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))
def run_weighted(ids, filename, verbose=False):
print "Calculated weighted scores..."
salaries = load_salaries(filename)
weighted_scores = get_weighted_scores(ids)
sorted_scores = sorted(weighted_scores.items(), key=lambda x: x[1],
reverse=True)
for player, score in sorted_scores:
if player in salaries:
print ('\t%s\t%.4f\t%d\t%s' % (player[:15], score[0], score[1],
salaries[player]))
elif verbose:
print '\t%s\t%.4f\t%d' % (player[:15], score[0], score[1])
def run_deltas(curr_ids, prev_ids, filename, verbose=False):
print "Calculating deltas..."
scores_curr = get_weighted_scores(curr_ids)
scores_prev = get_weighted_scores(prev_ids)
deltas = {}
for player in scores_curr:
if player in scores_curr and player in scores_prev:
deltas[player] = (scores_curr[player][0] - scores_prev[player][0],
scores_curr[player][1])
salaries = load_salaries(filename)
sorted_deltas = sorted(deltas.items(), key=lambda x: x[1], reverse=True)
for player, delta in sorted_deltas:
if delta[0] > 0:
if player in salaries:
print ('\t%s\t%.4f\t%d\t%s' % (player[:15], delta[0], delta[1],
salaries[player]))
elif verbose:
print '\t%s\t%.4f\t%d' % (player[:15], delta[0], delta[1])
return deltas
for player in sorted(players)]
print '%s %s' % (score, salary)
print '\t%s' % (', '.join(player_strs[:4]))
print '\t%s' % (', '.join(player_strs[-4:]))
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--salaries', type=str)
parser.add_argument('-p', '--prefill', type=str,
help=('Quoted list of comma-separated player names'
' (e.g. "Stephen Curry, James Harden")'))
parser.add_argument('-i', '--ignore', type=str,
help=('Quoted list of comma-separated player names'
' (e.g. "Stephen Curry, James Harden")'))
parser.add_argument('-m', '--min_score', type=float)
args = parser.parse_args()
if args.salaries:
print 'Loading score data...'
scores = get_scores_with_freq(get_ids(limit=7), min_games=3)
salaries = load_salaries(args.salaries)
prefill_players = ([x.strip() for x in args.prefill.split(',')]
if args.prefill else [])
ignore_players = ([x.strip() for x in args.ignore.split(',')]
if args.ignore else [])
min_score = args.min_score if args.min_score != None else -1
generate(scores, salaries, prefill_players=prefill_players,
ignore_players=ignore_players, min_score=min_score)
else:
print 'Unable to run script: requires a --salaries argument'
parser.add_argument('-s', '--salaries', type=str)
parser.add_argument('-p',
'--prefill',
type=str,
help=('Quoted list of comma-separated player names'
' (e.g. "Stephen Curry, James Harden")'))
parser.add_argument('-i',
'--ignore',
type=str,
help=('Quoted list of comma-separated player names'
' (e.g. "Stephen Curry, James Harden")'))
parser.add_argument('-m', '--min_score', type=float)
args = parser.parse_args()
if args.salaries:
print 'Loading score data...'
scores = get_scores_with_freq(get_ids(limit=7), min_games=3)
salaries = load_salaries(args.salaries)
prefill_players = ([x.strip() for x in args.prefill.split(',')]
if args.prefill else [])
ignore_players = ([x.strip() for x in args.ignore.split(',')]
if args.ignore else [])
min_score = args.min_score if args.min_score != None else -1
generate(scores,
salaries,
prefill_players=prefill_players,
ignore_players=ignore_players,
min_score=min_score)
else:
print 'Unable to run script: requires a --salaries argument'
salary_means = {}
for result in results[start:end]:
for position in positions.keys():
positions[position].append(result[position_index_map[position]])
for position, players in positions.iteritems():
pos_salaries = [salaries[player][0] for player in players]
salary_means[position] = numpy.mean(pos_salaries)
return salary_means
def run_and_plot(intervals, salaries):
POSITIONS = ['PG', 'SG', 'SF', 'PF', 'C', 'F', 'G', 'UTIL']
data = []
for i, end in enumerate(intervals[1:]):
start = intervals[i]
print start, end
data.append(run('results/contest-standings-13876064.csv',
salaries, start=start, end=end))
lines = {}
for pos in POSITIONS:
lines[pos], = plt.plot(intervals[1:], [x[pos] for x in data], label=pos)
plt.legend([lines[pos] for pos in POSITIONS], POSITIONS)
plt.show()
if __name__=='__main__':
salaries = load_salaries('salaries/dk_nba_salaries_2015_11_07.csv')
for salary in sorted(salaries.items(), key=lambda x: x[1][0], reverse=True)[:20]:
print salary
run_and_plot(range(0, 1001, 100), salaries)
run_and_plot(range(0, 10001, 1000), salaries)
run_and_plot(range(0, 100001, 10000), salaries)