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Implements a (hopefully) better rating system with an inflation test.

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This was SVN commit r15047.
This commit is contained in:
scythetwirler 2014-04-27 23:13:29 +00:00
parent a8b50a66da
commit fde558b4b2
1 changed files with 43 additions and 11 deletions
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54
source/tools/XpartaMuPP/ELO.py
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@ -1,4 +1,4 @@
"""Copyright (C) 2013 Wildfire Games.
"""Copyright (C) 2014 Wildfire Games.
* This file is part of 0 A.D.
*
* 0 A.D. is free software: you can redistribute it and/or modify
@ -19,12 +19,23 @@
# Difference between two ratings such that it is
# regarded as a "sure win" for the higher player.
# No points are gained or lost for such a game.
elo_sure_win_difference = 600
elo_sure_win_difference = 600.0
# Lower ratings "move faster" and change more
# dramatically than higher ones. Anything rating above
# this value moves at the same rate as this value.
elo_k_factor_constant_rating = 2200
elo_k_factor_constant_rating = 2200.0
# This preset number of games is the number of games
# where a player is considered "stable".
# Rating volatility is constant after this number.
volatility_constant = 20.0
# Fair rating adjustment loses against inflation
# This constant will battle inflation.
# NOTE: This can be adjusted as needed by a
# bot/server administrator
anti_inflation = 0.015
############ Functions ############
def get_rating_adjustment(rating, opponent_rating, games_played, opponent_games_played, result):
@ -44,15 +55,36 @@ def get_rating_adjustment(rating, opponent_rating, games_played, opponent_games_
TODO: Team games.
"""
opponent_volatility_influence = max(1, pow(min(games_played + 1, 50) / min(opponent_games_played + 1, 50), 0.5))
rating_k_factor = 0.75 * pow(elo_k_factor_constant_rating / min(elo_k_factor_constant_rating, (rating + opponent_rating) / 2), 0.5)
player_volatility = min(pow(1.1, games_played + 16), 25)
volatility = opponent_volatility_influence * player_volatility / rating_k_factor
player_volatility = (games_played / volatility_constant + 0.25) / 1.25
rating_k_factor = 50.0 * (min(rating, elo_k_factor_constant_rating) / elo_k_factor_constant_rating + 1.0) / 2.0
volatility = rating_k_factor * player_volatility
difference = opponent_rating - rating
if result == 1:
return round(max(0, (difference + result * elo_sure_win_difference) / volatility))
return round(max(0, (difference + result * elo_sure_win_difference) / volatility - anti_inflation))
elif result == -1:
return round(min(0, (difference + result * elo_sure_win_difference) / volatility))
return round(min(0, (difference + result * elo_sure_win_difference) / volatility - anti_inflation))
else:
return round(difference / volatility)
return round(difference / volatility - anti_inflation)
# Inflation test - A slightly negative is better than a slightly positive
# Lower rated players stop playing more often than higher rated players
# Uncomment to test.
# In this example, two evenly matched players play for 150000 games.
"""
from random import randrange
r1start = 1600
r2start = 1600
r1 = r1start
r2 = r2start
for x in range(0, 150000):
res = randrange(3)-1 # How often one wins against the other
if res >= 1:
res = 1
elif res <= -1:
res = -1
r1gain = get_rating_adjustment(r1, r2, 20, 20, res)
r2gain = get_rating_adjustment(r2, r1, 20, 20, -1 * res)
r1 += r1gain
r2 += r2gain
print(str(r1) + " " + str(r2) + " : " + str(r1 + r2-r1start - r2start))
"""