Football_Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = r\"C:\\Users\\MustaphaM\\Desktop\\sample-data-master\\data\\Sample_Game_1\\Sample_Game_1_RawEventsData.csv\"\n",
    "DATADIR2 = r\"C:\\Users\\MustaphaM\\Desktop\\sample-data-master\\data\\Sample_Game_2\\Sample_Game_2_RawEventsData.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "event1 = pd.read_csv(DATADIR)\n",
    "events = pd.read_csv(DATADIR2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Type</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Period</th>\n",
       "      <th>Start Frame</th>\n",
       "      <th>Start Time [s]</th>\n",
       "      <th>End Frame</th>\n",
       "      <th>End Time [s]</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Start X</th>\n",
       "      <th>Start Y</th>\n",
       "      <th>End X</th>\n",
       "      <th>End Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Away</td>\n",
       "      <td>SET PIECE</td>\n",
       "      <td>KICK OFF</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>Player23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>87</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Player23</td>\n",
       "      <td>Player20</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>5.84</td>\n",
       "      <td>186</td>\n",
       "      <td>7.44</td>\n",
       "      <td>Player20</td>\n",
       "      <td>Player18</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>248</td>\n",
       "      <td>9.92</td>\n",
       "      <td>283</td>\n",
       "      <td>11.32</td>\n",
       "      <td>Player18</td>\n",
       "      <td>Player17</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>12.64</td>\n",
       "      <td>346</td>\n",
       "      <td>13.84</td>\n",
       "      <td>Player17</td>\n",
       "      <td>Player16</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.58</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>Away</td>\n",
       "      <td>BALL LOST</td>\n",
       "      <td>INTERCEPTION</td>\n",
       "      <td>2</td>\n",
       "      <td>140122</td>\n",
       "      <td>5604.88</td>\n",
       "      <td>140183</td>\n",
       "      <td>5607.32</td>\n",
       "      <td>Player21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Home</td>\n",
       "      <td>CHALLENGE</td>\n",
       "      <td>AERIAL-LOST</td>\n",
       "      <td>2</td>\n",
       "      <td>140180</td>\n",
       "      <td>5607.20</td>\n",
       "      <td>140180</td>\n",
       "      <td>5607.20</td>\n",
       "      <td>Player1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>Away</td>\n",
       "      <td>CHALLENGE</td>\n",
       "      <td>AERIAL-LOST</td>\n",
       "      <td>2</td>\n",
       "      <td>140183</td>\n",
       "      <td>5607.32</td>\n",
       "      <td>140183</td>\n",
       "      <td>5607.32</td>\n",
       "      <td>Player24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>Home</td>\n",
       "      <td>RECOVERY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>140211</td>\n",
       "      <td>5608.44</td>\n",
       "      <td>140211</td>\n",
       "      <td>5608.44</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>Home</td>\n",
       "      <td>BALL OUT</td>\n",
       "      <td>CLEARANCE</td>\n",
       "      <td>2</td>\n",
       "      <td>140265</td>\n",
       "      <td>5610.60</td>\n",
       "      <td>140298</td>\n",
       "      <td>5611.92</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1935 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team       Type       Subtype  Period  Start Frame  Start Time [s]  \\\n",
       "0     Away  SET PIECE      KICK OFF       1           51            2.04   \n",
       "1     Away       PASS           NaN       1           51            2.04   \n",
       "2     Away       PASS           NaN       1          146            5.84   \n",
       "3     Away       PASS           NaN       1          248            9.92   \n",
       "4     Away       PASS           NaN       1          316           12.64   \n",
       "...    ...        ...           ...     ...          ...             ...   \n",
       "1930  Away  BALL LOST  INTERCEPTION       2       140122         5604.88   \n",
       "1931  Home  CHALLENGE   AERIAL-LOST       2       140180         5607.20   \n",
       "1932  Away  CHALLENGE   AERIAL-LOST       2       140183         5607.32   \n",
       "1933  Home   RECOVERY           NaN       2       140211         5608.44   \n",
       "1934  Home   BALL OUT     CLEARANCE       2       140265         5610.60   \n",
       "\n",
       "      End Frame  End Time [s]      From        To  Start X  Start Y  End X  \\\n",
       "0            51          2.04  Player23       NaN      NaN      NaN    NaN   \n",
       "1            87          3.48  Player23  Player20     0.50     0.50   0.40   \n",
       "2           186          7.44  Player20  Player18     0.43     0.50   0.44   \n",
       "3           283         11.32  Player18  Player17     0.47     0.19   0.31   \n",
       "4           346         13.84  Player17  Player16     0.29     0.32   0.26   \n",
       "...         ...           ...       ...       ...      ...      ...    ...   \n",
       "1930     140183       5607.32  Player21       NaN     0.54     0.77   0.29   \n",
       "1931     140180       5607.20   Player1       NaN     0.31     0.30    NaN   \n",
       "1932     140183       5607.32  Player24       NaN     0.32     0.30    NaN   \n",
       "1933     140211       5608.44  Player12       NaN     0.34     0.32    NaN   \n",
       "1934     140298       5611.92  Player12       NaN     0.33     0.16   0.41   \n",
       "\n",
       "      End Y  \n",
       "0       NaN  \n",
       "1      0.51  \n",
       "2      0.22  \n",
       "3      0.28  \n",
       "4      0.58  \n",
       "...     ...  \n",
       "1930   0.24  \n",
       "1931    NaN  \n",
       "1932    NaN  \n",
       "1933    NaN  \n",
       "1934  -0.04  \n",
       "\n",
       "[1935 rows x 14 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Team', 'Type', 'Subtype', 'Period', 'Start Frame', 'Start Time [s]',\n",
       "       'End Frame', 'End Time [s]', 'From', 'To', 'Start X', 'Start Y',\n",
       "       'End X', 'End Y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PASS              964\n",
       "CHALLENGE         311\n",
       "RECOVERY          248\n",
       "BALL LOST         233\n",
       "SET PIECE          80\n",
       "BALL OUT           49\n",
       "SHOT               24\n",
       "FAULT RECEIVED     20\n",
       "CARD                6\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events[\"Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>Team</th>\n",
       "      <th>Type</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Period</th>\n",
       "      <th>Start Frame</th>\n",
       "      <th>Start Time [s]</th>\n",
       "      <th>End Frame</th>\n",
       "      <th>End Time [s]</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
