def rate_at_date(self, dt):
"""
:param dt:
:return:
"""
games = util.get_games(dt)
self.rate_for_games(games)
data = [['UConn', 64, 'Kansas', 57],
['UConn', 82, 'Duke', 68],
['Minnesota', 71, 'UConn', 72],
['Kansas', 69, 'UConn', 62],
['Duke', 81, 'Minnesota', 70],
['Minnesota', 52, 'Kansas', 62]]
df = pd.DataFrame(data, columns=['hteam', 'hscore', 'ateam', 'ascore'])
df['home_outcome'] = df.hscore > df.ascore
df['neutral'] = False
df['hteam_id'] = df.hteam.map(lambda x: team_ids.get(x))
df['ateam_id'] = df.ateam.map(lambda x: team_ids.get(x))
return df
if __name__ == "__main__":
# df = rpi_test_data()
games = util.get_games(date(2015, 3, 15))
d1 = pd.read_sql("SELECT * FROM division_one WHERE year=2015", DB.conn)
games = games.merge(d1, left_on='hteam_id', right_on='ncaaid')
games = games.merge(d1, left_on='ateam_id', right_on='ncaaid')
teams = util.get_teams(games)
data = games[['hteam_id', 'ateam_id', 'home_outcome', 'neutral']].values
agg = RPIAggregator(teams)
map(lambda x: agg.update(x), data)
ratings = agg.evaluate()
teams['rpi'] = ratings[0]
teams['sos'] = ratings[1]
teams['w'] = agg.total_won
teams['l'] = agg.total_played - agg.total_won
all_teams = pd.read_sql("SELECT ncaa, ncaaid FROM teams", DB.conn)
df = teams.merge(all_teams, left_on="team_id", right_on="ncaaid")
df = pd.DataFrame(ratings_with_teams, columns=['rating', 'team_id'])
teams = pd.read_sql("SELECT ncaaid, ncaa FROM teams", DB.conn)
return df.merge(teams, left_on="team_id", right_on="ncaaid")
@staticmethod
def _calculate_rpi(wp, owp, oowp):
return 0.25 * wp + 0.5 * owp + 0.25 * oowp
@staticmethod
def win_factor(is_home, is_neutral=False):
if is_neutral:
return 1.
elif is_home:
return 0.6
else:
return 1.4
def get_true_rpi():
url = 'http://www.cbssports.com/collegebasketball/bracketology/nitty-gritty-report'
response = requests.get(url)
soup = BeautifulSoup(response.content, "html.parser")
rpi_table = soup.find('table', {'class': 'ncaa-rankings-table'})
if __name__ == "__main__":
games = util.get_games(2015)
teams = util.get_teams(games)
agg = RPIAggregator()
season_dict = agg.rate_for_every_game(teams, games)
# rpi = agg.infinite_depth()
