Tsokos, A;
Narayanan, S;
Kosmidis, I;
Baio, G;
Cucuringu, M;
Whitaker, G;
Király, F;
(2019)
Modeling outcomes of soccer matches.
Machine Learning
, 108
(1)
pp. 77-95.
10.1007/s10994-018-5741-1.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Modeling outcomes of soccer matches |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10994-018-5741-1 |
| Publisher version: | https://doi.org/10.1007/s10994-018-5741-1 |
| Language: | English |
| Additional information: | © The Author(s) 2018. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Bradley–Terry model; Poisson log-linear hierarchical model; Maximum penalized likelihood; Integrated nested laplace approximation; Temporal validation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10056079 |
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