Implied Odds#

[1]:

import penaltyblog as pb
import numpy as np
from pprint import pprint

Example starting odds (including overround)#

[2]:

odds = [2.7, 2.3, 4.4]

np.sum(1 / np.array(odds))

[2]:

np.float64(1.0324257063387499)

Remove the overround using basic multiplicative method#

[3]:

normalised = pb.implied.multiplicative(odds)
pprint(normalised)

{'implied_probabilities': array([0.35873804, 0.42112726, 0.2201347 ]),
 'margin': np.float64(0.03242570633874986),
 'method': 'multiplicative'}

[4]:

np.sum(normalised["implied_probabilities"])

[4]:

np.float64(0.9999999999999999)

Remove the overround using additive method#

[5]:

normalised = pb.implied.additive(odds)
normalised

[5]:

{'implied_probabilities': array([0.3595618 , 0.42397404, 0.21646416]),
 'method': 'additive',
 'margin': np.float64(0.03242570633874986)}

[6]:

np.sum(normalised["implied_probabilities"])

[6]:

np.float64(1.0)

Remove the overround using power method#

[7]:

normalised = pb.implied.power(odds)
normalised

[7]:

{'implied_probabilities': array([0.3591711 , 0.42373075, 0.21709815]),
 'method': 'power',
 'k': 1.0309132393169356,
 'margin': np.float64(0.03242570633874986)}

[8]:

np.sum(normalised["implied_probabilities"])

[8]:

np.float64(0.9999999999989682)

Remove the overround using Shin’s method#

[9]:

normalised = pb.implied.shin(odds)
normalised

[9]:

{'implied_probabilities': array([0.35934392, 0.42324385, 0.21741223]),
 'method': 'shin',
 'z': 0.016236442857291165,
 'margin': np.float64(0.03242570633874986)}

[10]:

np.sum(normalised["implied_probabilities"])

[10]:

np.float64(0.9999999999990125)

Remove the overround using Joseph Buchdahl’s margin weights proportional to the odds approach#

[11]:

normalised = pb.implied.differential_margin_weighting(odds)
normalised

[11]:

{'implied_probabilities': array([0.3595618 , 0.42397404, 0.21646416]),
 'method': 'differential_margin_weighting',
 'margin': np.float64(0.03242570633874986)}

[12]:

np.sum(normalised["implied_probabilities"])

[12]:

np.float64(0.9999999999999998)

Remove the overround using Keith Cheung’s odds ratio approach#

[13]:

normalised = pb.implied.odds_ratio(odds)
normalised

[13]:

{'implied_probabilities': array([0.35881036, 0.42256142, 0.21862822]),
 'method': 'odds_ratio',
 'c': 1.05116912729218,
 'margin': np.float64(0.03242570633874986)}

[14]:

np.sum(normalised["implied_probabilities"])

[14]:

np.float64(1.000000000000595)