324 lines
51 KiB
Plaintext
324 lines
51 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"#change this cell to change season and player name\n",
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"season = '2005/2006'\n",
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"ssn = '0506'\n",
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"length = int(20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import json\n",
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"from pandas.io.json import json_normalize\n",
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"import codecs\n",
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"import pandas as pd\n",
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"import codecs\n",
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"main_df = pd.DataFrame(data=None)\n",
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"path_match = \"\"\"C:\\\\Users\\\\koushik.r\\\\Documents\\\\open-data-master\\\\\\\\data\\\\events\\\\\"\"\" #location for play by play events\n",
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"for root, dirs, files in os.walk(r'C:\\Users\\koushik.r\\Documents\\open-data-master\\data\\matches'):\n",
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" for file in files:\n",
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" with open(os.path.join(root, file), \"r\") as auto:\n",
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" with codecs.open(root + str('\\\\') + file,encoding='utf-8') as data_file:\n",
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" data = json.load(data_file)\n",
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" df = pd.DataFrame(data=None)\n",
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" df = json_normalize(data, sep = \"_\")\n",
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" #for x in df.competition_country_name:\n",
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" # if x == 'Spain':\n",
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" # print(df.match_id)\n",
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" for i in range(len(df)):\n",
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" if df.iloc[i]['competition_country_name'] == 'Spain' and df.iloc[i]['season_season_name'] == season :\n",
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" match_no = df.iloc[i]['match_id'] #gets match with Spain as country\n",
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" match_no = str(match_no) # from int to str \n",
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" with codecs.open(path_match + match_no + str(r'.json'),encoding=\"utf8\") as event_file: #open the respective file\n",
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" df_match = json.load(event_file)\n",
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" df_match2 = pd.DataFrame(data=None)\n",
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" df_match2 = json_normalize(df_match,sep=\"_\") \n",
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" \n",
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" main_df = main_df.append(df_match2,ignore_index=True,sort=False) \n",
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"print('Done')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"Player = main_df.query('player_id == 5503 & type_id ==43 & play_pattern_id ==1 & duration >= 1.50')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"player_name = Player.player_name.iloc[0]\n",
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"#df[['a','b']]\n",
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"Player = Player [['location','carry_end_location']]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from math import sqrt\n",
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"distance= []\n",
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"for i in range(len(Player)):\n",
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" distance.append(sqrt((Player.iloc[i]['carry_end_location'][0] - Player.iloc[i]['location'][0])**2 + ((Player.iloc[i]['carry_end_location'][1] - Player.iloc[i]['location'][1])**2)))\n",
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" #using distance formula above (sqrt((x2-x1)^2 + (y2-y1)^2))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"Player['dribble_distance'] = distance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>location</th>\n",
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" <th>carry_end_location</th>\n",
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" <th>dribble_distance</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2791</th>\n",
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" <td>[81.9, 77.4]</td>\n",
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" <td>[85.6, 66.6]</td>\n",
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" <td>11.416217</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3160</th>\n",
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" <td>[78.7, 7.3]</td>\n",
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" <td>[115.6, 22.8]</td>\n",
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" <td>40.023243</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3765</th>\n",
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" <td>[45.5, 4.5]</td>\n",
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" <td>[48.2, 6.4]</td>\n",
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" <td>3.301515</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4811</th>\n",
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" <td>[43.1, 40.0]</td>\n",
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" <td>[50.3, 28.4]</td>\n",
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" <td>13.652839</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5012</th>\n",
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" <td>[77.0, 65.6]</td>\n",
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" <td>[97.8, 53.3]</td>\n",
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" <td>24.164644</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" location carry_end_location dribble_distance\n",
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"2791 [81.9, 77.4] [85.6, 66.6] 11.416217\n",
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"3160 [78.7, 7.3] [115.6, 22.8] 40.023243\n",
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"3765 [45.5, 4.5] [48.2, 6.4] 3.301515\n",
