The ranking lasso and its application to sport tournaments

TL;DR

This paper introduces a lasso-based method for ranking contestants in sports tournaments, improving interpretability and prediction accuracy over traditional models like Bradley-Terry.

Contribution

It proposes a novel lasso-type procedure for paired comparison models that groups contestants with similar abilities, enhancing ranking clarity and predictive performance.

Findings

Improved ranking interpretability through grouping contestants.

Significant enhancement in prediction accuracy.

Effective application to NFL and college hockey data.

Abstract

Ranking a vector of alternatives on the basis of a series of paired comparisons is a relevant topic in many instances. A popular example is ranking contestants in sport tournaments. To this purpose, paired comparison models such as the Bradley-Terry model are often used. This paper suggests fitting paired comparison models with a lasso-type procedure that forces contestants with similar abilities to be classified into the same group. Benefits of the proposed method are easier interpretation of rankings and a significant improvement of the quality of predictions with respect to the standard maximum likelihood fitting. Numerical aspects of the proposed method are discussed in detail. The methodology is illustrated through ranking of the teams of the National Football League 2010-2011 and the American College Hockey Men's Division I 2009-2010.