Alternative ranking measures to predict international football results

TL;DR

This paper compares statistical and machine learning models, including the Bayesian Bradley-Terry-Davidson model and FIFA rankings, to predict outcomes of major football tournaments like the 2022 FIFA World Cup and 2023 CAF Africa Cup of Nations.

Contribution

It provides a comparative evaluation of different ranking models and summaries of past performances for football result prediction, including Bayesian and goal-based approaches.

Findings

Bayesian Bradley-Terry-Davidson model performs well in predictions.

Alternative summaries of past performances improve predictive accuracy.

Comparison shows strengths and limitations of various ranking methods.

Abstract

Over the last few years, there has been a growing interest in the prediction and modelling of competitive sports outcomes, with particular emphasis placed on this area by the Bayesian statistics and machine learning communities. In this paper, we have carried out a comparative evaluation of statistical and machine learning models to assess their predictive performance for the 2022 FIFA World Cup and for the 2023 CAF Africa Cup of Nations by evaluating alternative summaries of past performances related to the involved teams. More specifically, we consider the Bayesian Bradley-Terry-Davidson model, which is a widely used statistical framework for ranking items based on paired comparisons that have been applied successfully in various domains, including football. The analysis was performed including in some canonical goal-based models both the Bradley-Terry-Davidson derived ranking and the widely recognized Coca-Cola FIFA ranking commonly adopted by football fans and amateurs.