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Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

Rémi Coulom 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : Whole-History Rating (WHR) is a new method to estimate the time-varying strengths of players involved in paired comparisons. Like many variations of the Elo rating system, the whole-history approach is based on the dynamic Bradley-Terry model. But, instead of using incremental approximations, WHR directly computes the exact maximum a posteriori over the whole rating history of all players. This additional accuracy comes at a higher computational cost than traditional methods, but computation is still fast enough to be easily applied in real time to large-scale game servers (a new game is added in less than 0.001 second). Experiments demonstrate that, in comparison to Elo, Glicko, TrueSkill, and decayed-history algorithms, WHR produces better predictions.
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Contributor : Rémi Coulom <>
Submitted on : Sunday, September 21, 2008 - 3:01:22 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:20 PM
Long-term archiving on: : Saturday, November 26, 2016 - 12:59:02 AM


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  • HAL Id : inria-00323349, version 1



Rémi Coulom. Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength. Computer and Games, Sep 2008, Beijing, China. pp.113--124. ⟨inria-00323349⟩



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