def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(event.matches, event.year)
if event.year == 2016:
organized_matches = MatchHelper.organizeMatches(event.matches)
match_predictions, match_prediction_stats = PredictionHelper.get_match_predictions(organized_matches['qm'])
ranking_predictions, ranking_prediction_stats = PredictionHelper.get_ranking_predictions(organized_matches['qm'], match_predictions)
matchstats_dict['match_predictions'] = match_predictions
matchstats_dict['match_prediction_stats'] = match_prediction_stats
matchstats_dict['ranking_predictions'] = ranking_predictions
matchstats_dict['ranking_prediction_stats'] = ranking_prediction_stats
if any([v != {} for v in matchstats_dict.values()]):
event.matchstats_json = json.dumps(matchstats_dict)
EventManipulator.createOrUpdate(event)
else:
logging.warn("Matchstat calculation for {} failed!".format(event_key))
template_values = {
'matchstats_dict': matchstats_dict,
}
path = os.path.join(os.path.dirname(__file__), '../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(
event.matches, event.year)
if event.year == 2016:
organized_matches = MatchHelper.organizeMatches(event.matches)
match_predictions, match_prediction_stats = PredictionHelper.get_match_predictions(
organized_matches['qm'])
ranking_predictions, ranking_prediction_stats = PredictionHelper.get_ranking_predictions(
organized_matches['qm'], match_predictions)
matchstats_dict['match_predictions'] = match_predictions
matchstats_dict['match_prediction_stats'] = match_prediction_stats
matchstats_dict['ranking_predictions'] = ranking_predictions
matchstats_dict[
'ranking_prediction_stats'] = ranking_prediction_stats
if any([v != {} for v in matchstats_dict.values()]):
event.matchstats_json = json.dumps(matchstats_dict)
EventManipulator.createOrUpdate(event)
else:
logging.warn(
"Matchstat calculation for {} failed!".format(event_key))
template_values = {
'matchstats_dict': matchstats_dict,
}
path = os.path.join(os.path.dirname(__file__),
'../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(event.matches)
if matchstats_dict != {}:
event.matchstats_json = json.dumps(matchstats_dict)
event.dirty = True # TODO: hacky
EventManipulator.createOrUpdate(event)
else:
logging.warn("Matchstat calculation for {} failed!".format(event_key))
template_values = {"matchstats_dict": matchstats_dict}
path = os.path.join(os.path.dirname(__file__), "../templates/math/event_matchstats_do.html")
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(event.matches)
if matchstats_dict != {}:
event.matchstats_json = json.dumps(matchstats_dict)
event.put()
else:
logging.warn("Matchstat calculation for {} failed!".format(event_key))
template_values = {
'matchstats_dict': matchstats_dict,
}
path = os.path.join(os.path.dirname(__file__), '../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(
event.matches, event.year)
if any([v != {} for v in matchstats_dict.values()]):
pass
else:
logging.warn(
"Matchstat calculation for {} failed!".format(event_key))
matchstats_dict = None
predictions_dict = None
if event.year in {
2016, 2017, 2018, 2019, 2020
} and event.event_type_enum in EventType.SEASON_EVENT_TYPES or event.enable_predictions:
sorted_matches = MatchHelper.play_order_sort_matches(event.matches)
match_predictions, match_prediction_stats, stat_mean_vars = PredictionHelper.get_match_predictions(
sorted_matches)
ranking_predictions, ranking_prediction_stats = PredictionHelper.get_ranking_predictions(
sorted_matches, match_predictions)
predictions_dict = {
'match_predictions': match_predictions,
'match_prediction_stats': match_prediction_stats,
'stat_mean_vars': stat_mean_vars,
'ranking_predictions': ranking_predictions,
'ranking_prediction_stats': ranking_prediction_stats
}
event_insights = EventInsightsHelper.calculate_event_insights(
event.matches, event.year)
event_details = EventDetails(
id=event_key,
matchstats=matchstats_dict,
predictions=predictions_dict,
insights=event_insights,
)
EventDetailsManipulator.createOrUpdate(event_details)
template_values = {
'matchstats_dict': matchstats_dict,
}
if 'X-Appengine-Taskname' not in self.request.headers: # Only write out if not in taskqueue
path = os.path.join(os.path.dirname(__file__),
'../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(event.matches)
if matchstats_dict != {}:
event.matchstats_json = json.dumps(matchstats_dict)
event.put()
else:
logging.warn(
"Matchstat calculation for {} failed!".format(event_key))
template_values = {
'matchstats_dict': matchstats_dict,
}
path = os.path.join(os.path.dirname(__file__),
'../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(event.matches)
if any([v != {} for v in matchstats_dict.values()]):
event.matchstats_json = json.dumps(matchstats_dict)
event.dirty = True # TODO: hacky
