Bayes-ically fair: A Bayesian Ranking of the Olympic Medal Table
Applications
2025-10-17 v1
Abstract
Evaluating a country's sporting success provides insight into its decision-making and infrastructure for developing athletic talent. The Olympic Games serve as a global benchmark, yet conventional medal rankings can be unduly influenced by population size. We propose a Bayesian ranking scheme to rank the performance of National Olympic Committees by their "long-run" medals-to-population ratio. The algorithm aims to mitigate the influence of large populations and reduce the stochastic fluctuations for smaller nations by applying shrinkage. These long-run rankings provide a more stable and interpretable ordering of national sporting performance across games compared to existing methods.
Cite
@article{arxiv.2510.14723,
title = {Bayes-ically fair: A Bayesian Ranking of the Olympic Medal Table},
author = {Cormac MacDermott and Carl J. Scarrott and John Ferguson},
journal= {arXiv preprint arXiv:2510.14723},
year = {2025}
}