Margin-dependent Elo ratings and predictions model
Requirements: Python 2.7 or 3.3+ with numpy and scipy.
Install the latest release with pip:
pip install melo
Example usage:
import pkgutil
import numpy as np
from melo import Melo
# the package comes pre-bundled with an example dataset
pkgdata = pkgutil.get_data('melo', 'nfl.dat').splitlines()
dates, teams_home, scores_home, teams_away, scores_away = zip(
*[l.split() for l in pkgdata[1:]])
# define a binary comparison statistic
spreads = [int(h) - int(a) for h, a
in zip(scores_home, scores_away)]
# hyperparameters and options
k = 0.245
lines = np.arange(-50.5, 51.5)
regress = lambda months: .413 if months > 3 else 0
regress_unit = 'month'
commutes = False
# initialize the estimator
nfl_spreads = Melo(k, lines=lines, commutes=commutes,
regress=regress, regress_unit=regress_unit)
# fit the estimator to the training data
nfl_spreads.fit(dates, teams_home, teams_away, spreads)
# specify a comparison time
time = nfl_spreads.last_update
# predict the mean outcome at that time
mean = nfl_spreads.mean(time, 'CLE', 'KC')
print('CLE VS KC: {}'.format(mean))
# rank nfl teams at end of 2018 regular season
rankings = nfl_spreads.rank(time, statistic='mean')
for team, rank in rankings:
print('{}: {}'.format(team, rank))