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Memory and long-range correlations in chess games

Author

Listed:
  • Schaigorodsky, Ana L.
  • Perotti, Juan I.
  • Billoni, Orlando V.

Abstract

In this paper we report the existence of long-range memory in the opening moves of a chronologically ordered set of chess games using an extensive chess database. We used two mapping rules to build discrete time series and analyzed them using two methods for detecting long-range correlations; rescaled range analysis and detrended fluctuation analysis. We found that long-range memory is related to the level of the players. When the database is filtered according to player levels we found differences in the persistence of the different subsets. For high level players, correlations are stronger at long time scales; whereas in intermediate and low level players they reach the maximum value at shorter time scales. This can be interpreted as a signature of the different strategies used by players with different levels of expertise. These results are robust against the assignation rules and the method employed in the analysis of the time series.

Suggested Citation

  • Schaigorodsky, Ana L. & Perotti, Juan I. & Billoni, Orlando V., 2014. "Memory and long-range correlations in chess games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 304-311.
    • Handle: RePEc:eee:phsmap:v:394:y:2014:i:c:p:304-311
    DOI: 10.1016/j.physa.2013.09.035
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    References listed on IDEAS

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    1. Haroldo V Ribeiro & Renio S Mendes & Ervin K Lenzi & Marcelo del Castillo-Mussot & Luís A N Amaral, 2013. "Move-by-Move Dynamics of the Advantage in Chess Matches Reveals Population-Level Learning of the Game," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-7, January.
    2. de Saá Guerra, Y. & Martín González, J.M. & Sarmiento Montesdeoca, S. & Rodríguez Ruiz, D. & García-Rodríguez, A. & García-Manso, J.M., 2012. "A model for competitiveness level analysis in sports competitions: Application to basketball," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(10), pages 2997-3004.
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    Cited by:

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    2. Petersen, Alexander M. & Penner, Orion, 2020. "Renormalizing individual performance metrics for cultural heritage management of sports records," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).

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