",
"91.2\n",
"90\n",
"108.0\n",
"90\n",
"108.0\n",
"84\n",
"100.8\n",
"10\n",
"12.0\n",
"15\n",
"18.0\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</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>Mins</th>\n",
" <th>Secs</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>X2</th>\n",
" <th>Y2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>FLU</td>\n",
" <td>Egídio</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>44.4</td>\n",
" <td>6.4</td>\n",
" <td>90</td>\n",
" <td>22.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>FLU</td>\n",
" <td>Abelito</td>\n",
" <td>Shot</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>99.6</td>\n",
" <td>23.2</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>FLU</td>\n",
" <td>Biel</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>73.2</td>\n",
" <td>4.0</td>\n",
" <td>80.4</td>\n",
" <td>9.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>FLU</td>\n",
" <td>CP</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>80.4</td>\n",
" <td>8.8</td>\n",
" <td>91.2</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>FLU</td>\n",
" <td>Biel</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>92.4</td>\n",
" <td>9.6</td>\n",
" <td>108</td>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>FLU</td>\n",
" <td>Egídio</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>108.0</td>\n",
" <td>6.4</td>\n",
" <td>108</td>\n",
" <td>30.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>FLU</td>\n",
" <td>CP</td>\n",
" <td>Shot</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>108.0</td>\n",
" <td>30.4</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>FLU</td>\n",
" <td>CP</td>\n",
" <td>Pass</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>80.4</td>\n",
" <td>48.8</td>\n",
" <td>100.8</td>\n",
" <td>30.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>FLU</td>\n",
" <td>Nene</td>\n",
" <td>Shot</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>106.8</td>\n",
" <td>40.0</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>FLU</td>\n",
" <td>Abelito</td>\n",
" <td>Falta</td>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>105.6</td>\n",
" <td>31.2</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>FLU</td>\n",
" <td>Martinelli</td>\n",
" <td>Pass</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>31.2</td>\n",
" <td>17.6</td>\n",
" <td>12</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>FLU</td>\n",
" <td>Biel</td>\n",
" <td>Shot</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>12.0</td>\n",
" <td>48.0</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>FLU</td>\n",
" <td>Kayky</td>\n",
" <td>Pass</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>39.6</td>\n",
" <td>18.4</td>\n",
" <td>18</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>FLU</td>\n",
" <td>Bobadilla</td>\n",
" <td>Shot</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>15.6</td>\n",
" <td>31.2</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Team Player Event Mins Secs X Y X2 Y2\n",
"0 FLU Egídio Pass 0 11 44.4 6.4 90 22.4\n",
"1 FLU Abelito Shot 0 13 99.6 23.2 - -\n",
"2 FLU Biel Pass 0 21 73.2 4.0 80.4 9.6\n",
"3 FLU CP Pass 0 23 80.4 8.8 91.2 4.8\n",
"4 FLU Biel Pass 0 28 92.4 9.6 108 6.4\n",
"5 FLU Egídio Pass 0 38 108.0 6.4 108 30.4\n",
"6 FLU CP Shot 0 38 108.0 30.4 - -\n",
"7 FLU CP Pass 0 42 80.4 48.8 100.8 30.4\n",
"8 FLU Nene Shot 0 55 106.8 40.0 - -\n",
"9 FLU Abelito Falta 0 43 105.6 31.2 - -\n",
"10 FLU Martinelli Pass 1 6 31.2 17.6 12 48\n",
"11 FLU Biel Shot 1 6 12.0 48.0 - -\n",
"12 FLU Kayky Pass 1 19 39.6 18.4 18 36\n",
"13 FLU Bobadilla Shot 1 20 15.6 31.2 - -"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 505
},
"id": "YcIsm8kNsfce",
"outputId": "51ae8667-3bd4-4322-84f6-d19d1cc7821c"
},
"source": [
"fig,ax = plt.subplots(figsize=(13.5,8))\n",
"fig.set_facecolor('#22312b')\n",
"ax.patch.set_facecolor('#22312b')\n",
"pitch = Pitch(pitch_type='statsbomb', orientation='horizontal', pitch_color='#22312b', line_color='#c7d5cc', figsize=(13, 8), constrained_layout=True, tight_layout=False)\n",
"pitch.draw(ax=ax)\n",
"bons_chutes = 0\n",
"bons_passes = 0 \n",
"for x in range(len(df['X'])):\n",
" if df['Event'][x] == 'Pass': \n",
" plt.plot((df['X'][x],df['X2'][x]),(df['Y'][x], df['Y2'][x]), color='green')\n",
" plt.plot(df['X'][x], df['Y'][x], 'go', markersize=10)\n",
" plt.plot(df['X2'][x], df['Y2'][x], 'go', markersize=10)\n",
" bons_passes += 1\n",
" if df['Event'][x] == 'Shot':\n",
" plt.plot(df['X'][x], df['Y'][x], 'ro', markersize=12)\n",
" bons_chutes += 1\n",
"\n",
"plt.title('Momentos-chave SPFC x FLU 29/05',color='white',size=20)\n",
"\n",
"fig_text(s=f'Chances Claras: {bons_chutes}', x=.30, y =.27, fontsize=14,fontfamily='Andale Mono',color='w')\n",
"fig_text(s=f'Bons Passes: {bons_passes}', x=.60, y =.27, fontsize=14,fontfamily='Andale Mono',color='w')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<highlight_text.htext.HighlightText at 0x7fd459bcab90>"
]
},
"metadata": {
"tags": []
},
"execution_count": 89
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 972x576 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ql5aFCwN0fx8"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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