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__)
scores = {}
alivetimes = {}
winners = []
+ losers = []
for (p,s,a,r,t) in session.query(PlayerGameStat.player_id,
PlayerGameStat.score, PlayerGameStat.alivetime,
PlayerGameStat.rank, PlayerGameStat.team).\
# 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()
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)
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
projects / xonotic / xonstat.git / blobdiff