Understanding distributions of chess performances
Regan, K. W., Macieja, B. and Haworth, G. (2012) Understanding distributions of chess performances. In: Advances in Computer Games. Lecture Notes in Computer Science, 7168. Springer-Verlag, Heidelberg, pp. 230-243.
To link to this article DOI: 10.1007/978-3-642-31866-5_20
This paper presents evidence for several features of the population of chess players, and the distribution of their performances measured in terms of 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 several forms: by showing that the population of strong players fits a simple logistic-curve model without inflation, by plotting players’ average error against the FIDE category of tournaments over time, and 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 hence 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.
1. Haworth, G.: Reference fallible endgame play. ICGA Journal 26 (2003) 81–91 2. Haworth, G.: Gentlemen, Stop Your Engines! ICGA Journal 30 (2007) 150–156 3. DiFatta, G., Haworth, G., Regan, K.: Skill rating by Bayesian inference. In: Proceedings, 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM’09), Nashville, TN, March 30–April 2, 2009. (2009) 89–94 4. Regan, K., Haworth, G.: Intrinsic chess ratings. In: Proceedings of AAAI 2011, San Francisco. (2011) 5. Guid, M., Bratko, I.: Computer analysis of world chess champions. ICGA Journal 29 (2006) 65–73 6. Guid, M., P´erez, A., Bratko, I.: How trustworthy is Crafty’s analysis of world chess champions? ICGA Journal 31 (2008) 131–144 7. Guid, M., Bratko, I.: Using heuristic-search based engines for estimating human skill at chess. ICGA Journal 34 (2011) 71–81 8. de Solla Price, D.J.: Science Since Babylon. Yale University Press (1961) 9. Goodstein, D.: The big crunch. In: Proceedings, 48th NCAR Symposium, Portland. (1994) 10. Verhulst, P.F.: Notice sur la loi que la population poursuit dans son accroissement (1838) 11. Rajlich, V., Kaufman, L.: Rybka 3 chess engine (2007) http://www.rybkachess.com. 12. Haworth, G., Regan, K., DiFatta, G.: Performance and prediction: Bayesian modelling of fallible choice in chess. In: Proceedings, 12th ICGA Conference on Advances in Computer Games, Pamplona, Spain, May 11–13, 2009. Volume 6048 of Lecture Notes in Computer Science., Springer-Verlag (2010) 99–110 13. Sonas, J.: Chessmetrics. http://www.chessmetrics.com (2011) 14. Sonas, J., Kaggle.com: Chess ratings: Elo versus the Rest of the World. http://www.kaggle.com/c/chess (2011)
Centaur Editors: Update this record