Tribes2/Foxox-T2-Player-Ratings
1
0
Fork 0
mirror of https://github.com/foxox/Foxox-T2-Player-Ratings.git synced 2026-07-14 16:04:42 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

added win rate / conditional probability of winning when on team thing

Browse source
This commit is contained in:
John Drake 2025-11-10 02:25:23 -05:00
parent 827d70088c
commit 2218f3ec10
1 changed files with 43 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

49
pubstatcruncher.py
View file

@ -1,8 +1,14 @@
# from yaml import safe_load, dump
# This script is an exploration into processing PUB/PUG match results into player rankings.
# Once I hack together something that seems useful, I may try to tidy this up.
# So far, "single team point whore" glicko ratings are the most useful result. This compares players only to their teammates, so when one team dominates another, the ratings are still fair. However, this still suffers from the problem that certain roles tend to score more than others: for example, a top capper is probably going to have a greater score than a top farmer, even though the farmer is critical to the team's success.
# The next thing I would like to compute is a historical conditional probability of winning a match given that a certain player is on the team.
import yaml
from operator import itemgetter
import glicko2
# from itertools import pairwise
from more_itertools import pairwise
# load file
@ -138,10 +144,14 @@ for player,roles in players_to_roles.items():
print(first_roles_to_players)
print(any_roles_to_players)
# quit()
player_to_win_count = dict()
player_to_match_count = dict()
# loop over all matches
for match in file_contents:
print()
@ -153,9 +163,9 @@ for match in file_contents:
merged_match_player_results = list()
for team in results:
print('team:', team)
# if results[team]['score'] > winning_team_score:
# winning_team_score = results[team]['score']
# winning_team_name = team
if results[team]['score'] > winning_team_score:
winning_team_score = results[team]['score']
winning_team_name = team
# print('input unsorted')
# for player in results[team]['players']:
# print('player:', player)
@ -204,6 +214,20 @@ for match in file_contents:
better_player_glicko = stpwglickos[better_player[0]]
stpwglickos[worse_player[0]].update_player([better_player_glicko.rating], [better_player_glicko.rd], [lose])
for team in results:
for player in results[team]['players']:
player_split = player.split(", ")
playername = player_split[0]
# player_tuple = (player_split[0], int(player_split[1]))
# 0 is name, 1 is score
if not playername in player_to_match_count:
player_to_match_count[playername] = 0
if not playername in player_to_win_count:
player_to_win_count[playername] = 0
player_to_match_count[playername]+=1
if team == winning_team_name:
player_to_win_count[playername]+=1
# for player in merged_match_player_results:
# print('inplayer:', player)
@ -282,3 +306,16 @@ for role, players in first_roles_to_players.items():
# print('unsorted:',role,players)
players.sort(key=lambda p: stpwglickos[p].rating if p in stpwglickos else 1400, reverse=True)
print('sorted:',role,players)
# Print conditional probabilities
player_to_win_rate = dict()
for matchkvp in player_to_match_count.items():
# Only use data with at least 10 samples (matches)
if matchkvp[1] < 10:
continue
player_to_win_rate[matchkvp[0]] = player_to_win_count[matchkvp[0]] / matchkvp[1]
player_to_win_rate_sorted = list(player_to_win_rate.items())
player_to_win_rate_sorted.sort(key=lambda p: p[1], reverse=True)
print(player_to_win_rate_sorted)
# print(player_to_match_count)
# print(player_to_win_count)