Statistics-Free Sports Prediction

TL;DR

This paper introduces a simple, statistics-free machine learning approach to predict sports game outcomes using only historical game data, showing varying success across different sports.

Contribution

The study demonstrates that a basic machine learning model can effectively predict sports outcomes without using traditional game statistics, highlighting sport-specific differences.

Findings

Best performance in basketball predictions

Baseball predictions are close to theoretical optimum

Football and hockey predictions can be improved

Abstract

We use a simple machine learning model, logistically-weighted regularized linear least squares regression, in order to predict baseball, basketball, football, and hockey games. We do so using only the thirty-year record of which visiting teams played which home teams, on what date, and what the final score was. No real "statistics" are used. The method works best in basketball, likely because it is high-scoring and has long seasons. It works better in football and hockey than in baseball, but in baseball the predictions are closer to a theoretical optimum. The football predictions, while good, can in principle be made much better, and the hockey predictions can be made somewhat better. These findings tells us that in basketball, most statistics are subsumed by the scores of the games, whereas in football, further study of game and player statistics is necessary to predict games as well as can be done. Baseball and hockey lie somewhere in between.