# print '1:1'
# print serv
# print retn
#
# print np.nanmean(serv)
# print np.nanmean(retn)
#
# print np.nanstd(serv)
# print np.nanstd(retn)
#
#
Player('Andy Murray')
Player('Novak Djokovic')
Player.calculate_all_ts()
for p in Player.cached_players:
if -1 in Player.cached_players[p].game_baseline.keys():
if Player.cached_players[p].match_baseline.serv_counts > 10:
if Player.cached_players[p].game_baseline[-1].serv_counts > 50:
if Player.cached_players[
p].match_baseline.serv_mean < Player.cached_players[
p].game_baseline[-1].serv_mean:
if Player.cached_players[
p].match_baseline.retn_mean < Player.cached_players[
p].game_baseline[-1].retn_mean:
if Player.cached_players[
p].match_impact.serv_mean > Player.cached_players[
p].game_impact[-1].serv_mean:
if Player.cached_players[
def get_multi_player():
start_year = int(request.args.get('start_year'))
end_year = int(request.args.get('end_year'))
surface = request.args.get('surface')
two_step = request.args.get('two_step')
if (start_year != Player.start_year) or (end_year != Player.end_year) or (surface != Player.surface):
Player.cached_players = {}
Player.start_year = start_year
Player.end_year = end_year
Player.surface = surface
player_names = request.args.get('player_name')
player_names = player_names.split(',')
metrics_level = request.args.get('metrics_level')
baseline_impact = request.args.get('baseline_impact')
serve_return = request.args.get('serve_return')
players = {}
for player_name in player_names:
players[player_name] = Player(player_name)
if two_step == 'true':
Player.calculate_all_ts()
all_players_stats = {}
# grand 5 sets missing
if metrics_level == 'set':
for player in players:
if two_step == 'true':
if baseline_impact == 'baseline':
player_stat = players[player].set_baseline_3_ts
player_match_stat = players[player].match_baseline_ts
elif baseline_impact == 'impact':
player_stat = players[player].set_impact_3_ts
player_match_stat = players[player].match_impact_ts
else:
if baseline_impact == 'baseline':
player_stat = players[player].set_baseline_3
player_match_stat = players[player].match_baseline
elif baseline_impact == 'impact':
player_stat = players[player].set_impact_3
player_match_stat = players[player].match_impact
if serve_return == 'serve':
player_stats = [player_match_stat.serv_mean,
player_stat['0:0'].serv_mean, player_stat['1:0'].serv_mean, player_stat['0:1'].serv_mean,
player_stat['1:1'].serv_mean]
player_stats_ci = [player_match_stat.serv_std * stats.t._ppf(0.9,player_match_stat.serv_counts),
player_stat['0:0'].serv_std * stats.t._ppf(0.9,player_stat['0:0'].serv_counts),
player_stat['1:0'].serv_std * stats.t._ppf(0.9,player_stat['1:0'].serv_counts),
player_stat['0:1'].serv_std * stats.t._ppf(0.9,player_stat['0:1'].serv_counts),
player_stat['1:1'].serv_std * stats.t._ppf(0.9,player_stat['1:1'].serv_counts)]
elif serve_return == 'return':
player_stats = [player_match_stat.retn_mean,
player_stat['0:0'].retn_mean, player_stat['1:0'].retn_mean, player_stat['0:1'].retn_mean,
player_stat['1:1'].retn_mean]
player_stats_ci = [player_match_stat.retn_std * stats.t._ppf(0.9, player_match_stat.retn_counts),
player_stat['0:0'].retn_std * stats.t._ppf(0.9, player_stat['0:0'].retn_counts),
player_stat['1:0'].retn_std * stats.t._ppf(0.9, player_stat['1:0'].retn_counts),
player_stat['0:1'].retn_std * stats.t._ppf(0.9, player_stat['0:1'].retn_counts),
player_stat['1:1'].retn_std * stats.t._ppf(0.9, player_stat['1:1'].retn_counts)]
for x in range(player_stats.__len__()):
if np.isnan(player_stats[x]):
player_stats[x] = float(0.0)
for x in range(player_stats_ci.__len__()):
if np.isnan(player_stats_ci[x]):
player_stats_ci[x] = float(0.0)
all_players_stats[player] = {'mean':player_stats,'ci':player_stats_ci}
header = ['match', '0:0', '1:0', '0:1', '1:1']
elif metrics_level == 'game':
for player in players:
if two_step == 'true':
if baseline_impact == 'baseline':
player_stat = players[player].game_baseline_ts
player_match_stat = players[player].match_baseline_ts
elif baseline_impact == 'impact':
