def analyze_by_metric():
factors_by_metric = {}
count = 0
for race in race_feed(2000):
if len([
s for s in race
if s.final_odds < 0.01 or len(s.entry_indicator.strip()) > 0
]) > 0:
continue
assign_metrics(race)
for m in race.metrics:
if m not in factors_by_metric:
factors_by_metric[m] = [
FactorStats(f) for f in [just_broke_the_maiden]
]
try:
race.takeout = 1.0 - 1.0 / sum(
[1.0 / (1.0 + s.final_odds) for s in race])
for starter in race:
starter.crowd_probability = (1.0 - race.takeout) / (
1.0 + starter.final_odds)
for m in race.metrics:
factor_stats = factors_by_metric[m]
map(lambda f: f.add(race), factor_stats)
count += 1
except Exception as e:
print 'here', e, race.parent.track, race.parent.date, race.number
for m in factors_by_metric:
print m
factor_stats = factors_by_metric[m]
map(lambda f: f.show_details(), factor_stats)
def analyze_by_metric():
factors_by_metric = {}
count = 0
for race in race_feed(2000):
if len([s for s in race if s.final_odds < 0.01 or len(s.entry_indicator.strip()) > 0]) > 0:
continue
assign_metrics(race)
for m in race.metrics:
if m not in factors_by_metric:
factors_by_metric[m] = [FactorStats(f) for f in [just_broke_the_maiden]]
try:
race.takeout = 1.0 - 1.0 / sum([1.0 / (1.0 + s.final_odds) for s in race])
for starter in race:
starter.crowd_probability = (1.0 - race.takeout) / (1.0 + starter.final_odds)
for m in race.metrics:
factor_stats = factors_by_metric[m]
map(lambda f: f.add(race), factor_stats)
count += 1
except Exception as e:
print "here", e, race.parent.track, race.parent.date, race.number
for m in factors_by_metric:
print m
factor_stats = factors_by_metric[m]
map(lambda f: f.show_details(), factor_stats)
def analyze_favorites():
#factor_stats =[all_favorites, first_time_out, short_layoff, layoff, long_layoff, second_of_layoff, third_of_layoff, deep_form_cycle]
#factor_stats = [ FactorStats(f) for f in factor_stats]
factor_stats = []
for days_off in range(20,200,15):
f = foo(days_off)
f.__name__ = 'layoff_{0}'.format(days_off)
func = FactorStats(f)
factor_stats.append(func)
f = slo(days_off)
f.__name__ = '2nd of layoff_{0}'.format(days_off)
func = FactorStats(f)
factor_stats.append(func)
f = tlo(days_off)
f.__name__ = '3rd of layoff_{0}'.format(days_off)
func = FactorStats(f)
factor_stats.append(func)
count = 0
for race in race_feed():
if len([s for s in race if s.final_odds < 0.01 or len(s.entry_indicator.strip()) > 0]) > 0:
continue
try:
race.takeout = 1.0 - 1.0 / sum( [ 1.0 / (1.0 + s.final_odds) for s in race] )
for starter in race:
starter.crowd_probability = 1.0 / ( (race.takeout + 1.0) * ( 1.0 + starter.final_odds))
map(lambda f : f.add(race.favorite), factor_stats)
count += 1
except Exception as e:
print 'here',e, race.parent.track, race.parent.date, race.number
s = '{0:35} '.format('factor')
s += '{0:12} '.format('expected')
s += '{0:12}'.format('observed')
s += '{0:12}'.format('rate')
s += '{0:12}'.format('roi')
print s
print
for factor in factor_stats:
print str(factor)
#factor.show_details()
print 'number of races:', count
def recency_histogram():
counter = Counter()
for race in race_feed():
if len([s for s in race if s.final_odds < 0.01 or len(s.entry_indicator.strip()) > 0]) > 0:
continue
try:
for starter in race:
if first_time_out(starter):
pass
else:
counter[int(starter.days_off)] += 1
except Exception as e:
print 'here',e, race.parent.track, race.parent.date, race.number
keys = sorted(counter.keys())
for k in keys:
print '{0},{1}'.format(k, counter[k])
def analyze(factors):
factor_stats = [FactorStats(f) for f in factors]
count = 0
for race in race_feed():
if len([s for s in race if s.final_odds < 0.01 or len(s.entry_indicator.strip()) > 0]) > 0:
continue
try:
race.takeout = 1.0 - 1.0 / sum([1.0 / (1.0 + s.final_odds) for s in race])
for starter in race:
starter.crowd_probability = (1.0 - race.takeout) / (1.0 + starter.final_odds)
map(lambda f: f.add(race), factor_stats)
count += 1
except Exception as e:
print "here", e, race.parent.track, race.parent.date, race.number
map(lambda f: f.show_details(), factor_stats)
def analyze(factors):
factor_stats = [FactorStats(f) for f in factors]
count = 0
for race in race_feed():
if len([
s for s in race
if s.final_odds < 0.01 or len(s.entry_indicator.strip()) > 0
]) > 0:
continue
try:
race.takeout = 1.0 - 1.0 / sum(
[1.0 / (1.0 + s.final_odds) for s in race])
for starter in race:
starter.crowd_probability = (1.0 - race.takeout) / (
1.0 + starter.final_odds)
map(lambda f: f.add(race), factor_stats)
count += 1
except Exception as e:
print 'here', e, race.parent.track, race.parent.date, race.number
map(lambda f: f.show_details(), factor_stats)
def evaluate_opening_call_of_race(pp):
return get_evaluator().evaluate_opening_call_of_race(pp)
def evaluate_final_time_of_race(pp):
return get_evaluator().evaluate_final_time_of_race(pp)
def evaluate_closing_for_starter(pp):
return get_evaluator().evaluate_closing_for_starter(pp)
def get_closing_time_for_starter(pp):
return get_evaluator().get_closing_time_for_starter(pp)
def get_fractions_for_starter(pp):
return get_evaluator().get_fractions_for_starter(pp)
if __name__ == '__main__':
from card_loader import race_feed
for race in race_feed():
for starter in race.starters:
for pp in starter.past_performances:
print fancy_format(evaluate_opening_call_of_race(pp), 'OPEN'),
print evaluate_opening_call_of_race(pp),
print fancy_format(evaluate_final_time_of_race(pp), 'FINAL'),
print evaluate_final_time_of_race(pp),
print evaluate_closing_for_starter(pp)
if distance_metrics is None:
distance_metrics = DistanceMetrics()
assert(None is not distance_metrics)
return distance_metrics
def evaluate_opening_call_of_race(pp):
return get_evaluator().evaluate_opening_call_of_race(pp)
def evaluate_final_time_of_race(pp):
return get_evaluator().evaluate_final_time_of_race(pp)
def evaluate_closing_for_starter(pp):
return get_evaluator().evaluate_closing_for_starter(pp)
def get_closing_time_for_starter(pp):
return get_evaluator().get_closing_time_for_starter(pp)
def get_fractions_for_starter(pp):
return get_evaluator().get_fractions_for_starter(pp)
if __name__ == '__main__':
from card_loader import race_feed
for race in race_feed():
for starter in race.starters:
for pp in starter.past_performances:
print fancy_format(evaluate_opening_call_of_race(pp), 'OPEN'),
print evaluate_opening_call_of_race(pp),
print fancy_format(evaluate_final_time_of_race(pp), 'FINAL'),
print evaluate_final_time_of_race(pp),
print evaluate_closing_for_starter(pp)