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       "      <th>Start Y</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Away</td>\n",
       "      <td>SET PIECE</td>\n",
       "      <td>KICK OFF</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>87</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Player23</td>\n",
       "      <td>Player20</td>\n",
       "      <td>-53.00</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-1176.60</td>\n",
       "      <td>12.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>5.84</td>\n",
       "      <td>186</td>\n",
       "      <td>7.44</td>\n",
       "      <td>Player20</td>\n",
       "      <td>Player18</td>\n",
       "      <td>-839.52</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-727.16</td>\n",
       "      <td>-1328.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>248</td>\n",
       "      <td>9.92</td>\n",
       "      <td>283</td>\n",
       "      <td>11.32</td>\n",
       "      <td>Player18</td>\n",
       "      <td>Player17</td>\n",
       "      <td>-390.08</td>\n",
       "      <td>-1467.44</td>\n",
       "      <td>-2187.84</td>\n",
       "      <td>-1051.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>12.64</td>\n",
       "      <td>346</td>\n",
       "      <td>13.84</td>\n",
       "      <td>Player17</td>\n",
       "      <td>Player16</td>\n",
       "      <td>-2412.56</td>\n",
       "      <td>-866.32</td>\n",
       "      <td>-2749.64</td>\n",
       "      <td>335.92</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>Away</td>\n",
       "      <td>BALL LOST</td>\n",
       "      <td>INTERCEPTION</td>\n",
       "      <td>2</td>\n",
       "      <td>140122</td>\n",
       "      <td>5604.88</td>\n",
       "      <td>140183</td>\n",
       "      <td>5607.32</td>\n",
       "      <td>Player21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>396.44</td>\n",
       "      <td>1214.48</td>\n",
       "      <td>-2412.56</td>\n",
       "      <td>-1236.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Home</td>\n",
       "      <td>CHALLENGE</td>\n",
       "      <td>AERIAL-LOST</td>\n",
       "      <td>2</td>\n",
       "      <td>140180</td>\n",
       "      <td>5607.20</td>\n",
       "      <td>140180</td>\n",
       "      <td>5607.20</td>\n",
       "      <td>Player1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2187.84</td>\n",
       "      <td>-958.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>Away</td>\n",
       "      <td>CHALLENGE</td>\n",
       "      <td>AERIAL-LOST</td>\n",
       "      <td>2</td>\n",
       "      <td>140183</td>\n",
       "      <td>5607.32</td>\n",
       "      <td>140183</td>\n",
       "      <td>5607.32</td>\n",
       "      <td>Player24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2075.48</td>\n",
       "      <td>-958.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>Home</td>\n",
       "      <td>RECOVERY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>140211</td>\n",
       "      <td>5608.44</td>\n",
       "      <td>140211</td>\n",
       "      <td>5608.44</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1850.76</td>\n",
       "      <td>-866.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>Home</td>\n",
       "      <td>BALL OUT</td>\n",
       "      <td>CLEARANCE</td>\n",
       "      <td>2</td>\n",
       "      <td>140265</td>\n",
       "      <td>5610.60</td>\n",
       "      <td>140298</td>\n",
       "      <td>5611.92</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1963.12</td>\n",
       "      <td>-1606.16</td>\n",
       "      <td>-1064.24</td>\n",
       "      <td>-2530.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1935 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team       Type       Subtype  Period  Start Frame  Start Time [s]  \\\n",
       "0     Away  SET PIECE      KICK OFF       1           51            2.04   \n",
       "1     Away       PASS           NaN       1           51            2.04   \n",
       "2     Away       PASS           NaN       1          146            5.84   \n",
       "3     Away       PASS           NaN       1          248            9.92   \n",
       "4     Away       PASS           NaN       1          316           12.64   \n",
       "...    ...        ...           ...     ...          ...             ...   \n",
       "1930  Away  BALL LOST  INTERCEPTION       2       140122         5604.88   \n",
       "1931  Home  CHALLENGE   AERIAL-LOST       2       140180         5607.20   \n",
       "1932  Away  CHALLENGE   AERIAL-LOST       2       140183         5607.32   \n",
       "1933  Home   RECOVERY           NaN       2       140211         5608.44   \n",
       "1934  Home   BALL OUT     CLEARANCE       2       140265         5610.60   \n",
       "\n",
       "      End Frame  End Time [s]      From        To  Start X  Start Y    End X  \\\n",
       "0            51          2.04  Player23       NaN      NaN      NaN      NaN   \n",
       "1            87          3.48  Player23  Player20   -53.00   -34.00 -1176.60   \n",
       "2           186          7.44  Player20  Player18  -839.52   -34.00  -727.16   \n",
       "3           283         11.32  Player18  Player17  -390.08 -1467.44 -2187.84   \n",
       "4           346         13.84  Player17  Player16 -2412.56  -866.32 -2749.64   \n",
       "...         ...           ...       ...       ...      ...      ...      ...   \n",
       "1930     140183       5607.32  Player21       NaN   396.44  1214.48 -2412.56   \n",
       "1931     140180       5607.20   Player1       NaN -2187.84  -958.80      NaN   \n",
       "1932     140183       5607.32  Player24       NaN -2075.48  -958.80      NaN   \n",
       "1933     140211       5608.44  Player12       NaN -1850.76  -866.32      NaN   \n",
       "1934     140298       5611.92  Player12       NaN -1963.12 -1606.16 -1064.24   \n",
       "\n",
       "        End Y  \n",
       "0         NaN  \n",
       "1       12.24  \n",
       "2    -1328.72  \n",
       "3    -1051.28  \n",
       "4      335.92  \n",
       "...       ...  \n",
       "1930 -1236.24  \n",
       "1931      NaN  \n",
       "1932      NaN  \n",
       "1933      NaN  \n",
       "1934 -2530.96  \n",
       "\n",
       "[1935 rows x 14 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a function that converts positions from Metrica units to meters (with origin at centre circle)\n",
    "\n",
    "def to_metric_coordinates(data, field_dimen = (106., 68.), inplace = False):\n",
    "    ''' \n",
    "    convert positions from Metrica units to meters (with origin at centre circle)\n",
    "    '''\n",
    "    new_data = data.copy()\n",
    "    if inplace == True:\n",
    "        x_columns = [c for c in data.columns if c[-1].lower()==\"x\"]\n",
    "        y_columns = [c for c in data.columns if c[-1].lower()==\"y\"]\n",
    "        data[x_columns]= (data[x_columns]-0.5)*field_dimen[0]\n",