player_stat = players[player].game_impact_ts
player_match_stat = players[player].match_impact_ts
else:
if baseline_impact == 'baseline':
player_stat = players[player].game_baseline
player_match_stat = players[player].match_baseline
elif baseline_impact == 'impact':
player_stat = players[player].game_impact
player_match_stat = players[player].match_impact
if serve_return == 'serve':
player_stats = [player_match_stat.serv_mean,
player_stat[3].serv_mean, player_stat[2].serv_mean, player_stat[1].serv_mean,
player_stat[0].serv_mean, player_stat[-1].serv_mean, player_stat[-2].serv_mean,
player_stat[-3].serv_mean]
player_stats_ci = [player_match_stat.serv_std * stats.t._ppf(0.9, player_match_stat.serv_counts),
player_stat[3].serv_std * stats.t._ppf(0.9, player_stat[3].serv_counts),
player_stat[2].serv_std * stats.t._ppf(0.9, player_stat[2].serv_counts),
player_stat[1].serv_std * stats.t._ppf(0.9, player_stat[1].serv_counts),
player_stat[0].serv_std * stats.t._ppf(0.9, player_stat[0].serv_counts),
player_stat[-1].serv_std * stats.t._ppf(0.9, player_stat[-1].serv_counts),
player_stat[-2].serv_std * stats.t._ppf(0.9, player_stat[-2].serv_counts),
player_stat[-3].serv_std * stats.t._ppf(0.9, player_stat[-3].serv_counts)]
elif serve_return == 'return':
player_stats = [player_match_stat.retn_mean,
player_stat[3].retn_mean, player_stat[2].retn_mean, player_stat[1].retn_mean,
player_stat[0].retn_mean, player_stat[-1].retn_mean, player_stat[-2].retn_mean,
player_stat[-3].retn_mean]
player_stats_ci = [player_match_stat.retn_std * stats.t._ppf(0.9, player_match_stat.retn_counts),
player_stat[3].retn_std * stats.t._ppf(0.9, player_stat[3].retn_counts),
player_stat[2].retn_std * stats.t._ppf(0.9, player_stat[2].retn_counts),
player_stat[1].retn_std * stats.t._ppf(0.9, player_stat[1].retn_counts),
player_stat[0].retn_std * stats.t._ppf(0.9, player_stat[0].retn_counts),
player_stat[-1].retn_std * stats.t._ppf(0.9, player_stat[-1].retn_counts),
player_stat[-2].retn_std * stats.t._ppf(0.9, player_stat[-2].retn_counts),
player_stat[-3].retn_std * stats.t._ppf(0.9, player_stat[-3].retn_counts)]
for x in range(player_stats.__len__()):
if np.isnan(player_stats[x]):
player_stats[x] = float(0.0)
for x in range(player_stats_ci.__len__()):
if np.isnan(player_stats_ci[x]):
player_stats_ci[x] = float(0.0)
all_players_stats[player] = {'mean': player_stats, 'ci': player_stats_ci}
header = ['match', '3', '2', '1', '0', '-1', '-2', '-3']
elif metrics_level == 'point':
for player in players:
if two_step == 'true':
if baseline_impact == 'baseline':
player_stat = players[player].point_baseline_ts
player_match_stat = players[player].match_baseline_ts
elif baseline_impact == 'impact':
player_stat = players[player].point_impact_ts
player_match_stat = players[player].match_impact_ts
else:
if baseline_impact == 'baseline':
player_stat = players[player].point_baseline
player_match_stat = players[player].match_baseline
elif baseline_impact == 'impact':
player_stat = players[player].point_impact
player_match_stat = players[player].match_impact
if serve_return == 'serve':
player_stats = [player_match_stat.serv_mean,
player_stat['0:0'].serv_mean, player_stat['15:15'].serv_mean, player_stat['30:30'].serv_mean,
player_stat['40:40'].serv_mean, player_stat['15:0'].serv_mean, player_stat['30:15'].serv_mean,
player_stat['40:30'].serv_mean, player_stat['AD:40'].serv_mean, player_stat['30:0'].serv_mean,
player_stat['40:15'].serv_mean, player_stat['40:0'].serv_mean, player_stat['0:15'].serv_mean,
player_stat['15:30'].serv_mean, player_stat['30:40'].serv_mean, player_stat['40:AD'].serv_mean,
player_stat['0:30'].serv_mean, player_stat['15:40'].serv_mean, player_stat['0:40'].serv_mean]
player_stats_ci = [player_match_stat.serv_std * stats.t._ppf(0.9, player_match_stat.serv_counts),
player_stat['0:0'].serv_std * stats.t._ppf(0.9, player_stat['0:0'].serv_counts),
player_stat['15:15'].serv_std * stats.t._ppf(0.9, player_stat['15:15'].serv_counts),
player_stat['30:30'].serv_std * stats.t._ppf(0.9, player_stat['30:30'].serv_counts),
