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https://github.com/foxox/Foxox-T2-Player-Ratings.git
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Copied script and started cleaning it up,
Focusing on the win rate stat first since the glicko stuff was less useful
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John Drake
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310
statcruncher.py
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310
statcruncher.py
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# This script is an exploration into processing PUB/PUG match results into player rankings.
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# Once I hack together something that seems useful, I may try to tidy this up.
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import yaml
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from operator import itemgetter
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from more_itertools import pairwise, distinct_combinations
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print()
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# load file
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with open('pubresults.yaml', 'r') as file:
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file_contents = yaml.full_load(file)
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class PlayerResult:
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def __init__(self, yaml_player_result):
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self.name = yaml_player_result[0]
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self.score = yaml_player_result[1]
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def __str__(self):
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return "PlayerResult(name={}, score={})".format(self.name, self.score)
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class TeamResult:
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def __init__(self, yaml_team_result):
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self.score = yaml_team_result['score']
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self.players = [PlayerResult(player) for player in yaml_team_result['players']]
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def __str__(self):
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return "TeamResult(score={}, players={})".format(self.score, self.players)
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class MatchResult:
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def __init__(self, yaml_match_result):
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self.date = yaml_match_result['date']
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self.mission = yaml_match_result['mission']
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results = yaml_match_result['results']
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self.team_results = dict()
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for (team, team_result) in results.items():
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self.team_results[team] = TeamResult(team_result)
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def __str__(self):
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return "MatchResult(date={}, mission={}, team_results={})".format(self.date, self.mission, self.team_results)
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# Guesses at player primary roles based on information provided by the community and my observations
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players_to_roles = {
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'stormcrow':['ld','lof'],
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'jacob':['ld','lo','cap'],
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'bizzy':['ld','lo'],
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'slush':['cap'],
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'astralis':['cap','flex'],
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'domestic':['ld','chase'],
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'danno':['ho','ho'],
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'hybrid':['lof','ho'],
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'vaxity':['ho','shrike'],
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'mistcane':['ld','cap'],
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'nevares':['cap'],
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'haggis':['ho'],
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'devil':['cap','ho'],
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'efx':['ld','lof'],
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'hexy':['ld','shrike'],
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'halo2':['ho'],
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'blake':['lof'],
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'future':['flex'],
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'thaen':['offense'],
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'strazz':['hof'],
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'history':['cap','shrike','ho'],
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'sliderzero':['shrike','flex'],
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'jerry':['ld'],
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'wingedwarrior':['ld','snipe'],
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'sylock':['ho'],
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'darrell':['ld'],
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'pedro':['ld'],
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'coorslightman':['ld'],
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'hautsoss':['flex'],
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'sajent':['ld','ho'],
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'turtle':['ld'],
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'irvin':['cap'],
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'redeye':['lo','ho','flex'],
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'mlgru':['shrike','ho','cap'],
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'actionswanson':['flex'],
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'bendover':['ho'],
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'warchilde':['ho'],
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'johnwayne':['flex'],
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'lsecannon':['farm'],
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'hp':['ld','lof'],
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'sake':['ld'],
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'anthem':['ho'],
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'taco':['ho'],
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'exogen':['cap'],
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'mp40':['hd'],
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'gunther':['ho'],
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'ipkiss':['snipe'],
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'alterego':['hd'],
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'homer':['ho'],
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'spartanonyx':['ld'],
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'bish':['ho'],
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'flyersfan':['ld'],
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'geekofwires':['ho'],
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'aromatomato':['ho'],
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'heat':['ho','hd','farm'],
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'daddyroids':['ld'],
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'pupecki':['ld'],
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'yuanz':['farm','hd','ho'],
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'm80':['lof'],
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'andycap':['hof'],
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'tetchy':['cap','shrike'],
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'systeme':['hd','farm','ho'],
