Understanding Distributions of Chess Performances

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7168)


This paper studies the population of chess players and the distribution of their performances measured by Elo ratings and by computer analysis of moves. Evidence that ratings have remained stable since the inception of the Elo system in the 1970’s is given in three forms: (1) by showing that the population of strong players fits a straightforward logistic-curve model without inflation, (2) by plotting players’ average error against the FIDE category of tournaments over time, and (3) by skill parameters from a model that employs computer analysis keeping a nearly constant relation to Elo rating across that time. The distribution of the model’s Intrinsic Performance Ratings can therefore be used to compare populations that have limited interaction, such as between players in a national chess federation and FIDE, and ascertain relative drift in their respective rating systems.


Average Error Intrinsic Rating Chess Player World Championship National Federation 
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  1. 1.
    Haworth, G.: Reference fallible endgame play. ICGA Journal 26, 81–91 (2003)Google Scholar
  2. 2.
    Haworth, G.: Gentlemen, Stop Your Engines! ICGA Journal 30, 150–156 (2007)Google Scholar
  3. 3.
    DiFatta, G., Haworth, G., Regan, K.: Skill rating by Bayesian inference. In: Proceedings, 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009), Nashville, TN, March 30-April 2, pp. 89–94 (2009)Google Scholar
  4. 4.
    Regan, K., Haworth, G.: Intrinsic chess ratings. In: Proceedings of AAAI 2011, San Francisco (2011)Google Scholar
  5. 5.
    Guid, M., Bratko, I.: Computer analysis of world chess champions. ICGA Journal 29(2), 65–73 (2006)Google Scholar
  6. 6.
    Guid, M., Pérez, A., Bratko, I.: How trustworthy is Crafty’s analysis of world chess champions? ICGA Journal 31(3), 131–144 (2008)Google Scholar
  7. 7.
    Guid, M., Bratko, I.: Using heuristic-search based engines for estimating human skill at chess. ICGA Journal 34(2), 71–81 (2011)Google Scholar
  8. 8.
    de Solla Price, D.J.: Science Since Babylon. Yale University Press (1961)Google Scholar
  9. 9.
    Goodstein, D.: The big crunch. In: Proceedings, 48th NCAR Symposium, Portland (1994)Google Scholar
  10. 10.
    Verhulst, P.F.: Notice sur la loi que la population poursuit dans son accroissement (1838)Google Scholar
  11. 11.
    Rajlich, V., Kaufman, L.: Rybka 3 chess engine (2007),
  12. 12.
    Haworth, G., Regan, K., Di Fatta, G.: Performance and Prediction: Bayesian Modelling of Fallible Choice in Chess. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 99–110. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Sonas, J.: Chessmetrics (2011),
  14. 14.
    Sonas, J., Chess ratings: Elo versus the Rest of the World (2011),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of CSEUniversity at BuffaloAmherstUSA
  2. 2.WarsawPoland
  3. 3.School of Systems EngineeringUniversity of ReadingUK

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