Analyzing Player Motion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Player Motion: Making a Movie, Measuring Player Speed, and Creating Summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import ffmpeg\n",
    "\n",
    "import scipy.signal as signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR2 = r\"C:\\Users\\MustaphaM\\Desktop\\sample-data-master\\data\\Sample_Game_2\\Sample_Game_2_RawEventsData.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the events data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = pd.read_csv(DATADIR2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>To</th>\n",
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       "      <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",
       "  </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     0.50     0.50   0.40   0.51  \n",
       "2          7.44  Player20  Player18     0.43     0.50   0.44   0.22  \n",
       "3         11.32  Player18  Player17     0.47     0.19   0.31   0.28  \n",
       "4         13.84  Player17  Player16     0.29     0.32   0.26   0.58  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Away</td>\n",
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       "      <td>2.04</td>\n",
       "      <td>51</td>\n",
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       "      <td>Player23</td>\n",
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       "      <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",
       "    <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>4.24</td>\n",
       "      <td>-18.36</td>\n",
       "      <td>-22.26</td>\n",
       "      <td>17.68</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>-20.14</td>\n",
       "      <td>13.60</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>-19.08</td>\n",
       "      <td>13.60</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>-16.96</td>\n",
       "      <td>12.24</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>-18.02</td>\n",
       "      <td>23.12</td>\n",
       "      <td>-9.54</td>\n",
       "      <td>36.72</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     0.00    -0.00 -10.60   \n",
       "2           186          7.44  Player20  Player18    -7.42    -0.00  -6.36   \n",
       "3           283         11.32  Player18  Player17    -3.18    21.08 -20.14   \n",
       "4           346         13.84  Player17  Player16   -22.26    12.24 -25.44   \n",
       "...         ...           ...       ...       ...      ...      ...    ...   \n",
       "1930     140183       5607.32  Player21       NaN     4.24   -18.36 -22.26   \n",
       "1931     140180       5607.20   Player1       NaN   -20.14    13.60    NaN   \n",
       "1932     140183       5607.32  Player24       NaN   -19.08    13.60    NaN   \n",
       "1933     140211       5608.44  Player12       NaN   -16.96    12.24    NaN   \n",
       "1934     140298       5611.92  Player12       NaN   -18.02    23.12  -9.54   \n",
       "\n",
       "      End Y  \n",
       "0       NaN  \n",
       "1     -0.68  \n",
       "2     19.04  \n",
       "3     14.96  \n",
       "4     -5.44  \n",
       "...     ...  \n",
       "1930  17.68  \n",
       "1931    NaN  \n",
       "1932    NaN  \n",
       "1933    NaN  \n",
       "1934  36.72  \n",
       "\n",
       "[1935 rows x 14 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
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    }
   ],
   "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]= -1*(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]= -1*(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": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_metr = events.copy()\n",
    "events = to_metric_coordinates(events_metr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "    <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",
       "    <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>4.24</td>\n",
       "      <td>-18.36</td>\n",
       "      <td>-22.26</td>\n",
       "      <td>17.68</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>-20.14</td>\n",
       "      <td>13.60</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>-19.08</td>\n",
       "      <td>13.60</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>-16.96</td>\n",
       "      <td>12.24</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>-18.02</td>\n",
       "      <td>23.12</td>\n",
       "      <td>-9.54</td>\n",
       "      <td>36.72</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     0.00    -0.00 -10.60   \n",
       "2           186          7.44  Player20  Player18    -7.42    -0.00  -6.36   \n",
       "3           283         11.32  Player18  Player17    -3.18    21.08 -20.14   \n",
       "4           346         13.84  Player17  Player16   -22.26    12.24 -25.44   \n",
       "...         ...           ...       ...       ...      ...      ...    ...   \n",
       "1930     140183       5607.32  Player21       NaN     4.24   -18.36 -22.26   \n",
       "1931     140180       5607.20   Player1       NaN   -20.14    13.60    NaN   \n",
       "1932     140183       5607.32  Player24       NaN   -19.08    13.60    NaN   \n",
       "1933     140211       5608.44  Player12       NaN   -16.96    12.24    NaN   \n",
       "1934     140298       5611.92  Player12       NaN   -18.02    23.12  -9.54   \n",
       "\n",
       "      End Y  \n",
       "0       NaN  \n",
       "1     -0.68  \n",
       "2     19.04  \n",
       "3     14.96  \n",
       "4     -5.44  \n",
       "...     ...  \n",
       "1930  17.68  \n",
       "1931    NaN  \n",
       "1932    NaN  \n",
       "1933    NaN  \n",
       "1934  36.72  \n",
       "\n",
       "[1935 rows x 14 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading team: home\n",
      "Reading team: away\n"
     ]
    }
   ],
   "source": [
    "DataDir= r\"C:\\Users\\MustaphaM\\Desktop\\sample-data-master\\data\"\n",
    "tracking_Hom = tracking_data(DataDir, 2, \"Home\")\n",
    "tracking_Awa = tracking_data(DataDir,2,\"Away\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert positions from metrica units to meters"
   ]
  },
  {
   "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>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>14.54860</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>14.54860</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>14.54860</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>14.54860</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>14.54860</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>...</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>141152</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.08</td>\n",
       "      <td>-46.3379</td>\n",
       "      <td>-0.84524</td>\n",
       "      <td>-12.19106</td>\n",
       "      <td>23.55792</td>\n",
       "      <td>-29.08852</td>\n",
       "      <td>5.87860</td>\n",
       "      <td>-24.22948</td>\n",
       "      <td>12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.71278</td>\n",
       "      <td>24.17468</td>\n",
       "      <td>1.09392</td>\n",
       "      <td>-30.29672</td>\n",
       "      <td>-23.36028</td>\n",
       "      <td>9.04400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141153</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.12</td>\n",
       "      <td>-46.3379</td>\n",
       "      <td>-0.84524</td>\n",
       "      <td>-12.19106</td>\n",
       "      <td>23.55792</td>\n",
       "      <td>-29.08852</td>\n",
       "      <td>5.87860</td>\n",
       "      <td>-24.22948</td>\n",
       "      <td>12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.71278</td>\n",
       "      <td>24.17468</td>\n",
       "      <td>1.09286</td>\n",
       "      <td>-30.34568</td>\n",
       "      <td>-23.31046</td>\n",
       "      <td>9.03176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141154</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.16</td>\n",
       "      <td>-46.3379</td>\n",
       "      <td>-0.84524</td>\n",
       "      <td>-12.19106</td>\n",
       "      <td>23.55792</td>\n",
       "      <td>-29.08852</td>\n",
       "      <td>5.87860</td>\n",
       "      <td>-24.22948</td>\n",
       "      <td>12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.71278</td>\n",
       "      <td>24.17468</td>\n",
       "      <td>1.09074</td>\n",
       "      <td>-30.40348</td>\n",
       "      <td>-23.28714</td>\n",
       "      <td>9.02632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141155</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.20</td>\n",
       "      <td>-46.3379</td>\n",
       "      <td>-0.84524</td>\n",
       "      <td>-12.19106</td>\n",
       "      <td>23.55792</td>\n",
       "      <td>-29.08852</td>\n",
       "      <td>5.87860</td>\n",
       "      <td>-24.22948</td>\n",
       "      <td>12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.71278</td>\n",
       "      <td>24.17468</td>\n",
       "      <td>1.08968</td>\n",
       "      <td>-30.45856</td>\n",
       "      <td>-23.25746</td>\n",
       "      <td>9.03992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141156</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.24</td>\n",
       "      <td>-46.3379</td>\n",
       "      <td>-0.84524</td>\n",
       "      <td>-12.19106</td>\n",
       "      <td>23.55792</td>\n",
       "      <td>-29.08852</td>\n",
       "      <td>5.87860</td>\n",
       "      <td>-24.22948</td>\n",
       "      <td>12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.71278</td>\n",
       "      <td>24.17468</td>\n",
       "      <td>1.09074</td>\n",
       "      <td>-30.49528</td>\n",
       "      <td>-23.25110</td>\n",
       "      <td>9.18612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141156 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  14.54860  18.81712   \n",
       "2            1      0.08    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "3            1      0.12    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "4            1      0.16    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "5            1      0.20    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "...        ...       ...        ...        ...       ...       ...       ...   \n",
       "141152       2   5646.08   -46.3379   -0.84524 -12.19106  23.55792 -29.08852   \n",
       "141153       2   5646.12   -46.3379   -0.84524 -12.19106  23.55792 -29.08852   \n",
       "141154       2   5646.16   -46.3379   -0.84524 -12.19106  23.55792 -29.08852   \n",
       "141155       2   5646.20   -46.3379   -0.84524 -12.19106  23.55792 -29.08852   \n",
       "141156       2   5646.24   -46.3379   -0.84524 -12.19106  23.55792 -29.08852   \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",
       "141152   5.87860 -24.22948  12.09788  ...        NaN        NaN   -9.71278   \n",
       "141153   5.87860 -24.22948  12.09788  ...        NaN        NaN   -9.71278   \n",
       "141154   5.87860 -24.22948  12.09788  ...        NaN        NaN   -9.71278   \n",
       "141155   5.87860 -24.22948  12.09788  ...        NaN        NaN   -9.71278   \n",