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'friendo':['hof','farm','ld','ho'],
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'coastal':['shrike','ld'],
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'caution':['ho','cap'],
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'jx':['ld'],
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'nightwear':['flex'],
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'piata':['ho'],
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'foxox':['snipe','farm'],
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'elliebackwards':['ld'],
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'nutty':['ld'],
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'sweetcheeks':['farm'],
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'carpenter':['hd','ld'],
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'eeor':['ld'],
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'cooter':['cap'],
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'flakpyro':['flex','d'],
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'doug':['ld','ho','snipe'],
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'raynian':['ho','mo'],
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'legelos':['ld'],
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'7thbishop':['cap','hd'],
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'dirkdiggler':['ho'],
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'lazer':['ld'],
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'iroc':['ld'],
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'ember':['ld'],
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'2short':['hd','ho','cap'],
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'earth':['tank','hd','hof'],
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'lolcaps':['cap'],
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'aftermath':['ld'],
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'fnatic':['ld'],
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'cooljuke':['snipe'],
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'sterio':['ld'],
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'jazz':['ho','ld','cap'],
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}
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first_roles_to_players = dict()
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any_roles_to_players = dict()
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for player,roles in players_to_roles.items():
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if roles[0] is None:
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# print('')
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continue
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# first_role_players = first_roles_to_players[roles[0]]
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if not roles[0] in first_roles_to_players:
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first_roles_to_players[roles[0]] = list()
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# print('adding', player,'to role',roles[0])
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first_roles_to_players[roles[0]].append(player)
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for role in roles:
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if not role in any_roles_to_players:
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any_roles_to_players[role] = list()
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any_roles_to_players[role].append(player)
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# Some roles imply other roles or role categories, such as HO implying O.
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# D doesn't include farm and O doesn't include cap
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role_relationships = {'defense':['tank','hd','lof','hof','ld','flex','shrike','snipe'],'offense':['shrike','ho','snipe','flex','lo','snipe']}
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any_roles_to_players['defense'] = list()
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print("any_roles_to_players['defense']:",any_roles_to_players['defense'])
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print("any_roles_to_players['offense']:",any_roles_to_players['offense'])
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for (role, related_roles) in role_relationships.items():
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if role not in any_roles_to_players:
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any_roles_to_players[role] = list()
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for related_role in related_roles:
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any_roles_to_players[role].append(any_roles_to_players[related_role])
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print("expanded any_roles_to_players['defense']:",any_roles_to_players['defense'])
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print("any_roles_to_players['offense']:",any_roles_to_players['offense'])
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player_to_win_count = dict()
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player_to_match_count = dict()
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duo_to_win_count = dict()
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duo_to_match_count = dict()
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trio_to_win_count = dict()
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trio_to_match_count = dict()
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# loop over all matches
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for match in file_contents:
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match_result = MatchResult(match)
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print(match_result)
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winning_team_score = 0
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winning_team_name = None
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results = match['results']
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# match
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for team in results:
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# print('team:', team)
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if results[team]['score'] > winning_team_score:
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winning_team_score = results[team]['score']
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winning_team_name = team
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# WIN RATE STATS GATHERING. SINGLES, DUOS, TRIOS, ETC.
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for team in results:
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# SINGLES
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for player in results[team]['players']:
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player_split = player.split(", ")
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playername = player_split[0]
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# player_tuple = (player_split[0], int(player_split[1]))
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# 0 is name, 1 is score
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if not playername in player_to_match_count:
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player_to_match_count[playername] = 0
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if not playername in player_to_win_count:
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player_to_win_count[playername] = 0
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player_to_match_count[playername]+=1
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if team == winning_team_name:
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player_to_win_count[playername]+=1
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# DUOS
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for duo in distinct_combinations(results[team]['players'], 2):
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duo0split = duo[0].split(", ")
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duo1split = duo[1].split(", ")
