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import json, dateutil
from sqlalchemy import func
from itertools import combinations
from os import path
import __main__
from f1elo.model import *
class Elo:
def __init__(self, session):
self.session = session
self.config = json.load(
open(path.join(path.dirname(__main__.__file__), 'config', 'elo.json')))
def get_ranking(self, driver, rank_date=None):
rank = driver.get_ranking(rank_date)
if rank:
return rank.ranking
return self.config['initial_ranking']
def get_entry_ranking(self, entry, date=None):
return sum([self.get_ranking(d, date) for d in entry.drivers]) / len(entry.drivers)
def rank_race(self, race):
race_disparity = self.config['disparity']['base_disparity']
if self.config['disparity']['adjust']:
recent_date = race.date - dateutil.relativedelta.relativedelta(months=3)
recent_ratings = self.session.query(
func.min(Ranking.ranking).label('min'),
func.max(Ranking.ranking).label('max')
).filter(
Ranking.rank_date >= recent_date
).group_by(
Ranking._driver
)
changes_query = self.session.query(
func.avg(
recent_ratings.subquery().columns.max - recent_ratings.subquery().columns.min
)
)
recent_rank_change = changes_query.scalar()
if not recent_rank_change:
recent_rank_change = 0
recent_rank_change = min(self.config['disparity']['base_rating_change'], recent_rank_change)
race_disparity *= (2.5 + (self.config['disparity']['base_rating_change']/(recent_rank_change - 2.0 * self.config['disparity']['base_rating_change']))) * 0.5
entries = race.entries
entries_to_compare = []
rankings = {}
new_rankings = {}
for e in entries:
rankings[e] = self.get_entry_ranking(e, race.date)
new_rankings[e] = 0.0
if e.result_group:
entries_to_compare.append(e)
for c in combinations(entries_to_compare, 2):
score = self.get_score(
rankings[c[0]] - rankings[c[1]],
self.get_outcome(c),
self.get_importance(race,
[rankings[c[0]],
rankings[c[1]]]),
race_disparity
)
new_rankings[c[0]] += score
new_rankings[c[1]] -= score
return new_rankings
def get_importance(self, race, rankings):
base_importance = self.config['importance'][race.type.code]
min_rank = min(rankings)
if min_rank < min(self.config['importance_threshold']):
return base_importance
if min_rank <= max(self.config['importance_threshold']):
return base_importance * 0.75
return base_importance / 2
def get_outcome(self, entries):
if entries[0].result_group < entries[1].result_group:
return 1
elif entries[0].result_group > entries[1].result_group:
return 0
return 0.5
def get_score(self, difference, outcome, importance, disparity):
return importance * (outcome - 1 / (1 + (10 ** (-difference / disparity))))
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