       "141156   5.87860 -24.22948  12.09788  ...        NaN        NaN   -9.71278   \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",
       "141152   24.17468    1.09392  -30.29672  -23.36028    9.04400     NaN     NaN  \n",
       "141153   24.17468    1.09286  -30.34568  -23.31046    9.03176     NaN     NaN  \n",
       "141154   24.17468    1.09074  -30.40348  -23.28714    9.02632     NaN     NaN  \n",
       "141155   24.17468    1.08968  -30.45856  -23.25746    9.03992     NaN     NaN  \n",
       "141156   24.17468    1.09074  -30.49528  -23.25110    9.18612     NaN     NaN  \n",
       "\n",
       "[141156 rows x 32 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tracking_HomeConv = tracking_Hom.copy()\n",
    "tracking_AwayConv = tracking_Awa.copy()\n",
    "\n",
    "tracking_Home = to_metric_coordinates(tracking_HomeConv)\n",
    "tracking_Away = to_metric_coordinates(tracking_AwayConv)\n",
    "\n",
    "tracking_Home "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse direction of player so that home team is always attacking right --> left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_single_playing_direction(home,away,events):\n",
    "    '''\n",
    "    Flip coordinates in second half so that each team always shoots in the same direction through the match.\n",
    "    '''\n",
    "    for team in [home,away,events]:\n",
    "        second_half_idx = team.Period.idxmax(2)\n",
    "        columns = [c for c in team.columns if c[-1].lower() in ['x','y']]\n",
    "        team.loc[second_half_idx:,columns] *= -1\n",
    "    return home,away,events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_Home, tracking_Away, events = to_single_playing_direction(tracking_Home, tracking_Away, events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_match_clip(hometeam,awayteam, fpath, fname='clip_test', figax=None, frames_per_second=25, team_colors=('r','b'), field_dimen = (106.0,68.0), include_player_velocities=False, PlayerMarkerSize=10, PlayerAlpha=0.7):\n",
    "    \"\"\" save_match_clip( hometeam, awayteam, fpath )\n",
    "    \n",
    "    Generates a movie from Metrica tracking data, saving it in the 'fpath' directory with name 'fname'\n",
    "    \n",
    "    Parameters\n",
    "    -----------\n",
    "        hometeam: home team tracking data DataFrame. Movie will be created from all rows in the DataFrame\n",
    "        awayteam: away team tracking data DataFrame. The indices *must* match those of the hometeam DataFrame\n",
    "        fpath: directory to save the movie\n",
    "        fname: movie filename. Default is 'clip_test.mp4'\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",
    "        frames_per_second: frames per second to assume when generating the movie. Default is 25.\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",
    "        \n",
    "    Returrns\n",
    "    -----------\n",
    "       fig,ax : figure and aixs objects (so that other data can be plotted onto the pitch)\n",
    "    \"\"\"\n",
    "    # check that indices match first\n",
    "    assert np.all( hometeam.index==awayteam.index ), \"Home and away team Dataframe indices must be the same\"\n",
    "    # in which case use home team index\n",
    "    index = hometeam.index\n",
    "    # Set figure and movie settings\n",
    "    FFMpegWriter = animation.writers['ffmpeg']\n",
    "    metadata = dict(title='Tracking Data', artist='Matplotlib', comment='Metrica tracking data clip')\n",
    "    writer = FFMpegWriter(fps=frames_per_second, metadata=metadata)\n",
    "    fname = fpath + '/' +  fname + '.mp4' # path and filename\n",
    "    # create football pitch\n",
    "    if figax is None:\n",
    "        fig,ax = plot_pitch(field_dimen=field_dimen)\n",
    "    else:\n",
    "        fig,ax = figax\n",
    "    fig.set_tight_layout(True)\n",
    "    # Generate movie\n",
    "    print(\"Generating movie...\",end='')\n",
    "    with writer.saving(fig, fname, 100):\n",
    "        for i in index:\n",
    "            figobjs = [] # this is used to collect up all the axis objects so that they can be deleted after each iteration\n",
    "            for team,color in zip( [hometeam.loc[i],awayteam.loc[i]], 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",
    "                objs, = ax.plot( team[x_columns], team[y_columns], color+'o', MarkerSize=PlayerMarkerSize, alpha=PlayerAlpha ) # plot player positions\n",
    "                figobjs.append(objs)\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",
    "                    objs = 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",
    "                    figobjs.append(objs)\n",
    "            # plot ball\n",
    "            objs, = ax.plot( team['ball_x'], team['ball_y'], 'ko', MarkerSize=6, alpha=1.0, LineWidth=0)\n",
    "            figobjs.append(objs)\n",
    "            # include match time at the top\n",
    "            frame_minute =  int( team['Time [s]']/60. )\n",
    "            frame_second =  ( team['Time [s]']/60. - frame_minute ) * 60.\n",
    "            timestring = \"%d:%1.2f\" % ( frame_minute, frame_second  )\n",
    "            objs = ax.text(-2.5,field_dimen[1]/2.+1., timestring, fontsize=14 )\n",
    "            figobjs.append(objs)\n",
    "            writer.grab_frame()\n",
    "            # Delete all axis objects (other than pitch lines) in preperation for next frame\n",
    "            for figobj in figobjs:\n",
    "                figobj.remove()\n",
    "    print(\"done\")\n",
    "    plt.clf()\n",
    "    plt.close(fig) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating movie...done\n"
     ]
    }
   ],
   "source": [
    "PLOTDIR = r\"C:\\Users\\MustaphaM\\Desktop\\sample-data-master\\data\\Sample_Game_2\"\n",
    "save_match_clip(tracking_Home.iloc[73600: 73600+500], tracking_Away.iloc[73600:73600+500], PLOTDIR, fname= \"home_goal_2\", include_player_velocities = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate player velocities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_player_velocities(team, smoothing=True, filter_='moving average', window=7, polyorder=1, maxspeed = 12):\n",
    "    \"\"\" calc_player_velocities( tracking_data )\n",
    "    \n",
    "    Calculate player velocities in x & y direciton, and total player speed at each timestamp of the tracking data\n",
    "    \n",
    "    Parameters\n",
    "    -----------\n",
    "        team: the tracking DataFrame for home or away team\n",
    "        smoothing: boolean variable that determines whether velocity measures are smoothed. Default is True.\n",
    "        filter: type of filter to use when smoothing the velocities. Default is Savitzky-Golay, which fits a polynomial of order 'polyorder' to the data within each window\n",
    "        window: smoothing window size in # of frames\n",
    "        polyorder: order of the polynomial for the Savitzky-Golay filter. Default is 1 - a linear fit to the velcoity, so gradient is the acceleration\n",
    "        maxspeed: the maximum speed that a player can realistically achieve (in meters/second). Speed measures that exceed maxspeed are tagged as outliers and set to NaN. \n",
    "        \n",
    "    Returns\n",
    "    -----------\n",
    "       team : the tracking DataFrame with columns for speed in the x & y direction and total speed added\n",
    "    \"\"\"\n",
    "    # remove any velocity data already in the dataframe\n",
    "    team = remove_player_velocities(team)\n",
    "    \n",
    "    # Get the player ids\n",
    "    player_ids = np.unique( [ c[:-2] for c in team.columns if c[:4] in ['Home','Away'] ] )\n",
    "\n",
    "    # Calculate the timestep from one frame to the next. Should always be 0.04 within the same half\n",
    "    dt = team['Time [s]'].diff()\n",
    "    \n",
    "    # index of first frame in second half\n",
    "    second_half_idx = team.Period.idxmax(2)\n",
    "    \n",
    "    # estimate velocities for players in team\n",
    "    for player in player_ids: # cycle through players individually\n",
    "        # difference player positions in timestep dt to get unsmoothed estimate of velocity\n",
    "        vx = team[player+\"_x\"].diff() / dt\n",
    "        vy = team[player+\"_y\"].diff() / dt\n",
    "\n",
    "        if maxspeed>0:\n",
    "            # remove unsmoothed data points that exceed the maximum speed (these are most likely position errors)\n",
    "            raw_speed = np.sqrt( vx**2 + vy**2 )\n",
    "            vx[ raw_speed>maxspeed ] = np.nan\n",
    "            vy[ raw_speed>maxspeed ] = np.nan\n",
    "            \n",
    "        if smoothing:\n",
    "            if filter_=='Savitzky-Golay':\n",
    "                # calculate first half velocity\n",
    "                vx.loc[:second_half_idx] = signal.savgol_filter(vx.loc[:second_half_idx],window_length=window,polyorder=polyorder)\n",
    "                vy.loc[:second_half_idx] = signal.savgol_filter(vy.loc[:second_half_idx],window_length=window,polyorder=polyorder)        \n",
    "                # calculate second half velocity\n",
    "                vx.loc[second_half_idx:] = signal.savgol_filter(vx.loc[second_half_idx:],window_length=window,polyorder=polyorder)\n",
    "                vy.loc[second_half_idx:] = signal.savgol_filter(vy.loc[second_half_idx:],window_length=window,polyorder=polyorder)\n",
    "            elif filter_=='moving average':\n",
    "                ma_window = np.ones( window ) / window \n",
    "                # calculate first half velocity\n",
    "                vx.loc[:second_half_idx] = np.convolve( vx.loc[:second_half_idx] , ma_window, mode='same' ) \n",
    "                vy.loc[:second_half_idx] = np.convolve( vy.loc[:second_half_idx] , ma_window, mode='same' )      \n",
    "                # calculate second half velocity\n",
    "                vx.loc[second_half_idx:] = np.convolve( vx.loc[second_half_idx:] , ma_window, mode='same' ) \n",
    "                vy.loc[second_half_idx:] = np.convolve( vy.loc[second_half_idx:] , ma_window, mode='same' ) \n",
    "                \n",
    "        \n",
    "        # put player speed in x,y direction, and total speed back in the data frame\n",
    "        team[player + \"_vx\"] = vx\n",
    "        team[player + \"_vy\"] = vy\n",
    "        team[player + \"_speed\"] = np.sqrt( vx**2 + vy**2 )\n",
    "\n",
    "    return team\n",
    "\n",
    "def remove_player_velocities(team):\n",
    "    \"\"\"\n",
    "    remove player velocities and accelration values that are already in the \"team\" dataframe \n",
    "    \"\"\"\n",
    "    columns = [c for c in team.columns if c.split('_')[-1] in ['vx','vy','ax','ay','speed','acceleration']] # Get the player ids\n",
    "    team = team.drop(columns = columns)\n",
    "    return team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the player velocities\n",
    "tracking_home = calc_player_velocities(tracking_Home, smoothing = True)\n",
    "tracking_away = calc_player_velocities(tracking_Away, smoothing = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>Time [s]</th>\n",
       "      <th>Away_25_x</th>\n",
       "      <th>Away_25_y</th>\n",
       "      <th>Away_15_x</th>\n",
