def ts_update_error(x, rA1, rA2, rB1, rB2, scoreA):
(beta, tau, dp) = x
env = trueskill.TrueSkill(beta=beta, tau=tau,
draw_probability=dp,
backend='mpmath')
tss = TrueSkillSystem('trueskill', env)
(rAp, rBp) = tss.rate2p(rA1, rB1, scoreA)
err = math.sqrt(distance(rAp, rA2)**2 + distance(rBp, rB2)**2)
return err
# Test it on a third game
goko_env = trueskill.TrueSkill(mu=mu0, sigma=sigma0,
beta=beta, tau=tau,
draw_probability=dp,
backend='mpmath')
goko_tss = TrueSkillSystem('Goko', goko_env)
# Game data for two experienced players with similar ratings
# Initial ratings
print()
print('Testing Parameters on Game 3...')
rA1 = Rating(6789.036432492558, 262.8329415491707)
rB1 = Rating(7108.43720451577, 266.9581590879031)
scoreA = 1
# Post-game ratings
rA2 = Rating(6822.354187108161, 262.6577478580567)
rB2 = Rating(7074.076798465825, 266.6845904766116)
(rAx, rBx) = goko_tss.rate2p(rA1, rB1, scoreA)
error = distance(rA2, rAx)**2 + distance(rB2, rBx)**2
print('Initial ratings:')
print(' A: %7.2f +/- %5.2f' % (rA1.mu, rA1.sigma))
print(' B: %7.2f +/- %5.2f' % (rB1.mu, rB1.sigma))
print('Expected post-game ratings:')
print(' A: %7.2f +/- %5.2f' % (rAx.mu, rAx.sigma))
print(' B: %7.2f +/- %5.2f' % (rBx.mu, rBx.sigma))
print('Observed post-game ratings:')
print(' A: %7.2f +/- %5.2f' % (rA2.mu, rA2.sigma))
print(' B: %7.2f +/- %5.2f' % (rB2.mu, rB2.sigma))
print('Rating prediction error: %6.4f' % error)
