The NFL (and the blog) is back

Hey everyone!  After a busy summer, I have made my way back from the desert in time for the return of the NFL tomorrow night.  As usual, my first order of business is to blithely act like I can predict who will win the week 1 games based on team performance from last year. 

The model is very straightforward.  The first game last year (2013), for example, was the Baltimore Ravens traveling to Denver to face the Broncos.  In 2012, Baltimore won 10 games and outscored their opponents by 54 points over the course of the season.  Denver won 13 games and had a 192 point advantage.  You could use either wins or point differential as a predictor of strength (or both, I suppose), and we want to know which team is more likely to win.  I like to use point differential.  As it turns out, Denver won.  As you can see in the link above to last year’s post, that’s what the model predicted.  In fact, the model picked the correct team 11 times out of 16, which isn’t too shabby.

What’s odd about the model is that only the home team’s strength is statistically significant – the away team’s performance last year has “nothing” to do with the outcome statistically speaking (actually, if you use point differential the away team’s weight is about half the home team, and if you use wins it’s about a third).  Incredibly, if you only use the away team’s strength, it isn’t statistically significant.  Apparently you can get pretty far just by knowing one team’s ability, roughly speaking, and who’s playing at home.  This has been true as long as I’ve been running this regression.  So to make this year’s predictions, I’m going to use a model based on the home and away teams’ point differential last year and an intercept that reflects home field advantage.  In theory the away team doesn’t matter, but I just find that a little hard to believe in a practical sense.

For example, tomorrow night we have Green Bay traveling to Seattle.  Normally you would say that this should be a close game; the Packers tend to be pretty good and Aaron Rodgers isn’t the kind of guy to fold just because he’s on the road.  But all the model cares about is that Seattle outscored their opponents by 186 points last year while the Packers were outscored by 11.  Here’s a little table with the games and the home team’s predicted win probability.

home away home win prob
Seattle Green Bay 78%
Atlanta New Orleans 45%
St. Louis Minnesota 63%
Pittsburgh Cleveland 66%
Philly Jacksonville 75%
Jets Oakland 57%
Baltimore Cincy 51%
Chicago Buffalo 60%
Houston Washington 52%
Kansas City Tennessee 73%
Miami New England 53%
Tampa Bay Carolina 43%
Dallas San Fran 54%
Denver Indy 77%
Detroit Giants 67%
Arizona San Diego 64%

The last thing to mention is the Hilton Supercontest.  I use it as one of a couple benchmarks to see how well my models do.  This first week isn’t really part of ‘the model’, and it isn’t meant to be used to pick against the spread.  But in the interest of getting a full season of picks in, here are the five games where the model prediction differs most from the lines, according to my eyes: Dallas +4.5, Carolina +2, Miami +5, Oakland +5.5, and Buffalo +7.  The Cowboys and Dolphins are home underdogs that the model thinks should be tiny favorites (less than home field advantage, but not getting over a field goal).  Carolina is a road underdog that the model thinks should be a favorite (the Bucs only have a 43% chance to win).  And the Raiders and Bills are road underdogs as appropriate, but the model thinks they should be closer to the home field number of +3 instead of at 5.5 and 7.  Take those with a big grain of salt.

Whoo!  Back to football tomorrow!

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2 Responses to The NFL (and the blog) is back

  1. Ton says:

    Hooray! So happy you’re back – rode the model to pool wins my last two years. Thanks!

  2. Pingback: Wrapping up NFL Week 1: Guessing in the Dark | Sport Skeptic

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