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"""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
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 2 of the License, or
* (at your option) any later version.
*
* 0 A.D. is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with 0 A.D. If not, see <http://www.gnu.org/licenses/>.
"""
############ Constants ############
# 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 = 600.0
# 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 = 2200.0
# 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):
"""
Calculates the rating adjustment after a 1v1 game finishes using simplified ELO.
Arguments:
rating, opponent_rating - Ratings of the players before this game.
games_played, opponent_games_played - Number of games each player has played
before this game.
result - 1 for the first player (rating, games_played) won, 0 for draw, or
-1 for the second player (opponent_rating, opponent_games_played) won.
Returns:
The integer that should be subtracted from the loser's rating and added
to the winner's rating to get their new ratings.
TODO: Team games.
"""
player_volatility = (min(games_played, volatility_constant) / 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 - anti_inflation))
elif result == -1:
return round(min(0, (difference + result * elo_sure_win_difference) / volatility - anti_inflation))
else:
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))
"""
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