From 71f979dedf515938663214a945df2f113e3c8d90 Mon Sep 17 00:00:00 2001 From: Ant Zucaro Date: Wed, 25 Jul 2012 08:32:20 -0400 Subject: [PATCH] Use traditional (win-based) Elo instead of score-based. Using traditional Elo instead of score-based Elo eliminates a lot of confusion and drama surrounding losing points when winning and gaining points when losing. I think this is a better reflection of what we're after - the highest ranking players have won the most. This has an intended side-effect of encouraging ALL players to play more, because even pros have no penalties by playing newer players. --- xonstat/models.py | 99 +++++++++++++++++++++-------------------------- 1 file changed, 44 insertions(+), 55 deletions(-) diff --git a/xonstat/models.py b/xonstat/models.py index 33c33ec..466bfa0 100644 --- a/xonstat/models.py +++ b/xonstat/models.py @@ -8,7 +8,7 @@ from sqlalchemy.orm import mapper from sqlalchemy.orm import scoped_session from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.declarative import declarative_base -from xonstat.elo import KREDUCTION, ELOPARMS +from xonstat.elo import ELOPARMS, KREDUCTION from xonstat.util import strip_colors, html_colors, pretty_date log = logging.getLogger(__name__) @@ -114,6 +114,7 @@ class Game(object): scores = {} alivetimes = {} winners = [] + losers = [] for (p,s,a,r,t) in session.query(PlayerGameStat.player_id, PlayerGameStat.score, PlayerGameStat.alivetime, PlayerGameStat.rank, PlayerGameStat.team).\ @@ -129,6 +130,8 @@ class Game(object): # team games are where the team is set (duh) if r == 1 or (t == self.winner and t is not None): winners.append(p) + else: + losers.append(p) player_ids = scores.keys() @@ -151,80 +154,64 @@ class Game(object): del(scores[pid]) del(alivetimes[pid]) - elos = self.update_elos(session, elos, scores, winners, ELOPARMS) + if pid in winners: + winners.remove(pid) + else: + losers.remove(pid) + + elos = self.update_elos(session, elos, scores, winners, losers, ELOPARMS) # add the elos to the session for committing for e in elos: session.add(elos[e]) - # no longer calculate DM elo for a duel game - # if game_type_cd == 'duel': - # self.process_elos(session, "dm") - - - def update_elos(self, session, elos, scores, winners, ep): - eloadjust = {} - for pid in elos.keys(): - eloadjust[pid] = 0 - - if len(elos) < 2: + def update_elos(self, session, elos, scores, winners, losers, ep): + if len(elos) < 2 or len(winners) == 0 or len(losers) == 0: return elos pids = elos.keys() - for i in xrange(0, len(pids)): - ei = elos[pids[i]] - for j in xrange(i+1, len(pids)): - ej = elos[pids[j]] - si = scores[ei.player_id] - sj = scores[ej.player_id] - - # normalize scores - ofs = min(0, si, sj) - si -= ofs - sj -= ofs - if si + sj == 0: - si, sj = 1, 1 # a draw + elo_deltas = {} + for w_pid in winners: + w_elo = elos[w_pid] + for l_pid in losers: + l_elo = elos[l_pid] - # real score factor - scorefactor_real = si / float(si + sj) + w_q = math.pow(10, float(w_elo.elo)/400.0) + l_q = math.pow(10, float(l_elo.elo)/400.0) - # estimated score factor by elo - elodiff = min(ep.maxlogdistance, max(-ep.maxlogdistance, - (float(ei.elo) - float(ej.elo)) * ep.logdistancefactor)) - scorefactor_elo = 1 / (1 + math.exp(-elodiff)) + w_delta = w_elo.k * ELOPARMS.global_K * (1 - w_q/(w_q + l_q)) + l_delta = l_elo.k * ELOPARMS.global_K * (0 - l_q/(l_q + w_q)) - # how much adjustment is good? - # scorefactor(elodiff) = 1 / (1 + e^(-elodiff * logdistancefactor)) - # elodiff(scorefactor) = -ln(1/scorefactor - 1) / logdistancefactor - # elodiff'(scorefactor) = 1 / ((scorefactor) (1 - scorefactor) logdistancefactor) - # elodiff'(scorefactor) >= 4 / logdistancefactor + elo_deltas[w_pid] = (elo_deltas.get(w_pid, 0.0) + w_delta) + elo_deltas[l_pid] = (elo_deltas.get(l_pid, 0.0) + l_delta) - # adjust'(scorefactor) = K1 + K2 + log.debug("Winner {0}'s elo_delta vs Loser {1}: {2}".format(w_pid, + l_pid, w_delta)) - # so we want: - # K1 + K2 <= 4 / logdistancefactor <= elodiff'(scorefactor) - # as we then don't overcompensate + log.debug("Loser {0}'s elo_delta vs Winner {1}: {2}".format(l_pid, + w_pid, l_delta)) - adjustment = scorefactor_real - scorefactor_elo - eloadjust[ei.player_id] += adjustment - eloadjust[ej.player_id] -= adjustment + log.debug("w_elo: {0}, w_k: {1}, w_q: {2}, l_elo: {3}, l_k: {4}, l_q: {5}".\ + format(w_elo.elo, w_elo.k, l_q, l_elo.elo, l_elo.k, l_q)) - elo_deltas = {} for pid in pids: - old_elo = elos[pid].elo - new_elo = max(float(elos[pid].elo) + eloadjust[pid] * elos[pid].k * ep.global_K / float(len(elos) - 1), ep.floor) - - # winners are not penalized with negative elo - if pid in winners and new_elo < elos[pid].elo: - log.debug("Not penalizing Player {0} for winning. Elo delta set to 0.0. Elo is unchanged at {1}".format(pid, old_elo)) - elo_deltas[pid] = 0.0 + # average the elo gain for team games + if pid in winners: + elo_deltas[pid] = elo_deltas.get(pid, 0.0) / len(losers) else: - elo_deltas[pid] = new_elo - float(elos[pid].elo) - log.debug("Setting Player {0}'s Elo delta to {1}. Elo is now {2} (was {3}).".format(pid, elo_deltas[pid], new_elo, old_elo)) - elos[pid].elo = new_elo + elo_deltas[pid] = elo_deltas.get(pid, 0.0) / len(winners) + + old_elo = float(elos[pid].elo) + new_elo = max(float(elos[pid].elo) + elo_deltas[pid], ep.floor) + # in case we've set a different delta from the above + elo_deltas[pid] = new_elo - old_elo + + elos[pid].elo = new_elo elos[pid].games += 1 + log.debug("Setting Player {0}'s Elo delta to {1}. Elo is now {2} (was {3}).".\ + format(pid, elo_deltas[pid], new_elo, old_elo)) self.save_elo_deltas(session, elo_deltas) @@ -253,6 +240,8 @@ class Game(object): log.debug("Unable to save Elo delta value for player_id {0}".format(pid)) + + class PlayerGameStat(object): def __init__(self, player_game_stat_id=None, create_dt=None): self.player_game_stat_id = player_game_stat_id -- 2.39.2