How to use touch_factor method in Airtest
Best Python code snippet using Airtest
ios.py
Source:ios.py 
...245 # å½åçæ¬: wda 4.1.4 è·åå°ç/window/sizeï¼å¨ipad+æ¡é¢æ¨ªå±ä¸æ¿å°çæ¯ height * heightï¼éè¦ä¿®æ£246 if self.is_pad and self.home_interface():247 self._size['window_width'] = int(width * self._touch_factor)248 @property249 def touch_factor(self):250 if not self._touch_factor:251 self._display_info()252 return self._touch_factor253 @touch_factor.setter254 def touch_factor(self, factor):255 """256 touch_factor is used to convert click coordinates: mobile phone real coordinates = touch_factor * screen coordinates257 In general, no manual settings are required258 touch_factorç¨äºæ¢ç®ç¹å»åæ ï¼ææºçå®åæ = touch_factor * å±å¹åæ 259 é»è®¤è®¡ç®æ¹å¼æ¯ï¼ self.display_info['window_height'] / self.display_info['height']260 ä½å¨é¨åç¹æ®åå·ææºä¸å¯è½ä¸åç¡®ï¼ä¾å¦iOS14.4ç7Pï¼é»è®¤å¼ä¸º 1/3ï¼ä½é¨å7Pç¹å»ä½ç½®ä¸åç¡®ï¼å¯èªè¡è®¾ç½®ä¸ºï¼self.touch_factor = 1 / 3.3261 ï¼ä¸è¬æ
åµä¸ï¼æ éæå¨è®¾ç½®ï¼ï¼262 Examples:263 >>> device = connect_device("iOS:///")264 >>> device.touch((100, 100)) # wrong position265 >>> print(device.touch_factor)266 0.333333267 >>> device.touch_factor = 1 / 3.3 # default is 1/3268 >>> device.touch((100, 100))...
Index.py
Source:Index.py
1from regression import shotRate2import numpy as np3from math import exp, factorial4import operator5import datetime6def matchEstimates(matchData, coefs, probs):7 expectedshots = []8 total_team_touches = []9 for teamId, value1 in matchData.iteritems():10 contributions=[]11 for key in matchData.keys():12 if key != teamId:13 opponent_teamId = key14 won_contest_sum = 015 yellow_card_opp_sum = 016 red_card_opp_sum = 017 accurate_pass_sum = 018 total_tackle_opp_sum = 019 touches_sum = 020 aerial_won_sum = 021 for player_name, value2 in matchData[teamId]["Player_stats"].iteritems():22 for key in matchData[teamId]["Player_stats"][player_name]["Match_stats"].keys():23 if key == "won_contest":24 won_contest_sum += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["won_contest"])25 if key == "accurate_pass":26 accurate_pass_sum += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["accurate_pass"])27 if key == "touches":28 touches_sum += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["touches"])29 if key == "aerial_won": 30 aerial_won_sum += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["aerial_won"]) 31 for player_name, value2 in matchData[opponent_teamId]["Player_stats"].iteritems():32 for key in matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"].keys():33 if key == "total_tackle":34 total_tackle_opp_sum += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["total_tackle"])35 if key == "yellow_card":36 yellow_card_opp_sum += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["yellow_card"])37 if key == "red_card":38 red_card_opp_sum += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["red_card"])39 if key == "second_yellow":40 red_card_opp_sum += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["second_yellow"])41 42 contributions.append(won_contest_sum)43 contributions.append(accurate_pass_sum)44 contributions.append(total_tackle_opp_sum)45 contributions.append(aerial_won_sum)46 contributions.append(yellow_card_opp_sum)47 contributions.append(red_card_opp_sum)48 total_team_touches.append(touches_sum)49 regression_pred = coefs[0] + coefs[1]*contributions[0] + coefs[2]*contributions[1] + coefs[3]*contributions[2] \50 + coefs[4]*contributions[3] + coefs[5]*contributions[4] + coefs[6]*contributions[5]51 expectedshots.append(regression_pred)52 expectedshots = np.array(expectedshots)53 lambda_ = probs[0]*expectedshots54 estimates = [lambda_, expectedshots, total_team_touches]55 return estimates56def goalProbabilities(lambda_):57 goal_probabilities = dict()58 sum_prob = 059 points_home = 060 points_away = 061 for i in range(5):62 for j in range(5):63 a = str(j) + "-" + str(i)64 prob = exp(-(lambda_[0] + lambda_[1]))*pow(lambda_[0], j)*pow(lambda_[1], i)/(factorial(j)*factorial(i))65 if j > i:66 points_home += 3*prob67 if j < i:68 points_away += 3*prob69 if j ==i:70 