Memory and long-range correlations in chess games

Autores
Schaigorodsky, Ana Laura; Perotti, Juan Ignacio; Billoni, Orlando Vito
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Schaigorodsky, Ana Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Perotti, Juan Ignacio. Aalto University; Finlandia
Fil: Billoni, Orlando Vito. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Materia
Long Range Correlations
Zip'S Law
Interdisciplinary Physics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/31658
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Memory and long-range correlations in chess gamesSchaigorodsky, Ana LauraPerotti, Juan IgnacioBilloni, Orlando VitoLong Range CorrelationsZip'S LawInterdisciplinary Physicshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In 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.Fil: Schaigorodsky, Ana Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Perotti, Juan Ignacio. Aalto University; FinlandiaFil: Billoni, Orlando Vito. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaElsevier Science2013-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/31658Perotti, Juan Ignacio; Schaigorodsky, Ana Laura; Billoni, Orlando Vito; Memory and long-range correlations in chess games; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 394; 9-2013; 304-3110378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.09.035info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437113009126info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:23:05Zoai:ri.conicet.gov.ar:11336/31658instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:23:06.025CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Memory and long-range correlations in chess games
title Memory and long-range correlations in chess games
spellingShingle Memory and long-range correlations in chess games
Schaigorodsky, Ana Laura
Long Range Correlations
Zip'S Law
Interdisciplinary Physics
title_short Memory and long-range correlations in chess games
title_full Memory and long-range correlations in chess games
title_fullStr Memory and long-range correlations in chess games
title_full_unstemmed Memory and long-range correlations in chess games
title_sort Memory and long-range correlations in chess games
dc.creator.none.fl_str_mv Schaigorodsky, Ana Laura
Perotti, Juan Ignacio
Billoni, Orlando Vito
author Schaigorodsky, Ana Laura
author_facet Schaigorodsky, Ana Laura
Perotti, Juan Ignacio
Billoni, Orlando Vito
author_role author
author2 Perotti, Juan Ignacio
Billoni, Orlando Vito
author2_role author
author
dc.subject.none.fl_str_mv Long Range Correlations
Zip'S Law
Interdisciplinary Physics
topic Long Range Correlations
Zip'S Law
Interdisciplinary Physics
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Schaigorodsky, Ana Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Perotti, Juan Ignacio. Aalto University; Finlandia
Fil: Billoni, Orlando Vito. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
description 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.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/31658
Perotti, Juan Ignacio; Schaigorodsky, Ana Laura; Billoni, Orlando Vito; Memory and long-range correlations in chess games; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 394; 9-2013; 304-311
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/31658
identifier_str_mv Perotti, Juan Ignacio; Schaigorodsky, Ana Laura; Billoni, Orlando Vito; Memory and long-range correlations in chess games; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 394; 9-2013; 304-311
0378-4371
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.09.035
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437113009126
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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