EventManipulator.createOrUpdate(event)
else:
logging.warn(
"Matchstat calculation for {} failed!".format(event_key))
template_values = {
'matchstats_dict': matchstats_dict,
}
path = os.path.join(os.path.dirname(__file__),
'../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get(self, event_key):
event = Event.get_by_id(event_key)
matchstats_dict = MatchstatsHelper.calculate_matchstats(event.matches, event.year)
if any([v != {} for v in matchstats_dict.values()]):
pass
else:
logging.warn("Matchstat calculation for {} failed!".format(event_key))
matchstats_dict = None
predictions_dict = None
if event.year in {2016, 2017, 2018} and event.event_type_enum in EventType.SEASON_EVENT_TYPES or event.enable_predictions:
sorted_matches = MatchHelper.play_order_sort_matches(event.matches)
match_predictions, match_prediction_stats, stat_mean_vars = PredictionHelper.get_match_predictions(sorted_matches)
ranking_predictions, ranking_prediction_stats = PredictionHelper.get_ranking_predictions(sorted_matches, match_predictions)
predictions_dict = {
'match_predictions': match_predictions,
'match_prediction_stats': match_prediction_stats,
'stat_mean_vars': stat_mean_vars,
'ranking_predictions': ranking_predictions,
'ranking_prediction_stats': ranking_prediction_stats
}
event_insights = EventInsightsHelper.calculate_event_insights(event.matches, event.year)
event_details = EventDetails(
id=event_key,
matchstats=matchstats_dict,
predictions=predictions_dict,
insights=event_insights,
)
EventDetailsManipulator.createOrUpdate(event_details)
template_values = {
'matchstats_dict': matchstats_dict,
}
if 'X-Appengine-Taskname' not in self.request.headers: # Only write out if not in taskqueue
path = os.path.join(os.path.dirname(__file__), '../templates/math/event_matchstats_do.html')
self.response.out.write(template.render(path, template_values))
def get_match_predictions(cls, matches):
if not matches:
return None, None
event_key = matches[0].event
event = event_key.get()
# Setup
team_list, team_id_map = MatchstatsHelper.build_team_mapping(matches)
last_event_stats = MatchstatsHelper.get_last_event_stats(team_list, event_key)
Minv = MatchstatsHelper.build_Minv_matrix(matches, team_id_map)
init_stats_sums = defaultdict(int)
init_stats_totals = defaultdict(int)
for _, stats in last_event_stats.items():
for stat, stat_value in stats.items():
init_stats_sums[stat] += stat_value
init_stats_totals[stat] += 1
init_stats_default = defaultdict(int)
for stat, stat_sum in init_stats_sums.items():
init_stats_default[stat] = float(stat_sum) / init_stats_totals[stat]
relevant_stats = [
'oprs',
'2016autoPointsOPR',
'2016crossingsOPR',
'2016bouldersOPR'
]
# Make predictions before each match
predictions = {}
played_matches = 0
played_matches_75 = 0
correct_predictions = 0
correct_predictions_75 = 0
score_differences = []
stats_sum = defaultdict(int)
for i, match in enumerate(matches):
# Calculate ixOPR
all_ixoprs = {}
for stat in relevant_stats:
all_ixoprs[stat] = MatchstatsHelper.calc_stat(
matches, team_list, team_id_map, Minv, stat,
init_stats=last_event_stats,
init_stats_default=init_stats_default[stat],
limit_matches=i)
for _ in xrange(2):
for stat in relevant_stats:
start = time.time()
all_ixoprs[stat] = MatchstatsHelper.calc_stat(
matches, team_list, team_id_map, Minv, stat,
init_stats=all_ixoprs,
init_stats_default=init_stats_default[stat],
limit_matches=i)
# Make prediction
is_champs = event.event_type_enum in EventType.CMP_EVENT_TYPES
prediction = cls._predict_match(match, all_ixoprs, is_champs)
predictions[match.key.id()] = prediction
# Benchmark prediction
if match.has_been_played:
played_matches += 1
if prediction['prob'] > 75:
played_matches_75 += 1
if match.winning_alliance == prediction['winning_alliance']:
correct_predictions += 1
if prediction['prob'] > 75:
correct_predictions_75 += 1
for alliance_color in ['red', 'blue']:
score_differences.append(abs(match.alliances[alliance_color]['score'] - prediction[alliance_color]['score']))
# Update init_stats
if match.has_been_played:
for alliance_color in ['red', 'blue']:
stats_sum['score'] += match.alliances[alliance_color]['score']
for stat in relevant_stats:
if stat == '2016autoPointsOPR':
init_stats_default[stat] += match.score_breakdown[alliance_color]['autoPoints']
elif stat == '2016bouldersOPR':
init_stats_default[stat] += (
match.score_breakdown[alliance_color]['autoBouldersLow'] +
match.score_breakdown[alliance_color]['autoBouldersHigh'] +
match.score_breakdown[alliance_color]['teleopBouldersLow'] +
match.score_breakdown[alliance_color]['teleopBouldersHigh'])
elif stat == '2016crossingsOPR':
init_stats_default[stat] += (
match.score_breakdown[alliance_color]['position1crossings'] +
match.score_breakdown[alliance_color]['position2crossings'] +