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       "      <th>Away_23_speed</th>\n",
       "      <th>Away_24_vx</th>\n",
       "      <th>Away_24_vy</th>\n",
       "      <th>Away_24_speed</th>\n",
       "      <th>Away_25_vx</th>\n",
       "      <th>Away_25_vy</th>\n",
       "      <th>Away_25_speed</th>\n",
       "      <th>Away_26_vx</th>\n",
       "      <th>Away_26_vy</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-51.70892</td>\n",
       "      <td>-1.19884</td>\n",
       "      <td>-18.13554</td>\n",
       "      <td>-21.65528</td>\n",
       "      <td>-20.42196</td>\n",
       "      <td>-3.52376</td>\n",
       "      <td>-18.83090</td>\n",
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       "      <td>0.08</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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       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-51.70892</td>\n",
       "      <td>-1.19884</td>\n",
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       "      <td>-3.52376</td>\n",
       "      <td>-18.83090</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.16</td>\n",
       "      <td>-51.70892</td>\n",
       "      <td>-1.19884</td>\n",
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       "      <td>-21.65596</td>\n",
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       "      <td>0.20</td>\n",
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       "      <td>1.15434</td>\n",
       "      <td>-3.54416</td>\n",
       "      <td>-10.37528</td>\n",
       "      <td>5.07824</td>\n",
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       "      <td>-0.552714</td>\n",
       "      <td>-0.364286</td>\n",
       "      <td>0.661965</td>\n",
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       "      <td>-1.14852</td>\n",
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       "      <td>-0.416429</td>\n",
       "      <td>-0.235571</td>\n",
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       "    <tr>\n",
       "      <th>141155</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.20</td>\n",
       "      <td>-43.62006</td>\n",
       "      <td>-1.14852</td>\n",
       "      <td>1.03032</td>\n",
       "      <td>-37.40000</td>\n",
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       "      <td>-10.36150</td>\n",
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       "      <td>-0.143286</td>\n",
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       "      <td>2</td>\n",
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       "      <td>-43.58296</td>\n",
       "      <td>-1.14784</td>\n",
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       "      <td>0.359651</td>\n",
       "      <td>-0.238500</td>\n",
       "      <td>-0.065571</td>\n",
       "      <td>0.247350</td>\n",
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       "<p>141156 rows × 64 columns</p>\n",
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      ],
      "text/plain": [
       "        Period  Time [s]  Away_25_x  Away_25_y  Away_15_x  Away_15_y  \\\n",
       "Frame                                                                  \n",
       "1            1      0.04  -51.70892   -1.19884  -18.13554  -21.65528   \n",
       "2            1      0.08  -51.70892   -1.19884  -18.13554  -21.65528   \n",
       "3            1      0.12  -51.70892   -1.19884  -18.13554  -21.65528   \n",
       "4            1      0.16  -51.70892   -1.19884  -18.19384  -21.65596   \n",
       "5            1      0.20  -51.70892   -1.19884  -18.25002  -21.65596   \n",
       "...        ...       ...        ...        ...        ...        ...   \n",
       "141152       2   5646.08  -43.68366   -1.14852    1.03032  -37.40000   \n",
       "141153       2   5646.12  -43.66246   -1.14852    1.03032  -37.40000   \n",
       "141154       2   5646.16  -43.63808   -1.14852    1.03032  -37.40000   \n",
       "141155       2   5646.20  -43.62006   -1.14852    1.03032  -37.40000   \n",
       "141156       2   5646.24  -43.58296   -1.14784    1.03032  -37.40000   \n",
       "\n",
       "        Away_16_x  Away_16_y  Away_17_x  Away_17_y  ...  Away_23_speed  \\\n",
       "Frame                                               ...                  \n",
       "1       -20.42196   -3.52376  -18.83090    7.51128  ...            NaN   \n",
       "2       -20.42196   -3.52376  -18.83090    7.51128  ...            NaN   \n",
       "3       -20.42196   -3.52376  -18.83090    7.51128  ...            NaN   \n",
       "4       -20.42196   -3.52376  -18.83090    7.51128  ...            NaN   \n",
       "5       -20.42196   -3.52376  -18.83090    7.51128  ...            0.0   \n",
       "...           ...        ...        ...        ...  ...            ...   \n",
       "141152    1.15434   -3.54416  -10.37528    5.07824  ...            0.0   \n",
       "141153    1.17236   -3.56252  -10.35302    5.10612  ...            0.0   \n",
       "141154    1.17872   -3.61488  -10.34878    5.13400  ...            0.0   \n",
       "141155    1.17130   -3.76924  -10.36150    5.16120  ...            0.0   \n",
       "141156    1.24444   -3.52580  -10.33606    5.18908  ...            0.0   \n",
       "\n",
       "        Away_24_vx  Away_24_vy  Away_24_speed  Away_25_vx  Away_25_vy  \\\n",
       "Frame                                                                   \n",
       "1              NaN         NaN            NaN         NaN         NaN   \n",
       "2              NaN         NaN            NaN         NaN         NaN   \n",
       "3              NaN         NaN            NaN         NaN         NaN   \n",
       "4              NaN         NaN            NaN         NaN         NaN   \n",
       "5              0.0         0.0            0.0   -1.169786    1.245857   \n",
       "...            ...         ...            ...         ...         ...   \n",
       "141152         0.0         0.0            0.0    1.022143    0.483286   \n",
       "141153         0.0         0.0            0.0    0.961571    0.400714   \n",
       "141154         0.0         0.0            0.0    0.753357    0.291429   \n",
       "141155         0.0         0.0            0.0    0.530000    0.143286   \n",
       "141156         0.0         0.0            0.0    0.359643    0.002429   \n",
       "\n",
       "        Away_25_speed  Away_26_vx  Away_26_vy  Away_26_speed  \n",
       "Frame                                                         \n",
       "1                 NaN         NaN         NaN            NaN  \n",
       "2                 NaN         NaN         NaN            NaN  \n",
       "3                 NaN         NaN         NaN            NaN  \n",
       "4                 NaN         NaN         NaN            NaN  \n",
       "5            1.708964         NaN         NaN            NaN  \n",
       "...               ...         ...         ...            ...  \n",
       "141152       1.130637   -0.552714   -0.364286       0.661965  \n",
       "141153       1.041725   -0.503500   -0.279286       0.575771  \n",
       "141154       0.807761   -0.416429   -0.235571       0.478442  \n",
       "141155       0.549027   -0.329357   -0.143286       0.359175  \n",
       "141156       0.359651   -0.238500   -0.065571       0.247350  \n",
       "\n",
       "[141156 rows x 64 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "<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>14.54860</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>14.54860</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>14.54860</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>14.54860</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>14.54860</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>...</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>141152</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.08</td>\n",
       "      <td>46.3379</td>\n",
       "      <td>0.84524</td>\n",
       "      <td>12.19106</td>\n",
       "      <td>-23.55792</td>\n",
       "      <td>29.08852</td>\n",
       "      <td>-5.87860</td>\n",
       "      <td>24.22948</td>\n",
       "      <td>-12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.71278</td>\n",
       "      <td>-24.17468</td>\n",
       "      <td>-1.09392</td>\n",
       "      <td>30.29672</td>\n",
       "      <td>23.36028</td>\n",
       "      <td>-9.04400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141153</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.12</td>\n",
       "      <td>46.3379</td>\n",
       "      <td>0.84524</td>\n",
       "      <td>12.19106</td>\n",
       "      <td>-23.55792</td>\n",
       "      <td>29.08852</td>\n",
       "      <td>-5.87860</td>\n",
       "      <td>24.22948</td>\n",
       "      <td>-12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.71278</td>\n",
       "      <td>-24.17468</td>\n",
       "      <td>-1.09286</td>\n",
       "      <td>30.34568</td>\n",
       "      <td>23.31046</td>\n",
       "      <td>-9.03176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141154</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.16</td>\n",
       "      <td>46.3379</td>\n",
       "      <td>0.84524</td>\n",
       "      <td>12.19106</td>\n",
       "      <td>-23.55792</td>\n",
       "      <td>29.08852</td>\n",
       "      <td>-5.87860</td>\n",
       "      <td>24.22948</td>\n",
       "      <td>-12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.71278</td>\n",
       "      <td>-24.17468</td>\n",
       "      <td>-1.09074</td>\n",
       "      <td>30.40348</td>\n",
       "      <td>23.28714</td>\n",
       "      <td>-9.02632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141155</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.20</td>\n",
       "      <td>46.3379</td>\n",
       "      <td>0.84524</td>\n",
       "      <td>12.19106</td>\n",
       "      <td>-23.55792</td>\n",
       "      <td>29.08852</td>\n",
       "      <td>-5.87860</td>\n",
       "      <td>24.22948</td>\n",
       "      <td>-12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.71278</td>\n",
       "      <td>-24.17468</td>\n",
       "      <td>-1.08968</td>\n",
       "      <td>30.45856</td>\n",
       "      <td>23.25746</td>\n",
       "      <td>-9.03992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141156</th>\n",
       "      <td>2</td>\n",
       "      <td>5646.24</td>\n",
       "      <td>46.3379</td>\n",
       "      <td>0.84524</td>\n",
       "      <td>12.19106</td>\n",
       "      <td>-23.55792</td>\n",
       "      <td>29.08852</td>\n",
       "      <td>-5.87860</td>\n",
       "      <td>24.22948</td>\n",
       "      <td>-12.09788</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.71278</td>\n",
       "      <td>-24.17468</td>\n",
       "      <td>-1.09074</td>\n",
       "      <td>30.49528</td>\n",
       "      <td>23.25110</td>\n",
       "      <td>-9.18612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141156 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  14.54860  18.81712   \n",
