Who's good this year? Comparing the Information Content of Games in the Four Major US Sports

TL;DR

This paper quantifies and compares the information content of games across the four major US sports by analyzing how well models predict outcomes based on game data, revealing significant differences in informational value.

Contribution

It introduces a method to measure the information content of sports games by predictive modeling, providing a comparative analysis across different sports.

Findings

Football games are more informative than baseball games.

Predictive accuracy varies significantly between sports.

The number of games correlates with the information content.

Abstract

In the four major North American professional sports (baseball, basketball, football, and hockey), the primary purpose of the regular season is to determine which teams most deserve to advance to the playoffs. Interestingly, while the ultimate goal of identifying the best teams is the same, the number of regular season games played differs dramatically between the sports, ranging from 16 (football) to 82 (basketball and hockey) to 162 (baseball). Though length of season is partially determined by many factors including travel logistics, rest requirements, playoff structure and television contracts, it is hard to reconcile the 10-fold difference in the number of games between, for example, the NFL and MLB unless football games are somehow more "informative" than baseball games. In this paper, we aim to quantify the amount of information games yield about the relative strength of the teams involved. Our strategy is to assess how well simple paired comparison models fitted from $X%$ of the games within a season predict the outcomes of the remaining $(100-X)%$ of games, for multiple values of $X$. We compare the resulting predictive accuracy curves between seasons within the same sport and across all four sports, and find dramatic differences in the amount of information yielded by individual game results in the four major U.S. sports.