match.score_breakdown[alliance_color]['position3crossings'] +
match.score_breakdown[alliance_color]['position4crossings'] +
match.score_breakdown[alliance_color]['position5crossings'])
init_stats_default['oprs'] = float(stats_sum['score']) / (i + 1) / 6 # Initialize with 1/3 of average scores (2 alliances per match)
for stat in relevant_stats:
if stat != 'oprs':
init_stats_default[stat] = float(stats_sum[stat]) / (i + 1) / 6 # Initialize with 1/3 of average scores (2 alliances per match)
prediction_stats = {
'wl_accuracy': None if played_matches == 0 else 100 * float(correct_predictions) / played_matches,
'wl_accuracy_75': None if played_matches_75 == 0 else 100 * float(correct_predictions_75) / played_matches_75,
'err_mean': np.mean(score_differences) if score_differences else None,
'err_var': np.var(score_differences) if score_differences else None,
}
return predictions, prediction_stats
def get_match_predictions(cls, matches):
if not matches:
return None, None
event_key = matches[0].event
event = event_key.get()
# Setup
team_list, team_id_map = MatchstatsHelper.build_team_mapping(matches)
last_event_stats = MatchstatsHelper.get_last_event_stats(
team_list, event_key)
Minv = MatchstatsHelper.build_Minv_matrix(matches, team_id_map)
init_stats_sums = defaultdict(int)
init_stats_totals = defaultdict(int)
for _, stats in last_event_stats.items():
for stat, stat_value in stats.items():
init_stats_sums[stat] += stat_value
init_stats_totals[stat] += 1
init_stats_default = defaultdict(int)
for stat, stat_sum in init_stats_sums.items():
init_stats_default[stat] = float(
stat_sum) / init_stats_totals[stat]
relevant_stats = [
'oprs', '2016autoPointsOPR', '2016crossingsOPR', '2016bouldersOPR'
]
# Make predictions before each match
predictions = {}
played_matches = 0
played_matches_75 = 0
correct_predictions = 0
correct_predictions_75 = 0
score_differences = []
stats_sum = defaultdict(int)
for i, match in enumerate(matches):
# Calculate ixOPR
all_ixoprs = {}
for stat in relevant_stats:
all_ixoprs[stat] = MatchstatsHelper.calc_stat(
matches,
team_list,
team_id_map,
Minv,
stat,
init_stats=last_event_stats,
init_stats_default=init_stats_default[stat],
limit_matches=i)
for _ in xrange(2):
for stat in relevant_stats:
start = time.time()
all_ixoprs[stat] = MatchstatsHelper.calc_stat(
matches,
team_list,
team_id_map,
Minv,
stat,
init_stats=all_ixoprs,
init_stats_default=init_stats_default[stat],
limit_matches=i)
# Make prediction
is_champs = event.event_type_enum in EventType.CMP_EVENT_TYPES
prediction = cls._predict_match(match, all_ixoprs, is_champs)
predictions[match.key.id()] = prediction
# Benchmark prediction
if match.has_been_played:
played_matches += 1
if prediction['prob'] > 75:
played_matches_75 += 1
if match.winning_alliance == prediction['winning_alliance']:
correct_predictions += 1
if prediction['prob'] > 75:
correct_predictions_75 += 1
for alliance_color in ['red', 'blue']:
score_differences.append(
abs(match.alliances[alliance_color]['score'] -
prediction[alliance_color]['score']))
# Update init_stats
if match.has_been_played and match.score_breakdown:
for alliance_color in ['red', 'blue']:
stats_sum['score'] += match.alliances[alliance_color][
'score']
for stat in relevant_stats:
if stat == '2016autoPointsOPR':
init_stats_default[stat] += match.score_breakdown[
alliance_color]['autoPoints']
elif stat == '2016bouldersOPR':
init_stats_default[stat] += (
match.score_breakdown[alliance_color]
['autoBouldersLow'] +
match.score_breakdown[alliance_color]
['autoBouldersHigh'] +
match.score_breakdown[alliance_color]
['teleopBouldersLow'] +
match.score_breakdown[alliance_color]
['teleopBouldersHigh'])
elif stat == '2016crossingsOPR':
init_stats_default[stat] += (
match.score_breakdown[alliance_color]
['position1crossings'] +
match.score_breakdown[alliance_color]
['position2crossings'] +
match.score_breakdown[alliance_color]
['position3crossings'] +
match.score_breakdown[alliance_color]
['position4crossings'] +
match.score_breakdown[alliance_color]
['position5crossings'])
init_stats_default['oprs'] = float(stats_sum['score']) / (
i + 1
) / 6 # Initialize with 1/3 of average scores (2 alliances per match)
for stat in relevant_stats:
if stat != 'oprs':
init_stats_default[stat] = float(stats_sum[stat]) / (
i + 1
) / 6 # Initialize with 1/3 of average scores (2 alliances per match)
prediction_stats = {
'wl_accuracy':
None if played_matches == 0 else 100 * float(correct_predictions) /
played_matches,
'wl_accuracy_75':
None if played_matches_75 == 0 else 100 *
float(correct_predictions_75) / played_matches_75,
'err_mean':
np.mean(score_differences) if score_differences else None,
'err_var':
np.var(score_differences) if score_differences else None,
}
return predictions, prediction_stats