       "2            1      0.08    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "3            1      0.12    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "4            1      0.16    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "5            1      0.20    46.9315   -0.28084  15.70072  14.54860  18.81712   \n",
       "...        ...       ...        ...        ...       ...       ...       ...   \n",
       "141152       2   5646.08    46.3379    0.84524  12.19106 -23.55792  29.08852   \n",
       "141153       2   5646.12    46.3379    0.84524  12.19106 -23.55792  29.08852   \n",
       "141154       2   5646.16    46.3379    0.84524  12.19106 -23.55792  29.08852   \n",
       "141155       2   5646.20    46.3379    0.84524  12.19106 -23.55792  29.08852   \n",
       "141156       2   5646.24    46.3379    0.84524  12.19106 -23.55792  29.08852   \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",
       "141152  -5.87860  24.22948 -12.09788  ...        NaN        NaN    9.71278   \n",
       "141153  -5.87860  24.22948 -12.09788  ...        NaN        NaN    9.71278   \n",
       "141154  -5.87860  24.22948 -12.09788  ...        NaN        NaN    9.71278   \n",
       "141155  -5.87860  24.22948 -12.09788  ...        NaN        NaN    9.71278   \n",
       "141156  -5.87860  24.22948 -12.09788  ...        NaN        NaN    9.71278   \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",
       "141152  -24.17468   -1.09392   30.29672   23.36028   -9.04400     NaN     NaN  \n",
       "141153  -24.17468   -1.09286   30.34568   23.31046   -9.03176     NaN     NaN  \n",
       "141154  -24.17468   -1.09074   30.40348   23.28714   -9.02632     NaN     NaN  \n",
       "141155  -24.17468   -1.08968   30.45856   23.25746   -9.03992     NaN     NaN  \n",
       "141156  -24.17468   -1.09074   30.49528   23.25110   -9.18612     NaN     NaN  \n",
       "\n",
       "[141156 rows x 32 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_Home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a random frame and show player velocities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
    "        PlayerAlhttp://localhost:8888/notebooks/Untitled31.ipynb?kernel_name=python3#Plot-a-random-framepha: 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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0xd680bffc08>)"
      ]
     },
     "execution_count": 68,
     "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": [
    "#frame is 10000\n",
    "plot_frame(tracking_home.loc[10000], tracking_away.loc[10000], include_player_velocities = True, annotate = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Player velocities are indicated by the arrow lengths, the jersey numbers are also shown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the physical summary for the home players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_players = np.unique([c.split(\"_\")[1] for c in tracking_home.columns if c[:4] == \"Home\"])\n",
    "#extracts the jersey numbers\n",
    "home_summary = pd.DataFrame(index = home_players)\n",
    "#creates a new dataframe to hold the summary later, each row is the player jersey number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [1, 10, 11, 12, 13, 14, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
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     "execution_count": 70,
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   "source": [
    "home_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the minutes played for each player\n",
    "minutes = []\n",
    "for player in home_players:\n",
    "    # search for first and last frames that we have a \n",
    "      #position observation for each player (when a player is not on the pitch, positions are NaN)\n",
    "    column = \"Home_\"+player + \"_x\" #use player x-position coordinate\n",
    "    player_minutes = (tracking_home[column].last_valid_index() - tracking_home[column].first_valid_index() + 1)/25/60\n",
    "    #there are 25 frames per second, hence the division. Division by 60 is to convert to minutes\n",
    "    minutes.append(player_minutes)\n",
    "home_summary[\"Minutes Played\"] = minutes\n",
    "home_summary = home_summary.sort_values([\"Minutes Played\"], ascending = False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Minutes Played</th>\n",
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       "      <td>94.104000</td>\n",
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       "      <th>12</th>\n",
       "      <td>30.302667</td>\n",
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      ],
      "text/plain": [
       "    Minutes Played\n",
       "1        94.104000\n",
       "11       94.104000\n",
       "2        94.104000\n",
       "3        94.104000\n",
       "5        94.104000\n",
       "6        94.104000\n",
       "7        94.104000\n",
       "9        94.104000\n",
       "10       83.825333\n",
       "8        70.740667\n",
       "4        63.802000\n",
       "12       30.302667\n",
       "13       23.364000\n",
       "14       10.279333"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#distance covered\n",
    "distance = []\n",
    "for player in home_summary.index:\n",
    "    column = \"Home_\"+player+\"_speed\"\n",
    "    player_distance = tracking_home[column].sum()/25./1000 #speed times time. covert to km\n",
    "    distance.append(player_distance)\n",
    "home_summary[\"Distance [km]\"] = distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>9.845300</td>\n",
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       "      <td>94.104000</td>\n",
       "      <td>9.546312</td>\n",
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       "    <tr>\n",
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       "      <td>94.104000</td>\n",
       "      <td>11.909182</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.535956</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.068739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.765225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83.825333</td>\n",
       "      <td>8.959440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70.740667</td>\n",
       "      <td>9.012902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63.802000</td>\n",
       "      <td>7.177593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30.302667</td>\n",
       "      <td>3.036616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.364000</td>\n",
       "      <td>2.369891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.279333</td>\n",
       "      <td>1.087950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Minutes Played  Distance [km]\n",
       "1        94.104000      10.369966\n",
       "11       94.104000       5.203820\n",
       "2        94.104000       9.845300\n",
       "3        94.104000       9.546312\n",
       "5        94.104000      11.909182\n",
       "6        94.104000      11.535956\n",
       "7        94.104000      11.068739\n",
       "9        94.104000       9.765225\n",
       "10       83.825333       8.959440\n",
       "8        70.740667       9.012902\n",
       "4        63.802000       7.177593\n",
       "12       30.302667       3.036616\n",
       "13       23.364000       2.369891\n",
       "14       10.279333       1.087950"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the goalkeeper (i.e player 11) covers one of the smallest distances for the entire duration of the game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Distance covered [km]')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#a bar chart showing the distance covered by each player\n",
    "plt.subplots()\n",
    "ax = home_summary[\"Distance [km]\"].plot.bar(rot = 0)\n",
    "ax.set_xlabel(\"Player\")\n",
    "ax.set_ylabel(\"Distance covered [km]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# distance covered: walking, joggng, running, and sprinting\n",
    "walking = []\n",
    "jogging = []\n",
    "running = []\n",
    "sprinting = []\n",
    "\n",
    "\n",
    "for player in home_summary.index:\n",
    "    column = \"Home_\"+player+\"_speed\"\n",
    "    \n",
    "    #walking (less than 2 m/s)\n",
    "    player_distance = tracking_home.loc[tracking_home[column]<2, column].sum()/25./1000\n",
    "    walking.append(player_distance)\n",
    "    \n",
    "    #jogging (between 2 and 4 m/s)\n",
    "    player_distance = tracking_home.loc[(tracking_home[column]>= 2) & (tracking_home[column]<4), column].sum()/25./1000\n",
    "    jogging.append(player_distance)\n",
    "    \n",
    "    #running (between 4 and 7 m/s)\n",
    "    player_distance = tracking_home.loc[(tracking_home[column]>=4) & (tracking_home[column]<7), column].sum()/25./1000\n",
    "    running.append(player_distance)\n",
    "    \n",
    "    #sprinting (greater than 7 m/s)\n",
    "    player_distance = tracking_home.loc[tracking_home[column] >= 7, column].sum()/25./1000\n",
    "    sprinting.append(player_distance)\n",
    "\n",
    "home_summary[\"Walking [km]\"] = walking\n",
    "home_summary[\"Jogging [km]\"] = jogging\n",
    "home_summary[\"Running [km]\"] = running\n",
    "home_summary[\"Sprinting [km]\"] = sprinting\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Minutes Played</th>\n",
       "      <th>Distance [km]</th>\n",
       "      <th>Walking [km]</th>\n",
       "      <th>Jogging [km]</th>\n",
       "      <th>Running [km]</th>\n",
       "      <th>Sprinting [km]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>10.369966</td>\n",
       "      <td>3.701953</td>\n",
       "      <td>4.436083</td>\n",
       "      <td>2.149219</td>\n",
       "      <td>0.082712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>5.203820</td>\n",
       "      <td>4.394682</td>\n",
       "      <td>0.777765</td>\n",
       "      <td>0.029655</td>\n",
       "      <td>0.001718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.845300</td>\n",
       "      <td>4.085937</td>\n",
       "      <td>4.066061</td>\n",
       "      <td>1.612491</td>\n",
       "      <td>0.080811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.546312</td>\n",
       "      <td>4.178461</td>\n",
       "      <td>3.832849</td>\n",
       "      <td>1.382836</td>\n",
       "      <td>0.152166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.909182</td>\n",
       "      <td>3.583949</td>\n",
       "      <td>4.652462</td>\n",
       "      <td>3.519102</td>\n",
       "      <td>0.153669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.535956</td>\n",
       "      <td>3.385540</td>\n",
       "      <td>5.145440</td>\n",
       "      <td>2.829522</td>\n",
       "      <td>0.175454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.068739</td>\n",
       "      <td>3.606788</td>\n",
       "      <td>4.956883</td>\n",
       "      <td>2.324597</td>\n",
       "      <td>0.180470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.765225</td>\n",
       "      <td>4.428081</td>\n",
       "      <td>3.322470</td>\n",
       "      <td>1.863521</td>\n",
       "      <td>0.151153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83.825333</td>\n",
       "      <td>8.959440</td>\n",
       "      <td>3.858442</td>\n",
       "      <td>3.430553</td>\n",
       "      <td>1.404684</td>\n",
