def win_probability(a, b):
deltaMu = sum([x.mu for x in a]) - sum([x.mu for x in b])
sumSigma = sum([x.sigma**2 for x in a]) + sum([x.sigma**2 for x in b])
playerCount = len(a) + len(b)
denominator = math.sqrt(playerCount * (trueskill.BETA * trueskill.BETA) +
sumSigma)
return cdf(deltaMu / denominator)
def win_probability(self, rating_a, rating_b):
from trueskill import BETA
from math import sqrt
from trueskill.backends import cdf
delta_mu = rating_a.mu - rating_b.mu
denom = sqrt(2 * (BETA * BETA) + pow(rating_a.sigma, 2) \
+ pow(rating_b.sigma, 2))
return cdf(delta_mu / denom)
def predict_win_probability(team_1, team_2):
a = [role.rating for role in team_1.ratings]
b = [role.rating for role in team_2.ratings]
deltaMu = sum([x.mu for x in a]) - sum([x.mu for x in b])
sumSigma = sum([x.sigma ** 2 for x in a]) + sum([x.sigma ** 2 for x in b])
playerCount = len(a) + len(b)
denominator = math.sqrt(playerCount * (BETA * BETA) + sumSigma)
return cdf(deltaMu / denominator)
def _calculate_win_probability_internal(a: Collection[Rating],
b: Collection[Rating]) -> int:
delta_mu = sum([x.mu for x in a]) - sum([x.mu for x in b])
sum_sigma = sum([x.sigma**2 for x in a]) + sum([x.sigma**2 for x in b])
player_count = len(a) + len(b)
denominator = max(
trueskill.DELTA,
sqrt(player_count * (trueskill.BETA**2) + sum_sigma).real)
return cdf(delta_mu / denominator)
def predict(self, red_alliance, blue_alliance):
self.init_teams(red_alliance, blue_alliance)
a = [self.trueskills[t] for t in red_alliance]
b = [self.trueskills[t] for t in blue_alliance]
delta_mu = sum([x.mu for x in a]) - sum([x.mu for x in b])
sum_sigma = sum([x.sigma ** 2 for x in a + b])
player_count = len(a) + len(b)
denominator = (player_count * (self.env.beta**2) + sum_sigma) ** 0.5
return backends.cdf(delta_mu / denominator)
def win_probability(player_rating, opponent_rating):
delta_mu = player_rating.mu - opponent_rating.mu
denom = sqrt(2 * (BETA * BETA) + pow(player_rating.sigma, 2) + pow(opponent_rating.sigma, 2))
return cdf(delta_mu / denom)
def Pwin(rAlist=[Rating()], rBlist=[Rating()]):
deltaMu = sum([x.mu for x in rAlist]) - sum([x.mu for x in rBlist])
rsss = sqrt(sum([x.sigma**2 for x in rAlist]) + sum([x.sigma**2 for x in rBlist]))
return cdf(deltaMu / rsss)
def win_prob(r1, r2):
delta = r1.mu - r2.mu
#Perhaps we should weight the uncertainty higher...
rsss = sqrt(r1.sigma**2 + r2.sigma**2)
return cdf(delta / rsss)
def win_probability(player_rating, opponent_rating):
delta_mu = player_rating.mu - opponent_rating.mu
denom = sqrt(2 * (BETA * BETA) + pow(player_rating.sigma, 2) + pow(opponent_rating.sigma, 2))
return cdf(delta_mu / denom)
def win_pct(self, player, opp=None):
return cdf(self.win_stdev(player, opp))
def win_probability(self, player, opponent):
delta_mu = player.rating.mu - opponent.rating.mu - calc_draw_margin(self.draw, 2, self.env)
denom = sqrt(2 * (self.beta * self.beta) + pow(player.rating.sigma, 2) + pow(opponent.rating.sigma, 2))
return cdf(delta_mu / denom)
def Pwin(rA=Rating(), rB=Rating()):
deltaMu = rA.mu - rB.mu
rsss = sqrt(rA.sigma**2 + rB.sigma**2)
return cdf(deltaMu/rsss)
def Pwin(rA=trueskill.Rating(), rB=trueskill.Rating()):
deltaMu = rA.mu - rB.mu
rsss = math.sqrt(rA.sigma**2 + rB.sigma**2)
_prob = cdf(deltaMu / rsss)
return _prob
