def betting_stats(self, stat_names=None, window=None):
data = self.game_stats()
data['PACE'] = team_stats.pace(data)
data['POSSESSIONS'] = team_stats.possessions(data)
data['TEAM_OFF_RTG'] = team_stats.off_rating(data)
data['TEAM_DEF_RTG'] = team_stats.def_rating(data)
data['TEAM_NET_RTG'] = data['TEAM_OFF_RTG'] - data['TEAM_DEF_RTG']
data['TEAM_EFG'] = stats.eff_fg_pct(data, 'TEAM_')
data['TEAM_TOV_PCT'] = stats.tov_pct(data, 'TEAM_')
data['TEAM_OREB_PCT'] = team_stats.oreb_pct(data)
data['TEAM_DREB_PCT'] = team_stats.dreb_pct(data)
data['TEAM_FT_PER_FGA'] = stats.ft_per_fga(data, 'TEAM_')
efg = data.TEAM_EFG
oreb = data.TEAM_OREB_PCT
dreb = data.TEAM_DREB_PCT
ftr = data.TEAM_FT_PER_FGA
tov = data.TEAM_TOV_PCT
data[
'TEAM_FOUR_FACTORS'] = 0.4 * efg + 0.2 * oreb + 0.15 * ftr - 0.25 * tov
data[
'TEAM_FOUR_FACTORS_REB'] = 0.4 * efg + 0.1 * oreb + 0.1 * dreb + 0.15 * ftr - 0.25 * tov
if stat_names is None:
stat_names = [
'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'OREB', 'DREB',
'REB', 'AST', 'TOV', 'STL', 'BLK'
]
stat_names = ['TEAM_' + s for s in stat_names] + ['OPP_' + s for s in stat_names] +\
['TEAM_OFF_RTG', 'TEAM_DEF_RTG', 'TEAM_NET_RTG', 'TEAM_EFG', 'TEAM_TOV_PCT',
'TEAM_OREB_PCT', 'TEAM_DREB_PCT', 'TEAM_FT_PER_FGA', 'TEAM_FOUR_FACTORS',
'TEAM_FOUR_FACTORS_REB', 'PACE', 'POSSESSIONS']
data = data[['SEASON', 'GAME_ID', 'TEAM_ID'] + stat_names]
data = self.windowed_stats(data, stat_names, window=window)
games = pd.read_sql(
'SELECT * FROM games JOIN betting ON games.ID is betting.GAME_ID',
self.__conn)
games = games.merge(data,
left_on=['SEASON', 'ID', 'HOME_TEAM_ID'],
right_on=['SEASON', 'GAME_ID', 'TEAM_ID'])
games = games.merge(data,
left_on=['SEASON', 'ID', 'AWAY_TEAM_ID'],
right_on=['SEASON', 'GAME_ID', 'TEAM_ID'],
suffixes=('', '_AWAY'))
games = games[games.HOME_SPREAD_WL != 'P']
return games
def season_stats(self):
query = '''
SELECT SEASON,
TEAM_ID,
AVG(TEAM_MIN) AS TEAM_MIN,
AVG(TEAM_FGM) AS TEAM_FGM,
AVG(TEAM_FGA) AS TEAM_FGA,
AVG(TEAM_FG3M) AS TEAM_FG3M,
AVG(TEAM_FG3A) AS TEAM_FG3A,
AVG(TEAM_FTM) AS TEAM_FTM,
AVG(TEAM_FTA) AS TEAM_FTA,
AVG(TEAM_OREB) AS TEAM_OREB,
AVG(TEAM_DREB) AS TEAM_DREB,
AVG(TEAM_REB) AS TEAM_REB,
AVG(TEAM_AST) AS TEAM_AST,
AVG(TEAM_TOV) AS TEAM_TOV,
AVG(TEAM_STL) AS TEAM_STL,
AVG(TEAM_BLK) AS TEAM_BLK,
AVG(TEAM_PTS) AS TEAM_PTS,
AVG(TEAM_PLUS_MINUS) AS TEAM_PLUS_MINUS,
AVG(OPP_MIN) AS OPP_MIN,
AVG(OPP_FGM) AS OPP_FGM,
AVG(OPP_FGA) AS OPP_FGA,
AVG(OPP_FG3M) AS OPP_FG3M,
AVG(OPP_FG3A) AS OPP_FG3A,
AVG(OPP_FTM) AS OPP_FTM,
AVG(OPP_FTA) AS OPP_FTA,
AVG(OPP_OREB) AS OPP_OREB,
AVG(OPP_DREB) AS OPP_DREB,
AVG(OPP_REB) AS OPP_REB,
AVG(OPP_AST) AS OPP_AST,
AVG(OPP_TOV) AS OPP_TOV,
AVG(OPP_STL) AS OPP_STL,
AVG(OPP_BLK) AS OPP_BLK,
AVG(OPP_PTS) AS OPP_PTS,
AVG(OPP_PLUS_MINUS) AS OPP_PLUS_MINUS
FROM
({})
GROUP BY SEASON, TEAM_ID
'''.format(self.__game_query)
data = pd.read_sql(query, self.__conn)
data['PACE'] = team_stats.pace(data)
data['POSSESSIONS'] = team_stats.possessions(data)
data['TEAM_OFF_RTG'] = team_stats.off_rating(data)
data['TEAM_DEF_RTG'] = team_stats.def_rating(data)
data['TEAM_NET_RTG'] = data['TEAM_OFF_RTG'] - data['TEAM_DEF_RTG']
data['TEAM_EFG'] = stats.eff_fg_pct(data, 'TEAM_')
data['TEAM_TOV_PCT'] = stats.tov_pct(data, 'TEAM_')
data['TEAM_OREB_PCT'] = team_stats.oreb_pct(data)
data['TEAM_DREB_PCT'] = team_stats.dreb_pct(data)
data['TEAM_FT_PER_FGA'] = stats.ft_per_fga(data, 'TEAM_')
efg = data.TEAM_EFG
oreb = data.TEAM_OREB_PCT
dreb = data.TEAM_DREB_PCT
ftr = data.TEAM_FT_PER_FGA
tov = data.TEAM_TOV_PCT
data[
'TEAM_FOUR_FACTORS'] = 0.4 * efg + 0.2 * oreb + 0.15 * ftr - 0.25 * tov
data[
'TEAM_FOUR_FACTORS_REB'] = 0.4 * efg + 0.1 * oreb + 0.1 * dreb + 0.15 * ftr - 0.25 * tov
query = '''
SELECT SEASON, TEAM_ID, OPP_ID, COUNT(OPP_ID) AS GAMES_PLAYED
FROM
({})
GROUP BY SEASON, TEAM_ID, OPP_ID
'''.format(self.__game_query)
opponents = pd.read_sql(query, self.__conn)
for season in pd.unique(data.SEASON):
season_opponents = opponents[opponents.SEASON == season]
teams = pd.unique(season_opponents.TEAM_ID)
schedule = np.zeros([len(teams), len(teams)])
for team in teams:
index = np.array([
x in season_opponents[season_opponents.TEAM_ID ==
team].OPP_ID.values for x in teams
])
schedule[team == teams, index] = season_opponents[
season_opponents.TEAM_ID == team].GAMES_PLAYED
schedule /= sum(season_opponents.GAMES_PLAYED) / len(teams)
point_diff = data[data.SEASON == season].TEAM_PLUS_MINUS.values
srs = point_diff
for i in range(10):
srs = point_diff + schedule.dot(srs)
data.loc[data.SEASON == season, 'TEAM_SRS'] = srs
return data