Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning

Nipoti Bernardo () and Schiavon Lorenzo ()
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Nipoti Bernardo: Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
Schiavon Lorenzo: Department of Economics, Ca’ Foscari University of Venice, Venice, Italy

Journal of Quantitative Analysis in Sports, 2025, vol. 21, issue 1, 37-50

Abstract: While the use of expected goals (xG) as a metric for assessing soccer performance is increasingly prevalent, the uncertainty associated with their estimates is often overlooked. This work bridges this gap by providing easy-to-implement methods for uncertainty quantification in xG estimates derived from Bayesian models. Based on a convenient posterior approximation, we devise an online prior-to-posterior update scheme, aligning with the typical in-season model training in soccer. Additionally, we present a novel framework to assess and compare the performance dynamics of two teams during a match, while accounting for evolving match scores. Our approach is well-suited for graphical representation and improves interpretability. We validate the accuracy of our methods through simulations, and provide a real-world illustration using data from the Italian Serie A league.

Keywords: Bayesian statistics; expected goals; online learning; quadratic approximation; uncertainty quantification (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/jqas-2024-0081

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