MELO

Margin-dependent Elo ratings and predictions model

.. image:: https://travis-ci.org/morelandjs/melo.svg?branch=master :target: https://travis-ci.org/morelandjs/melo

Documentation

moreland.dev/projects/melo <https://moreland.dev/projects/melo>_

Quick start

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))

.. _numpy: http://www.numpy.org .. _pip: https://pip.pypa.io .. _scipy: https://www.scipy.org