    "        data[y_columns]= (data[y_columns]-0.5)*field_dimen[1]\n",
    "        return data\n",
    "    else:\n",
    "        x_columns = [c for c in new_data.columns if c[-1].lower()==\"x\"]\n",
    "        y_columns = [c for c in new_data.columns if c[-1].lower()==\"y\"]\n",
    "        new_data[x_columns]= (new_data[x_columns]-0.5)*field_dimen[0]\n",
    "        new_data[y_columns]= (new_data[y_columns]-0.5)*field_dimen[1]\n",
    "        return new_data\n",
    "        \n",
    "\n",
    "#conversion\n",
    "\n",
    "to_metric_coordinates(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = to_metric_coordinates(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Type</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Period</th>\n",
       "      <th>Start Frame</th>\n",
       "      <th>Start Time [s]</th>\n",
       "      <th>End Frame</th>\n",
       "      <th>End Time [s]</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Start X</th>\n",
       "      <th>Start Y</th>\n",
       "      <th>End X</th>\n",
       "      <th>End Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Away</td>\n",
       "      <td>SET PIECE</td>\n",
       "      <td>KICK OFF</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>Player23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2.04</td>\n",
       "      <td>87</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Player23</td>\n",
       "      <td>Player20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-10.60</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>5.84</td>\n",
       "      <td>186</td>\n",
       "      <td>7.44</td>\n",
       "      <td>Player20</td>\n",
       "      <td>Player18</td>\n",
       "      <td>-7.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.36</td>\n",
       "      <td>-19.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>248</td>\n",
       "      <td>9.92</td>\n",
       "      <td>283</td>\n",
       "      <td>11.32</td>\n",
       "      <td>Player18</td>\n",
       "      <td>Player17</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-21.08</td>\n",
       "      <td>-20.14</td>\n",
       "      <td>-14.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Away</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>12.64</td>\n",
       "      <td>346</td>\n",
       "      <td>13.84</td>\n",
       "      <td>Player17</td>\n",
       "      <td>Player16</td>\n",
       "      <td>-22.26</td>\n",
       "      <td>-12.24</td>\n",
       "      <td>-25.44</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Team       Type   Subtype  Period  Start Frame  Start Time [s]  End Frame  \\\n",
       "0  Away  SET PIECE  KICK OFF       1           51            2.04         51   \n",
       "1  Away       PASS       NaN       1           51            2.04         87   \n",
       "2  Away       PASS       NaN       1          146            5.84        186   \n",
       "3  Away       PASS       NaN       1          248            9.92        283   \n",
       "4  Away       PASS       NaN       1          316           12.64        346   \n",
       "\n",
       "   End Time [s]      From        To  Start X  Start Y  End X  End Y  \n",
       "0          2.04  Player23       NaN      NaN      NaN    NaN    NaN  \n",
       "1          3.48  Player23  Player20     0.00     0.00 -10.60   0.68  \n",
       "2          7.44  Player20  Player18    -7.42     0.00  -6.36 -19.04  \n",
       "3         11.32  Player18  Player17    -3.18   -21.08 -20.14 -14.96  \n",
       "4         13.84  Player17  Player16   -22.26   -12.24 -25.44   5.44  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# events by team\n",
    "\n",
    "home_events = events[events[\"Team\"] == \"Home\"]\n",
    "away_events = events[events[\"Team\"] == \"Away\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Events type and count by team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PASS              543\n",
       "CHALLENGE         160\n",
       "RECOVERY          135\n",
       "BALL LOST         120\n",
       "SET PIECE          34\n",
       "BALL OUT           27\n",
       "SHOT               13\n",
       "FAULT RECEIVED      9\n",
       "CARD                3\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#event types and count of the home team\n",
    "home_events[\"Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PASS              421\n",
       "CHALLENGE         151\n",
       "RECOVERY          113\n",
       "BALL LOST         113\n",
       "SET PIECE          46\n",
       "BALL OUT           22\n",
       "FAULT RECEIVED     11\n",
       "SHOT               11\n",
       "CARD                3\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#event types and count of the away team\n",
    "\n",
    "away_events[\"Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shots by team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = events[events[\"Type\"]==\"SHOT\"]\n",
    "home_shots = home_events[home_events.Type == \"SHOT\"]\n",
    "away_shots = away_events[away_events.Type == \"SHOT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HEAD-OFF TARGET-OUT    4\n",
       "ON TARGET-SAVED        4\n",
       "OFF TARGET-OUT         2\n",
       "ON TARGET-GOAL         2\n",
       "HEAD-ON TARGET-GOAL    1\n",
       "Name: Subtype, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_shots['Subtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OFF TARGET-OUT         3\n",
       "ON TARGET-SAVED        2\n",
       "BLOCKED                2\n",
       "ON TARGET-GOAL         2\n",
       "HEAD-OFF TARGET-OUT    1\n",
       "OFF TARGET-HEAD-OUT    1\n",
       "Name: Subtype, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "away_shots[\"Subtype\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player9     4\n",
       "Player10    3\n",
       "Player5     2\n",
       "Player7     1\n",
       "Player12    1\n",
       "Player3     1\n",
       "Player4     1\n",
       "Name: From, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of shots, by whom?\n",
    "home_shots[\"From\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals by team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_goals= home_shots[home_shots[\"Subtype\"].str.contains(\"-GOAL\")].copy()\n",
    "away_goals = away_shots[away_shots[\"Subtype\"].str.contains(\"-GOAL\")].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Type</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Period</th>\n",
       "      <th>Start Frame</th>\n",
       "      <th>Start Time [s]</th>\n",
       "      <th>End Frame</th>\n",
       "      <th>End Time [s]</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Start X</th>\n",
       "      <th>Start Y</th>\n",
       "      <th>End X</th>\n",
       "      <th>End Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>ON TARGET-GOAL</td>\n",
       "      <td>1</td>\n",
       "      <td>12202</td>\n",
       "      <td>488.08</td>\n",
       "      <td>12212</td>\n",
       "      <td>488.48</td>\n",
