# Example¶

The melo package comes pre-bundled with a text file containing the final score of all regular season NFL games 2009–2018. Let’s load this dataset and use the model to predict the point spread and point total of “future” games using the historical game data.

## Training data¶

First, let’s import Melo and load the nfl.dat package data. Let’s also load numpy for convenience.

import pkgutil
import numpy as np

from melo import Melo

# the package comes pre-bundled with an example NFL dataset
pkgdata = pkgutil.get_data('melo', 'nfl.dat').splitlines()


The nfl.dat package data looks like this:

# date, home, score, away, score
2009-09-10 PIT 13 TEN 10
2009-09-13 ATL 19 MIA  7
2009-09-13 BAL 38 KC  24
2009-09-13 CAR 10 PHI 38
2009-09-13 CIN  7 DEN 12
2009-09-13 CLE 20 MIN 34
2009-09-13 HOU  7 NYJ 24
2009-09-13 IND 14 JAC 12
2009-09-13 NO  45 DET 27
2009-09-13 TB  21 DAL 34
2009-09-13 ARI 16 SF  20
2009-09-13 NYG 23 WAS 17
...


After we’ve loaded the package data, we’ll need to split the game data into separate columns.

dates, teams_home, scores_home, teams_away, scores_away = zip(
*[l.split() for l in pkgdata[1:]])


Let’s start by analyzing the home team point spreads.

spreads = [int(h) - int(a) for h, a in zip(scores_home, scores_away)]


Note, if I swap the order of scores_home and scores_away, my definition of the point spread picks up a minus sign. This means the point spread binary comparison anti-commutes under label interchange. Let’s define a new constant to pass this information to the Melo class constructor.

commutes = False


Much like traditional Elo ratings, the melo model includes a hyperparameter k which controls how fast the ratings update. Prior experience indicates that

k = 0.245


is a good choice for NFL games. Generally speaking, this hyperparameter must be tuned for each use case.

Next, we need to specify a one-dimensional array of point spread thresholds. The vast majority of NFL spreads fall between -59.5 and 59.5 points, so let’s partition the point spreads within this range. Here I choose half-point lines in one-point increments so there is no ambiguity as to whether a comparison falls above or below a given threshold.

lines = np.arange(-59.5, 60.5)


Additionally, let’s also specify a function which describes how much the ratings should regress to the mean as a function of elapsed time between games. Here I regress the ratings to the mean by a fixed fraction each off season. To accomplish this, I create a function

def regress(dormant_months):
"""
Regress ratings to the mean by 40% if the team
has not played for three or more months

"""
return .4 if dormant_months > 3 else 0


and define a constant to specify the units of the decay function time argument (see Usage for available options).

regress_unit = 'month'


Using the previous components, the model estimator is initialized as follows:

nfl_spreads = Melo(k, lines=lines, commutes=commutes,
regress=regress, regress_unit=regress_unit)


Note, at this point we have not trained the model on any data yet. We’ve simply specified some necessary hyperparameters and options. Assembling the previous components, the model is trained by calling its fit function on the previously defined training data:

nfl_spreads.fit(dates, teams_home, teams_away, spreads)


Once the model is fit to the data, we can easily generate predictions by calling its various instance methods:

# time one day after the last model update
time = nfl_spreads.last_update + np.timedelta64(1, 'D')

# predict the mean outcome at 'time'

# predict the median outcome at 'time'

# predict the interquartile range at 'time'
nfl_spreads.quantile(time, 'CLE', 'KC', q=[.25, .5, .75])

# predict the win probability at 'time'

# generate prediction samples at 'time'


Furthermore, the model can rank teams by their expected performance against a league average opponent on a neutral field. Let’s evaluate this ranking at the end of the 2018–2019 season.

# end of the 2018–2019 season
time = nfl_spreads.last_update + np.timedelta64(1, 'D')

# rank teams by expected mean spread against average team


Or alternatively, we can rank teams by their expected win probability against a league average opponent:

# rank teams by expected win prob against average team


## Point total predictions¶

Everything demonstrated so far can also be applied to point total comparisons with a few small changes. First, let’s create the array of point total comparisons.

totals = [int(h) + int(a) for h, a in zip(scores_home, scores_away)]


Next, we’ll need to change our lines so they cover the expected range of point total comparisons:

lines = np.arange(-0.5, 105.5)


commutes = True


since the point total comparisons are invariant under label interchange. Finally, we’ll want to provide somewhat different inputs for the k and regress arguments. Putting the pieces together:

nfl_totals = Melo(.245, lines=lines, commutes=True,
regress=lambda months: .3 if months > 3 else 0,
regress_unit='month')

nfl_totals.fit(dates, teams_home, teams_away, totals)


And voila! We can easily predict the outcome of a future point total comparison.

# time one day after the last model update
time = nfl_totals.last_update + np.timedelta64(1, 'D')

# predict the mean outcome at 'time'
nfl_totals.mean(time, 'CLE', 'KC')