  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.