       "      <td>Player10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-47.70</td>\n",
       "      <td>3.40</td>\n",
       "      <td>-54.06</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>HEAD-ON TARGET-GOAL</td>\n",
       "      <td>2</td>\n",
       "      <td>73983</td>\n",
       "      <td>2959.32</td>\n",
       "      <td>73995</td>\n",
       "      <td>2959.80</td>\n",
       "      <td>Player9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.70</td>\n",
       "      <td>1.36</td>\n",
       "      <td>54.06</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>ON TARGET-GOAL</td>\n",
       "      <td>2</td>\n",
       "      <td>121027</td>\n",
       "      <td>4841.08</td>\n",
       "      <td>121055</td>\n",
       "      <td>4842.20</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.74</td>\n",
       "      <td>5.44</td>\n",
       "      <td>54.06</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team  Type              Subtype  Period  Start Frame  Start Time [s]  \\\n",
       "198   Home  SHOT       ON TARGET-GOAL       1        12202          488.08   \n",
       "1118  Home  SHOT  HEAD-ON TARGET-GOAL       2        73983         2959.32   \n",
       "1723  Home  SHOT       ON TARGET-GOAL       2       121027         4841.08   \n",
       "\n",
       "      End Frame  End Time [s]      From   To  Start X  Start Y  End X  End Y  \n",
       "198       12212        488.48  Player10  NaN   -47.70     3.40 -54.06   2.04  \n",
       "1118      73995       2959.80   Player9  NaN    47.70     1.36  54.06   2.04  \n",
       "1723     121055       4842.20  Player12  NaN    30.74     5.44  54.06   2.72  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Pitch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_pitch( field_dimen = (106.0,68.0), field_color ='green', linewidth=2, markersize=20):\n",
    "    \"\"\" plot_pitch\n",
    "    \n",
    "    Plots a soccer pitch. All distance units converted to meters.\n",
    "    \n",
    "    Parameters\n",
    "    -----------\n",
    "        field_dimen: (length, width) of field in meters. Default is (106,68)\n",
    "        field_color: color of field. options are {'green','white'}\n",
    "        linewidth  : width of lines. default = 2\n",
    "        markersize : size of markers (e.g. penalty spot, centre spot, posts). default = 20\n",
    "        \n",
    "    Returrns\n",
    "    -----------\n",
    "       fig,ax : figure and aixs objects (so that other data can be plotted onto the pitch)\n",
    "    \"\"\"\n",
    "    fig,ax = plt.subplots(figsize=(12,8)) # create a figure \n",
    "    # decide what color we want the field to be. Default is green, but can also choose white\n",
    "    if field_color=='green':\n",
    "        ax.set_facecolor('mediumseagreen')\n",
    "        lc = 'whitesmoke' # line color\n",
    "        pc = 'w' # 'spot' colors\n",
    "    elif field_color=='white':\n",
    "        lc = 'k'\n",
    "        pc = 'k'\n",
    "    # ALL DIMENSIONS IN m\n",
    "    border_dimen = (3,3) # include a border arround of the field of width 3m\n",
    "    meters_per_yard = 0.9144 # unit conversion from yards to meters\n",
    "    half_pitch_length = field_dimen[0]/2. # length of half pitch\n",
    "    half_pitch_width = field_dimen[1]/2. # width of half pitch\n",
    "    signs = [-1,1] \n",
    "    # Soccer field dimensions typically defined in yards, so we need to convert to meters\n",
    "    goal_line_width = 8*meters_per_yard\n",
    "    box_width = 20*meters_per_yard\n",
    "    box_length = 6*meters_per_yard\n",
    "    area_width = 44*meters_per_yard\n",
    "    area_length = 18*meters_per_yard\n",
    "    penalty_spot = 12*meters_per_yard\n",
    "    corner_radius = 1*meters_per_yard\n",
    "    D_length = 8*meters_per_yard\n",
    "    D_radius = 10*meters_per_yard\n",
    "    D_pos = 12*meters_per_yard\n",
    "    centre_circle_radius = 10*meters_per_yard\n",
    "    # plot half way line # center circle\n",
    "    ax.plot([0,0],[-half_pitch_width,half_pitch_width],lc,linewidth=linewidth)\n",
    "    ax.scatter(0.0,0.0,marker='o',facecolor=lc,linewidth=0,s=markersize)\n",
    "    y = np.linspace(-1,1,50)*centre_circle_radius\n",
    "    x = np.sqrt(centre_circle_radius**2-y**2)\n",
    "    ax.plot(x,y,lc,linewidth=linewidth)\n",
    "    ax.plot(-x,y,lc,linewidth=linewidth)\n",
    "    for s in signs: # plots each line seperately\n",
    "        # plot pitch boundary\n",
    "        ax.plot([-half_pitch_length,half_pitch_length],[s*half_pitch_width,s*half_pitch_width],lc,linewidth=linewidth)\n",
    "        ax.plot([s*half_pitch_length,s*half_pitch_length],[-half_pitch_width,half_pitch_width],lc,linewidth=linewidth)\n",
    "        # goal posts & line\n",
    "        ax.plot( [s*half_pitch_length,s*half_pitch_length],[-goal_line_width/2.,goal_line_width/2.],pc+'s',markersize=6*markersize/20.,linewidth=linewidth)\n",
    "        # 6 yard box\n",
    "        ax.plot([s*half_pitch_length,s*half_pitch_length-s*box_length],[box_width/2.,box_width/2.],lc,linewidth=linewidth)\n",
    "        ax.plot([s*half_pitch_length,s*half_pitch_length-s*box_length],[-box_width/2.,-box_width/2.],lc,linewidth=linewidth)\n",
    "        ax.plot([s*half_pitch_length-s*box_length,s*half_pitch_length-s*box_length],[-box_width/2.,box_width/2.],lc,linewidth=linewidth)\n",
    "        # penalty area\n",
    "        ax.plot([s*half_pitch_length,s*half_pitch_length-s*area_length],[area_width/2.,area_width/2.],lc,linewidth=linewidth)\n",
    "        ax.plot([s*half_pitch_length,s*half_pitch_length-s*area_length],[-area_width/2.,-area_width/2.],lc,linewidth=linewidth)\n",
    "        ax.plot([s*half_pitch_length-s*area_length,s*half_pitch_length-s*area_length],[-area_width/2.,area_width/2.],lc,linewidth=linewidth)\n",
    "        # penalty spot\n",
    "        ax.scatter(s*half_pitch_length-s*penalty_spot,0.0,marker='o',facecolor=lc,linewidth=0,s=markersize)\n",
    "        # corner flags\n",
    "        y = np.linspace(0,1,50)*corner_radius\n",
    "        x = np.sqrt(corner_radius**2-y**2)\n",
    "        ax.plot(s*half_pitch_length-s*x,-half_pitch_width+y,lc,linewidth=linewidth)\n",
    "        ax.plot(s*half_pitch_length-s*x,half_pitch_width-y,lc,linewidth=linewidth)\n",
    "        # draw the D\n",
    "        y = np.linspace(-1,1,50)*D_length # D_length is the chord of the circle that defines the D\n",
    "        x = np.sqrt(D_radius**2-y**2)+D_pos\n",
    "        ax.plot(s*half_pitch_length-s*x,y,lc,linewidth=linewidth)\n",
    "        \n",
    "    # remove axis labels and ticks\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    # set axis limits\n",
    "    xmax = field_dimen[0]/2. + border_dimen[0]\n",
    "    ymax = field_dimen[1]/2. + border_dimen[1]\n",
    "    ax.set_xlim([-xmax,xmax])\n",
    "    ax.set_ylim([-ymax,ymax])\n",
    "    ax.set_axisbelow(True)\n",
    "    return fig,ax\n",
