{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gaussian-process-priors-for-dynamic-paired","title":"Gaussian Process Priors for Dynamic Paired Comparison Modelling","arxiv_id":"1902.07378","date":"2019-02-20","proceeding":null,"authors":["Martin Ingram"],"abstract":"Dynamic paired comparison models, such as Elo and Glicko, are frequently used\nfor sports prediction and ranking players or teams. We present an alternative\ndynamic paired comparison model which uses a Gaussian Process (GP) as a prior\nfor the time dynamics rather than the Markovian dynamics usually assumed. In\naddition, we show that the GP model can easily incorporate covariates. We\nderive an efficient approximate Bayesian inference procedure based on the\nLaplace Approximation and sparse linear algebra. We select hyperparameters by\nmaximising their marginal likelihood using Bayesian Optimisation, comparing the\nresults against random search. Finally, we fit and evaluate the model on the\n2018 season of ATP tennis matches, where it performs competitively,\noutperforming Elo and Glicko on log loss, particularly when surface covariates\nare included.","url_abs":"http://arxiv.org/abs/1902.07378v1","url_pdf":"http://arxiv.org/pdf/1902.07378v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gaussian-process-priors-for-dynamic-paired","repo_url":"https://github.com/martiningram/paired-comparison-gp-laplace","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}