       "      <td>0.265760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70.740667</td>\n",
       "      <td>9.012902</td>\n",
       "      <td>2.604774</td>\n",
       "      <td>4.133859</td>\n",
       "      <td>2.183218</td>\n",
       "      <td>0.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63.802000</td>\n",
       "      <td>7.177593</td>\n",
       "      <td>2.658048</td>\n",
       "      <td>3.248758</td>\n",
       "      <td>1.257342</td>\n",
       "      <td>0.013444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30.302667</td>\n",
       "      <td>3.036616</td>\n",
       "      <td>1.140396</td>\n",
       "      <td>1.034231</td>\n",
       "      <td>0.819097</td>\n",
       "      <td>0.042892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.364000</td>\n",
       "      <td>2.369891</td>\n",
       "      <td>0.874073</td>\n",
       "      <td>0.858117</td>\n",
       "      <td>0.581497</td>\n",
       "      <td>0.056203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.279333</td>\n",
       "      <td>1.087950</td>\n",
       "      <td>0.436613</td>\n",
       "      <td>0.402641</td>\n",
       "      <td>0.235706</td>\n",
       "      <td>0.012990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Minutes Played  Distance [km]  Walking [km]  Jogging [km]  Running [km]  \\\n",
       "1        94.104000      10.369966      3.701953      4.436083      2.149219   \n",
       "11       94.104000       5.203820      4.394682      0.777765      0.029655   \n",
       "2        94.104000       9.845300      4.085937      4.066061      1.612491   \n",
       "3        94.104000       9.546312      4.178461      3.832849      1.382836   \n",
       "5        94.104000      11.909182      3.583949      4.652462      3.519102   \n",
       "6        94.104000      11.535956      3.385540      5.145440      2.829522   \n",
       "7        94.104000      11.068739      3.606788      4.956883      2.324597   \n",
       "9        94.104000       9.765225      4.428081      3.322470      1.863521   \n",
       "10       83.825333       8.959440      3.858442      3.430553      1.404684   \n",
       "8        70.740667       9.012902      2.604774      4.133859      2.183218   \n",
       "4        63.802000       7.177593      2.658048      3.248758      1.257342   \n",
       "12       30.302667       3.036616      1.140396      1.034231      0.819097   \n",
       "13       23.364000       2.369891      0.874073      0.858117      0.581497   \n",
       "14       10.279333       1.087950      0.436613      0.402641      0.235706   \n",
       "\n",
       "    Sprinting [km]  \n",
       "1         0.082712  \n",
       "11        0.001718  \n",
       "2         0.080811  \n",
       "3         0.152166  \n",
       "5         0.153669  \n",
       "6         0.175454  \n",
       "7         0.180470  \n",
       "9         0.151153  \n",
       "10        0.265760  \n",
       "8         0.091051  \n",
       "4         0.013444  \n",
       "12        0.042892  \n",
       "13        0.056203  \n",
       "14        0.012990  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Distance covered [km]')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a clustered bar chart\n",
    "ax = home_summary[[\"Walking [km]\", \"Jogging [km]\", \"Running [km]\", \"Sprinting [km]\"]].plot.bar(colormap=\"coolwarm\")\n",
    "ax.set_xlabel(\"Player\")\n",
    "ax.set_ylabel(\"Distance covered [km]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sustained sprints: how many sustained sprints per match?\n",
    "# sustained sprint occcurs when a player maintains a speed greater than 7m/s for more than a second\n",
    "nsprints = []\n",
    "sprint_threshold = 7 #minimum speed to be defined as a sprint (m/s)\n",
    "sprint_window = 1*25 #minimum duration sprint should be sustained (in this case, 1 second = 25 consecutive frames)\n",
    "for player in home_summary.index:\n",
    "    column = \"Home_\"+player+\"_speed\" \n",
    "    # trick here is to convolve speed with a window of size \"sprint_window\", and find the number of occassions that sprint was sustained for at least one window length\n",
    "    # diff helps us to identify when the window starts\n",
    "    player_sprints = np.diff( 1*( np.convolve( 1*(tracking_home[column]>=sprint_threshold), np.ones(sprint_window), mode='same' ) >= sprint_window ) )\n",
    "    nsprints.append( np.sum( player_sprints == 1 ) )\n",
    "home_summary['# sprints'] = nsprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Minutes Played</th>\n",
       "      <th>Distance [km]</th>\n",
       "      <th>Walking [km]</th>\n",
       "      <th>Jogging [km]</th>\n",
       "      <th>Running [km]</th>\n",
       "      <th>Sprinting [km]</th>\n",
       "      <th># sprints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>10.369966</td>\n",
       "      <td>3.701953</td>\n",
       "      <td>4.436083</td>\n",
       "      <td>2.149219</td>\n",
       "      <td>0.082712</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>5.203820</td>\n",
       "      <td>4.394682</td>\n",
       "      <td>0.777765</td>\n",
       "      <td>0.029655</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.845300</td>\n",
       "      <td>4.085937</td>\n",
       "      <td>4.066061</td>\n",
       "      <td>1.612491</td>\n",
       "      <td>0.080811</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.546312</td>\n",
       "      <td>4.178461</td>\n",
       "      <td>3.832849</td>\n",
       "      <td>1.382836</td>\n",
       "      <td>0.152166</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.909182</td>\n",
       "      <td>3.583949</td>\n",
       "      <td>4.652462</td>\n",
       "      <td>3.519102</td>\n",
       "      <td>0.153669</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.535956</td>\n",
       "      <td>3.385540</td>\n",
       "      <td>5.145440</td>\n",
       "      <td>2.829522</td>\n",
       "      <td>0.175454</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>11.068739</td>\n",
       "      <td>3.606788</td>\n",
       "      <td>4.956883</td>\n",
       "      <td>2.324597</td>\n",
       "      <td>0.180470</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>94.104000</td>\n",
       "      <td>9.765225</td>\n",
       "      <td>4.428081</td>\n",
       "      <td>3.322470</td>\n",
       "      <td>1.863521</td>\n",
       "      <td>0.151153</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83.825333</td>\n",
       "      <td>8.959440</td>\n",
       "      <td>3.858442</td>\n",
       "      <td>3.430553</td>\n",
       "      <td>1.404684</td>\n",
       "      <td>0.265760</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70.740667</td>\n",
       "      <td>9.012902</td>\n",
       "      <td>2.604774</td>\n",
       "      <td>4.133859</td>\n",
       "      <td>2.183218</td>\n",
       "      <td>0.091051</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63.802000</td>\n",
       "      <td>7.177593</td>\n",
       "      <td>2.658048</td>\n",
       "      <td>3.248758</td>\n",
       "      <td>1.257342</td>\n",
       "      <td>0.013444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30.302667</td>\n",
       "      <td>3.036616</td>\n",
       "      <td>1.140396</td>\n",
       "      <td>1.034231</td>\n",
       "      <td>0.819097</td>\n",
       "      <td>0.042892</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.364000</td>\n",
       "      <td>2.369891</td>\n",
       "      <td>0.874073</td>\n",
       "      <td>0.858117</td>\n",
       "      <td>0.581497</td>\n",
       "      <td>0.056203</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.279333</td>\n",
       "      <td>1.087950</td>\n",
       "      <td>0.436613</td>\n",
       "      <td>0.402641</td>\n",
       "      <td>0.235706</td>\n",
       "      <td>0.012990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Minutes Played  Distance [km]  Walking [km]  Jogging [km]  Running [km]  \\\n",
       "1        94.104000      10.369966      3.701953      4.436083      2.149219   \n",
       "11       94.104000       5.203820      4.394682      0.777765      0.029655   \n",
       "2        94.104000       9.845300      4.085937      4.066061      1.612491   \n",
       "3        94.104000       9.546312      4.178461      3.832849      1.382836   \n",
       "5        94.104000      11.909182      3.583949      4.652462      3.519102   \n",
       "6        94.104000      11.535956      3.385540      5.145440      2.829522   \n",
       "7        94.104000      11.068739      3.606788      4.956883      2.324597   \n",
       "9        94.104000       9.765225      4.428081      3.322470      1.863521   \n",
       "10       83.825333       8.959440      3.858442      3.430553      1.404684   \n",
       "8        70.740667       9.012902      2.604774      4.133859      2.183218   \n",
       "4        63.802000       7.177593      2.658048      3.248758      1.257342   \n",
       "12       30.302667       3.036616      1.140396      1.034231      0.819097   \n",
       "13       23.364000       2.369891      0.874073      0.858117      0.581497   \n",
       "14       10.279333       1.087950      0.436613      0.402641      0.235706   \n",
       "\n",
       "    Sprinting [km]  # sprints  \n",
       "1         0.082712          5  \n",
       "11        0.001718          0  \n",
       "2         0.080811          3  \n",
       "3         0.152166          8  \n",
       "5         0.153669          8  \n",
       "6         0.175454          7  \n",
       "7         0.180470          9  \n",
       "9         0.151153          7  \n",
       "10        0.265760         13  \n",
       "8         0.091051          6  \n",
       "4         0.013444          0  \n",
       "12        0.042892          2  \n",
       "13        0.056203          2  \n",
