def test_case_optimize_parameters():
player_data, player_stats_data, team_data, match_data = first_data_preparation(
)
countries = [
'France',
]
min_date = '2013-07-31'
max_date = '2014-07-31'
match_data = match_data.loc[match_data['league_country'].isin(countries)]
match_data = match_data[match_data['date'] >= min_date]
match_data = match_data[match_data['date'] < max_date]
mask_home = team_data['team_api_id'].isin(match_data['home_team_api_id'])
mask_away = team_data['team_api_id'].isin(match_data['away_team_api_id'])
team_universe = list(team_data[mask_home | mask_away]['team_long_name'])
# print(len(team_universe), team_universe)
# print('nb matches', match_data.shape[0])
# on the below: non effective way to use team names as id (easier for human-checking and debugging)
team_id_to_name, team_name_to_id = create_dict_involved_teams(
match_data, team_data)
match_data['home_team_id'] = match_data.apply(
lambda x: team_id_to_name[x['home_team_api_id']], axis=1)
match_data['away_team_id'] = match_data.apply(
lambda x: team_id_to_name[x['away_team_api_id']], axis=1)
model = DixonColes(team_universe)
# print("... fit dixon coles parameters ...")
opti_params = model.optimize_parameters(match_data,
max_date,
verbose=VERBOSE,
home_goals_key='home_team_goal',
away_goals_key='away_team_goal')
def test_dixon_coles_multi_countries_prediction():
player_data, player_stats_data, team_data, match_data = first_data_preparation(
)
countries = ['France', 'England', 'Germany', 'Spain', 'Italy']
min_date_str = '2013-07-31'
# filter by countries
mask_countries = match_data['league_country'].isin(countries)
match_data = match_data.loc[mask_countries]
# convert date input string to actual python date
match_data['date'] = match_data.apply(
lambda x: datetime.strptime(x['date'], "%Y-%m-%d %H:%M:%S").date(),
axis=1)
# on the below: non effective way to use team names as id (easier for human-checking and debugging)
team_id_to_name, team_name_to_id = create_dict_involved_teams(
match_data, team_data)
match_data['home_team_id'] = match_data.apply(
lambda x: team_id_to_name[x['home_team_api_id']], axis=1)
match_data['away_team_id'] = match_data.apply(
lambda x: team_id_to_name[x['away_team_api_id']], axis=1)
# save full match history
full_history = match_data
# filter on recent matchs only (to make predictions on them)
min_date = datetime.strptime(min_date_str, "%Y-%m-%d").date()
mask_date = match_data['date'] >= min_date
matches_to_predict = match_data[mask_date]
print("nb matches full history", full_history.shape[0])
print("nb matches_to_predict", matches_to_predict.shape[0])
# define weight fct (default is one)
dixon_coles_params = {
'weight_fct': lambda t1, t2: np.exp(-0.3 * (t2 - t1).days / 365.25)
}
predictions = dixon_coles_predictions(
matches_to_predict,
full_history,
dixon_coles_params=dixon_coles_params,
verbose=1)
# save predictions
# predictions.to_csv("D:/Football_betting/predictions/" + "dixon_coles_predictions.csv", index=False)
np.savetxt("D:/Football_betting/predictions/" +
"dixon_coles_predictions.csv",
predictions,
delimiter=',',
fmt='%.6e')
def test_case_dixon_coles_one_country_predictions():
player_data, player_stats_data, team_data, match_data = first_data_preparation(
)
countries = [
'France',
]
# countries = ['England', ]
min_date = datetime.strptime('2016-04-30', "%Y-%m-%d").date()
mask_countries = match_data['league_country'].isin(countries)
match_data = match_data[mask_countries]
# input(match_data['league_country'].unique())
# convert date input string to actual python date
match_data['date'] = match_data.apply(
lambda x: datetime.strptime(x['date'], "%Y-%m-%d %H:%M:%S").date(),
axis=1)
# on the below: non effective way to use team names as id (easier for human-checking and debugging)
team_id_to_name, team_name_to_id = create_dict_involved_teams(
match_data, team_data)
match_data['home_team_id'] = match_data.apply(
lambda x: team_id_to_name[x['home_team_api_id']], axis=1)
match_data['away_team_id'] = match_data.apply(
lambda x: team_id_to_name[x['away_team_api_id']], axis=1)
# on the below: we define our team universe (teams we calibrate parameters on)
mask_home = team_data['team_api_id'].isin(match_data['home_team_api_id'])
mask_away = team_data['team_api_id'].isin(match_data['away_team_api_id'])
team_universe = list(team_data[mask_home | mask_away]['team_long_name'])
printv(1, VERBOSE, len(team_universe), team_universe)
printv(1, VERBOSE, 'nb matches', match_data.shape[0])
# save full_history before selecting recent matches to predict
full_history = match_data
mask_date = match_data['date'] >= min_date
match_data = match_data[mask_date]
exp_weight_fct = lambda t1, t2: np.exp(-0.3 * (t2 - t1).days / 365.25)
model = DixonColes(team_universe, weight_fct=exp_weight_fct)
printv(1, VERBOSE,
" ... fit dixon coles parameters and predict match outcomes ... ")
predictions, param_histo = model.fit_and_predict(
match_data,
full_history,
nb_obs_years=1,
verbose=VERBOSE,
home_goals_key='home_team_goal',
away_goals_key='away_team_goal')
printv(1, VERBOSE, " ... match outcomes predicted ... ")
match_outcomes = match_outcomes_hot_vectors(match_data)
bkm_quotes = pd.DataFrame()
bkm_quotes['W'], bkm_quotes['D'], bkm_quotes['L'] = match_data[
'B365H'], match_data['B365D'], match_data['B365A']
analysis = analyze_predictions(match_outcomes,
predictions,
bkm_quotes,
verbose=VERBOSE,
nb_max_matchs_displayed=40,
fully_labelled_matches=match_data)
model_log_loss, model_rps, (log_loss_comparison_l,
rps_comparison_l) = analysis
remove_nan_mask = [
not contain_nan(bkm_quotes.iloc[i]) for i in range(bkm_quotes.shape[0])
]
bkm_quotes_r = bkm_quotes.iloc[remove_nan_mask]
match_outcomes_r = match_outcomes.iloc[remove_nan_mask]
predictions_r = predictions[remove_nan_mask]
constant_invest_stgy = ConstantAmountInvestStrategy(
1.) # invest 1 in each match (if expected return > 1% actually)
constant_sigma_invest_stgy = ConstantStdDevInvestStrategy(
0.01) # stdDev of each bet is 1% of wealth
kelly_invest_stgy = KellyInvestStrategy(
) # Kelly's ratio investment to maximize's wealth long term return
constant_percent_stgy = ConstantPercentInvestStrategy(
0.01) # invest 1% of money each time
for invest_stgy in [
constant_invest_stgy, constant_sigma_invest_stgy,
kelly_invest_stgy, constant_percent_stgy
]:
printv(1, VERBOSE, "\n#### results for ",
invest_stgy.__class__.__name__, "####")
init_wealth = 100
df_recap_stgy = invest_stgy.apply_invest_strategy(
predictions_r,
bkm_quotes_r,
match_outcomes_r,
init_wealth=init_wealth)
printv(
1, VERBOSE, df_recap_stgy[[
'invested_amounts', 'exp_gain_amounts', 'gain_amounts'
]].sum())
printv(1, VERBOSE, 'wealth: from', init_wealth, 'to',
round(df_recap_stgy['wealth'].iloc[-1], 4))