Modeling outcomes of soccer matches

Journal article

We compare various extensions of the Bradley–Terry model and a hierarchical Poisson log-linear model in terms of their performance in predicting the outcome of soccer matches (win, draw, or loss). The parameters of the Bradley–Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the posterior densities of the parameters of the hierarchical Poisson log-linear model are approximated using integrated nested Laplace approximations. The prediction performance of the various modeling approaches is assessed using a novel, context-specific framework for temporal validation that is found to deliver accurate estimates of the test error. The direct modeling of outcomes via the various Bradley–Terry extensions and the modeling of match scores using the hierarchical Poisson log-linear model demonstrate similar behavior in terms of predictive performance.

Authors: Tsokos, A; Narayanan, S; Kosmidis, I; Baio, G; Cucuringu, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Verlag
• Journal: Machine Learning
• Volume: 108
• Issue: 1
• Pages: 77–95
• Publication date: 2018-08-01
• Acceptance date: 2018-06-25
• DOI: 10.1007/s10994-018-5741-1
• EISSN: 1573-0565
• ISSN: 0885-6125
• Keywords: poisson log-linear hierarchical model; integrated nested laplace approximation; Bradley–Terry model; temporal validation; maximum penalized likelihood