player_stat['40:40'].serv_std * stats.t._ppf(0.9, player_stat['40:40'].serv_counts),
player_stat['15:0'].serv_std * stats.t._ppf(0.9, player_stat['15:0'].serv_counts),
player_stat['30:15'].serv_std * stats.t._ppf(0.9, player_stat['30:15'].serv_counts),
player_stat['40:30'].serv_std * stats.t._ppf(0.9, player_stat['40:30'].serv_counts),
player_stat['AD:40'].serv_std * stats.t._ppf(0.9, player_stat['AD:40'].serv_counts),
player_stat['30:0'].serv_std * stats.t._ppf(0.9, player_stat['30:0'].serv_counts),
player_stat['40:15'].serv_std * stats.t._ppf(0.9, player_stat['40:15'].serv_counts),
player_stat['40:0'].serv_std * stats.t._ppf(0.9, player_stat['40:0'].serv_counts),
player_stat['0:15'].serv_std * stats.t._ppf(0.9, player_stat['0:15'].serv_counts),
player_stat['15:30'].serv_std * stats.t._ppf(0.9, player_stat['15:30'].serv_counts),
player_stat['30:40'].serv_std * stats.t._ppf(0.9, player_stat['30:40'].serv_counts),
player_stat['40:AD'].serv_std * stats.t._ppf(0.9, player_stat['40:AD'].serv_counts),
player_stat['0:30'].serv_std * stats.t._ppf(0.9, player_stat['0:30'].serv_counts),
player_stat['15:40'].serv_std * stats.t._ppf(0.9, player_stat['15:40'].serv_counts),
player_stat['0:40'].serv_std * stats.t._ppf(0.9, player_stat['0:40'].serv_counts)]
elif serve_return == 'return':
player_stats = [player_match_stat.retn_mean,
player_stat['0:0'].retn_mean, player_stat['15:15'].retn_mean, player_stat['30:30'].retn_mean,
player_stat['40:40'].retn_mean, player_stat['15:0'].retn_mean, player_stat['30:15'].retn_mean,
player_stat['40:30'].retn_mean, player_stat['AD:40'].retn_mean, player_stat['30:0'].retn_mean,
player_stat['40:15'].retn_mean, player_stat['40:0'].retn_mean, player_stat['0:15'].retn_mean,
player_stat['15:30'].retn_mean, player_stat['30:40'].retn_mean, player_stat['40:AD'].retn_mean,
player_stat['0:30'].retn_mean, player_stat['15:40'].retn_mean, player_stat['0:40'].retn_mean]
player_stats_ci = [player_match_stat.retn_std * stats.t._ppf(0.9, player_match_stat.retn_counts),
player_stat['0:0'].retn_std * stats.t._ppf(0.9, player_stat['0:0'].retn_counts),
player_stat['15:15'].retn_std * stats.t._ppf(0.9, player_stat['15:15'].retn_counts),
player_stat['30:30'].retn_std * stats.t._ppf(0.9, player_stat['30:30'].retn_counts),
player_stat['40:40'].retn_std * stats.t._ppf(0.9, player_stat['40:40'].retn_counts),
player_stat['15:0'].retn_std * stats.t._ppf(0.9, player_stat['15:0'].retn_counts),
player_stat['30:15'].retn_std * stats.t._ppf(0.9, player_stat['30:15'].retn_counts),
player_stat['40:30'].retn_std * stats.t._ppf(0.9, player_stat['40:30'].retn_counts),
player_stat['AD:40'].retn_std * stats.t._ppf(0.9, player_stat['AD:40'].retn_counts),
player_stat['30:0'].retn_std * stats.t._ppf(0.9, player_stat['30:0'].retn_counts),
player_stat['40:15'].retn_std * stats.t._ppf(0.9, player_stat['40:15'].retn_counts),
player_stat['40:0'].retn_std * stats.t._ppf(0.9, player_stat['40:0'].retn_counts),
player_stat['0:15'].retn_std * stats.t._ppf(0.9, player_stat['0:15'].retn_counts),
player_stat['15:30'].retn_std * stats.t._ppf(0.9, player_stat['15:30'].retn_counts),
player_stat['30:40'].retn_std * stats.t._ppf(0.9, player_stat['30:40'].retn_counts),
player_stat['40:AD'].retn_std * stats.t._ppf(0.9, player_stat['40:AD'].retn_counts),
player_stat['0:30'].retn_std * stats.t._ppf(0.9, player_stat['0:30'].retn_counts),
player_stat['15:40'].retn_std * stats.t._ppf(0.9, player_stat['15:40'].retn_counts),
player_stat['0:40'].retn_std * stats.t._ppf(0.9, player_stat['0:40'].retn_counts)]
for x in range(player_stats.__len__()):
if np.isnan(player_stats[x]):
player_stats[x] = float(0.0)
for x in range(player_stats_ci.__len__()):
if np.isnan(player_stats_ci[x]):
player_stats_ci[x] = float(0.0)
all_players_stats[player] = {'mean': player_stats, 'ci': player_stats_ci}
header = ['match', '0:0', '15:15', '30:30', '40:40', '15:0', '30:15', '40:30', 'AD:40', '30:0',
'40:15', '40:0', '0:15', '15:30', '30:40', '40:AD', '0:30', '15:40', '0:40']
return flask.jsonify({'player_stats': all_players_stats, 'header': header})
