Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications

TL;DR

This paper develops a modified Glicko-2 rating system to evaluate European and South American football leagues, incorporating factors like draw probability and home advantage, and demonstrates its improved accuracy over traditional methods.

Contribution

The authors introduce a Glicko-2 based rating approach with modifications for league strength assessment and match prediction, enhancing accuracy and interpretability in soccer analytics.

Findings

Glicko-2 based ratings outperform Poisson regression in accuracy.

Modified Glicko-2 accounts for draw probability and home advantage.

Ratings are applicable for predicting player performance and match outcomes.

Abstract

One of the key problems in the field of soccer analytics is predicting how a player performance changes when transitioning from one league to another. One potential solution to address this issue lies in the evaluation of the respective league strength. This article endeavors to compute club ratings of the first and second European and South American leagues. In order to calculate these ratings, the authors have designed the Glicko-2 rating system-based approach, which overcomes some Glicko-2 limitations. Particularly, the authors took into consideration the probability of the draw, the home-field advantage, and the property of teams to become stronger or weaker following their league transitions. Furthermore, authors have constructed a predictive model for forecasting match results based on the number of goals scored in previous matches. The metrics utilized in the analysis reveal that the Glicko-2 based approach exhibits a marginally superior level of accuracy when compared to the commonly used Poisson regression-based approach. In addition, Glicko-2 based ratings offer greater interpretability and can find application in various analytics tasks, such as predicting soccer player metrics for forthcoming seasons or the detailed analysis of a player performance in preceding matches. The implementation of the approach is available on github.com/andreyshelopugin/GlickoSoccer.