points_away += prob71 points_home += prob72 sum_prob += prob73 goal_probabilities.update({a : prob})74 goal_probabilities = sorted(goal_probabilities.items(), key=operator.itemgetter(1))75 print points_home, points_away76def playerPoints(matchData, estimates, coefs, probs):77 lambda_ = estimates[0]78 expectedshots = estimates[1]79 total_team_touches = estimates[2]80 total_team_touches_home = total_team_touches[0]81 total_team_touches_away = total_team_touches[1]82 derivative_home = 083 derivative_away = 084 cross_positions_1 = ["ML", "MR", "AML", "AMR"]85 cross_positions_2 = ["FWL", "FWR", "DR", "DL", "DMR", "DML"]86 87 for i in range(5):88 for j in range(5):89 a = str(j) + "-" + str(i)90 prob = exp(-(lambda_[0] + lambda_[1]))*pow(lambda_[0], j)*pow(lambda_[1], i)/(factorial(j)*factorial(i))91 if j > i:92 derivative_home += prob*(j/lambda_[0] - 1)93 if j < i:94 derivative_away += prob*(i/lambda_[1] - 1)95 exp_pts = dict() 96 # count to keep track of home or away team97 for teamId, value1 in matchData.iteritems():98 clean_sheet_away = 099 clean_sheet_home = 0100 for key in matchData.keys():101 if key != teamId:102 opponent_teamId = key103 if "goals" in matchData[teamId]["aggregate_stats"].keys():104 home_team_goal = float(matchData[teamId]["aggregate_stats"]["goals"])105 else:106 home_team_goal = 0107 clean_sheet_away = 1108 if "goals" in matchData[opponent_teamId]["aggregate_stats"].keys():109 away_team_goal = float(matchData[opponent_teamId]["aggregate_stats"]["goals"])110 else:111 away_team_goal = 0112 clean_sheet_home = 1113 114 if "total_scoring_att" in matchData[teamId]["aggregate_stats"].keys():115 home_team_shots = float(matchData[teamId]["aggregate_stats"]["total_scoring_att"])116 else:117 home_team_shots = 0118 if "total_scoring_att" in matchData[opponent_teamId]["aggregate_stats"].keys():119 away_team_shots = float(matchData[opponent_teamId]["aggregate_stats"]["total_scoring_att"])120 else:121 away_team_shots = 0122 if "total_tackle" in matchData[teamId]["aggregate_stats"].keys():123 home_team_tackle = float(matchData[teamId]["aggregate_stats"]["total_tackle"])124 else:125 home_team_tackle = 0126 if "total_tackle" in matchData[opponent_teamId]["aggregate_stats"].keys():127 away_team_tackle = float(matchData[opponent_teamId]["aggregate_stats"]["total_tackle"])128 else:129 away_team_tackle = 0130 131 if home_team_goal >= away_team_goal:132 if home_team_goal == away_team_goal:133 game_result_home = 1134 game_result_away = 1135 else:136 game_result_away = 0137 game_result_home = 3138 else:139 game_result_away = 3140 game_result_home = 0141 142 for player_name, value2 in matchData[teamId]["Player_stats"].iteritems():143 player_id = str(matchData[teamId]["Player_stats"][player_name]["player_details"]["player_id"])144 date = datetime.datetime.strptime(matchData[teamId]["team_details"]["date"], "%d/%m/%Y").strftime("%m/%d/%Y")145 won_contest = 0146 yellow_card_opp = 0147 red_card_opp = 0148 accurate_pass = 0149 total_tackle_opp = 0150 touches = 0151 aerial_won = 0152 saves = 0153 goals = 0154 assists = 0155 shots = 0156 cross_factor = 0157 player_position = int(matchData[teamId]["Player_stats"][player_name]["player_details"]["player_position_value"])158 player_position_info = str(matchData[teamId]["Player_stats"][player_name]["player_details"]["player_position_info"])159 if player_position < 5:160 game_time = 0.85/11161 if player_position == 1:162 defensive_actions = 0.3163 if player_position == 2:164 defensive_actions = 0.14165 if player_position == 3 and str(matchData[teamId]["Player_stats"][player_name]["player_details"]["player_position_info"]) == "DMC":166 defensive_actions = 0.14 167 else:168 game_time = 0.15/7169 if player_position_info in cross_positions_1:170 cross_factor = 0.30171 elif player_position_info in cross_positions_2:172 cross_factor = 0.20173 elif player_position_info == "GK" or player_position_info == "Sub":174 cross_factor = 0175 else :176 cross_factor = 0.05177 for key in matchData[teamId]["Player_stats"][player_name]["Match_stats"].keys():178 if key == "won_contest":179 won_contest += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["won_contest"])180 if key == "accurate_pass":181 accurate_pass += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["accurate_pass"])182 if key == "touches":183 touches += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["touches"])184 if key == "aerial_won": 185 aerial_won += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["aerial_won"])186 if key == "saves":187 saves += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["saves"])188 if key == "total_tackle":189 total_tackle_opp += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["total_tackle"])190 if key == "yellow_card":191 yellow_card_opp += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["yellow_card"])192 if key == "red_card":193 red_card_opp += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["red_card"])194 if key == "second_yellow":195 red_card_opp += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["second_yellow"])196 if key == "goals":197 goals += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["goals"])198 if key == "goal_assist":199 assists += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["goal_assist"])200 if key == "total_scoring_att":201 shots += float(matchData[teamId]["Player_stats"][player_name]["Match_stats"]["total_scoring_att"])202 touch_factor = touches/total_team_touches_home203 if home_team_goal != 0:204 goal_factor = goals/home_team_goal205 assist_factor = assists/home_team_goal206 else :207 goal_factor = 0208 assist_factor = 0209 if home_team_shots != 0:210 shot_factor = shots/home_team_shots211 else :212 shot_factor = 0213 if home_team_tackle != 0:214 tackle_factor = total_tackle_opp/home_team_tackle215 else:216 tackle_factor = 0217 contributions = np.array([won_contest, accurate_pass, touch_factor, aerial_won, saves, total_tackle_opp, yellow_card_opp, red_card_opp, goal_factor, assist_factor, shot_factor, cross_factor, tackle_factor])218 exp_pt_1 = 2*derivative_home*probs[0]*(coefs[1]*won_contest + coefs[2]*accurate_pass + coefs[4]*aerial_won) \219 - derivative_away*probs[0]*(coefs[3]*total_tackle_opp + coefs[5]*yellow_card_opp + coefs[6]*red_card_opp) \220 + derivative_away*probs[1]*saves/(expectedshots[1]*probs[2])*1.5221 exp_pt_2 = game_result_home*touch_factor222 exp_pt_3 = 1.37*game_time223 exp_pt_4 = 1.02*goals224 exp_pt_5 = 1.02*assists225 exp_pt_6 = 2.73*defensive_actions*clean_sheet_home226 exp_pt = exp_pt_1 + exp_pt_2*0.25 + exp_pt_3*0.15 + exp_pt_4*0.05 + exp_pt_5*0.05 + exp_pt_6*0.13227 exp_pts.update({player_id : [player_name, teamId, player_position, contributions, [exp_pt], date]})228 for player_name, value2 in matchData[opponent_teamId]["Player_stats"].iteritems():229 player_id = str(matchData[opponent_teamId]["Player_stats"][player_name]["player_details"]["player_id"])230 date = datetime.datetime.strptime(matchData[opponent_teamId]["team_details"]["date"], "%d/%m/%Y").strftime("%m/%d/%Y")231 won_contest = 0232 yellow_card_opp = 0233 red_card_opp = 0234 accurate_pass = 0235 total_tackle_opp = 0236 touches = 0237 aerial_won = 0238 saves = 0239 goals = 0240 assists = 0241 shots = 0242 player_position = int(matchData[opponent_teamId]["Player_stats"][player_name]["player_details"]["player_position_value"])243 player_position_info = str(matchData[opponent_teamId]["Player_stats"][player_name]["player_details"]["player_position_info"])244 if player_position < 5:245 game_time = 0.85/11246 if player_position == 1:247 defensive_actions = 0.3248 if player_position == 2:249 defensive_actions = 0.13250 if player_position == 3 and str(matchData[opponent_teamId]["Player_stats"][player_name]["player_details"]["player_position_info"]) == "DMC":251 defensive_actions = 0.13252 else:253 game_time = 0.15/7254 if player_position_info in cross_positions_1:255 cross_factor = 0.30256 elif player_position_info in cross_positions_2:257 cross_factor = 0.20258 elif player_position_info == "GK" or player_position_info == "Sub":259 cross_factor = 0260 else :261 cross_factor = 0.05262 for key in matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"].keys():263 if key == "won_contest":264 won_contest += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["won_contest"])265 if key == "accurate_pass":266 accurate_pass += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["accurate_pass"])267 if key == "touches":268 touches += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["touches"])269 if key == "aerial_won": 270 aerial_won += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["aerial_won"])271 if key == "saves":272 saves += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["saves"])273 if key == "total_tackle":274 total_tackle_opp += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["total_tackle"])275 if key == "yellow_card":276 yellow_card_opp += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["yellow_card"])277 if key == "red_card":278 red_card_opp += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["red_card"])279 if key == "second_yellow":280 red_card_opp += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["second_yellow"])281 if key == "goals":282 goals += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["goals"])283 if key == "goal_assist":284 assists += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["goal_assist"])285 if key == "total_scoring_att":286 shots += float(matchData[opponent_teamId]["Player_stats"][player_name]["Match_stats"]["total_scoring_att"]) 287 288 touch_factor = touches/total_team_touches_away289 if away_team_goal != 0:290 goal_factor = goals/away_team_goal291 assist_factor = assists/away_team_goal292 else :293 goal_factor = 0294 assist_factor = 0295 if away_team_shots != 0:296 shot_factor = shots/away_team_shots297 else :298 shot_factor = 0299 if away_team_tackle != 0:300 tackle_factor = total_tackle_opp/away_team_tackle301 else:302 tackle_factor = 0303 304 contributions = np.array([won_contest, accurate_pass, touch_factor, aerial_won, saves, total_tackle_opp, yellow_card_opp, red_card_opp, goal_factor, assist_factor, shot_factor, cross_factor, tackle_factor])305 exp_pt_1 = 2*derivative_away*probs[0]*(coefs[1]*won_contest + coefs[2]*accurate_pass + coefs[4]*aerial_won) \306 - derivative_home*probs[0]*(coefs[3]*total_tackle_opp + coefs[5]*yellow_card_opp + coefs[6]*red_card_opp) \307 + derivative_home*saves*probs[1]/(expectedshots[0]*probs[2])*1.5308 exp_pt_2 = game_result_away*touch_factor309 exp_pt_3 = 1.37*game_time310 exp_pt_4 = 1.02*goals311 exp_pt_5 = 1.02*assists312 exp_pt_6 = 2.73*defensive_actions*clean_sheet_away313 exp_pt = exp_pt_1 + exp_pt_2*0.25 + exp_pt_3*0.15 + exp_pt_4*0.05 + exp_pt_5*0.05 + exp_pt_6*0.13314 exp_pts.update({player_id : [player_name, opponent_teamId, player_position, contributions,[exp_pt], date]})315 break316 return exp_pts317def dict_update(dict1, matchUpdate):318 for key, value in matchUpdate.iteritems():319 if key in dict1.keys():320 dict1[key].append(matchUpdate[key][4][0])321 else:322 dict1[key] = matchUpdate[key][4]323def position_update(dict1, matchUpdate):324 # position vector is [others, postion1, position2, position3, position4, position5] 325 for key, value in matchUpdate.iteritems():326 position = int(matchUpdate[key][2])327 if key in dict1.keys():328 dict1[key][position] += 1329 else:330 dict1[key] = []331 for i in range(6):332 if i == position:333 dict1[key].append(1)334 else:335 dict1[key].append(0)336def contributions_update(dict1, matchUpdate):337 # contributions vector is [won_contest, accurate_pass, touch_factor, aerial_won, saves, total_tackle_opp, yellow_card_opp, red_card_opp, goals, assists]338 for key, value in matchUpdate.iteritems():339 contributions = matchUpdate[key][3]340 if key in dict1.keys():341 dict1[key] = dict1[key] + contributions342 else:343 dict1[key] = contributions344def player_name_update(dict1, matchUpdate):345 for key, value in matchUpdate.iteritems():346 player_name = matchUpdate[key][0]347 player_team = matchUpdate[key][1]348 if key in dict1.keys():349 if player_team in dict1[key][1].keys():350 dict1[key][1][player_team] += 1351 else:352 dict1[key][1][player_team] = 1353 else:354 dict1[key]=[]355 dict1[key].append(player_name)356 