Soccer matches as experiments: how often does the 'best' team win?

TL;DR

This paper uses a Bayesian approach to analyze soccer matches as experiments, quantifying the likelihood that the outcome reflects the true superiority of the teams, revealing that typical scores often lead to misleading conclusions.

Contribution

It introduces a Bayesian framework to assess the probability that a soccer match outcome accurately indicates team superiority, highlighting limitations of current scoring levels.

Findings

High probability of misleading results with typical scores

Increasing goals scored improves confidence but requires radical rule changes

Current scoring levels often do not reliably indicate the better team

Abstract

Models in which the number of goals scored by a team in a soccer match follow a Poisson distribution, or a closely related one, have been widely discussed. We here consider a soccer match as an experiment to assess which of two teams is superior and examine the probability that the outcome of the experiment (match) truly represents the relative abilities of the two teams. Given a final score, it is possible by using a Bayesian approach to quantify the probability that it was or was not the case that 'the best team won'. For typical scores, the probability of a misleading result is significant. Modifying the rules of the game to increase the typical number of goals scored would improve the situation, but a level of confidence that would normally be regarded as satisfactory could not be obtained unless the character of the game was radically changed.