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player_name_duo=(duo0split[0],duo1split[0])
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# print('Duo ',player_name_duo,' appeared in match ',match)
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if not player_name_duo in duo_to_win_count:
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duo_to_win_count[player_name_duo] = 0
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if not player_name_duo in duo_to_match_count:
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duo_to_match_count[player_name_duo] = 0
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duo_to_match_count[player_name_duo]+=1
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if team == winning_team_name:
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duo_to_win_count[player_name_duo]+=1
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# Count a team win as an individual win for each winning team player against all losing team players (and vice versa for losses)
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# todo: maybe it should only count as a personal win if your personal score is higher than the other team's player
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assert(len(results) == 2)
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winning_team_name = 0
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losing_team_name = 0
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lose = 0
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win = 1
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team_names = list(results.keys())
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if results[team_names[0]]['score'] > results[team_names[1]]['score']:
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winning_team_name = team_names[0]
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losing_team_name = team_names[1]
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elif results[team_names[0]]['score'] < results[team_names[1]]['score']:
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winning_team_name = team_names[1]
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losing_team_name = team_names[0]
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else:
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lose = 0.5
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win = 0.5
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# Print conditional probabilities
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player_to_win_rate = dict()
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match_count_high_threshold = 40
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match_count_low_threshold = 27
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for matchkvp in player_to_match_count.items():
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if matchkvp[1] < match_count_high_threshold:
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continue
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player_to_win_rate[matchkvp[0]] = player_to_win_count[matchkvp[0]] / matchkvp[1]
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player_to_win_rate_sorted = list(player_to_win_rate.items())
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player_to_win_rate_sorted.sort(key=lambda p: p[1], reverse=True)
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print('Higher confidence Best (and worst) player win rates:\n','\n'.join([str(x) for x in player_to_win_rate_sorted]))
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player_to_win_rate = dict()
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for matchkvp in player_to_match_count.items():
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if matchkvp[1] > match_count_high_threshold or matchkvp[1] < match_count_low_threshold:
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continue
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player_to_win_rate[matchkvp[0]] = player_to_win_count[matchkvp[0]] / matchkvp[1]
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player_to_win_rate_sorted = list(player_to_win_rate.items())
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player_to_win_rate_sorted.sort(key=lambda p: p[1], reverse=True)
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print('Lower confidence Best (and worst) player win rates:\n','\n'.join([str(x) for x in player_to_win_rate_sorted]))
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print('')
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# As above but per role
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for role, players in first_roles_to_players.items():
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# print([str((p,player_to_match_count[p])) for p in players if p in player_to_match_count])
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player_match_counts = [player_to_match_count[p] for p in players if p in player_to_match_count]
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player_match_counts.sort()
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top_third_match_count = player_match_counts[len(player_match_counts)*2//3]
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middle_third_match_count = player_match_counts[len(player_match_counts)//3]
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# print('Role:',role,'player match counts:',player_match_counts,'top third cutoff:',top_third_match_count,'middle third cutoff:',middle_third_match_count)
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significant_players = [(p, player_to_win_count[p] / player_to_match_count[p]) for p in players if p in player_to_match_count and player_to_match_count[p] >= top_third_match_count]
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significant_players.sort(key=lambda p: p[1], reverse=True)
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print('Higher confidence',role,[p[0]+' '+format(p[1],'.2f') for p in significant_players])
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significant_players = [(p, player_to_win_count[p] / player_to_match_count[p]) for p in players if p in player_to_match_count and player_to_match_count[p] >= middle_third_match_count and player_to_match_count[p] < top_third_match_count]
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significant_players.sort(key=lambda p: p[1], reverse=True)
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print('Middle confidence',role,[p[0]+' '+format(p[1],'.2f') for p in significant_players])
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significant_players = [(p, player_to_win_count[p] / player_to_match_count[p]) for p in players if p in player_to_match_count and player_to_match_count[p] < middle_third_match_count and player_to_match_count[p] > 1]
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significant_players.sort(key=lambda p: p[1], reverse=True)
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print('Lower confidence',role,[p[0]+' '+format(p[1],'.2f') for p in significant_players])
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print()
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print()
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duo_to_win_rate = dict()
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duo_count_threshold = 22
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for matchkvp in duo_to_match_count.items():
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if matchkvp[1] < duo_count_threshold:
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continue
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duo_to_win_rate[matchkvp[0]] = duo_to_win_count[matchkvp[0]] / matchkvp[1]
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duo_to_win_rate_sorted = list(duo_to_win_rate.items())
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duo_to_win_rate_sorted.sort(key=lambda p: p[1], reverse=True)
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print('Duo win rates (n >= ',duo_count_threshold,'):\n','\n'.join([str(x) for x in duo_to_win_rate_sorted]))
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print()
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