    "fig,ax = plot_pitch()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-47.7, 3.400000000000003, '')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plot_pitch()\n",
    "#on-target shot position\n",
    "ax.plot( events.loc[198][\"Start X\"], events.loc[198][\"Start Y\"], \"ro\")\n",
    "\n",
    "ax.plot( events.loc[198][\"End X\"], events.loc[198][\"End Y\"], \"bo\")\n",
    "\n",
    "\n",
    "#on-target shot direction\n",
    "ax.annotate(\"\", xy = events.loc[198][[\"End X\", \"End Y\"]], xytext = events.loc[198][[\"Start X\", \"Start Y\"]],\\\n",
    "           alpha = 0.6, arrowprops = dict(arrowstyle = \"->\", color = \"b\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting all the passes\n",
    "All the passes that led up to the goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_events( events, figax=None, field_dimen = (106.0,68), \\\n",
    "                indicators = ['Marker','Arrow'], color='r', \\\n",
    "                marker_style = 'o', alpha = 0.5, annotate=False):\n",
    "    \"\"\" plot_events( events )\n",
    "    \n",
    "    Plots Metrica event positions on a football pitch. event data can be a single or \n",
    "    several rows of a data frame. All distances should be in meters.\n",
    "    \n",
    "    Parameters\n",
    "    -----------\n",
    "        events: row (i.e. instant) of the home team tracking data frame\n",
    "        fig,ax: Can be used to pass in the (fig,ax) objects of a previously \n",
    "        generated pitch. \n",
    "        Set to (fig,ax) to use an existing figure, or None (the default) to generate a new pitch plot, \n",
    "        field_dimen: tuple containing the length and width of the pitch in meters. Default is (106,68)\n",
    "        indicators: List containing choices on how to plot the event.\n",
    "        'Marker' places a marker at the 'Start X/Y' location of the event;\n",
    "        'Arrow' draws an arrow from the start to end locations. Can choose one or both.\n",
    "        color: color of indicator. Default is 'r' (red)\n",
    "        marker_style: Marker type used to indicate the event position. Default is 'o' (filled circle).\n",
    "        alpha: alpha of event marker. Default is 0.5    \n",
    "        annotate: Boolean determining whether text annotation from event data 'Type' and 'From' fields is shown on plot. Default is False.\n",
    "        \n",
    "    Returns\n",
    "    -----------\n",
    "       fig,ax : figure and aixs objects (so that other data can be plotted onto the pitch)\n",
    "    \"\"\"\n",
    "\n",
    "    if figax is None: # create new pitch \n",
    "        fig,ax = plot_pitch( field_dimen = field_dimen )\n",
    "    else: # overlay on a previously generated pitch\n",
    "        fig,ax = figax \n",
    "    for i,row in events.iterrows():\n",
    "        if 'Marker' in indicators:\n",
    "            ax.plot(  row['Start X'], row['Start Y'], color+marker_style, alpha=alpha )\n",
    "        if 'Arrow' in indicators:\n",
    "            ax.annotate(\"\", xy=row[['End X','End Y']], xytext=row[['Start X','Start Y']],\\\n",
    "                        alpha=alpha, arrowprops=dict(alpha=alpha,width=0.5,headlength=4.0,\\\n",
    "                                                     headwidth=4.0,color=color),annotation_clip=False)\n",
    "        if annotate:\n",
    "            textstring = row['Type'] + ': ' + row['From']\n",
    "            ax.text( row['Start X'], row['Start Y'], textstring, fontsize=10, color=color)\n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x584d708d48>)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plot_pitch()\n",
    "plot_events(events.loc[190:198], indicators = [\"Marker\",\"Arrow\"], figax = (fig, ax))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating the Players and Event Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x584d8e8348>)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plot_pitch()\n",
    "plot_events(events.loc[190:198], indicators = [\"Marker\",\"Arrow\"], figax = (fig, ax), annotate = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in the Tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (0,1,2,3,5,7,9,11,13,15,17,19,21,23,25,27) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (0,1,2,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    " DataDir= r\"C:\\Users\\MustaphaM\\Desktop\\sample-data-master\\data\"\n",
    "tracking_away = pd.read_csv(AwayT_dir)\n",
    "tracking_home = pd.read_csv(HomeT_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141158, 29)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_away.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141158, 33)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_home.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Home</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Home.1</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Home.2</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Home.3</th>\n",
       "      <th>...</th>\n",
       "      <th>Home.10</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Home.11</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "      <th>Home.12</th>\n",
       "      <th>Unnamed: 28</th>\n",
       "      <th>Home.13</th>\n",
       "      <th>Unnamed: 30</th>\n",
       "      <th>Unnamed: 31</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Period</td>\n",
       "      <td>Frame</td>\n",
       "      <td>Time [s]</td>\n",
       "      <td>Player11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Player1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Player2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Player3</td>\n",
       "      <td>...</td>\n",
       "      <td>Player10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Player13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Player14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141153</th>\n",
       "      <td>2</td>\n",
       "      <td>141152</td>\n",
       "      <td>5646.08</td>\n",
       "      <td>0.06285</td>\n",
       "      <td>0.51243</td>\n",
       "      <td>0.38499</td>\n",
       "      <td>0.15356</td>\n",
       "      <td>0.22558</td>\n",
       "      <td>0.41355</td>\n",
       "      <td>0.27142</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40837</td>\n",
       "      <td>0.14449</td>\n",
       "      <td>0.51032</td>\n",
       "      <td>0.94554</td>\n",
       "      <td>0.27962</td>\n",
       "      <td>0.36700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141154</th>\n",
       "      <td>2</td>\n",
       "      <td>141153</td>\n",
       "      <td>5646.12</td>\n",
       "      <td>0.06285</td>\n",
       "      <td>0.51243</td>\n",
       "      <td>0.38499</td>\n",
       "      <td>0.15356</td>\n",
       "      <td>0.22558</td>\n",
       "      <td>0.41355</td>\n",
       "      <td>0.27142</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40837</td>\n",
       "      <td>0.14449</td>\n",
       "      <td>0.51031</td>\n",
       "      <td>0.94626</td>\n",