dict1[key].append({player_team: 1})357 358def summary_update(dict_1, matchUpdate):359 for key, value in matchUpdate.iteritems():360 contributions = matchUpdate[key][3]361 position = matchUpdate[key][2]362 pts = matchUpdate[key][4][0]363 date = matchUpdate[key][5]364 updated_array = np.append(contributions, position)365 updated_array = np.append(updated_array, pts)366 if key in dict_1.keys():367 dict_1[key][date] = updated_array368 369 else:370 dict_1[key] = {}371 dict_1[key][date] = updated_array372 373 374def roster_update(dict1, matchUpdate):375 for player_id, value in matchUpdate.iteritems():376 team_id = value[1]377 if team_id in dict1.keys():378 if player_id in dict1[team_id].keys():379 if value[2] < 5:380 dict1[team_id][player_id][0] += 1/38.0381 else:382 dict1[team_id][player_id][1] += 1/38.0383 else:384 dict1[team_id][player_id] = [0, 0]385 if value[2] < 5:386 dict1[team_id][player_id][0] += 1/38.0387 else:388 dict1[team_id][player_id][1] += 1/38.0389 else:390 dict1[team_id] = dict()391 dict1[team_id][player_id] = [0, 0]392 if value[2] < 5:393 dict1[team_id][player_id][0] += 1/38.0394 else:395 dict1[team_id][player_id][1] += 1/38.0396 397def aggregate_to_mean_contribution(dict1, aggregate_dict):398 for player_id, player_data in aggregate_dict.iteritems():399 beta = 0.9400 num_starts = 0401 num_subs = 0402 sorteddates = aggregate_dict[player_id].keys()403 sorteddates.sort()404 for date in sorteddates:405 match_data = aggregate_dict[player_id][date]406 contribution = match_data[:-2]407 if match_data[-2] < 5:408 if num_starts == 0:409 player_start = contribution410 num_starts += 1411 else:412 player_start = beta*player_start + (1-beta)*contribution413 num_starts += 1414 else:415 if num_subs == 0:416 player_sub = contribution417 num_subs += 1418 else:419 player_sub = beta*player_sub + (1-beta)*contribution420 num_subs += 1421 if num_starts > 0:422 player_start = player_start423 else:424 player_start = 0*player_sub425 if num_subs > 0:426 player_sub = player_sub427 else:428 player_sub = 0*player_start429 430 dict1[player_id] = [player_start, player_sub] 431def match_contributions(dict1, mean_contribution_dict, team_roster_dict):432 for team_id, home_players in team_roster_dict.iteritems():433 count_home = 0434 for player, appearances in home_players.iteritems():435 if count_home > 0:436 home_contribution = home_contribution + mean_contribution_dict[player][0][:-5]*appearances[0] \437 + mean_contribution_dict[player][1][:-5]*appearances[1]438 else:439 home_contribution = mean_contribution_dict[player][0][:-5]*appearances[0] \440 + mean_contribution_dict[player][1][:-5]*appearances[1]441 442 count_home += 1443 dict1[team_id] = home_contribution444def game_result(home_team_id, away_team_id, teamMeanContribution_dict, coefs, probs):445 home_team_contribution = teamMeanContribution_dict[home_team_id]446 away_team_contribution = teamMeanContribution_dict[away_team_id]447 448 expectedshots_home = coefs[0] + coefs[1]*home_team_contribution[0] + coefs[2]*home_team_contribution[1] + \449 coefs[3]*away_team_contribution[5] + coefs[4]*home_team_contribution[3] + coefs[5]*away_team_contribution[6] + \450 coefs[6]*away_team_contribution[7]451 expectedshots_away = coefs[0] + coefs[1]*away_team_contribution[0] + coefs[2]*away_team_contribution[1] + \452 coefs[3]*home_team_contribution[5] + coefs[4]*away_team_contribution[3] + coefs[5]*home_team_contribution[6] + \453 coefs[6]*home_team_contribution[7]454 expectedshots = np.array([expectedshots_home, expectedshots_away])455 expectedshots = [1.2, 1]*expectedshots456 lambda_ = probs[0]*expectedshots457 home_win_prob = 0458 draw_prob = 0459 away_win_prob = 0460 461 for i in range(10):462 for j in range(10):463 prob = exp(-(lambda_[0] + lambda_[1]))*pow(lambda_[0], j)*pow(lambda_[1], i)/(factorial(j)*factorial(i))464 if j > i:465 home_win_prob += prob466 if j < i:467 away_win_prob += prob468 if j == i:469 draw_prob += prob470 sum_prob = home_win_prob + away_win_prob + draw_prob471 home_win_prob = home_win_prob/sum_prob472 away_win_prob = away_win_prob/sum_prob473 draw_prob = draw_prob/sum_prob...
