+ def process_elos(self, session, game_type_cd=None):
+ if game_type_cd is None:
+ game_type_cd = self.game_type_cd
+
+ # we do not have the actual duration of the game, so use the
+ # maximum alivetime of the players instead
+ duration = 0
+ for d in session.query(sfunc.max(PlayerGameStat.alivetime)).\
+ filter(PlayerGameStat.game_id==self.game_id).\
+ one():
+ duration = d.seconds
+
+ scores = {}
+ alivetimes = {}
+ for (p,s,a) in session.query(PlayerGameStat.player_id,
+ PlayerGameStat.score, PlayerGameStat.alivetime).\
+ filter(PlayerGameStat.game_id==self.game_id).\
+ filter(PlayerGameStat.alivetime > timedelta(seconds=0)).\
+ filter(PlayerGameStat.player_id > 2).\
+ all():
+ # scores are per second
+ scores[p] = s/float(a.seconds)
+ alivetimes[p] = a.seconds
+
+ player_ids = scores.keys()
+
+ elos = {}
+ for e in session.query(PlayerElo).\
+ filter(PlayerElo.player_id.in_(player_ids)).\
+ filter(PlayerElo.game_type_cd==game_type_cd).all():
+ elos[e.player_id] = e
+
+ # ensure that all player_ids have an elo record
+ for pid in player_ids:
+ if pid not in elos.keys():
+ elos[pid] = PlayerElo(pid, game_type_cd)
+
+ for pid in player_ids:
+ elos[pid].k = KREDUCTION.eval(elos[pid].games, alivetimes[pid],
+ duration)
+ if elos[pid].k == 0:
+ del(elos[pid])
+ del(scores[pid])
+ del(alivetimes[pid])
+
+ elos = self.update_elos(elos, scores, ELOPARMS)
+
+ # add the elos to the session for committing
+ for e in elos:
+ session.add(elos[e])
+
+ if game_type_cd == 'duel':
+ self.process_elos(session, "dm")
+
+ def update_elos(self, elos, scores, ep):
+ eloadjust = {}
+ for pid in elos.keys():
+ eloadjust[pid] = 0
+
+ if len(elos) < 2:
+ 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
+
+ # real score factor
+ scorefactor_real = si / float(si + sj)
+
+ # 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))
+
+ # 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
+
+ # adjust'(scorefactor) = K1 + K2
+
+ # so we want:
+ # K1 + K2 <= 4 / logdistancefactor <= elodiff'(scorefactor)
+ # as we then don't overcompensate
+
+ adjustment = scorefactor_real - scorefactor_elo
+ eloadjust[ei.player_id] += adjustment
+ eloadjust[ej.player_id] -= adjustment
+ for pid in pids:
+ elos[pid].elo = max(float(elos[pid].elo) + eloadjust[pid] * elos[pid].k * ep.global_K / float(len(elos) - 1), ep.floor)
+ elos[pid].games += 1
+ return elos
+