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

  • Rémi Coulom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5131)

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.

Keywords

Wiener Process Prediction Rate Rating Algorithm Rating Uncertainty Incremental Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rémi Coulom
    • 1
  1. 1.Université Charles de Gaulle, INRIA SEQUEL, CNRS GRAPPALilleFrance

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