       "      <td>0.28009</td>\n",
       "      <td>0.36718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141155</th>\n",
       "      <td>2</td>\n",
       "      <td>141154</td>\n",
       "      <td>5646.16</td>\n",
       "      <td>0.06285</td>\n",
       "      <td>0.51243</td>\n",
       "      <td>0.38499</td>\n",
       "      <td>0.15356</td>\n",
       "      <td>0.22558</td>\n",
       "      <td>0.41355</td>\n",
       "      <td>0.27142</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40837</td>\n",
       "      <td>0.14449</td>\n",
       "      <td>0.51029</td>\n",
       "      <td>0.94711</td>\n",
       "      <td>0.28031</td>\n",
       "      <td>0.36726</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141156</th>\n",
       "      <td>2</td>\n",
       "      <td>141155</td>\n",
       "      <td>5646.2</td>\n",
       "      <td>0.06285</td>\n",
       "      <td>0.51243</td>\n",
       "      <td>0.38499</td>\n",
       "      <td>0.15356</td>\n",
       "      <td>0.22558</td>\n",
       "      <td>0.41355</td>\n",
       "      <td>0.27142</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40837</td>\n",
       "      <td>0.14449</td>\n",
       "      <td>0.51028</td>\n",
       "      <td>0.94792</td>\n",
       "      <td>0.28059</td>\n",
       "      <td>0.36706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141157</th>\n",
       "      <td>2</td>\n",
       "      <td>141156</td>\n",
       "      <td>5646.24</td>\n",
       "      <td>0.06285</td>\n",
       "      <td>0.51243</td>\n",
       "      <td>0.38499</td>\n",
       "      <td>0.15356</td>\n",
       "      <td>0.22558</td>\n",
       "      <td>0.41355</td>\n",
       "      <td>0.27142</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40837</td>\n",
       "      <td>0.14449</td>\n",
       "      <td>0.51029</td>\n",
       "      <td>0.94846</td>\n",
       "      <td>0.28065</td>\n",
       "      <td>0.36491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141158 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0 Unnamed: 1 Unnamed: 2      Home  Unnamed: 4   Home.1  \\\n",
       "0             NaN        NaN        NaN        11         NaN        1   \n",
       "1          Period      Frame   Time [s]  Player11         NaN  Player1   \n",
       "2               1          1       0.04   0.94275     0.50413  0.64812   \n",
       "3               1          2       0.08   0.94275     0.50413  0.64812   \n",
       "4               1          3       0.12   0.94275     0.50413  0.64812   \n",
       "...           ...        ...        ...       ...         ...      ...   \n",
       "141153          2     141152    5646.08   0.06285     0.51243  0.38499   \n",
       "141154          2     141153    5646.12   0.06285     0.51243  0.38499   \n",
       "141155          2     141154    5646.16   0.06285     0.51243  0.38499   \n",
       "141156          2     141155     5646.2   0.06285     0.51243  0.38499   \n",
       "141157          2     141156    5646.24   0.06285     0.51243  0.38499   \n",
       "\n",
       "        Unnamed: 6   Home.2  Unnamed: 8   Home.3  ...   Home.10 Unnamed: 24  \\\n",
       "0              NaN        2         NaN        3  ...        10         NaN   \n",
       "1              NaN  Player2         NaN  Player3  ...  Player10         NaN   \n",
       "2          0.28605  0.67752     0.42803  0.69882  ...   0.50194     0.61123   \n",
       "3          0.28605  0.67752     0.42803  0.69882  ...   0.50194     0.61123   \n",
       "4          0.28605  0.67752     0.42803  0.69882  ...   0.50194     0.61123   \n",
       "...            ...      ...         ...      ...  ...       ...         ...   \n",
       "141153     0.15356  0.22558     0.41355  0.27142  ...       NaN         NaN   \n",
       "141154     0.15356  0.22558     0.41355  0.27142  ...       NaN         NaN   \n",
       "141155     0.15356  0.22558     0.41355  0.27142  ...       NaN         NaN   \n",
       "141156     0.15356  0.22558     0.41355  0.27142  ...       NaN         NaN   \n",
       "141157     0.15356  0.22558     0.41355  0.27142  ...       NaN         NaN   \n",
       "\n",
       "         Home.11 Unnamed: 26   Home.12 Unnamed: 28   Home.13 Unnamed: 30  \\\n",
       "0             12         NaN        13         NaN        14         NaN   \n",
       "1       Player12         NaN  Player13         NaN  Player14         NaN   \n",
       "2            NaN         NaN       NaN         NaN       NaN         NaN   \n",
       "3            NaN         NaN       NaN         NaN       NaN         NaN   \n",
       "4            NaN         NaN       NaN         NaN       NaN         NaN   \n",
       "...          ...         ...       ...         ...       ...         ...   \n",
       "141153   0.40837     0.14449   0.51032     0.94554   0.27962     0.36700   \n",
       "141154   0.40837     0.14449   0.51031     0.94626   0.28009     0.36718   \n",
       "141155   0.40837     0.14449   0.51029     0.94711   0.28031     0.36726   \n",
       "141156   0.40837     0.14449   0.51028     0.94792   0.28059     0.36706   \n",
       "141157   0.40837     0.14449   0.51029     0.94846   0.28065     0.36491   \n",
       "\n",
       "        Unnamed: 31 Unnamed: 32  \n",
       "0               NaN         NaN  \n",
       "1              Ball         NaN  \n",
       "2               NaN         NaN  \n",
       "3               NaN         NaN  \n",
       "4               NaN         NaN  \n",
       "...             ...         ...  \n",
       "141153          NaN         NaN  \n",
       "141154          NaN         NaN  \n",
       "141155          NaN         NaN  \n",
       "141156          NaN         NaN  \n",
       "141157          NaN         NaN  \n",
       "\n",
       "[141158 rows x 33 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to read in the Data as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "def tracking_data(DATADIR,game_id,teamname):\n",
    "    '''\n",
    "    tracking_data(DATADIR,game_id,teamname):\n",
    "    read Metrica tracking data for game_id and return as a DataFrame. \n",
    "    teamname is the name of the team in the filename. For the sample data this is either 'Home' or 'Away'.\n",
    "    '''\n",
    "    teamfile = '/Sample_Game_%d/Sample_Game_%d_RawTrackingData_%s_Team.csv' % (game_id,game_id,teamname)\n",
    "    # First:  deal with file headers so that we can get the player names correct\n",
    "    csvfile =  open('{}/{}'.format(DATADIR, teamfile), 'r') # create a csv file reader\n",
    "    reader = csv.reader(csvfile) \n",
    "    teamnamefull = next(reader)[3].lower()\n",
    "    print(\"Reading team: %s\" % teamnamefull)\n",
    "    # construct column names\n",
    "    jerseys = [x for x in next(reader) if x != ''] # extract player jersey numbers from second row\n",
    "    columns = next(reader)\n",
    "    for i, j in enumerate(jerseys): # create x & y position column headers for each player\n",
