Main Article Content
Originally designed as a way to reflect past performance, chess ratings are now widely used to reflect players strength with many important aspects in tournament scheduling, advertising and premium shares. The ELO system has been officially adopted by World Chess Federation (FIDE). We used Bayesian analysis of actual data from elite chess players to fit parametric statistical models that could subsidize proposals for rating system improvement. Although most of the considered options are not new, since based on well known preference models, the use of a weighed likelihood function to emulate dynamic rating systems via Bayesian inference is novel. We compared descriptive ability using marginal likelihood based information criteria. Akaike information criterion was used to compare predictions. Many of the considered options improve on Elo ratings and there is strong evidence that dynamic models considering both white advantage and propensity to draws would result in more accurate systems.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).