def revise_history(PvA_df, web_df, weight_factor, date, HCA, stdev):
yesterday = max(web_df['Date'])
planned_picklehood = False
while date < yesterday:
planned_picklehood = True
date += timedelta(days=1)
# updata is an updated, temporary copy of the data trawled from the web. In addition to the trawled data it contains a weighting factor. It is temporary because we will be going back in time to make historical predictions, and we want the weights to be what they would actually have been back in the day
updata = introduce_weighting(web_df, weight_factor, date) # add weights column
PwrRt = PowerRater(updata[updata['Date']<date]).power_ratings
todays_data = updata[updata['Date']==date]
for i in xrange(todays_data.shape[0]):
home = todays_data.iloc[i]['Home']
away = todays_data.iloc[i]['Away']
prob = predict(todays_data.iloc[i], PwrRt, HCA, stdev)
outcome = [0,1][todays_data.iloc[i]['Home Score']>todays_data.iloc[i]['Away Score']]
PvA_df.loc[PvA_df.shape[0]] = (date, away, home, prob, outcome)
return PvA_df, planned_picklehood
coredump(website, csv_file)
#load data from csv as a pandas DataFrame
data = get_trawled_data(csv_file, ['Date', 'Away', 'Away Score', 'Home', 'Home Score'])
# compile a list of historical predictions and actual outcomes
history_file = 'Predictive_outcomes_2015'
past_predictions = historical(data, weight_factor, history_file, HCA, sigma)
# get the fit parameters needed to correct errors in the historical model
beta_correct = model_the_model(past_predictions)
print 'Checking on the model parameters: %s' % beta_correct
# make predictions
todays_schedule = pickle.load(open('Today', 'rb'))
data = introduce_weighting(data, weight_factor,home_court_advantage=HCA) # add weights column
PwrRt = PowerRater(data).power_ratings # generate latest ratings
print PwrRt.sort_values(by='rating', ascending=False)
prob = []
for i in xrange(todays_schedule.shape[0]):
prob.append(predict(todays_schedule.iloc[i], PwrRt, HCA, sigma))
todays_schedule['Prob'] = prob
# pull in odds
odds = read_odds('Odds.csv', todays_schedule)
# determine optimal betting strategy
print wager(odds)
# pull in 538 predictions
