class EloParms:
- def __init__(self, global_K = 15, initial = 100, floor = 100, logdistancefactor = math.log(10)/float(400), maxlogdistance = math.log(10)):
+ def __init__(self, global_K=15, initial=100, floor=100,
+ logdistancefactor=math.log(10)/float(400), maxlogdistance=math.log(10),
+ latencyfactor=0.2):
self.global_K = global_K
self.initial = initial
self.floor = floor
self.logdistancefactor = logdistancefactor
self.maxlogdistance = maxlogdistance
+ self.latencyfactor = latencyfactor
class KReduction:
return k
+# parameters for K reduction
+# this may be touched even if the DB already exists
+KREDUCTION = KReduction(600, 120, 0.5, 0, 32, 0.2)
+
+# parameters for chess elo
+# only global_K may be touched even if the DB already exists
+# we start at K=200, and fall to K=40 over the first 20 games
+ELOPARMS = EloParms(global_K = 200)
+
+
class EloWIP:
"""EloWIP is a work-in-progress Elo value. It contains all of the
attributes necessary to calculate Elo deltas for a given game."""
self.wip[pid].elo = PlayerElo(pid, game.game_type_cd, ELOPARMS.initial)
# determine k reduction
- self.wip[pid].k = KREDUCTION.eval(self.wip[pid].elo.games,
- self.wip[pid].alivetime, self.duration)
+ self.wip[pid].k = KREDUCTION.eval(self.wip[pid].elo.games, self.wip[pid].alivetime,
+ self.duration)
# we don't process the players who have a zero K factor
- self.wip = { e.player_id:e for e in self.wip.values() if e.k > 0.0}
+ self.wip = {e.player_id:e for e in self.wip.values() if e.k > 0.0}
# now actually process elos
self.process()
- # DEBUG
- # for w in self.wip.values():
- # log.debug(w.player_id)
- # log.debug(w)
-
def scorefactor(self, si, sj):
"""Calculate the real scorefactor of the game. This is how players
actually performed, which is compared to their expected performance as
return scorefactor_real
+ def pingfactor(self, pi, pj):
+ """ Calculate the ping differences between the two players, but only if both have them. """
+ if pi is None or pj is None or pi < 0 or pj < 0:
+ return None
+
+ else:
+ return float(pi)/(pi+pj)
+
def process(self):
"""Perform the core Elo calculation, storing the values in the "wip"
dict for passing upstream."""
# log.debug("(New) adjustment j: {0}".format(adjustmentj))
if scorefactor_elo > 0.5:
- # player i is expected to win
+ # player i is expected to win
if scorefactor_real > 0.5:
- # he DID win, so he should never lose points.
+ # he DID win, so he should never lose points.
adjustmenti = max(0, adjustmenti)
else:
- # he lost, but let's make it continuous (making him lose less points in the result)
+ # he lost, but let's make it continuous
+ # (making him lose less points in the result)
adjustmenti = (2 * scorefactor_real - 1) * scorefactor_elo
else:
- # player j is expected to win
+ # player j is expected to win
if scorefactor_real > 0.5:
- # he lost, but let's make it continuous (making him lose less points in the result)
+ # he lost, but let's make it continuous
+ # (making him lose less points in the result)
adjustmentj = (1 - 2 * scorefactor_real) * (1 - scorefactor_elo)
else:
- # he DID win, so he should never lose points.
+ # he DID win, so he should never lose points.
adjustmentj = max(0, adjustmentj)
self.wip[pids[i]].adjustment += adjustmenti
w.elo.games += 1
w.elo.update_dt = datetime.datetime.utcnow()
- # log.debug("Setting Player {0}'s Elo delta to {1}. Elo is now {2}\
- # (was {3}).".format(pid, w.elo_delta, new_elo, old_elo))
-
def save(self, session):
"""Put all changed PlayerElo and PlayerGameStat instances into the
session to be updated or inserted upon commit."""
except:
log.debug("Unable to save Elo delta value for player_id {0}".format(w.player_id))
-
-# parameters for K reduction
-# this may be touched even if the DB already exists
-KREDUCTION = KReduction(600, 120, 0.5, 0, 32, 0.2)
-
-# parameters for chess elo
-# only global_K may be touched even if the DB already exists
-# we start at K=200, and fall to K=40 over the first 20 games
-ELOPARMS = EloParms(global_K = 200)
projects / xonotic / xonstat.git / blobdiff