import datetime
import logging
import math
-import random
-import sys
-from xonstat.models import *
+from xonstat.models import PlayerElo
log = logging.getLogger(__name__)
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),
+ latency_trend_factor=0.2):
self.global_K = global_K
self.initial = initial
self.floor = floor
self.logdistancefactor = logdistancefactor
self.maxlogdistance = maxlogdistance
+ self.latency_trend_factor = latency_trend_factor
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."""
# used to determine if a pgstat record is elo-eligible
def elo_eligible(pgs):
- return pgs.player_id > 2 and pgs.alivetime > timedelta(seconds=0)
+ return pgs.player_id > 2 and pgs.alivetime > datetime.timedelta(seconds=0)
+
+ elostats = filter(elo_eligible, pgstats)
# only process elos for elo-eligible players
- for pgstat in filter(elo_eligible, pgstats):
+ for pgstat in elostats:
self.wip[pgstat.player_id] = EloWIP(pgstat.player_id, pgstat)
# determine duration from the maximum alivetime
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:
+ # default to a draw
+ return 0.5
+
+ else:
+ return float(pi)/(pi+pj)
+
def process(self):
"""Perform the core Elo calculation, storing the values in the "wip"
dict for passing upstream."""
pids = self.wip.keys()
for i in xrange(0, len(pids)):
ei = self.wip[pids[i]].elo
+ pi = self.wip[pids[i]].pgstat.avg_latency
for j in xrange(i+1, len(pids)):
ej = self.wip[pids[j]].elo
si = self.wip[pids[i]].score_per_second
sj = self.wip[pids[j]].score_per_second
+ pj = self.wip[pids[j]].pgstat.avg_latency
# normalize scores
ofs = min(0, si, sj)
(float(ei.elo) - float(ej.elo)) * ep.logdistancefactor))
scorefactor_elo = 1 / (1 + math.exp(-elodiff))
+ # adjust the elo prediction according to ping
+ ping_ratio = self.pingfactor(pi, pj)
+ scorefactor_ping = ep.latency_trend_factor * (0.5 - ping_ratio)
+ scorefactor_elo_adjusted = max(0.0, min(1.0, scorefactor_elo + scorefactor_ping))
+
# initial adjustment values, which we may modify with additional rules
- adjustmenti = scorefactor_real - scorefactor_elo
- adjustmentj = scorefactor_elo - scorefactor_real
+ adjustmenti = scorefactor_real - scorefactor_elo_adjusted
+ adjustmentj = scorefactor_elo_adjusted - scorefactor_real
# DEBUG
- # log.debug("(New) Player i: {0}".format(ei.player_id))
- # log.debug("(New) Player i's K: {0}".format(self.wip[pids[i]].k))
- # log.debug("(New) Player j: {0}".format(ej.player_id))
- # log.debug("(New) Player j's K: {0}".format(self.wip[pids[j]].k))
- # log.debug("(New) Scorefactor real: {0}".format(scorefactor_real))
- # log.debug("(New) Scorefactor elo: {0}".format(scorefactor_elo))
- # log.debug("(New) adjustment i: {0}".format(adjustmenti))
- # log.debug("(New) adjustment j: {0}".format(adjustmentj))
+ log.debug("(New) Player i: {0}".format(ei.player_id))
+ log.debug("(New) Player i's K: {0}".format(self.wip[pids[i]].k))
+ log.debug("(New) Player j: {0}".format(ej.player_id))
+ log.debug("(New) Player j's K: {0}".format(self.wip[pids[j]].k))
+ log.debug("(New) Ping ratio: {0}".format(ping_ratio))
+ log.debug("(New) Scorefactor real: {0}".format(scorefactor_real))
+ log.debug("(New) Scorefactor elo: {0}".format(scorefactor_elo))
+ log.debug("(New) Scorefactor ping: {0}".format(scorefactor_ping))
+ log.debug("(New) adjustment i: {0}".format(scorefactor_real - scorefactor_elo))
+ log.debug("(New) adjustment j: {0}".format(scorefactor_elo - scorefactor_real))
+ log.debug("(New) adjustment i with ping: {0}".format(adjustmenti))
+ log.debug("(New) adjustment j with ping: {0}\n".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
new_elo = max(float(w.elo.elo) + w.adjustment * w.k * ep.global_K / float(len(pids) - 1), ep.floor)
w.elo_delta = new_elo - old_elo
+ log.debug("{}'s Old Elo: {} New Elo: {} Delta {}"
+ .format(pid, old_elo, new_elo, w.elo_delta))
+
w.elo.elo = new_elo
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