This is my CG2008 paper, for statisticians:

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

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.


Feedback is welcome.


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