def get_weighted_scores(ids):
# sum(log(dollars_per_player_i) / N for i in N)
scores_weighted_all = {}
mean = numpy.mean([i for i, _ in enumerate(ids)])
for i, contest_id in enumerate(ids):
results = load_results('results/contest-standings-%s.csv' % contest_id)
numentries = len(results)
prize_map = get_prize_map(numentries)
print contest_id, numentries
# Weights: [0, ..., 2]
weight = 1 # i / mean
scores_weighted = analyze_results(
results,
lambda x: sum([get_weighted_score(y, prize_map) for y in x])
/ float(len(x)) * weight
)
for player, score in scores_weighted.iteritems():
if player in scores_weighted_all:
scores_weighted_all[player].append(score)
else:
scores_weighted_all[player] = [score]
for player, scores in scores_weighted_all.iteritems():
scores_weighted_all[player] = (
numpy.median(scores),
#numpy.mean(scores),
len(scores)
)
# Return { player: (score, num data) }
return scores_weighted_all
def run():
salaries = load_salaries('salaries/dk_nba_salaries_2015_11_07.csv')
results = load_results('results/contest-standings-13876064.csv')
numentries = len(results)
prize_map = get_prize_map(numentries)
scores_mean = analyze_results(
results, lambda x: numpy.mean(x) / len(results)
)
scores_own = analyze_results(
results, lambda x: len(x) / float(len(results))
)
scores_weighted = analyze_results(
results,
lambda x: sum([get_weighted_score(y, prize_map) for y in x])
/ float(len(x))
)
sorted_mean = sorted(scores_mean.items(), key=lambda x: x[1],
reverse=False)
sorted_own = sorted(scores_own.items(), key=lambda x: x[1], reverse=True)
sorted_weighted = sorted(scores_weighted.items(), key=lambda x: x[1],
reverse=True)
print 'Sorted by score:'
print '\tPlayer\t\tScore\tOwn\tWeighted'
for player, score in sorted_mean[:30]:
if player in salaries:
print ('\t%s\t%.4f\t%.2f\t%.4f\t%s'
% (player[:15], score, scores_own[player] * 100,
scores_weighted[player], salaries[player]))
else:
print ('\t%s\t%.4f\t%.2f\t%.4f'
% (player[:15], score, scores_own[player] * 100,
scores_weighted[player]))
print 'Sorted by ownership:'
print '\tPlayer\t\tScore\tOwn\tWeighted'
for player, own in sorted_own[:30]:
if player in salaries:
print ('\t%s\t%.4f\t%.2f\t%.4f\t%s'
% (player[:15], scores_mean[player], own * 100,
scores_weighted[player], salaries[player]))
else:
print ('\t%s\t%.4f\t%.2f\t%.4f'
% (player[:15], scores_mean[player], own * 100,
scores_weighted[player]))
print 'Sorted by weighted score:'
print '\tPlayer\t\tScore\tOwn\tWeighted'
for player, weighted_score in sorted_weighted[:50]:
if player in salaries:
print ('\t%s\t%.4f\t%.2f\t%.4f\t%s'
% (player[:15], scores_mean[player],
scores_own[player] * 100, weighted_score,
salaries[player]))
else:
print ('\t%s\t%.4f\t%.2f\t%.4f'
% (player[:15], scores_mean[player],
scores_own[player] * 100, weighted_score))