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"4811 [43.1, 40.0] [50.3, 28.4] 13.652839\n",
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"5012 [77.0, 65.6] [97.8, 53.3] 24.164644"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Player.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw_pitch(ax):\n",
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" # focus on only half of the pitch\n",
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" #Pitch Outline & Centre Line\n",
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" Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)\n",
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" #Left, Right Penalty Area and midline\n",
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" LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)\n",
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" RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)\n",
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" midline = ConnectionPatch([60,0], [60,80], \"data\", \"data\")\n",
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"\n",
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" #Left, Right 6-yard Box\n",
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" LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)\n",
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" RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)\n",
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"\n",
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"\n",
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" #Prepare Circles\n",
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" centreCircle = plt.Circle((60,40),8.1,color=\"white\", fill = False)\n",
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" centreSpot = plt.Circle((60,40),0.71,color=\"white\")\n",
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" #Penalty spots and Arcs around penalty boxes\n",
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" leftPenSpot = plt.Circle((9.7,40),0.71,color=\"white\")\n",
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" rightPenSpot = plt.Circle((110.3,40),0.71,color=\"white\")\n",
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" leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=\"white\")\n",
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" rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=\"white\")\n",
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" \n",
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" element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, \n",
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" centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]\n",
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" for i in element:\n",
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" ax.add_patch(i)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'plt.rcParams.update({\\n \"lines.color\": \"white\",\\n \"patch.edgecolor\": \"white\",\\n \"text.color\": \"black\",\\n \"axes.facecolor\": \"white\",\\n \"axes.edgecolor\": \"lightgray\",\\n \"axes.labelcolor\": \"white\",\\n \"xtick.color\": \"white\",\\n \"ytick.color\": \"white\",\\n \"grid.color\": \"lightgray\",\\n \"figure.facecolor\": \"black\",\\n \"figure.edgecolor\": \"black\",\\n \"savefig.facecolor\": \"black\",\\n \"savefig.edgecolor\": \"black\"})'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"'''plt.rcParams.update({\n",
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" \"lines.color\": \"white\",\n",
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" \"patch.edgecolor\": \"white\",\n",
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" \"text.color\": \"black\",\n",
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" \"axes.facecolor\": \"white\",\n",
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" \"axes.edgecolor\": \"lightgray\",\n",
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" \"axes.labelcolor\": \"white\",\n",
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" \"xtick.color\": \"white\",\n",
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" \"ytick.color\": \"white\",\n",
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" \"grid.color\": \"lightgray\",\n",
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" \"figure.facecolor\": \"black\",\n",
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" \"figure.edgecolor\": \"black\",\n",
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" \"savefig.facecolor\": \"black\",\n",
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" \"savefig.edgecolor\": \"black\"})'''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1152x648 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
|
||
|
|
"from matplotlib.offsetbox import OffsetImage\n",
|
||
|
|
"plt.style.use('dark_background')\n",
|
||
|
|
"fig=plt.figure() #set up the figures\n",
|
||
|
|
"fig.set_size_inches(16, 9)\n",
|
||
|
|
"ax=fig.add_subplot(1,1,1)\n",
|
||
|
|
"draw_pitch(ax) #overlay our different objects on the pitch\n",
|
||
|
|
"plt.ylim(-2, 82)\n",
|
||
|
|
"plt.xlim(-2, 122)\n",
|
||
|
|
"plt.axis('off')\n",
|
||
|
|
"y_cary_end = 0\n",
|
||
|
|
"y_loc = 0\n",
|
||
|
|
"for i in range(len(Player)):\n",
|
||
|
|
" # # y - y' = -2(y' +c) reflection \n",
|
||
|
|
" #mirror image of a point https://math.stackexchange.com/questions/1013230/how-to-find-coordinates-of-reflected-point\n",
|
||
|
|
" y_cary_end = -2*(Player.iloc[i]['carry_end_location'][1] - 40) + Player.iloc[i]['carry_end_location'][1] \n",
|
||
|
|
" y_loc = -2*(Player.iloc[i]['location'][1] - 40) + Player.iloc[i]['location'][1] \n",
|
||
|
|
" if Player.iloc[i]['carry_end_location'][0] >= 90 and Player.iloc[i]['dribble_distance'] >= length:\n",
|
||
|
|
" ax.annotate(\"\", xy = (Player.iloc[i]['carry_end_location'][0],y_cary_end), xycoords = 'data',\n",
|
||
|
|
" xytext = (Player.iloc[i]['location'][0],y_loc ), textcoords = 'data',\n",
|
||
|
|
" arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"blue\"),)\n",
|
||
|
|
" ax.set_title(player_name)\n",
|
||
|
|
" ax.text(40, -5, 'dribbles '+ season, fontsize=14)\n",
|
||
|
|
"fname = 'C:\\\\Users\\\\koushik.r\\\\Desktop\\\\Messi data\\\\'+ player_name + str(' dribbles ')+ str(ssn)+'.png'\n",
|
||
|
|
"plt.savefig(fname,orientation='landscape')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.7.0"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|