Abstract


 
 
 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.
 
 

Highlights

  • Chess is one of the most popular games in the world, being practiced by millions of people

  • Our objective is to find parametric statistical models that could subsidize proposals for changes in the Federacao Mundial de Xadrez (FIDE) system

  • For easy reading among chess players and arbiters, we present rating estimates corrected to FIDE-ELO’s scale, like above

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Summary

Introduction

Chess is one of the most popular games in the world, being practiced by millions of people (formally or informally). There is a lot of literature on chess, thousands of books and magazines, websites and data banks to retrieve games played from 15 century on and many sources of information on tournaments and players history. Introduction of rating systems to estimate players’ relative strengths played an important role in chess popularity. World Chess Federation (FIDE) and many national federations, like United States Chess Federation (USCF), Rev. Bras.

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