       "14        0.012990          1  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Player 10 made the most number of sprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fnsLR8ACj0xqq284spv/jO8Lyy3j5hhF8MrGvlq8SERE5BSqv0uQcZhNLh3Zi2eCODF+9l9veXsbvP1mL1WLmm3O7UxDs89t38itceUcu91or1320hqs/XUdOqD9/fPZytndub3QsERERl6HyKs3GYTZR6eVBWF4JR9v5UebvyS3vreSKLzewfFASm3p0AJMJj1obFpsds91R93UmsJtN1LpZqHF3o9rTjXIfD4oDvOmx/TB/fGsp3jV1y2u50o5cPdMO8+ArC0jIKOD7Md146aaRlPl5GR1LRETEpai8SpNwr7USm1lIYnoe0UeKiMouptP+HBLT83GYIKKgjIiCMgCCSquYsCCNCQvSmuTYDTtytdTy6l1Zw63vLGfyd5vJCg/QhgEiIiJnQeVVTpvJ7iAuI58eO7PouiebLnuOkpiej5utbq1WuwmKA7wJLKmk1M+TeSO7kB0ZSLG/FyX+XpT7eBKVXcT5i3bQb2smlR4WfhjTjS8uSqEg2A+Tw4HFbsfNasez2opnjRXfimqCSip58u/fnnRP45a6I1evtEwee2EukbklfHJJH964dhhVWr5KRETkjKm8ym9zOEg6mEf/zYdI2ZZJr+1HCCytAqDEz5OdHSP4cHI/9iSEcSA2hJDCcv7+1FcciAvl7hlTTrpI/uYe0Xw/rgfxh/K5+tN1XDxvGxct2M4PY7vy0aX9yegQfNIoOWFLiTzJlrItbUcuj2orf3h/JVd8uZ6siEDu+NvlbO3ewehYIiIiLk/lVU7Kv7SKgRvTGbT+IAM3HiSksAKAjPZBLBucxJZuHdjWNYqMqCAw/XSVfPLeozz9zDccDfPnnqcu+83dnQ7GhvD0fRfw9u8G87sv1nPhgjQmzNvGioGJfDKxX+O82AavTxvGQy8vwLva2nhbS9uRK+lALtOf+474zALmnN+TV24cQaW3h9GxREREWgWVV6njcJBwKJ8hqQcYsu4APXccwWJ3UOLnybrecaztF8+63rHk/soIZ/SRQv5v+heU+nly74zLjtsh6rccaR/E87eP5a3fDebS7zZz6XdbGL5mNruSwpl9SR8WDU+m1t2tcV5rS11t4IKFaTzw6kLKfD1Pe8MBERER+W0qr21ZRQVD1+5j6NoDDF5/kIj6eaO7E8OZNWUAq/snsD058oRdsk4mPLeUFx//DID7/jr5V0vurykM9uWtq4cya8pAzl+8g8vnbODxF+fyx7eW8tX5PZlzYS8WjOraYspqA7PNzm3vLOPKLzewvlcMTz5wIYXBvkbHEhERaXVUXtuYkIIyhq3Zj+e/L8W8ZAnPVldT4e3Out5xzLxyEKv7xZ928QwsruT5v3yOX3k1dz89hYzodmeds8bTja/P78nX5/Wg35YMLvt6I9fOXss1n65jVf8EvjmvB6v7J2A7hWLd3HzLq3niH98xZP1BPp3Qm5dvHIHNzWJ0LBERkVZJ5bUNiMksYMSqvQxfs4/uu7IBsCckYP3DH3gwMovN3aOxup9Z2fItr+b/pn9O+5xi7n9yMrs7RjRldDCZWJ8Sy/qUWNpnFzNh3jbGL0zjnLX7yWvny/dju/H92O6/eIFXc4vIKeG5J78k9nAhz/1xLF9f0MuQHCIiIm2FymtrVD9/ddSKPYxasYfEQ/kA7OwYzn+vGcrywUm89vv/1BXDRY+f8WE8q2p59q9f0vFAHo8+djGbe0Q31SM4qazIQN6YNoy3rh7CkHUHuHjuVq76LJVrZ69jR8cIFo7ozI/nJJMT5pyVBzrty+EfT36BR42NB568lPUpsU45roiISFum8tqKdDhSxLlLdjJ26S7iMwuwm2Bz92j+efMolg3ueHypO+YK/jPhVmvj6b99TY+dWUx/cDyrBiSeZfpTZ7OYWT44ieWDkwgpKOPcxTsZu2wXd7y1lDveWsrmblEsG9yRZYOTONI+qFky9Nt8iKef/poyX0/ueW4KB2NDmuU4IiIicjyVVxcXWFzJ2GW7OG/xDrrvym4srJ9d3JslQzo2y0VDZpudx1/4gUEb0vnbXeey+JzkJj/Gqcpv58dHk/vz0eT+RB8pZOzSXYxesaexyO5ODGPx0E4sG9KRgzHtzrq0A4xetos/v/ADh6Lb8cD0S8kL8WuCRyIiIiKnQuXVBbnV2hi6bj8XLtzO4PUHcbPZ2RsfyqvXD2fBiM5nfKX/KXE4uOvNJYxdvptXrh/Od+f2aL5jnabMqGBmXjmYmVcOpn12MSNW72XU8j3cPGslN89aSUb7IFYOTGTVgAQ2d+twRvN8xy9I46F/z2dr1ygeefwSyvy8muGRiIiIyC9ReXUhiQfzmDBvK+cu3klQaRW57Xz5ZGIf5o3qyr6EMKdkuOLLDVz2zSY+mtSXjyb3d8oxz0RWZCAfT+rHx5P6EZJfxvA1+xi2Zj+XfruZK+ZsoMLbnfW9YlnVP55VAxJPafR00rebuf+1RaztE8ejj15MtbZ5FRERcTqV1xbOp6Kasct2c9H8bXTflU2Nm4Vlg5P4fmw31vWJO6U1WJvK6Pp5pYuGdeLV60c47bhnKz/Ejy/Hp/Dl+BS8K2voszWDIakHGZx6gOFr9gEL2Rsfytq+cazpG8/WblHUuh9/akz5agN3v7GE5QMTeeJPF1HjoVNHRETECHoFbuGu/jSVabPXciCmHf++cSQ/jOlKScCvb7naHPpsyeDxF+aypWsUT993AQ7z2c8dNUKltwcrByaxcmDScbuKDVp/kKlfbeSqz9dT4eXOxp7RrO6XwJp+8QxfvY87/7eEJUM6Mv3B8We8rJiIiIicPZXXFu7L8b1YMSiR7cmRTXKx0ZmIT8/jmae/4nD7QB7+88TWM+poMnEgLpQDcaF8cNmAxlHZwakHGbThIMPWHWj8q5ntA/l+TFfcbHaVVxEREQO1khbSeuWG+jfvBVi/ISS/jH88+SXVnm48MP1SSv1b7wVKx47KWqw2HntxLucu3UV+kA+h+eX8/emvqXUzs61LFBt6xbC2Txw7O0U4depGcxi3eAe3vLuC8LxSckL9eX3asBa3/a6IiEgDlVf5RT4V1fzjyS8JKKvizmemkhMeYHQkp/CqquWvz37LkNQDvH9Zf16fdg7uVhs9tx9m4MZ0+m3O4PoPV3HjB6so9vdiTb94VvVLILV3LEVBPkbHPy3jFu/goZcX4F1tBSAyt5SHXl4AoAIrIiItksqrnJRbrY0Zf/uGhEP5/OkvE5t+29cWyr+0iuee/JKue7L5x+1j+erCuu1eazzcWN87jvW94wAIKKmk/+ZDDFl3gMGpBzhv8U4A9iSEkdo7lvW9YtjSrQOVPh7Ar49uelXV0mXPUXruOEJETgmeNVYsNjtmhwOz3QGA3WzCZjZjr59rbHI4aJhE4gAcJhMOE0DD/+v+jsXuAAdY7Hbca2141Nhwt9rwqLHiWWMjMT0XN5vjuH8D72ort7y7QuVVRERaJJVXOZHDwcMvzWPApkM8fc95rO0bb3QipwjJL+P5Jz4n5nARf354AsuGdPzFv1sS4M2i4Z1ZNLwzZpud5H05DNh0iH6bD3HZ15v43RfrsVrMbE+OpCDIhyGpB/CstQF1o5uP/Gse4xekEVhaTeLBXNzqS2pBkA/VHm5YLWYcZlNjWTXbHZjtdsw2Bw2t1WH6qchSf7PJ4cBU30Xt9V9vN5nAVFfAa9ws1HhYqPJyp8Tfi077c076+MLzSpvgX1RERKTpqbzKCW5+bwXnL97Jf68Zyg9juxsdxynaZxfxz8c+I6i0koemT2J9Suwpf63dYmZnciQ7kyN57/KBeFbV0mNXFv02H2LAhnRGrtrLzy+187Da6bclgw29Ypg1dSA7kiPZ2jXK6XOKZ9/wJpG5JxbVHAPnWYuIiPwalVc5ziXfb+Ha2euYc35P3rt84HGfi80owLPGyt6EMJddKutkorKKeOnRT/GuquHuGVPYmRx5ZnfkcBCZU0L3Xdl025VF911ZJKbnn1Bcf/r7cO+MKWcau0m8Pm3YcXNeASo93Xh92jADU4mIiPwylVdpNHzVXu57bRGr+sXz4m1jTlia64o567lk7jYKA71Z0zeeFYMSWdMnvnFepyuqK66z8ay2cvfTU9ibGH5aXx9fv05s951Z9Nh5hJDCCgCqPNzY2SmCTy/pzYUL0gguqTrha3PCjB/dbJjXqtUGRETEVai8CgBjl+zk0X/OY2enCP7y8ARsJ1n+6a2rhrClWwcGbExnSOoBLvhxBzVuFjb1jGb5oERWDEh0qRUJoo8U8q9HP8Wjxso9T085oy12B60/yO1vLyOzfSCpKbFs6xJFWpf27I8Pbfw33JMQ1qJHNxeM6qqyKiIiLkPlVRi/II1H/jWPTd078Ohjl1Dl5X7Sv5ffzo+5Y7oxd0w3LDY73Xce4Zw1+zlnzT7ue+1H7nvtR3YnhrFiYCLLByWxOyncsI0VfktUVhEvPTIbN5udu5+Zyv740DO6n+/GdWfu6K6/ukSWRjdFRESajsprG9dv8yEefHkBa3vH8vBfJlLrfmrfEjaLmS3do9nSPZpXbxhBTGYB56zdz9C1+5n2yVqu/2gNue18WTK0E/NHdmF7Z+N2CPu5iJwS/vXYp7jX2rjrb1M5EHdmxRU45QusNLopIiLSNFRe2zBTWhoznvma9Ohg/vLwhFMurieTEd2OD6Pb8eHk/gQWVzJ4/QGGr97HxXO3MuWbTRyODGT+yC4sGNmZ9JiQJnwUpycsr5SXHp2Nb0UN98y47KyKq4iIiDifymtblZ2N52WXUeDlzkN/mUS5r2eT3XVxoHfj9ALf8mqGr97LuUt2ce3stfz+4zXsTgxjwcguLDwn2alzZIMLy/nn458RUFrFvU9d1mY2XhAREWlNVF7bII9qK56XX44pP58/PT2pWQtkua8nP4ztzg9ju9OusJzRy3Zz7tKd3P72Mm5/exnbkyNZPKwTi4d2IisysNly+JZX83/TvyAsr5T7/zr5zJfDEhEREUOpvLY19btnmTfspuajj9jts9pphy4I9uWzS/rw2SV9iMoqYtSKPYxasaexyO7sGM6Pw5JZPKwTR9oHnfXxGrdkzS3F6m7BYrPz0BOT2NqtQxM8GhERETGCymsbM+2TtZy7dBc106djmzABFjmvvB7rSPsgPpgygA+mDKB9djEjV+5h1Mo93DZzObfNXM7uxDAWD0tm5YAE9sWHnvbFXuMW7zhueSqPWhs1bmYCSiqb4+GIiIiIk6i8tiGjVuzmplkr+WF0V0Y88IDRcRplRQby0eT+fDS5P+E5JYxauZdRK3dz83sruPm9FeQH+7C2Txxr+8ST2ieWosBfXpaqwS3vrjhuXVWo25L1lndX6Kp/ERERF6by2kYkHcjl0RfnsrVLe567cxwjWsiyVT+XEx7AJ5P68smkvoTklzFwYzoDN6YzZN0BLly0A4BdSeGs7RvHxh4x7EiOoMzvxOWqwvNKT3r/v3S7iIiIuAaV1zYgqKiCvz81h1I/Lx5/9OKzWhLLmfJD/Ph+XHe+H9cds81O8r4cBm5MZ8DGdH73WSrXzl4HwMHodmzvHMn2zpGkdW7PgbhQckL9icw9sajmhBq/JauIiIicOddoMXLGLFYbT/39G4KLK7j92SsoCPY1OtIZsVvM7EyOZGdyJO9eMQifimq67jlKt51ZdNudzZDUA4xfuB2o23o1L9gXq9mEm93ReB9VHi1nS1YRERE5Myqvrdwf31pK77TD/PX+C1rVuqYVPp6sT4llfUps3Q0OB+2PFtNtVzY9dmaRcCifgNIqAsurG7/Gq8bKrTNXcNGCNDKigsmKDKQ4wJsSPy+K/b0oCfCixM+LWncLNrMZu9mE3WLCbjZjM5tazA5hIiIibZlh5XXumBlGHRqA8xc9bujxneG8H3cw9etNfHJJH+a39ouUTCayIoPIigxi4cgujTd7V9bQIauImMOFxB4uJOZwITFHihi3dBf+xxTbU2E30Vhk7ea6Ums3m7CZTTjqb2ssvT/72G45/ut+fh/2n33OYQI3mwOz3Y7Z7sBsd2Cx2bHU/9liq/+/3YGpfnTZYTbhABymuq8HE3Yz1LpZsLpZqHW3UOXpRom/F6V+XpT415X1kgAvjoYFkBUeQFGgt0r6GfI0u1e5+QUAACAASURBVBPl3Y4onxA6eIcQ5RNCr6D4xs/f02USRyryOVyZz5GKfI5UFlBtrzUusIi0OkZ3K2fRyGsr1WlfDg++vICNPaJ59frhRscxTKW3B3sTw9mbGH78JxwOfCprCCypwr+sisCSKgJKKwkoq8K91t5YGi32nxXIho9tx3xsd2B2/PT3LD8vncd9fGIhdbOeeCyTA2zHjPraLHUl1+rmVv/xT7c7TIADTIDZ7gDqvt7kcGB21E0d8ai14VtRTUhBGV32VBNQWoVXjfWEf68qDzeywwPIDg/gUHQw++JD2R8XyoHYEKq93J3xlLVoXhaPuoJaX047eIc0FtZQz1/f7OPCqP4n3JZXXdJYZH8qtXV/rrLVNNfDEBFxaYaVV6NGPtvCbyVBRRX8bcZXFAd48cRD47G5WYyO1PKYTFT4eFLh40kWzbezV0vmUW3Fv6yKoJJKInJLaH+0hMicuv/aHy0hJS2zcbkxuwkORwaxPz60sdDuSwjlcGQQDnPrHant6B/F6IhedAmIpr13CCGev3zBX43dSnZlAUcqCxpHWLMrC3m693UA/HvXV40jslHe7Wjv3Y5QzwBCPQPoFZxwwv3lV5eSVZnPzpJMfjy6hb2lR5rtcYpI69LaO5ZGXlsZi83OX5/9lqCSugu0Cl30Ai1pfjWebuR7+pEf4se+hLATPm+22Yk6WkziwTwS0/NIOpBHUnoew1fvxVx/HVyZjwe7Okaws1Pdfzs6RXI0zN+lpx60927HmIgURkf0Isb3+H+XGruVrPpy2jBCerj+49yqYuw4fuFe4ZvDa4/7sxkTYV6BRHmH0KG+0DaM6Lb3bkeIpz8hnv70CIpnSuw5ZJTnsujoZn48uoWsyoJmeewiIq5A5bWVueWd5fTZlslT97WuC7TE+ewWM5lRwWRGBbN0aKfG2z2raonPyKfT/lw678uhy55sLp+zAXerHYDCQG92dopkxzGFtijotzeWMJKH2Y3z2vdlXGQfugbGNN5eWFPGkqNbWZO3k4yKPPKrS361oJ4OOw6OVhVxtKqIjYX7jvucGRMhngHE+IQyKLQLoyJ6EuMbxnWJ47gucRw7ijNYkL2ReVkbqLGfOP1DRKQ1U3ltRUat2M3vvlzPZxelMG90K79ASwxT7eXOrk6R7OoUyTf1t3nUWEk6mEeXPdl03X2ULnuyGbT+QOMIbXaYPzs7RbCtaxSbundgb2I4NovZsMfQwMfiyYToQUyOGUqwhx8AFdZqVuZu58ejW9hQuA+7w+70XHYc5FYXk1tdzIbCfby+93v6BicxOjKFYaFd6RoYQ9fAGK5JGMPnh1bwzeG1VNhO7wJEERFXpfLaSsQfyueRf80jrXMkL9840ug40sbUeLixIzmSHcmRfHFR3W3eFTUk78+hy56jdN2TTdfd2YxauReACm93tnaNYnP3aDZ3j2Jnp0hqPJz348jfzZtLY4YyMXowfu7eAOwqyeTzjJWsyt3R4lYBsDvspBbsIbVgDy+Z3RkS1pXJMUPpHBDNjR3P5/K4EXyVuZovMlZSaq00Oq6ISLNSeW0FvCtqmPHM11R5uvPnhydgddcFWmK8Sh8PNveIZnOP6MbbQvLLSNl+mJRth0lJy+Tm91YAUO1uYUdyJBt7RrOpRzTbukRR49n0P57MmBjfYQDXJY4jwL1uKsPmwgN8dHAxG3721n1LVW2vZfHRLSw+uoW+7Tryu7iR9ApO4OqE0VwcPYh39s3n+yOpTTa9QUSkpVF5dXUOBw+8upDorCLumTGFXG1/Ki1Yfogfi4Z3ZtHwzgAElFTSa/sRUtIySdl2mGmfrOX6j9ZQ42Zhe+dINvWIZlPPaLZ1bn/WS3V1D4zj9uSL6OgfBcCmwv3M3L+A7cWHzvpxGWVDwV42FOyle2Ac0xLH0js4kbu6TGR8hwG8uvtb0orTjY4oItLkVF5d3PgFaZy3ZCdvXj2ETT2jf/sLRFqQkgBvlg9OYvngJAB8y6vptf0wvbdl0ntrJtfOXsvvP15DrZuZHZ3qRmbX9YkjrXP7U36HwdPszk0dL+Di6EEAHK0q4r97vmN57vZme1zOllaczp82vsU5Yd25udOFdPSP4oV+N/FV5mre3Du3xU2DEBE5GyqvLiw+PY97X/uR1JQY3ps60Og4Imet3NeTVQMSWTUgEQCfimp6bj9Cn22Z9N6WydWfruO6T9ZS4e3Ohp4xrOsTx7o+cWREBZ10ea7OAdE81G0K0T6h1NqtfJy+lE/Sl7XaMrc8N411+bu5Im4El8cN55LowfRt15Hntn/KrpJMo+OJiDQJlVcX5VFjZfr/fU+FjwdP3X8h9tO4ctvfzbvx477BSS4z10/angofT9b0T2BN/7pF/P3KquizNZOBG9MZsDGdc9buByArPIB1fWJZ1yeO1JRYyvy8uDxuBL9PGIvFbGF/WTb/2P4p+8uyjXw4TlFtr+XdAwtZnpvGQ92mkOAXyYt9b+KdAwv5JH2p0fFEmlzfdh0bP/Z389ZFi22AyquLuu3tZSQdzOPBJyZRcBYbETzd+zre3b+Qj9KX4tAFHtLClfl5sWxIR5YNqXuxisoqYsCmdAZsPMSYZbu5ZO42bGYTpb264XuhF7YLQ/i4XRkz03+kto2th7q/LJs7U1/juoSxTI0bzo1J59HJP4rnd3yurWelVTBh4sr4kUxLGGN0FHEylVcXNGTdfqZ8s4lPLunD6v4nbiv5W37+W+nvk84lzi+C53d83uZe4MW1HWkfxJz2Qcy5MAWLzc6wg2Xck+lLu2VrMD/7LO5/+xsTgn1oNyCRFQMTSU2JPesLv1xJrd3Km/vmsrXoIH/qPpUR4T2I9QnjL1ve42hVkdHxRM6Yu9mNB7pOZlREr+PWYtaoa9ug8upiggvLeeRf89gbH8rr151z1vf3xJZZPNL9CkZH9CLMM4DpW97XyS8uKTagPbdOuw5fzwB2l+fw/LI3iF++nqFr9zNm2W4unreNag8L63vFsmJgIisHJpIX4md0bKdYk7+Lu1Jf44meVxHvF8EL/W7msU0zOVh+1OhoIqfN382b6b2uoUdQHOXWKv6W9gkzUqYZHUucSOXVlTgcPPTyAnwqarjr6SlNsqj72vzd3L/hDf7a61p6BMXzXJ8beGTTOxTVljdBYBHn6BEYx5O9rsHP3ZvNhQeYvmUWFR7V7BrTjbljuuFWayMlLZNha/czbO1+hqYegFcXsispnKVDOrJkaEfSY0KMfhjNKrMij7tTX2d6r2tICU7g+b5/4Ikts9im5bTEhQS5+/K3PteT6BdJblUxf978Lgf0S1ibY/z+jHLKxi9I45y1+3n9unM4GBfaZPe7vyybe9a/TkZ5Lon+7fm/vn+gnUfbGJES19c9MI4ZKdPwc/dmeU4aj22eecJWqVZ3C+t7x/HSzaO54o0buPbla3lt2jBq3SzcNGsls25/l3dvn8mNs1aSdCAXHK1z/neFrZrHNs9keU4afu7ezEiZRvfAOKNjiZySdh5+/F/fP5DoF0lGeS73rH9dxbWNUnl1EeE5Jdz1xhI29ojm04v7NPn951WXcP+GN9lfmkWMbxh/7309gfU7EIm0VF0DYpiRMg1vN0/mZ23k6W0f/fa8bZOJg3GhvD91ILf935Vc+s5NvHjLaAqDfLh29lreuWsWH97yNre+s4yuu7NbXZGttVt5ettHLMjaiLebJzNSptE1IMboWCK/KtDdh7/3vp4Y3zD2l2Zx/4Y3yasuMTqWGETl1RU4HDz87/mYHA7+dvd5OMwnrmfZFIpry3l409uklx0lzi+CGSnX4WXxaJZjiZytWJ8wnkqZho+bJ4uyN/PCjs/PaEvUvBA/Pp/Qm7ufmcqkmTfz3B/HciQyiCu+3MB/7/+QT2/4H3e+sZheaZmYbfbfvkMXYMfB8zs+58fszfi4efJUyjRifcKMjiVyUl4WD55OuY44vwjSy47yp01vU6ypbW2a5ry6gAnztjFg0yGev20MWZGBzXqs4toKHt70Ns/3u5nkgA482v0Kpm99/7irOUWM1s7Djxm9r8Pf3ZsVudv5x47Pzqi4/lxRkA9fX9CLry/ohX9pFcPW7mPkyr1M/H4Ll3+1kfxgH5YO7siSYZ3Y1CMa22msr9zS2HHw3I7P8LC4MyysG0+lTOPe9a9TUFNmdDSRRmaTmUe7X0GngA4cqSzg4U1vU1JbYXQsMZjr/uRtI0Lyy7j97WVs7BHNnAt6OeWYBTVlPL5pJsU15QwK7cxNSec75bgip8Ld7Mb0XtcQ4RXE9uJDPJs2u1l+uSr19+KHsd155M8TuXjWrUx/cDxbunXggkXb+efjn/Hlta/z0L/nM2BDOharrcmP7wx2h51n02azoziDSO9gpve6BnezxjSk5bgp6XwGhXamuKacxzbN1C9XAmjktcW75/Uf8ai18tyd45ptusDJHK7M58mtH/Bsn+uZHDuMvWVZLMze5LTji/ySO5MvpnNANFmVBUzfMsspW71W+niwcERnFo7ojGdVLYM2HGTkyr2MXbaLi+dto8jfi6VDO/LjsGQ29opxqRHZanstT2x5j5f630bngGjuSL6YF3d+YXQsEcZG9mZy7DBq7Vae3PoBRyrzjY4kLYTKaws2KPUAo1bt5fVpw8iMCnb68dOK03l197fc3WUid3W+hD0lhzlUkev0HCINLmjfj/Oj+lFlq+GvWz+g2IC3D6u93Fk6tBNLh3bCo9rKoA0HGbViD+OW7uKSudsoCvBm6ZAkFp2TzKaernEhVHFtBU9ufZ9/9ruFC6L6saP4ED9krTc6lrRhcb7h3NX5EgBe2f0NaVrSTY6h8tpCWaw27vjfUjKigvhoUj/Dcnx3ZB3dAmM5t30fHutxJXek/ke7cIkhon1CuS35IgBe2vUV+8uyDU4ENZ5ujdvVNhTZ0ct3M27JT0XWfUoptsmTsdjsLXpEdn9ZNv/aNYeHuk3htuSL2Fp0kMMa6RIDeJjdeLT7FXhZPJiXtYHvj6QaHUlamJb7k7SNm/TdFuIzC3j5xhFY3S2GZnl599dklOcS7xfBdQljDc0ibZPFZOZP3abiZfFgQfamFjmFpaHI/vXB8Vw861YeffRiUnvH4vbxx3hNmMAX1/2X+19ZQO+tGZjsLXP5rYX1/7ZeFg/+1H0qFpNeIsT5rkscR7xfBBnlubyy+xuj40gLpJ9MLZB/aRU3fLiKtb1jWTkg0eg4VNlq+Mf2T7E57FwWO0xrQorTXRZ7DskBHciuLOSVXV8bHec3NRTZJx8cT+XBg1R/8AEbesVw3uKd/PvRT/n0hje5/a2ldNyf0+LWkX1519dkVxbSOSCay2KGGR1H2phuATFMjhmKzW7jH9s/pcpWY3QkaYFUXlugaz9Zi195Na/eMAJMzrtI69fsKj3M7PRlmE1m7u4yUSMy4jRR3u24Jn40AP/a+eUJu2e1eD4+2CZOZPpDF3HJe7cw/cHx7E4KZ+pXG3n77vd574/vcu3HawjPLTU6KVC3C9e/ds0B4JqEMUR5tzM4kbQVFpOZu7pMxGwyM/vQcnaVHjY6krRQaiAtTGh+GZO/3cQPY7qxL6FlLRr+/sEfOVJZQIJfJJOihxgdR9qIWztdhKfFnQVZG9lQuM/oOGel2sudhSM688ifJzLx3Zv5x+1jKQrw5uZZK5l945s8/5fPGbNsFx41xs4r31CwlwXZm/C0uHNrp/GGZpG2Y1L0EBL8IjlSkc/7B380Oo60YCqvLcw1n67DYnfw9u8GGx3lBDV2K/+pn390VfwobR8rza5/u04MCu1MubWKN/b+YHScJlUS4M1XF/bizr9fzuVv3MDMKwYRm1nAk899xxfX/Zd7XltE8t6jhk0reGPP95RbqxgU2oV+7ToakkHajkB3H65OqHuH5dU931KjC4PlV6i8tiAh+WVMmLuV78d2IzuieXfSOlNr83eTmr8bP3dvrowbaXQcacVMmLixY90GGR8cXExRK94OMisykLeuHsrlb97IPU9NZnW/BCbM38b/7v2At++axdQ5Gwgqdu6yYEW15Xx4cDEAf+h4ASZaxhQmaZ1+Fz8KXzcvUvN3sy5/t9FxpIVz2lJZX46bTohXQOOfl014AYD8qhImLZjurBgt2lWfp2Kx2Xlv6gCjo/yq/+2bR/+QZCZED+LzjJXkVhcbHUlaoRHh3Un0iySnqog5mauNjuMUDrOJ9b3jWN87jhfLRjN22S7Gz0/jrjeXcNs7y1gxIJHvzu3O2r7xTll268vM1UyMHkKiXyTDw7uzNGdbsx9T2p4wz0Au6jAQu8POm3vnGh1HzoCzO57TRl