    "        columns[i*2+3] = \"{}_{}_x\".format(teamname, j)\n",
    "        columns[i*2+4] = \"{}_{}_y\".format(teamname, j)\n",
    "    columns[-2] = \"ball_x\" # column headers for the x & y positions of the ball\n",
    "    columns[-1] = \"ball_y\"\n",
    "    # Second: read in tracking data and place into pandas Dataframe\n",
    "    tracking = pd.read_csv('{}/{}'.format(DATADIR, teamfile), names=columns, index_col='Frame', skiprows=3)\n",
    "    return tracking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invoking the Function and reading in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading team: home\n",
      "Reading team: away\n"
     ]
    }
   ],
   "source": [
    "tracking_Home = tracking_data(DataDir, 2, \"Home\")\n",
    "tracking_Away = tracking_data(DataDir,2,\"Away\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141156, 32)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_Home.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141156, 28)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_Away.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>Period</th>\n",
       "      <th>Time [s]</th>\n",
       "      <th>Home_11_x</th>\n",
       "      <th>Home_11_y</th>\n",
       "      <th>Home_1_x</th>\n",
       "      <th>Home_1_y</th>\n",
       "      <th>Home_2_x</th>\n",
       "      <th>Home_2_y</th>\n",
       "      <th>Home_3_x</th>\n",
       "      <th>Home_3_y</th>\n",
       "      <th>...</th>\n",
       "      <th>Home_10_x</th>\n",
       "      <th>Home_10_y</th>\n",
       "      <th>Home_12_x</th>\n",
       "      <th>Home_12_y</th>\n",
       "      <th>Home_13_x</th>\n",
       "      <th>Home_13_y</th>\n",
       "      <th>Home_14_x</th>\n",
       "      <th>Home_14_y</th>\n",
       "      <th>ball_x</th>\n",
       "      <th>ball_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>0.55606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>0.55606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>0.55606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>0.55606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.94275</td>\n",
       "      <td>0.50413</td>\n",
       "      <td>0.64812</td>\n",
       "      <td>0.28605</td>\n",
       "      <td>0.67752</td>\n",
       "      <td>0.42803</td>\n",
       "      <td>0.69882</td>\n",
       "      <td>0.55606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50194</td>\n",
       "      <td>0.61123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Period  Time [s]  Home_11_x  Home_11_y  Home_1_x  Home_1_y  Home_2_x  \\\n",
       "Frame                                                                         \n",
       "1           1      0.04    0.94275    0.50413   0.64812   0.28605   0.67752   \n",
       "2           1      0.08    0.94275    0.50413   0.64812   0.28605   0.67752   \n",
       "3           1      0.12    0.94275    0.50413   0.64812   0.28605   0.67752   \n",
       "4           1      0.16    0.94275    0.50413   0.64812   0.28605   0.67752   \n",
       "5           1      0.20    0.94275    0.50413   0.64812   0.28605   0.67752   \n",
       "\n",
       "       Home_2_y  Home_3_x  Home_3_y  ...  Home_10_x  Home_10_y  Home_12_x  \\\n",
       "Frame                                ...                                    \n",
       "1       0.42803   0.69882   0.55606  ...    0.50194    0.61123        NaN   \n",
       "2       0.42803   0.69882   0.55606  ...    0.50194    0.61123        NaN   \n",
       "3       0.42803   0.69882   0.55606  ...    0.50194    0.61123        NaN   \n",
       "4       0.42803   0.69882   0.55606  ...    0.50194    0.61123        NaN   \n",
       "5       0.42803   0.69882   0.55606  ...    0.50194    0.61123        NaN   \n",
       "\n",
       "       Home_12_y  Home_13_x  Home_13_y  Home_14_x  Home_14_y  ball_x  ball_y  \n",
       "Frame                                                                         \n",
       "1            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "2            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "3            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "4            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "5            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_Home.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x and y represents the positions of the different players. Substitute players 12 through 14 have no positions.\n",
    "The ball has no position until the start of the game i.e. frame 51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ball_x    0.50216\n",
       "ball_y    0.50376\n",
       "Name: 51, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_Home.loc[51][[\"ball_x\", \"ball_y\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert Pitch Coordinates to metres and origin to centre\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "tracking_Home = to_metric_coordinates(tracking_Home)\n",
    "tracking_Away = to_metric_coordinates(tracking_Away)\n",
    "\n",
    "tracking_Home = tracking_Home\n",
    "tracking_Away = tracking_Away\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>Period</th>\n",
       "      <th>Time [s]</th>\n",
       "      <th>Home_11_x</th>\n",
       "      <th>Home_11_y</th>\n",
       "      <th>Home_1_x</th>\n",
       "      <th>Home_1_y</th>\n",
       "      <th>Home_2_x</th>\n",
       "      <th>Home_2_y</th>\n",
       "      <th>Home_3_x</th>\n",
       "      <th>Home_3_y</th>\n",
       "      <th>...</th>\n",
       "      <th>Home_10_x</th>\n",
       "      <th>Home_10_y</th>\n",
       "      <th>Home_12_x</th>\n",
       "      <th>Home_12_y</th>\n",
       "      <th>Home_13_x</th>\n",
       "      <th>Home_13_y</th>\n",
       "      <th>Home_14_x</th>\n",
       "      <th>Home_14_y</th>\n",
       "      <th>ball_x</th>\n",
       "      <th>ball_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>-14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>-4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20564</td>\n",
       "      <td>7.56364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>-14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>-4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20564</td>\n",
       "      <td>7.56364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>-14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>-4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20564</td>\n",
       "      <td>7.56364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>-14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>-4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20564</td>\n",
       "      <td>7.56364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>-14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>-4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20564</td>\n",
       "      <td>7.56364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Period  Time [s]  Home_11_x  Home_11_y  Home_1_x  Home_1_y  Home_2_x  \\\n",
