{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from highlight_text import fig_text\n",
    "import matplotlib as mpl\n",
    "from mplsoccer.pitch import Pitch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set general use colors\n",
    "text_color = 'w'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('shotmaps.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>team</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>42</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>50.88</td>\n",
       "      <td>85.20</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>34.96</td>\n",
       "      <td>91.68</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>44.40</td>\n",
       "      <td>101.64</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>27.92</td>\n",
       "      <td>96.36</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>50.40</td>\n",
       "      <td>104.88</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>21.60</td>\n",
       "      <td>108.00</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>47.84</td>\n",
       "      <td>107.64</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>47.04</td>\n",
       "      <td>89.76</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>57</td>\n",
       "      <td>31</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>48.64</td>\n",
       "      <td>107.04</td>\n",
       "      <td>Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>33.76</td>\n",
       "      <td>101.64</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64</td>\n",
       "      <td>29</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>37.36</td>\n",
       "      <td>98.28</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>66</td>\n",
       "      <td>58</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>50.72</td>\n",
       "      <td>107.52</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>69</td>\n",
       "      <td>12</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>37.36</td>\n",
       "      <td>98.28</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>69</td>\n",
       "      <td>57</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>54.56</td>\n",
       "      <td>104.04</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>52.32</td>\n",
       "      <td>89.76</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>80</td>\n",
       "      <td>9</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>46.24</td>\n",
       "      <td>91.44</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>82</td>\n",
       "      <td>51</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>41.12</td>\n",
       "      <td>97.80</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>27.44</td>\n",
       "      <td>87.84</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>28.88</td>\n",
       "      <td>96.36</td>\n",
       "      <td>Missed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>42.08</td>\n",
       "      <td>99.36</td>\n",
       "      <td>Saved</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    minute  second       team      x       y outcome\n",
       "0       21      42  Barcelona  50.88   85.20   Saved\n",
       "1       29      33  Barcelona  34.96   91.68  Missed\n",
       "2       35       3  Barcelona  44.40  101.64   Saved\n",
       "3       41       3  Barcelona  27.92   96.36   Saved\n",
       "4       42       9  Barcelona  50.40  104.88   Saved\n",
       "5       42      13  Barcelona  21.60  108.00  Missed\n",
       "6       45      32  Barcelona  47.84  107.64   Saved\n",
       "7       46      51  Barcelona  47.04   89.76   Saved\n",
       "8       57      31  Barcelona  48.64  107.04    Post\n",
       "9       60      12  Barcelona  33.76  101.64  Missed\n",
       "10      64      29  Barcelona  37.36   98.28   Saved\n",
       "11      66      58  Barcelona  50.72  107.52  Missed\n",
       "12      69      12  Barcelona  37.36   98.28  Missed\n",
       "13      69      57  Barcelona  54.56  104.04   Saved\n",
       "14      77       5  Barcelona  52.32   89.76   Saved\n",
       "15      80       9  Barcelona  46.24   91.44  Missed\n",
       "16      82      51  Barcelona  41.12   97.80   Saved\n",
       "17      84      15  Barcelona  27.44   87.84  Missed\n",
       "18      87      51  Barcelona  28.88   96.36  Missed\n",
       "19      89       6  Barcelona  42.08   99.36   Saved"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.22, 0.14, '@mckayjohns / twitter')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,8.5))\n",
    "fig.set_facecolor('#22312b')\n",
    "ax.patch.set_facecolor('#22312b')\n",
    "\n",
    "#The statsbomb pitch from mplsoccer\n",
    "pitch = Pitch(pitch_type='statsbomb', orientation='vertical',\n",
    "              pitch_color='#22312b', line_color='#c7d5cc', figsize=(13, 8),\n",
    "              constrained_layout=False, tight_layout=True, view='half')\n",
    "\n",
    "pitch.draw(ax=ax)\n",
    "\n",
    "#I invert the axis to make it so I am viewing it how I want\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "#plot the points, you can use a for loop to plot the different outcomes if you want\n",
    "plt.scatter(data['x'],data['y'], s=100,c='#ea6969',alpha=.7)\n",
    "\n",
    "s='Barcelona Shot Chart vs Juventus'\n",
    "fig_text(s=s,\n",
    "        x=.27,y=.9,\n",
    "        fontfamily='Andale Mono',\n",
    "        highlight_weights=['bold'],\n",
    "        fontsize=24,\n",
    "        color=text_color\n",
    "\n",
    ")\n",
    "\n",
    "total_shots = len(df)\n",
    "\n",
    "fig_text(s=f'Total Shots: {total_shots}',\n",
    "        x=.27, y =.67, fontsize=14,fontfamily='Andale Mono',color=text_color)\n",
    "fig_text(s=f'xG: .85',\n",
    "        x=.49, y =.67, fontsize=14,fontfamily='Andale Mono',color=text_color)\n",
    "fig_text(s=f'Goals: 0',\n",
    "        x=.68, y =.67, fontsize=14,fontfamily='Andale Mono',color=text_color)\n",
    "\n",
    "fig.text(.22,.14,f'@mckayjohns / twitter',fontstyle='italic',fontsize=12,fontfamily='Andale Mono',color=text_color)\n",
    "\n",
    "#plt.savefig('bcnjuveshots.png',dpi=300,bbox_inches = 'tight',facecolor='#22312b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}