def brier(model, surface, sy, ey, ts, r):
matches = db_conn.all_matches_year(sy, ey, surface)
c = 0
prob = 0
for match in matches:
end_date = match[3] + datetime.timedelta(days=-1)
start_date = end_date + datetime.timedelta(days=-r)
if not Player.end_year == end_date.__str__():
Player.start_year = start_date.__str__()
Player.end_year = end_date.__str__()
Player.surface = surface
Player.clean_cached_players()
p = Prediction(match[0], match[1], model, bo=3, ts=ts)
p.predict_set_score()
pbp = match[5]
pbp = pbp.split('.')
actual_length = 0
for pbp_ in pbp:
actual_length += pbp_.split(';').__len__()
if p.match_winner_prob['p1'] > 1 or p.match_winner_prob['p1'] < 0:
continue
if not np.isnan(p.match_winner_prob['p1']):
c += 1
prob += (p.match_length_mean - actual_length)**2
return [c, prob]
def brier(models, surface, sy, ey, ts, r):
matches = db_conn.all_matches_year(sy, ey, surface)
rs = {}
for model in models:
rs[model] = [0, 0, 0]
for match in matches:
end_date = match[3] + datetime.timedelta(days=-1)
start_date = end_date + datetime.timedelta(days=-r)
if not Player.end_year == end_date.__str__():
Player.start_year = start_date.__str__()
Player.end_year = end_date.__str__()
Player.surface = surface
Player.clean_cached_players()
for model in models:
p = Prediction(match[0], match[1], model, bo=3, ts=ts)
p.predict_set_score()
if p.match_winner_prob['p1'] > 1 or p.match_winner_prob['p1'] < 0:
print p.match_winner_prob['p1']
continue
if not np.isnan(p.match_winner_prob['p1']):
rs[model][0] += 1
if match[2] == 1:
if p.match_winner_prob['p1'] > 0.5:
rs[model][1] += 1
rs[model][2] += (1 - p.match_winner_prob['p1'])**2
else:
if p.match_winner_prob['p2'] > 0.5:
rs[model][1] += 1
rs[model][2] += (1 - p.match_winner_prob['p2'])**2
return rs
def brier(model, surface, sy, ey, mixture, ts):
matches = db_conn.all_matches_year(sy, ey, surface)
c = 0
prob = 0
right = 0
for match in matches:
end_date = match[3] + datetime.timedelta(days=-1)
start_date = end_date + datetime.timedelta(days=-365)
if not Player.end_year == end_date.__str__():
Player.start_year = start_date.__str__()
Player.end_year = end_date.__str__()
Player.surface = surface
Player.clean_cached_players()
p = PredictionSetend(match[0],
match[1],
model,
points_played=match[5],
mixture=mixture,
ts=ts)
p.predict_set_score()
# print p.match_winner_prob['p1'], p.match_winner_prob['p2'], match
if not np.isnan(p.match_winner_prob['p1']):
c += 1
if match[2] == 1:
if p.match_winner_prob['p1'] > 0.5:
right += 1
prob += (1 - p.match_winner_prob['p1'])**2
else:
if p.match_winner_prob['p2'] > 0.5:
right += 1
prob += (1 - p.match_winner_prob['p2'])**2
return [c, right, prob]
def brier(model, surface, sy, ey, ts, r):
matches = db_conn.all_matches_year(sy, ey, surface)
c = 0
prob = 0
right = 0
for match in matches:
end_date = match[3] + datetime.timedelta(days=-1)
start_date = end_date + datetime.timedelta(days=-r)
if not Player.end_year == end_date.__str__():
Player.start_year = start_date.__str__()
Player.end_year = end_date.__str__()
Player.surface = surface
Player.clean_cached_players()
winner = match[2]
score = match[4]
set_length = score.split().__len__()
ms = ''
if winner == 1:
if set_length == 3:
ms = '2:1'
else:
ms = '2:0'
else:
if set_length == 3:
ms = '1:2'
else:
ms = '0:2'
p = Prediction(match[0], match[1], model, bo=3, ts=ts)
p.predict_set_score()
ms_20 = p.match_score_prob['2:0']
ms_21 = p.match_score_prob['2:1']
ms_02 = p.match_score_prob['0:2']
ms_12 = p.match_score_prob['1:2']
if not np.isnan(p.match_winner_prob['p1']):
c += 1
prob += (1 - p.match_score_prob[ms])**2
if ms == '2:0' and ms_20 >= ms_21 and ms_20 >= ms_02 and ms_20 >= ms_12:
right += 1
if ms == '2:1' and ms_21 >= ms_20 and ms_21 >= ms_02 and ms_21 >= ms_12:
right += 1
if ms == '1:2' and ms_12 >= ms_21 and ms_12 >= ms_02 and ms_12 >= ms_20:
right += 1
if ms == '0:2' and ms_02 >= ms_20 and ms_02 >= ms_21 and ms_02 >= ms_12:
right += 1
return [c, right, prob]
Player.surface = 'hard' match_baseline_s = [] match_impact_s = [] set_baseline_s = [] set_impact_s = [] match_baseline_r = [] match_impact_r = [] set_baseline_r = [] set_impact_r = [] Player.end_year = '2012-12-31' Player.start_year = '2012-01-01' Player.clean_cached_players() p = Player(player) Player.calculate_all_ts() match_baseline_s.append(p.match_baseline_ts.serv_mean) match_impact_s.append(p.match_impact_ts.serv_mean) set_baseline_s.append(p.set_baseline_3_ts[set_level].serv_mean) set_impact_s.append(p.set_impact_3_ts[set_level].serv_mean) match_baseline_r.append(p.match_baseline_ts.retn_mean) match_impact_r.append(p.match_impact_ts.retn_mean) set_baseline_r.append(p.set_baseline_3_ts[set_level].retn_mean) set_impact_r.append(p.set_impact_3_ts[set_level].retn_mean) Player.end_year = '2013-12-31' Player.start_year = '2013-01-01'