       "Frame                                                                         \n",
       "1           1      0.04    46.9315    0.28084  15.70072  -14.5486  18.81712   \n",
       "2           1      0.08    46.9315    0.28084  15.70072  -14.5486  18.81712   \n",
       "3           1      0.12    46.9315    0.28084  15.70072  -14.5486  18.81712   \n",
       "4           1      0.16    46.9315    0.28084  15.70072  -14.5486  18.81712   \n",
       "5           1      0.20    46.9315    0.28084  15.70072  -14.5486  18.81712   \n",
       "\n",
       "       Home_2_y  Home_3_x  Home_3_y  ...  Home_10_x  Home_10_y  Home_12_x  \\\n",
       "Frame                                ...                                    \n",
       "1      -4.89396  21.07492   3.81208  ...    0.20564    7.56364        NaN   \n",
       "2      -4.89396  21.07492   3.81208  ...    0.20564    7.56364        NaN   \n",
       "3      -4.89396  21.07492   3.81208  ...    0.20564    7.56364        NaN   \n",
       "4      -4.89396  21.07492   3.81208  ...    0.20564    7.56364        NaN   \n",
       "5      -4.89396  21.07492   3.81208  ...    0.20564    7.56364        NaN   \n",
       "\n",
       "       Home_12_y  Home_13_x  Home_13_y  Home_14_x  Home_14_y  ball_x  ball_y  \n",
       "Frame                                                                         \n",
       "1            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "2            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "3            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "4            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "5            NaN        NaN        NaN        NaN        NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_Home.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x584b147a88>]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_pitch()\n",
    "\n",
    "#we can plot the players trajectories over time using their start and end positions.\n",
    "# E.g the first 60s implies 1500 frames (25 frames per second)\n",
    "#overlaying the trajectories on the pitch\n",
    "ax.plot(tracking_Home[\"Home_11_x\"].iloc[:1500], tracking_Home[\"Home_11_y\"].iloc[:1500], \"r.\", MarkerSize = 1)\n",
    "ax.plot(tracking_Home[\"Home_1_x\"].iloc[:1500], tracking_Home[\"Home_1_y\"].iloc[:1500], \"b.\", MarkerSize = 1)\n",
    "ax.plot(tracking_Home[\"Home_2_x\"].iloc[:1500], tracking_Home[\"Home_2_y\"].iloc[:1500], \"g.\", MarkerSize = 1)\n",
    "ax.plot(tracking_Home[\"Home_3_x\"].iloc[:1500], tracking_Home[\"Home_3_y\"].iloc[:1500], \"k.\", MarkerSize = 1)\n",
    "ax.plot(tracking_Home[\"Home_4_x\"].iloc[:1500], tracking_Home[\"Home_4_y\"].iloc[:1500], \"c.\", MarkerSize = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The red line shows the movement of the goalkeeper. i.e player 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the positions at kick off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_frame( hometeam, awayteam, figax=None, team_colors=('r','b'), field_dimen = (106.0,68.0), include_player_velocities=False, PlayerMarkerSize=10, PlayerAlpha=0.7, annotate=False ):\n",
    "    \"\"\" plot_frame( hometeam, awayteam )\n",
    "    \n",
    "    Plots a frame of Metrica tracking data (player positions and the ball) on a football pitch. All distances should be in meters.\n",
    "    \n",
    "    Parameters\n",
    "    -----------\n",
    "        hometeam: row (i.e. instant) of the home team tracking data frame\n",
    "        awayteam: row of the away team tracking data frame\n",
    "        fig,ax: Can be used to pass in the (fig,ax) objects of a previously generated pitch. Set to (fig,ax) to use an existing figure, or None (the default) to generate a new pitch plot, \n",
    "        team_colors: Tuple containing the team colors of the home & away team. Default is 'r' (red, home team) and 'b' (blue away team)\n",
    "        field_dimen: tuple containing the length and width of the pitch in meters. Default is (106,68)\n",
    "        include_player_velocities: Boolean variable that determines whether player velocities are also plotted (as quivers). Default is False\n",
    "        PlayerMarkerSize: size of the individual player marlers. Default is 10\n",
    "        PlayerAlpha: alpha (transparency) of player markers. Defaault is 0.7\n",
    "        annotate: Boolean variable that determines with player jersey numbers are added to the plot (default is False)\n",
    "        \n",
    "    Returrns\n",
    "    -----------\n",
    "       fig,ax : figure and aixs objects (so that other data can be plotted onto the pitch)\n",
    "    \"\"\"\n",
    "    if figax is None: # create new pitch \n",
    "        fig,ax = plot_pitch( field_dimen = field_dimen )\n",
    "    else: # overlay on a previously generated pitch\n",
    "        fig,ax = figax # unpack tuple\n",
    "    # plot home & away teams in order\n",
    "    for team,color in zip( [hometeam,awayteam], team_colors) :\n",
    "        x_columns = [c for c in team.keys() if c[-2:].lower()=='_x' and c!='ball_x'] # column header for player x positions\n",
    "        y_columns = [c for c in team.keys() if c[-2:].lower()=='_y' and c!='ball_y'] # column header for player y positions\n",
    "        ax.plot( team[x_columns], team[y_columns], color+'o', MarkerSize=PlayerMarkerSize, alpha=PlayerAlpha ) # plot player positions\n",
    "        if include_player_velocities:\n",
    "            vx_columns = ['{}_vx'.format(c[:-2]) for c in x_columns] # column header for player x positions\n",
    "            vy_columns = ['{}_vy'.format(c[:-2]) for c in y_columns] # column header for player y positions\n",
    "            ax.quiver( team[x_columns], team[y_columns], team[vx_columns], team[vy_columns], color=color, scale_units='inches', scale=10.,width=0.0015,headlength=5,headwidth=3,alpha=PlayerAlpha)\n",
    "        if annotate:\n",
    "            [ ax.text( team[x]+0.5, team[y]+0.5, x.split('_')[1], fontsize=10, color=color  ) for x,y in zip(x_columns,y_columns) if not ( np.isnan(team[x]) or np.isnan(team[y]) ) ] \n",
    "    # plot ball\n",
    "    ax.plot( hometeam['ball_x'], hometeam['ball_y'], 'ko', MarkerSize=6, alpha=1.0, LineWidth=0)\n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the positions at kick off\n",
    "fig, ax = plot_frame(tracking_Home.loc[51], tracking_Away.loc[51])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The black marker shows the position of the ball at kick off\\\n",
    "As evident in the event data, it is the away team that kicks off (blue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the positions at goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_events(events.loc[198:198], indicators = [\"Marker\", \"Arrow\"], annotate = True)\n",
    "frame = events.loc[198][\"Start Frame\"]\n",
    "fig, ax = plot_frame(tracking_Home.loc[frame], tracking_Away.loc[frame], figax = (fig,ax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}