Compute 2017 Top Teams

gistfile1.txt

import datetime
from models.event import Event

events = Event.query().filter(
          Event.start_date >= datetime.datetime(2017, 3, 29)).filter(
          Event.start_date <= datetime.datetime(2017, 4, 2)).order(
          Event.start_date).fetch()

gear_stats = {}
pressure_stats = {}
for event in events:
  predictions = event.details.predictions
  if predictions and predictions.get('stat_mean_vars'):
    for team, val in predictions['stat_mean_vars']['qual']['gears']['mean'].items():
      gear_stats[team] = val
    for team, val in predictions['stat_mean_vars']['qual']['pressure']['mean'].items():
      pressure_stats[team] = val

aggregate_stats = {}
for team, gear_stat in gear_stats.items():
  pressure_stat = pressure_stats[team]
  aggregate_stats[team] = gear_stat/max(gear_stats.values()) + pressure_stat/max(pressure_stats.values())


for team, val in sorted(aggregate_stats.items(), key=lambda x: -x[1]):
  print team, val, gear_stats[team], pressure_stats[team]