6PfVCncntb419axcVztzJ/VBeyIoOMjvOr9pdl8+PRLXiY3ZgSe47RcaQVMmHi6oQxQN2oa1tcW7jMz4s5F6ZwywtXMe3la/lsQm9Sth/mub/O4dMb3uSWd5YRk1nQrBlq7VY+qB99vSZ+tEZfpVlMjT0HD7MbS3K2caD8qNFx5Aw4u+Np2kALMfGHLXhXW/nYwA0JTsdHB5cAcGFUf819lSY3MCSZON9wcqqKmJ+10eg4hjsQF8orN47k0ndu4tFHL2ZHpwiu/GI9H9w2k1ce+pjx87fhXdk8SwrNy9pAblUxcX4RDAjp1CzHkLYr0N2XC6L6AzROUxH5LSqvLYDFZmfSd5tJTYlpcSsM/JKD5UdZk7cTT4s746Na9jQHcT2X1Y/of5GxEqvDZnCalsPmZmHZkI48+vhELnvnJl69fjgBpVU88tJ8vpz2X/700jx6bj/cpBd5WR02vshYCaB3WqTJje8wAE+LO6vzdpJenmN0HHERKq8twDmr9xGRV8anF/cxOspp+SJjFQATogdh1rqv0kTifMNJCU6gwlrND0fWGx2nxSoI9uXDyf259tVp3PqPK1g0vDNjlu/m1T99wqzbZnLVZ+toV9g0F7l9fySVCms1KcGJxPq4xi/Y0vKZTWYmdBgI0PgLksipUONoASZ/u4ms8ABW9U8wOspp2Vi4j4zyXEI9AxjQTm8nStO4sP4txB+Pbna9DQmMYDKR1iWKZ+86l0kzb+Zvd59HUaA3t72znM9+/wbPzJjDsDX7sFjrRrDHLd7B7BveZMklLzL7hjcZt3jHbx6iwlbNj0e3AHUjZSJNYWBIMqGeAWSU57KpcL/RccSFOG21gfyqkpNO3M2vKnFWhBYpLiOfvlsz+c9152B3wtXDTe2HrPXc1PECzmvflzX5u4yOIy7OYjIzOiIFgO+OpBqcxvVUenvw3bjufDeuOzGZBYxfkMaFi7YzfM1+8oN82Nkxgn6bD+FVW1dkI3NLeejlBQAsGNX1V+/7uyPruKjDAEZH9OKNvT9gc9ib/fFI63ZuZN27jT9k6R0WV+fsjue08tqwVMLcMTMAOH/R4846dIt20fw0rBYz343rbnSUM/Jj9mZuTDqPgSHJ+Fg8NVImZ6Vvu44EefiSXp7D3tIjRsdxaRnR7Xj998N589phDFp/gIvmpzF89b4T1gvwrrZyy7srfrO87i09wqHyHGJ9w+kTnERqwZ7mCy+tno/Fk4EhydgddhZlbzY6jpwlZ3c81xvqa0XMNjvnLd7Bqv4JFAW55hX7+TWlbC1Kx8PizqDQzkbHERc3IrwHAIvr36KWs2ezmFk5MInHHruEX7qMKzyv9JTuq2HqQMPzJHKmBoV2wcPiztaidApqTu37T6SByquB+mzNIKSwgrmjf33Eo6VbmbsdgKGhrv04xFhmTAwMSQZgRf33lDStnDD/k98eevLbf67heRkU2hmz1nyVszA0rO71YkVumsFJxBWpvBpo3NLdlHt7uNyFWj+3Om8nUPeWr1YdkDPVKaADQR5+ZFUWaMmcZvL6tGFUeh4/W6zS043Xpw07pa9PL88hq7KAIA8/OgV0aI6I0gaYTWb6BicBsDpP10rI6VPTMIjFamP46r2sGJRIjafTph43i+yqQg5X5OHn7k2yf5TRccRF9Q5OBGB9vuZSNpcFo7ry3B3jyA7zx26C7DB/nrtj3G/Odz3WhoK9AKTUP18ip6uzfwf83L3JrMjjaFWh0XHEBbl2a3JhfbZmElhaxY/Dko2O0iQ2FR6gg08oPYPi2VmSaXQccUENZUhL5jSvBaO6nlZZ/blNhfu5qMNAegcn8kn60iZMJm1Fz6B4ADbrXJczpJFXg4xYtZdKTzfW9okzOkqT2FZ0EIDuga3j8YhzmTDRJSAagLTiQwankV+zrSgdqBs9M2neq5yBboGxwE/fSyKnS+XVCA4HQ9ftZ23feJefMtBgV/1oa0dNG5AzEO0Tgq+bF7lVxbryuIUrqCklt6oYP3dvon1CjI4jLqhhvrTepZMzpfJqgKSDeUTklbFyYOuZM3aksoByaxVhXoEEuLvmsl9inES/SACt7eoiGp6nhPrnTeRUBbr7EOoZQLm1iqzKAqPjiItSeTXAkNQDAKzuF29skCbkwMGh8lygbm96kdMR5xsBoFUGXETD86RzXU7Xsee64xdXHhb5dSqvBhi4IZ3diWEUBPsaHaVJZVTUldcO3norUU5Ph/q3nw/Vfw9Jy9Zwrkf7hBqcRFxNw7meWZFncBJxZSqvTuZZVUuPnUdI7R1rdJQml11Zt+RJe+92BicRVxPpFQz89D0kLVvD8xRR/7yJnCqd69IUVF6drOeOI7hb7azv1frKa251MQAhngEGJxFXE+YVCEBOVZHBSeRUHK1/nsLrnzeRUxVa//rQ8HohciZUXp2s97ZMrGYTW7u2vqvyC6rrrhIP9vAzOIm4mkD3uik0RTVlBieRU1FUWw6gizPltAV71m1F3PB6IXImVF6drNf2I+xJDKfSx8PoKE2u1FoJgL+7t8FJxJX4WDxxM1uosFZT67AZHUdOQa3dSoW1GnezGz4WT6PjiAvxd6t7fSitrTA4ibgylVcncq+10m13Flu7tb5RV4AKaxUAvm5eBicRV+LjVld+Km3VBieR01FpqwHA29L6fhGX5tNwvlfofJezoPLqRJ335uBZY8Nv9Pk82esarowb2ap2qKm2WwHwMLeOjRfEOTzN7gBU22oNTtL0zMec363ufK8vr14qr3IaGs73qlZ2vpsxcWXcyFb52t4SqWU4Ubdd2QCMmngzhEYwOLQLAB+lLzEyVpOx2uve8rWY9DuRnDqzqe6HvM1hNzhJ07s8bkTjx9cnnQu0nvO94flqeP5ETkVrPd8vjxvReI63ttf2lkgtw4m67smmKioCIiJ+ui0wxsBETUsvYSLH+/n53ZrOd5Gz0dpeL3SuO5fKqxN12ZNNZrfjt4TdUZxhUJqm514/XaDWrotu5NRZ60dg3MwWg5M0vZ+f3zrfpa1r+H5xa2XTy1rzud4Sta7vnhbMr6yK6KxiXjvPgm3ffLoGxrCjOIOP05caHa3JeFrq5jLV2FvXXCZpXq157uTH6Usb30p8e9/8VnW+e9Wf79U63+U0NLw+NHz/tBYN53ZrfG1viVRenaTT/rrtFHclhZPaSufB+NUvgVJWv+qAyKkot9ZddezbCpdcOnbv9tY2/61hiawKq64al1NX1kpXpXHgaHXneEumaQNO0lBe9yaEGZyk+TQsWF6i9fvkNFTba6my1eBhcdeySy7Cx+KJh8WdKluNRl7ltDSs7xqoDS7kLKi8OknSwVzyg3woCmq9J2yIdk6RM1RUU7djUzsPf4OTyKlo2EWv4XkTOVUF9bvo6VyXs6Hy6iQJ6fnsjw81OkazCvcKArRntZy+o1VFAETUfw9JyxbhHQzA0apCg5OIq8mpP9fDda7LWVB5dQKT3UFcZgEHY9oZHaVZtfeue3xZlXpBk9PTUIIavoekZWvvVVdes3Wuy2lqeH2I0rkuZ0Hl1QnaFZbjU1XLoQ6t+2SN9ambz5tRkWtwEnE1h8pzAIj1DTc4iZyKuPrn6ZDOdTlNmfXfMzG+rff6D2l+Kq9OEFJYNy8sL8TP4CTNx91koYNPCHaHnYxyvaDJ6UmvL6/xfhG/8TelJWh4nhqeN5FTdag8F7vDTrRPKO6m1re2sziHyqsThBbUTVDPb+drcJLmk+AXibvZjYyKPF19LKdtb+kRADr5R2lP8BbOhImO/lEA7Kt/3kROVbW9lsyKPNzNbvplVc6YyqsTdMiqu4ApKyLA4CTNp0v9Vni7Sw4bnERcUUFNGblVxfi6eRHt07ovbHR1MT6h+Lp5kVNV1HjluMjpaHid6BqgLVTlzKi8OkFUdjHl3h4UBXgbHaXZ9AyMAyCtON3gJOKqthcfAqBXULyxQeRX9QpOAH56vkRO17b614meOtflDKm8OkFEbgnZEQFgap1vh5ox0btdEgBbCg8YnEZc1abC/QCN30vSMqUEJwI/PV8ip6vhdSIlOBGzpgnJGVB5dYLInFKOhrbeBZk7BXQgwN2H7MpCDlfmGx1HXNTGwn0A9AlOwmLSj6aWyGIy0ze47pcLlVc5U4cr88muLCTQw5dO9fOnRU6HXiGcICK3hOzw1jvfdUhoFwDW5u8yOIm4sqzKAg6V5+Dv7k2P+mko0rL0CIrHz92b9PIcsioLjI4jLqzh9WJwWFeDk4grUnltZp5VtQSUVZMb2nqXyTonrDsAK3N3GJxEXN2qvJ0AnBPe3eAkcjLD68/11fXPk8iZWlX/etHwPSVyOlRem1m7ogoA8oNb5zJZHf3aE+MbRlFNGZuLNN9Vzs7io1sAGBHeU1MHWhiLycyI8B7AT8+TyJnaXHSAopoyYnzDSPJrb3QccTFuzjrQl+OmE+L101vnyya8AEB+VQmTFkx3Vgyna1dUt0FBQZCPwUmax7j2fQBYcnQrdofd4DTi6vaXZZNenkOcbzgDQpI1wteCDAxJJtDDl/Syo+wvyzY6jrg4m8PO0pxtXBI9mHPb92HfniyjI8lZcHbHc9rQxrEP6lRuby3aFdaNvBa2wvLqYXZjXGRvAOZmbTA4jbQWZ/hU4AAAIABJREFU846sB2B8VH+Dk8ixLowaAOhcl6Yzt/5cHxfZGw+z08bSpBk4u+PpfblmFlhSCdAq13gdHZGCv7sPu0oy2Vem35qlaczL3kiN3cqAkGQivYKNjiNApFcwA0I6UWO3Mj97o9FxpJXYW5bFrpJM/N19GBXRy+g44kJUXpuZf3k1AKV+XgYnaVomTFwaMwSALzNWGZxGWpOS2goWH92C2WTm0pihRscRYHLMUMwmM4uPbqGktsLoONKKzMlcDf/f3p2H11nX+f9/nj0n+77va/clTbqlKy0tlKVsggrWQf1SUHG+zig6Xvib0VFn5jsq6oDaEVzKsAiigCwtdA1N931J0rRN06ZNmq1p9pyc7fdHSgakasEm9znJ63FdvTg5ycn9CmlzXvmc+35/GPw7JnK1VF6HWUR3Px6ziT6nzego19TMuEJywpNp6e+gvPmo0XFklHnpbAUAN6TOIMo2+k65CSZRtlCWp84A4HdntxmcRkab8qYjtLo6yQlPZlZckdFxJEiovA6z8G4X3WGOUbe71n051wHwUn0FHr/X4DQy2tT1NLGrtZoQi52PZc43Os6YdnfWAkIsdna2VnOmp9noODLKuP3eoV+K7s1ZbHAaCRYjVl7b+js/1P2jRWjfAL1Ou9ExrqmyhAkURqbR5urijfN7jI4jo9Ta05sAuCV9FrH20btDXSCLtUdwS9osAJ6u3WhwGhmt3ji/hzZXF0WR6ZQlTDA6jnwEI93xRuzyvndHJay/7jsALN/06Egd2lDOfveoOmXAZrLw2bxlADxbtxmXz21wIhmtTnY1sK35GPMSJ/J3uUv5YfUfjI405tyfdz0Oi41tzcc4qYsyZZi4fG6eq9vCF4tu4TN5y9jVelyv6AWZke54Om1gmIW4PPSHjJ7yujJjDmmh8ZztaebNhr1Gx5FR7qlTb+H2ebg+Zbr2QB9hBRGpLEspxu3z8NSp9UbHkVHujYY91Pe0kB4az23ps42OIwFO5XWY2Qc8uOyjY35dYkg0n7p8ruuaE2/i1aYEMswa+tp4uX4HZpOZvy9aiVm7bo0Is8nM34+7DRicJtLQd9HgRDLaef0+fn7iDWDwmooER5TBiSSQ6ZlgmNk8XjxWi9ExromHC28hxGKnvOkIey+eMDqOjBHP1G2mqf8SBZFp3J4+x+g4Y8Id6XMoiEilqf8S/1O32eg4MkbsvXiC8uajOK0OHi661eg4EsBUXoeZze3FZQ/+8ro8ZQYz44vocvfxs8u/HYuMhD7vAP91/FUAPp27lMzQBIMTjW6ZoQmsyl0KwE+qX6HfO2BwIhlLflbzOl3uPmbFF7E8ZYbRcSRAqbwOM7s7+Fde05xxPFSwAoAnal7j4kCXwYlkrNnTVsP6hn04LDa+NvFj2EzB/W8qUNlMFr4+8W4cFhvrGvbpFRYZcRcHuvhpzWsAPFiwglRnnMGJJBCpvA4zi9eHxxK8/5ttZivfmHQPTquDLU2H2dx0yOhIMkb97MTrNPS2kR+RygMFNxodZ1R6oOBG8iJSaOht42cnXjc6joxRm5oOsaXpMKFWB9+YdA828+i4bkSuneBtVUHC7PPjNwfvBgVfKrqV/IhUGnrb+HH1K0bHkTGszzvAvx17gQGfh1vTZ7M0eZrRkUaVpcnTuTV9NgM+D/927AWdLiCG+nH1KzT0XaQgIpUv6fxX+RMqr8PM5PfjNzrER3R7xlyWpRTT7x3gX48+R6/XZXQkGeNqus4PvaT4paKVjItMNzjR6DA+MmOoIDxx/I/UdJ03OJGMdb1eF/965Fn6vQMsSynm9oy5RkeSAKLyOtz84AvCldd5CRN4IP8GAH5Q9Xtquy8YnEhk0JsNe3n9/G4cFhvfmvIpUp2xRkcKaqnOOL415T4cFhuvn9/NusZ9RkcSAaC2+wI/qPo9AA/k36Ddt2SIyusIMAXZ0uvUmFy+NuFjmE1mnjr1FuXNR42OJPI+j9e8xp62GqLtYXx36qe1fexHFGuP4LtTVxFlD2NX63Eev7yqLRIoypuP8stTb2E2mfnahI8xNTrH6EgSAFReh5nfbMLsD572OiEqk29Nvhe7xcYfz+3ihTPlRkcS+QCf38d3jj5PTed5UkPj+I/p9xNtCzM6VlCJsYfz/6Z/htTQOGo6z/O9Y7/Fp41HJAD99kw5fzy36/KrLfcxISrT6EhiMJXXYdYSF05XeIjRMa7KlOgcvjf10zitDt5uPMATWoWRANbvHeAbB39NbfcFMsMS+X/TP0OsPdzoWEEh1h7Of0y7n4ywBGq7GvnGwV/rAi0JaE/UvMaGxgM4rQ6+O/XTTNEK7Jim8jrMHv73u/nR6sVGx/irZsYV8p2pq3BaHWy8cJAfVv+B4L3UTMaKLk8fXz/wS+q6m8gKT+KxGas1F/KvSHPG8diM1WSFJ1HX3cTXD/6KLk+f0bFE/iI/fn5Q/Qc2XThEqNXBd6auYmZcodGxxCAqr8LNaTP5l6ELNvbwn5Uv6eVDCRod7l4eOfAU1R31JDtjeGzG/6EoIs3oWAGpKCKNH854gGRnDNUd9Xz1wFN0uHuNjiVyVXx+H/9Z+TveOL8Hh8XGv0y5j5vTZhodSwyg8jrGPViwgoeLbsViMvPM6c385PgrWnGVoNPh7uVrB3/F3rYaou3hfL/4cyxNnm50rIByffJ0vl/8OaLtYexpq+GRA7+kU8VVgowPPz8+/grPnN6MxWTm4aJbefDyDpAydqi8jnG3Z8zF7fPw/cqXWHt6o9FxRD6yfu8A/3z4GV47twu7xcZXJ9zJ5wtuwjrGt5K1mix8vuAmvjLhTuwWG6+d28U/H/4fXD630dFEPrK1pzfy/cqXcPs8mgE7BmnPtTEoOSRm6HZLfwffPfo8VZ31BiYSuTY8fi//VfNHTnQ18IWiW1iZMYcJ0Vn8Z+XvONPTbHS8EZcVlsgjE+4iPyKVAZ+Hx4//kfWa4yqjxNsXDnCut5VHJ3+CeEckAEkhMTT1txucTIabVl7HoOb+S0O3v7DnpyquMuqsa9zHV/c/SePl7SUfL3mI2zPmYiL4Ngz5KEyYuD1jLo+XPER+RCqNfRf5yv4nVVxl1KnqrOcLu58Yevu9z28yemnldQzyveec1g53j4FJRIZPdec5Htr9OKsLVnBjagkPFqxgYeJkflrz2qje/rQwIo3PF97M+KgMYHBHsjUn3qBPo7BklLr0nucxXbMxNqi8isio1ecd4EfVL7OrtZqHi1YyPiqDH5esZn3jfn516u1R9ctblC2Mz+Qt44bUGQC0uTr5yfFX2dlabXAyEZFrS+VVREa9Ha3VHGyv5ZPZi7k9Yw43ppawIHESr5zbyR/qtwf1VfeRtlBuz5jLyvTZhFlDcPs8/KF+O8/WbdFqq4iMSiqvIjIm9HkHeOrUetY37uPBghWUxhXyyexF3JExl9fP7+Gls9toG+gyOuZVi7NHcFfmPFaklRJisQOwp62Gn594g3O9rQanExEZPiqvIjKmnOtt5dFDa5kQlcknshYyM76IOzPLuCV9FuVNR9jYdIiD7bUBuVGH2WRmWkwuS5KmsiBpMnbz4I/wXa3Hef7MVio7zhqcUERk+Km8isiYVNlxlm8efpq88BQ+nr2QeQkTWJoynaUp07no6mJr8xE2XTgUEBd3FUakcV3yVBYmTibWEQEM7jZU3nSE58+Uc6q70eCEIiIjR+VVRMa0U92NfPfo86Q4Y7kuaSrXJU8lPTSe2zPmcnvGXOp7Wtjddpz63lYa+i7S0NtGq6tzWK5qNmEi3hFJamgcqc5YMkLjmRU/jvTQ+KGPOdfbyqYLh9jUdIjGvovXPIOISKBTeRURARr7LvJM3Waeqdv8vpXOjLAEMsIS3vexA143jX0XB8tsXxvne9uGbrf0d7xvHN2fMmMiISSKVGccaZdLaqpz8L8pzljsFtsHHhNoK8EiIkZSeRUR+RM1Xeep6TrPf59cx7ToHAoj04eKZoozjjhHBFnhSWSFJ33gsQM+DxcuF9seT//Q/d+ach9pzjiSnTHYzH/+R2+bq4vGvsEyfL63jZrOcxy8dDogz8EVETGCYeV1/XXfMerQIiJXxef3sb/9FPvbT73vfqfFTsrlFdPBUnt5BTU0jnhHJJlhiWSGJb7vMbPjxw3dbnV10nB5tfZ8X9vl22009l3UeCsR+ZuN9o6llVcRkQ+pzztAbfcFarsvfOB9DrNtqMjOjCvkhtQSAL595NmhwuryuUc6sojIqGFYeV2+6VGjDi0iMmxcPjene5o43dNERUvlUHmtaKk0OJmIjHZGd6uRWvHVyqsElKVbqli9toLE1i6a4yNYs6qMDYvGGx1LREREAoTKqwSMpVuqeOTxDThdHgCSW7p45PENACqwIiIiAoDZ6AAi71q9tmKouL7L6fKwem2FQYlEREQk0Ki8SsBIbL3yvvKJLV3gv/YD4UVERCT4qLxKwGiOj7ji/Wbgv/7pRQpPNo1sIBEREQk4Kq8SMNasKqPP8f7TsPvsFl5bOoHs+ov84h+e5RuPrSfhz6zQioiIyOinC7YkYLx7UdaVpg083uNi1Qu7uOvVgyzeVsMLK4t55q4SekMdBqcWERGRkaTyKgFlw6LxV5ws0BPm4Gf3L+APK6bywNoKVr24m1vXH+E398zklRun4Lbpr7KIiMhYoNMGJKhcSIri219dwed++AlOZcfz97/YyjMP/oblmyoxe7X3u4iIyGin8ipB6XhBMv/3O3fyD9+6g86IEB59bD2/fvhpFmw/ockEIiIio5hea5XgZTKxpziLvdMyWbjjBJ97ejvf/bfXqM5P5FefmM320lwwmYxOKSIiIteQyqsEPb/ZxJayQt6Znc+yzVX83fM7+Y9/fZWa3ESeulclVkREZDRReZVRw2sx8+bSiby1aBzLtlSz6oVdQyX26btL2TqnAL9ZJVZERCSYqbzKqOO1WoZK7PIt1dz34m7+9d9f50xaDM/cVcpbi8bhtVqMjikiIiIfgS7YklHLa7XwxtKJ3PfTT/PPj6xgwG7lGz9+i2dX/5pb3zyMze0xOqKIiIh8SCqvMur5LGY2zS/iMz++l699cyWXokP56k838vz/+RW3vX5IJVZERCSI6LQBGTtMJrbPzGV7aQ4lB89y/3M7+cefb+K+3+3hf+4q5fVlE7XZgYiISIDTM7WMPSYTe6cPjtgqOXiWzzy3g3/8+SY+9eJunrmrlNeWTWLArn8aIiIigUjP0DJ2vafEzjg0uBL75TWb+dSLu3n2jhL+uHwy/SE2o1OKiIjIe6i8iphM7JuWxb6pmUw/co6/e34nX3pyK6t+u4uXbpnG72+aRmek0+iUIiIigsqryP8ymTgwJYMDUzKYVNXAvb/bw2ef3cknX9rLG9dP4sVbpnM+NdrolCIiImOayqvIFRwdn8o/fXMl2Wda+eTv93HrusPc/vpBts3K47k7Sjg6PtXoiCIiImOSyqvIX1CXFc/3vrycNZ8u47Y3DnPH6wdZsPMUR8an8NztJVTMzMVn0cQ5ERGRkaLyKnIV2mLDeeq+uTx7Zwkr3j7G3a/s53vf+yMNSZG8dPN0Xr9+Ij1hDqNjioiIjHoqryIfQp/Tzku3Tuflm6Yyb+cpPvbqfh5+aiuffXY76xZP4OUVUzidFW90TBERkVFL5VXkI/BazGwtK2BrWQGFJ5u4648HuOnto9zxxiEOTkzj5RunUD43X5seiIiIXGN6ZhX5G9XkJ/G9L9/AE59ZyE0bjnLruiP8y/ff5FJECG8tHs9ryyZpNVZEROQaUXkVuUY6opw8e2cpz91eQsnBs9z81hFuf+MQd796gGNFyay7bgIb5xfRFRFidFQREZGgpfIqco35zSb2FGexpziL6I5elm+u4sYNx/jHn23i4V9sZfvMHN5cMoFdxdl4rRaj44qIiAQVlVeRYXQpKpTf3jaD364spvBUM8s3V7FsSzWLtp+kPcrJ2wvH8eaSCZzMSQCTyei4IiIiAU/lVWQkmEzU5CdRk5/ET++fz6z9ddy4sZLb3jjM3a8eoDYzjg0Lx7FhQRGNyVFGpxUREQlYKq8iI8xrtbB9Zh7bZ+YR0dXPkneOs3RrNQ88XcEDT1dwtCiFTfML2TyvkNa4cKPjioiIBBSVVxEDdUWE8PKKqby8YipJzZ0sKT/O0vLjfOnJrXzhl+XsnZbJ+sXjeWd2Pv0hNqPjioiIGE7lVSRANCVG8uxdpTx7VykZ59u5fksVN2yq4v/7wTp6Q2zsnJFN+dwCtpfk0BdqNzquiIiIIVReRQJQfVoMv7x3Lr/6xBymVJ5n6dZq5u88xXUVJ3DZLeyckUP5nHy2l+bQHW7s6K2lW6pYvbaCxNYumuMjWLOqjA2LxhuaSURERi+VVwlYY6YU+f2EuDw4+wYIcXmwuz3Y3F6sHh9Wrw+T30/5nAJ2lOSQV9fKlMrzFB+uZ+GOk3jNJurSY6nJS+RkbgJd4SH4zCY8VgsDNgv9Dhv9Diuuy38GbFYG7BYGbFbcNgtumwWvxfyRoy/dUsUjj2/A6fIAkNzSxSOPbwAYnd8rERExnMqrBKSgLUV+P1Gd/cS19xDb3kPspV5iOnqJ7ugjuqOXqM4+ojr7Cet1Ed4zQGjf4B+Lz/+RDmfx+ck720be2TbYXPWRPofXbMJltzJgszBgv1xqrRY8VjNu62C59VpM+Mzmy8XYjM9sxms2UXLwDCED3vd9PqfLw+q1FYH9fRIRkaCl8ioBafXaiqHi+i5DS9HlUhp/sZvY9l6iO3tJaOsmobWb+LZuEi52E3exh7j2Hmwe3wce7rGYuRTl5FKkk45IJ+dSY+gJtV/+46AvxEav005/iJUBuxWPxYznPQXSYzHjsVnwmk0M2Cx4bIP3+Uwm8PvJaLxE6YGzlBw6S0FtM2Y/9Dus1OQmUpsZR316DBdjQrF6fDgGBld27ZdXd+0DHuwDXhwDHuxuL3a3B6vbi9Xrw+b2YvH6sPj8WLw+bG4fIS43Fq8Ps8+P40+K67sSW7uG+zsiIiJjlMqrBByTz09iy5XLz0iWok/9dhfTj54jsaWLpJYuQgY8H/iY7lA7rXHhtMSFc3ZyDG0xYbTFhtEWE8bFmDAuRofSHh1Kd5hjWDchaE6KYt+0LAAiuvqZcfgsMw7VM+PgWW6ragCg12mjqiCZqoIkqgqTqSpMpiUu/G/K9eJnniT5Ct+r5viIj/w5RURE/hKVVwkMfj85Z9tYuvU4y7ZU8efOwhzJUpTQ1k1Iv5va7Hh2lObQlBBBa1w4bdFhXIpy0hobHpBX/XdFhLClrJAtZYUAxLd1M+3IOSZVNzCpupGPv7wfq3dwdbg9ysmJnARq8hI5nRXP2bQYTmfG4brKsVxrVpW97/QOgD6HlTWryq79FyYiIoLKqxjI7PUxoeYCZbtOMX/nKbLOt+M1m9gzLYuK0hxWbDiG8z0vS490Kfrh55eM2LGGU2tcOBsWjWPDonEA2Ac85J9uYXzNBQpqWyiobeaeV/YPne7wlX++jV0lOVf1ud89hWNMXFgnIiIBQeVVRo7fT0bDJYoPn6X4UD0zDtcT1dWPx2LmwOR0fnfrdLbOyac9JgyAo+NTVYqGwYDdSmVRCpVFKUP3Wd1e0i5cIvNcO1WFyR/q821YND7ovi9jZpKFiMgopPIqw8bR76bg8grfxOpGJlc1kNjWDUBTfDjbS3PZUZLNnulZV5xVGoylKFh5bBbOZMRxJiPO6CjDLmgnWYiICKDyKteAxesjuamTrHNtZNdfJLeulby6VrLPtmG9PALqQkIEhyemcWBSOgemZFCfGj2sFzCJ/DkBN8lCREQ+FJVXg0Rf6uXrP3mLS1GhtEc5ab98EVBHRAhdESF0RoTQEeGkO8yB32xsybMPeIi72EP85XFQSS1dpDR1kNrUSWrjJVKbOt43Hqo5LpzarHi2zcrjeH4SVYVJtMWGG/gViEBsew8z99eRFACTLERE5KNTeTWIs3+AhLYeCmpbiOnoveJsUACfCXpCHXSFO+gKD6HXaacvxEaf00ZfiA2Xw4br8o5J787/dFvNeCyWocHyfhOY/IMrpDbP5fmeu/8dk8vF52vKsbs9OAYG53s6Lu/0FN7jIrK7n+iOPsJ7Bz6QqzvUTkNyFKcz43hnTj71aTGcSY/hTHqs4duVisDg3/eJ1Q3M3lfHrH11FNa2AIObMlxpUwiN9xIRCQ4qrwZpTI7msz++d/ANv5/wHhfRnX1EdvYT2d1PVGcfkV39RHb1E9HjIqK7n4huF86+AeIv9hDichPS7ybE5cY+4L3iDNK/7B38JhO32Qd3VXJd3lmp32GlP8RGV0QI51OiuRTlpD0qdGh2aWtcOM3xEXSFD+/cUpGPIq6tm9KDZ5i9t46ZB84Q0ePCYzZxdHwqa1aVsXt6Fpn1bTzyxEaN9xIRCVIqr4HAZKI7PGRwxTL1I34Ovx+rZ3BHJKvXh9XjxeLzY/b+74qu9/KK7IDdwsvLvwdWK8s3PXptvgYRA8S29zD16DmmHjtP8eF6cuovAtAWE0r5nHx2lOSwd1omPWGOocfU5CeByaRpAyIiQUrldbQwmQa3DLVZru7jrfrWS/BJbO5kSmUDU4+dY9rR82SfGyyrfQ4rhyemsW7JBHYVZ1ObFf8XzxXXJAsRkeClBiMiAcnq9pJ/umVwzFp1A5OqGkhqHRy11uO0c3hCKm9cP5GDk9KpyU3Aa73KX9xERCSoqbyKiOGsbi85Z9soqG2moLaFopNNFJ5qxuEe3GGtKT6Co+NTeW58KkfGp3IyJwGf5c9tIiwiIqOZyquIjBiz10dKUye5Z1rJPdNKzpk2cs62kdHQPjRxo9dp40ROAr+/aRqVRclUFqXQnKBJACIiMkjlVUSuufDuflIvdJDeOLjlbHb94AYW6efbh1ZTARqSIqnNimf7zFxO5CZwIjeRcynRhs82FhGRwKXyeo1EdPXzjz/dyK8/MZu6zNG/xaaMUZfHuiW0dZPQ2k1CWzdx7T3Etw3eTmrpIrm5k4ge19BDfCZoTIriTHosu6dncSY9ltrseOoyYulz2g38YkREJBipvF4j+adbmHngDAu3n+D16yfx9N0zaUqMNDqWjCGPPfo7Ms630xHppCs8hK4wB26bBZfDOrixRYid/hAbbqsZn9mMye/H4vNh8fqxeH1YvL7B+cEuD/YBD44BD6G9A4T3ugjvHty0IqLLhd3j/cCxOyJCaLk8A/jI+FQakyJpSI7iXGoM51KiGXDoR42IiFwbI/aMsnRL1eCe4q0/wp+eztK7J46qUTUHpmTwiTX3c//zO7ll3RFu2nCMrXPzefHW6Rwb91GHt4pcvV3F2TQlRBLd0Utkdz9Z53qwenyDJbRvgJB+N9Yr7Cz1Lp8J+h22ywV3cB5wd6iD3lA7Z9LD6IoIoSvcQVtMOK1xYbTEhdMSF05bbBhum8qpiIiMjBF5xlm6pYpHHt8wtKONqb6eRx5vBBhVBbYjysmPVi/mmTtLuPuV/dz09jGWvFPDsaJk/rBiKpvnFTJg15O8DI/n7yj5yx/g92N3e7F4fZh9fnwmE36zCa/FhNds1tX7IiISFEbk2Wr12or3bcUI4HR5WL22YiQOP+Ja4iN44rMLufNXn+MHD15HRLeLRx9bz+//7hd86b83k1vXanREGYtMJgbsVvqcdnrCHPSFXj6NwGZVcRURkaAxIsuAia1dH+r+0aLPaeflm6by8oopzDhczy3rjrDyzSN87I8HqSxM5tXlk9m4oIj+EJvRUUVERESCwoiU1+b4CJJbPlhUm+PHyOxGk4l9UzPZNzWTyM4+lm+u4pb1R/j6f73NF5/ayvrF43n1hinUZscbnVREREQkoI1IeV2zqux957zC4F7ka1aVjcThA0pnpJMXVxbz4q3TmVzZwMp1h7ll/VHufP0QlQVJrFsygU3ziuiIchodVURERCTgjEh5ffeirH/42SYiegfwpadj/va3+eo99/DVkQgQqJYAD4OntRV++1vGrV3LhJ9v5stPvoP3hhvw3ncf3uXLwa5ZmCIiIn/N+uu+Y3QEGQEjdpXGhkXjeereuQD0b9+O9557RurQgS8+Hs8XvkD/rl307diB56GHsOzejePjH8dZUIDtkUcwHTtmdEoZYZGdfax4+yjO3gGjo4iIiASMEZ3b1BE5+FK4qbWVZYceG8lDB5dlVixLPsHM/XWs2HCMsjU/w/nEE1TlJ7F+8Xg2LiziUlSo0SllmJXtruWffvI2X/75Zsrn5LNhYRF7p2VqpqqIyJ9YvulRoyMII7fyPaLPgq1xYQCYGhtH8rBByWsxs6M0lx2luUR39LJsczXLN1fxf3+xhS/+spwdJdmsu24C20tz8dgsRseVYfDmkgmcTY/hho2VXLethmVbq+kOtbOjJIctZQXsKs7GpUkVIiIyxoxoeW1KGJwuYDp7FtJH8sjB7VJUKC/cVswLtxWTc6aVGzZVsmxLNfN31XIpIoTN8wp5e+E4jo5PxW82GR1XrhWTiWPjUjk2LpUfP7CYmQfOMH/nSebtquX68uP0263sLs6iYlYeO2Zk0x4TZnRiERGRYTei5bU5PgK/xYK5rk7l9SM6nRXPz+5fwH+vmkfJgTPcuKmSGzdWcvubh7kYHcr20lzemZ3L3qlZ2k9+FPHYLGyfmcv2mbl83+tj6tFzLNhxknm7TrFg5ykATmXHs29KBvunZHBwUjo9YQ6DU4uIiFx7I9puvFYL/vR0THV1MC91JA896ngtZnaV5LCrJAdn7wBlu2sp21PLoooabn77KL0hNipm5rJhYRG7p2fr1IJRxGsxs39qJvunZvKj1YvJP93CrH11lB48y8p1h7n71QN4zCaqCpPZPyWDwxPTqCxMpjs8xOjoIiIif7MRX5rzZ2UNlldUXq+VvlA7GxaNY8OicVjdXqYfOcfC7SdYuP0E15cfpzPcweayQjbDVXVLAAAQwklEQVTNL+TgpHRtBTqamEyczE3kZG4iz3xsJvYBDxOON1J64CzFh+u593d7+PQLuwE4kxZDdUESx/OTqMlL5GROglZnRUQk6Ix8ec3MxLxp00gfdszw2CzsKc5iT3EWjz24mNKDZ7l+azXXb61m5fojXIwOpWJmLlvn5LNvaqZWZEeZAbuVg5MzODg5AwBn7wDjT1xgYnUj409cYMbhepZvqR76+HMpURzPS+J0Vhx1GXHUZsdzPjlKv+CIiEjAGvnymp6OqbERi8eL16riNJy8Vgs7S3LYWZKDo9/N7H11LK6o4bp3arjlraN0hTnYXprDttl57CrOps+pzRBGm75Q+9ApBu+Ku9hNfm0LhbUtFJ5sYvyJCyzZVjP0/n67lVM58ZzOjKM+NYZzqdGcT4mmMSmS3lCt1IqIiLFGvrympmLy+4lr76X58vQBGX6uEBtbywrYWlaAze2h5OBZFm4fvOBn+ZZqBqwWDk1KY0dJDlvn5NOcGGl0ZBkmbbHhtMWGs6skZ+i+kH43WfVt5J1pI+90C/mnW5iz9zQ3t79/c4zOcAfN8RG0xEfQEhdGS1w4LXERtMWGcTE6lEtRoVyKcjJgH54fLUu3VLF6bQWJrV00x0ewZlXZ0A5+IiIyNox4efWlpQEMPvmovBrCbbMOzZC1eH1Mrmxg7u5a5uw7zZee3MqXntxKdX4iu4qz2VWcTeW4FLx6GXlU6w+xcbwgmeMFye+7P7TXRVpjB2mNl0hp7iS5qZPE1i4S2ropPNlETEcvZv8HP193qJ1LUU46I5x0hznoCbXT77DRF2Kjz3n5vyF2ep02ekPt9DusuOxWvBYzbpuFvhA7bpsFt3XwbY/FwvztNXzpya04B7wAJLd08cjjGwBUYEVExpCRX3nNGDwXL7G1a6QPLVfgtZg5ODmdg5PT+elnF5De0M6C7Scp213LfZcv9ukMd7CrOJvtpbnsLMnWVetjSG+ogxN5iZzIS7zi+61uL/EXu4lt7yWuvYfozj5iLvUSc6mXqM4+orr6Ce9xkdjSRYjLTUi/G6fLjeNyAf1bOV0eVq+tUHkVERlDDDnnFSChtXukDy1X4VxqDM/eVcqzd5US3t1P6YEzzN5Xx5y9p7m+/Dges4nKohR2luRQPiePMxlxRkcWA3lsFi4kRXEhKepDPc7i9eHsGyC0z01orwvHgAfHgAezz4/D5cHpcmNze7G5vVg9PmweL1/67y1caf1fvwiLiIwtIz/FPjISf0gIce09I35o+XC6w0PYPL+IzfOLMPn8jK+5wNw9tczaX8cDT1fwwNMV1KXHUjErl22z8jhWlKIdvuSqeC1musNDLq/iX93pQx//wz6SWz5YVJvjdfqRiMhYMvLl1WTCn5ys8hpk/GYTleNSqByXwpOfKiO+rXtoh6d7Xt7PvS/tpS06lHdm57Fl3uA8WZ0nK9fSmlVlPPL4Bpwuz9B9fQ4ra1aVGZhKRERGmiH7h/pTUohvqzfi0HKNtMaF8/ubp/H7m6cR3t3PrH11LNhxkmVbqrlt3REuRYSwfWYuW+fms3da1rBdfS5jx7vntWragIjI2GZMeU1KIu5s9V//QAkK3eEhbFw4jo0Lx+HodzNrfx0Ld5xk/s5TrNhYSY/Tzs6SbHaU5LCrOJtL0aFGR5YgtWHReJVVEZExzpjymphI7KVeIw4tw8wVYqN8bgHlcwuwur0UH6ln8bYTzNlTy5J3BgfhV+cnsnt6NnumZ3FkfIo2qxAREZGrZtjKa0SPC5vbg9uml5NHK4/Nwu7ibHYXZ2Py+Sk81cTM/YPTCz750h5WvbibrjAHFTNzKZ+Tz+7pWbhCbEbHFhERkQBmTHlNHhyEHtveS5N2choT/GbT0BD8p++ZRViPi+LD9czfeYqy3ae4YXMV/XYre6dlUjEzlx2lObTFhhsdW0RERAKMMcueiYMDz2MvqbyOVT1hDt6Zk887c/KxeLxMPXaeBTtPMXd3LfN21wJQWZDEjtIctpfmUpOXCCaN4RIRERnrjFl5jRscbB/Z2WfE4SXAeK0W9k/NZP/UTH70wCLy6lqZu7uWuXtOc/9zO/nssztpio9g69x8Ns4vorIoWUVWRERkjDKmvCYkABDToYu25E+YTJzKSeBUTgJP3zOL6I5e5uw5zfydJ1n55mHufvUADUmRbFxQxNsLx3E6K97oxCIiIjKCDJs2AGjigPxVl6JCeXPpRN5cOpGwHhfzd57k+q3H+eRLe/nUi3s4lR3P2wuK2LBwnE5BERERGQOMOec1PJzeEBux7SqvcvV6whysWzKRdUsmEtPew+KKEyzdWs2Dayt4cG0FByem8daicWydW0BnpNPouCIiIjIMDJtT1R4dSoxWXuUjao8JG9rhK+VCB9dvreb6LdU88sRG/uHnm9k7NZMNC4vYNiuPnjCH0XFFRETkGjGsvHaFO4jo7jfq8DKKNCZHsfaeWay9eyaFp5q5blsN171Tw6OPrcd1edbspnmFVMzKpc9pNzquiIiI/A0MK69umwWbx2vU4WU0MpmoyU+iJj+Jn396HhOOX2DJO8dZVHGC+btO0eewUjErj03zCtk1I5sBuzbIEBERCTaGPXt7zWYsXr9Rh5fRzmSiclwKleNSePyzC5lcdZ7rt1SzaPtJlpYfpzPcwduLxvP60omcyEs0Oq2IiIhcJcPKq8nvx2sxG3V4GUP8ZhOHJ6ZzeGI6P3rwOooP1bNi4zFuXn+EO187yMnseN5eOI5N8wu5kBRldFwRERH5C/S6qYwpXouZPcVZ7CnOIry7n6Vbj3PD5koe+s02HvrNNioLk9k0r5DNZQU0a/SWiIhIwFF5lTGrOzyEl2+ayss3TSXlwiUWbzvBddtq+OIvy/niL8s5VpTM5rJCts7N14qsiIhIgDCsvPpNJkx+nfMqgaExOZpn7yrl2btKSW28xOJtNSyuODFUZKvzE9k6t4B3ZudxJj1W29OKiIgYxLgLtixmLF6fUYcX+bMaUqJ55mMzeeZjM0m5cIlF20+yqOIEq9dWsHptBQ1Jkewuzmbv1Ex2T8+iL1Tjt0REREaKgdMGTDhcKq8S2BqTo3nujhKeu6OExJYu5uytZc6e0yzbUsVtbx7GZbOwf2omO0qy2Tkjh8ZknV4gIiIynAwrrz6zGbNP5VWCR3NCBK/cOJVXbpyKxeNlUnUj83eeomz3KebsPQ1s5kxaDLtmZLOjJIdDk9Jw23RauYiIyLVk4CYFZhxubVIgwclrtXBoUjqHJqXz+OcWknG+nVn7TjN7Xx0r3zzM3a8eoM9h5eCkdI5MSOPgpDQqC5PxWi1GRxcREQlqhpXXjshQxp9oMurwItdUfVoM9Wkx/O7WYhz9boqP1DNrXx0zDtczZ18dAL0hNg5PSOPAlHQOTUyjJi/xqlZml26pYvXaChJbu2iOj2DNqjI2LBo/zF+RiIhIYDKsvF6MDiX6Ui8mnx+/WVduy+jhCrGxozSXHaW5AER29jHt6DlmHKqn+Eg9D/16GwBuq5lT2fGcyE3kVFY8Z9NjOZseQ3N8xNC/iaVbqnjk8Q04XR4Aklu6eOTxDQAqsCIiMiYZVl7bYsOw+vxEd/TSHhNmVAyRYdcZ6aR8bgHlcwsAiGnvYXJVA+NrLjDuZBPzd5zklreODn28y26hMSmKxsRIphw7P1Rc3+V0eVi9tkLlVURExiTDymtzfAQwuJKk8ipjSXtM2PvKLH4/ce09ZJy/RMb5i2SebyelqZOUpg5C+91X/ByJrV0jmFhERCRwGFZez6VGA5Bxvp2qwmSjYogYz2SiLTactthwDk5Of9+7XvzMkyS3fLCovvvLn4iIyFhjNurADclR+EyQeqHDqAgiAW/NqjL6HO//HbPPYWXNqjKDEomIiBjLsJXXAbuVlrhw0hvajYogEvDePa9V0wZEREQGGTpBvSYvUeOyRP6KDYvGq6yKiIhcZthpAwBHx6WSeb6d6I5eI2OIiIiISJAwtLwemZAKwOTKBiNjiIiIiEiQMLS8Vhck0eu0MXvfaSNjiIiIiEiQMLS8um1WKkpzWbD9JBaP18goIiIiIhIEDC2vAJvnFRLd1c+MQ/VGRxERERGRAGd4ed1Zkk13qJ1F208YHUVEREREApzh5dVts7KzJIey3bWYvT6j44iIiIhIADO8vAKUz84j9lIvk6s0dUBERERE/ryAKK87S3Jw2S0s3lZjdBQRERERCWABUV77nHa2zcpjSflxrG5NHRARERGRKwuI8grw1qLxRHf1U3rwjNFRRERERCRABUx53T09i46IEJZuPW50FBEREREJUAFTXj02CxUzc5mz9zQWTR0QERERkSsImPIKsKM0h4geF5M0dUBEREREriCgyuuu6dm4bBYW7DhpdBQRERERCUABVV77Qu3sLs5i4fYTmHx+o+OIiIiISIAJqPIKsHleIUmt3Uyq1qkDIiIiIvJ+AVdeK2bm4rJpwwIRERER+aCAK6+9oQ52zchmUcUJzJo6ICIiIiLvEXDlFWDDgiISLvYw/eg5o6OIiIiISAAJyPJaMTOPrjAHN244ZnQUEREREQkgAVleBxxW3l44jkUVJ4jo6jc6joiIiIgEiIAsrwCv3DgFh9vLTW8fNTqKiIiIiASIgC2vtdnxHJyYxu2vH9KFWyIiIiICBHB5BXhhZTGpzZ1c947GZomIiIhIgJfXbbPyOJUVx6d/u0urryIiIiIS2OXVbzbxm3tmkX3uIosrThgdR0REREQMFtDlFWBLWSH1qdHcuu6w0VFERERExGABX179ZhNbygqYcuy8xmaJiIiIjHEBX14BtswtwOrzs6T8uNFRRERERMRAI1peJ0VlDd2+NX02UbbQq3pcTV4iVflJ3Pn6QfD7hyueiIiIiHxIUbZQbk2fPfT2e/vecBjR8poRljB0+wuFN7N27ldY+Z4v9s8ymXjplmlk11+k9MDZYUwoIiIiIldrZfps1s79Cl8ovHnovvTQ+GE95oiW112t1UO3d7ceJ8Ri5/OFN7MqZ8lffeym+YW0xYRyx+sHhzOiiIiIiFyFVTlL+HzhzYRY7Oxu/d9TO3e3De9pniNaXi8OdA/d/ubhp/nu0efx+rzcm7OYSdHZf/GxbpuV9YvHM3tfHdEdvcOcVERERET+nEnR2dybsxivz8t3jz7PNw8/PfS+9/a94WDoBVvlzUd57sxWAO7JnP9XP/6tReOxen0s0sxXEREREcO829ueO7OV8uajI3psw6cNvHJuJwDTYvOwmP5ynFPZ8ZzOiNXUARERERGDWExmpsXmAf/b40aSdcSPeNn6677zvrftZitvLP72X32c9e/TSa+qYv3Cb4HFMlzxRESuqT/9mSciMhq8OP8bI35Mw8rrR+V56CGjI4iIiIiIQUa8vC7f9OhIH1JERERERgnDz3kVEREREblaKq8iIiIiEjRUXkVEREQkaKi8ioiIiEjQUHkVERERkaCh8ioiIiIiQUPlVURERESChsqriIiIiAQNlVcRERERCRoqryIiIiISNFReRURERCRoqLyKiIiISNBQeRURERGRoKHyKiIiIiJBQ+VVRERERIKGye/3X/0Hm0wtwJnhiyMiIiIiQpbf70+40js+VHkVERERETGSThsQERERkaCh8ioiIiIiQUPlVURERESChsqriIiIiAQNlVcRERERCRoqryIiIiISNFReRURERCRoqLyKiIiISNBQeRURERGRoPH/A87Z7GSyjrCKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the sprint trajectories for player 10\n",
    "\n",
    "# Plot the trajectories for each of player 10's sprints\n",
    "player = '10'\n",
    "column = 'Home_' + player + '_speed' # spped\n",
    "column_x = 'Home_' + player + '_x' # x position\n",
    "column_y = 'Home_' + player + '_y' # y position\n",
    "# same trick as before to find start and end indices of windows of size 'sprint_window' in which player speed was above the sprint_threshold\n",
    "player_sprints = np.diff( 1*( np.convolve( 1*(tracking_home[column]>=sprint_threshold), np.ones(sprint_window), mode='same' ) >= sprint_window ) )\n",
    "player_sprints_start = np.where( player_sprints == 1 )[0] - int(sprint_window/2) + 1 # adding sprint_window/2 because of the way that the convolution is centred\n",
    "player_sprints_end = np.where( player_sprints == -1 )[0] + int(sprint_window/2) + 1\n",
    "# now plot all the sprints\n",
    "fig,ax = plot_pitch()\n",
    "for s,e in zip(player_sprints_start,player_sprints_end):\n",
    "    ax.plot(tracking_home[column_x].iloc[s],tracking_home[column_y].iloc[s],'ro')\n",
    "    ax.plot(tracking_home[column_x].iloc[s:e+1],tracking_home[column_y].iloc[s:e+1],'r')"
   ]
  },
  {
   "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
}