Use score-scaling Elo for non-duels.

[xonotic/xonstat.git] / xonstat / models.py

diff --git a/xonstat/models.py b/xonstat/models.py
index 39c65868fc0e92b62c0fa34d80c404d18af2244f..84a64ea9a665fc6c9d49e52f12c041d8d48bca17 100644 (file)
--- a/xonstat/models.py
+++ b/xonstat/models.py
@@ -8,7 +8,6 @@ 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.util import strip_colors, html_colors, pretty_date
 
 log = logging.getLogger(__name__)
@@ -99,112 +98,6 @@ class Game(object):
     def fuzzy_date(self):
         return pretty_date(self.start_dt)
 
-    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
-
 
 class PlayerGameStat(object):
     def __init__(self, player_game_stat_id=None, create_dt=None):
@@ -287,13 +180,13 @@ class PlayerNick(object):
 
 
 class PlayerElo(object):
-    def __init__(self, player_id=None, game_type_cd=None):
+    def __init__(self, player_id=None, game_type_cd=None, elo=None):
 
         self.player_id = player_id
         self.game_type_cd = game_type_cd
+        self.elo = elo
         self.score = 0
         self.games = 0
-        self.elo = ELOPARMS.initial
 
     def __repr__(self):
         return "<PlayerElo(pid=%s, gametype=%s, elo=%s)>" % (self.player_id, self.game_type_cd, self.elo)