playerRating.py
Source:playerRating.py
1import json2import numpy as np3from regression import *4from shotEffectiveness import estimateProbabilities5from Index import *6from collections import OrderedDict7import operator8import unicodedata9file_pointer = open("/Users/shubhampawar/epl_data/xml_stats/season16-17/season_stats.json", "rb")10data = json.load(file_pointer, object_pairs_hook=OrderedDict)11# previous two seasons data and current season data12file_pointer = open("/Users/shubhampawar/epl_data/xml_stats/season17-18/season_stats.json", "rb")13data.update(json.load(file_pointer, object_pairs_hook=OrderedDict))14# file_pointer = open("/Users/shubhampawar/epl_data/xml_stats/season15-16/season_stats.json", "rb")15# data.update(json.load(file_pointer, object_pairs_hook=OrderedDict))16# file_pointer = open("/Users/shubhampawar/epl_data/xml_stats/season14-15/season_stats.json", "rb")17# data.update(json.load(file_pointer, object_pairs_hook=OrderedDict))18total_shots = []19won_contest = []20yellow_card_opp = []21red_card_opp = []22accurate_pass = []23total_tackle_opp= []24touches = []25aerial_won = []26shots = 027goals = 028onTarget = 029saves = 030blocks = 031aggregate_stats_dict = dict()32player_stats_dict = dict()33for matchId, value1 in data.iteritems():34 for teamId, value2 in data[matchId].iteritems():35 for key in data[matchId].keys():36 if key != teamId:37 opponent_teamId = key 38 for key in data[matchId][teamId]["aggregate_stats"].keys():39 if key in aggregate_stats_dict.keys():40 aggregate_stats_dict[key] +=141 else:42 aggregate_stats_dict.update({key: 1})43 total_shots.append(int(data[matchId][teamId]["aggregate_stats"]["total_scoring_att"]))44 45 if "total_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys():46 shots += float(data[matchId][teamId]["aggregate_stats"]["total_scoring_att"])47 if "goals" in data[matchId][teamId]["aggregate_stats"].keys():48 goals += float(data[matchId][teamId]["aggregate_stats"]["goals"])49 if ("ontarget_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()) and ("blocked_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()):50 onTarget += float(data[matchId][teamId]["aggregate_stats"]["ontarget_scoring_att"]) + float(data[matchId][teamId]["aggregate_stats"]["blocked_scoring_att"])51 if ("ontarget_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()) and not("blocked_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()):52 onTarget += float(data[matchId][teamId]["aggregate_stats"]["ontarget_scoring_att"])53 if not("ontarget_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()) and ("blocked_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()):54 onTarget += float(data[matchId][teamId]["aggregate_stats"]["blocked_scoring_att"])55 if ("ontarget_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()) and ("goals" in data[matchId][teamId]["aggregate_stats"].keys()):56 saves += float(data[matchId][teamId]["aggregate_stats"]["ontarget_scoring_att"]) - float(data[matchId][teamId]["aggregate_stats"]["goals"])57 if ("ontarget_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys()) and not("goals" in data[matchId][teamId]["aggregate_stats"].keys()):58 saves += float(data[matchId][teamId]["aggregate_stats"]["ontarget_scoring_att"])59 if "blocked_scoring_att" in data[matchId][teamId]["aggregate_stats"].keys():60 blocks += float(data[matchId][teamId]["aggregate_stats"]["blocked_scoring_att"])61 won_contest_sum = 062 yellow_card_opp_sum = 063 red_card_opp_sum = 064 accurate_pass_sum = 065 total_tackle_opp_sum = 066 touches_sum = 067 aerial_won_sum = 068 for player_name, value3 in data[matchId][teamId]["Player_stats"].iteritems():69 for key in data[matchId][teamId]["Player_stats"][player_name]["Match_stats"].keys():70 # if key in player_stats_dict.keys():71 # player_stats_dict[key] +=172 # else:73 # player_stats_dict.update({key: 1}) 74 if key == "won_contest":75 won_contest_sum += float(data[matchId][teamId]["Player_stats"][player_name]["Match_stats"]["won_contest"])76 if key == "accurate_pass":77 accurate_pass_sum += float(data[matchId][teamId]["Player_stats"][player_name]["Match_stats"]["accurate_pass"])78 if key == "touches":79 touches_sum += float(data[matchId][teamId]["Player_stats"][player_name]["Match_stats"]["touches"])80 if key == "aerial_won": 81 aerial_won_sum += float(data[matchId][teamId]["Player_stats"][player_name]["Match_stats"]["aerial_won"]) 82 83 won_contest.append(won_contest_sum)84 accurate_pass.append(accurate_pass_sum)85 touches.append(touches_sum)86 aerial_won.append(aerial_won_sum)87 for player_name, value3 in data[matchId][opponent_teamId]["Player_stats"].iteritems():88 for key in data[matchId][opponent_teamId]["Player_stats"][player_name]["Match_stats"].keys():89 if key == "total_tackle":90 total_tackle_opp_sum += float(data[matchId][opponent_teamId]["Player_stats"][player_name]["Match_stats"]["total_tackle"])91 if key == "yellow_card":92 yellow_card_opp_sum += float(data[matchId][opponent_teamId]["Player_stats"][player_name]["Match_stats"]["yellow_card"])93 if key == "red_card":94 red_card_opp_sum += float(data[matchId][opponent_teamId]["Player_stats"][player_name]["Match_stats"]["red_card"])95 if key == "second_yellow":96 red_card_opp_sum += float(data[matchId][opponent_teamId]["Player_stats"][player_name]["Match_stats"]["second_yellow"])97 98 total_tackle_opp.append(total_tackle_opp_sum) 99 yellow_card_opp.append(yellow_card_opp_sum)100 red_card_opp.append(red_card_opp_sum)101shots_data = np.transpose(np.array([total_shots, won_contest, accurate_pass, total_tackle_opp, aerial_won, yellow_card_opp, red_card_opp]))102#print shots_data.shape103coefficients = shotRegression(shots_data)104#print total_shots[0], won_contest[0], accurate_pass[0], total_tackle[0], touches[0], aerial_won[0], yellow_card_opp[0], red_card_opp[0]105probabilities = estimateProbabilities(shots, goals, onTarget, saves, blocks)106playerRatings = dict()107playerPosition = dict()108playerIdNameData = dict()109playerMatchSummary = dict()110playerMeanContributions = dict()111teamMeanContribution = dict()112teamRoster = dict()113for matchId, value in data.iteritems():114 estimates = matchEstimates(data[matchId], coefficients, probabilities) 115 #lambda_ = estimates[0]116 #goalProbabilities(lambda_)117 playerData = playerPoints(data[matchId], estimates, coefficients, probabilities)118 position_update(playerPosition, playerData)119 summary_update(playerMatchSummary, playerData)120 player_name_update(playerIdNameData, playerData)121 roster_update(teamRoster, playerData)122 aggregate_to_mean_contribution(playerMeanContributions, playerMatchSummary)123 dict_update(playerRatings, playerData)124match_contributions(teamMeanContribution, playerMeanContributions, teamRoster)125#result = game_result("13", "16", teamMeanContribution, coefficients, probabilities)126#print result127 128#playerRatings = sorted(playerRatings.items(), key=operator.itemgetter(1))129#print playerRatings130ratings_file = "playerRatings.csv"131positions_file = "playerPositions.csv"132contributions_file = "playerContributions.csv"133player_name_file = "playerNameId.csv"134team_roster = "teamRoster.csv"135mean_starts_contributions_file = "playerStartsMeanContributions.csv"136mean_subs_contributions_file = "playerSubsMeanContributions.csv"137team_mean_contributions_file = "teamMeanContributions.csv"138csvfiles = [ratings_file, positions_file, contributions_file, player_name_file]139fp = open(ratings_file, 'wb')140fp.write("Player_id, Ratings\n")141for player, ratings in playerRatings.iteritems():142 ratings_string = ""143 for rating in ratings:144 ratings_string += "," + str(rating)145 fp.write(str(player) + ratings_string + "\n")146fp.close()147fp = open(positions_file, 'wb')148fp.write("Player_id, Others, Goalkeeper, Defender, Midfielder, Forward, Substitute\n")149for player, positions in playerPosition.iteritems():150 positions_string = ""151 for position in positions:152 positions_string += "," + str(position)153 fp.write(str(player) + positions_string + "\n")154fp.close()155''' fp = open(contributions_file, 'wb')156# contributions vector is [won_contest, accurate_pass, touch_factor, aerial_won, saves, total_tackle_opp, yellow_card_opp, red_card_opp, goals, assists]157fp.write("Player_id, won_contest, accurate_pass, touch_factor, aerial_won, saves, total_tackle_opp, yellow_card_opp, red_card_opp, goals, assists\n")158for player, contributions in playerContributions.iteritems():159 contributions_string = ""160 for contribution in contributions:161 contributions_string += "," + str(contribution)162 fp.write(str(player) + contributions_string + "\n")163fp.close() '''164fp = open(player_name_file, 'wb')165fp.write("Player_id, Player_name, Player_team1, Appearances1, Player_team2, Appearances2\n")166for player, playerInfo in playerIdNameData.iteritems():167 player_name = unicodedata.normalize("NFKD", playerInfo[0]).encode('ascii', 'ignore')168 player_team_info_string = ""169 for team, appearances in playerInfo[1].iteritems():170 player_team_info_string += "," + str(team) + "," + str(appearances)171 fp.write(str(player) + "," + player_name + player_team_info_string + "\n")172fp.close()173fp = open(contributions_file, 'wb')174fp.write("Player_id, Date, won_contest, accurate_pass, touch_factor, aerial_won, saves, tackles, yellow_card, red_card, goal_factor, assist_factor, shot_factor, cross_factor, tackle_factor, Position, Rating\n")175for player, matchData in playerMatchSummary.iteritems():176 for date, contributions in matchData.iteritems():177 contributions_string_ = ""178 for contribution in contributions:179 contributions_string_ += "," + str(contribution)180 fp.write(str(player) + "," + str(date) + contributions_string_ + "\n") 181fp.close()182fp = open(team_roster, 'wb')183fp.write("Team_id, Player_id, Start_prob, Sub_prob\n")184for team, roster in teamRoster.iteritems():185 for player_id, data in teamRoster[team].iteritems():186 fp.write(str(team) + "," + str(player_id) + "," + str(data[0]) + "," + str(data[1]) + "\n")187fp.close()188fstart = open(mean_starts_contributions_file, 'wb')189fsub = open(mean_subs_contributions_file, 'wb')190fstart.write("Player_id, won_contest, accurate_pass, touch_factor, aerial_won, saves, tackles, yellow_card, red_card, goal_factor, assist_factor, shot_factor, cross_factor, tackle_factor\n")191fsub.write("Player_id, won_contest, accurate_pass, touch_factor, aerial_won, saves, tackles, yellow_card, red_card, goal_factor, assist_factor, shot_factor, cross_factor, tackle_factor\n")192for player, mean_data in playerMeanContributions.iteritems():193 mean_start_contribution_string = ""194 mean_sub_contribution_string = ""195 for contribution in mean_data[0]:196 mean_start_contribution_string += "," + str(contribution)197 for contribution in mean_data[1]:198 mean_sub_contribution_string += "," + str(contribution)199 fstart.write(str(player) + mean_start_contribution_string + "\n")200 fsub.write(str(player) + mean_sub_contribution_string + "\n")201fstart.close()202fsub.close()203fp = open(team_mean_contributions_file, 'wb')204fp.write("Team_id, won_contest, accurate_pass, touch_factor, aerial_won, saves, total_tackle_opp, yellow_card_opp, red_card_opp\n")205for team, contributions in teamMeanContribution.iteritems():206 contributions_string = ""207 for contribution in contributions:208 contributions_string += "," + str(contribution)209 fp.write(str(team) + contributions_string + "\n")210fp.close()211fp = open("info_file.csv", "wb")212fp.write("Constant, Won_contest, accurate_pass, tackle_opp, aerial_won, yellow_card, red_card\n")213count = 0214for coefs in coefficients:215 if count == 0:216 fp.write(str(coefs))217 count = 1218 continue219 fp.write("," + str(coefs))220fp.write("\n")221fp.write("P(G|S), P(onTarget|shot), P(saveOrBlock|onTarget)\n")222count = 0223for prob in probabilities:224 if count == 0:225 fp.write(str(prob))226 count = 1227 continue228 fp.write("," + str(prob))229fp.write("\n")...
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