Pythagorean Wins: Who Got Lucky in 2016

Although NFL sabermetrics aren’t nearly on the same level as MLB sabermetrics, one useful metric is Pythagorean wins. Pythagorean wins are used to determine the number of games a team “should have won” based off of their points for and points against. I’m going to look at which teams won more and less than expected in an attempt to find regression candidates and evaluate what that means for fantasy purposes.

The Formula

There are a few different formulations for Pythagorean wins. Specifically, I’ll be using the Pythagenpat formula for Pythagorean expectation, which is as follows:

PythW% = (Points for^EXP)/(Points for^EXP + Points against^EXP)

where the exponent EXP is given by:

EXP = (Points for + Points against/Games Played)^0.287.

This is chosen to reduce the mean square error, and provides a better fit across the range of point outcomes than the basic Pythagorean formula, which just uses a fixed constant for the exponent.1 To get Pythagorean wins (PythW), we just multiply PythW% by the number of games.

2016 Pythagorean Wins
  1. In the NFL the fixed exponent is ~2.37.  (back)

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