Investigating experiential effects in online chess using a hierarchical Bayesian analysis

Abstract The presence or absence of winner-loser effects is a widely discussed phenomenon across both sports and psychology research. Investigation of such effects is often hampered by the limited availability of data. Online chess has exploded in popularity in recent years and provides vast amounts of data which can be used to explore this question. With a hierarchical Bayesian regression model, we carefully investigate the presence of such experiential effects in online chess. Using a large quantity of online chess data, we see little evidence for experiential effects that are consistent across all players, with some individual players showing some evidence for such effects. Given the challenging temporal nature of this data, we discuss several methods for assessing the suitability of our model and carefully check its validity.

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Publication Details

Journal
Journal of Quantitative Analysis in Sports
Published
2025-09-19
DOI
https://doi.org/10.1515/jqas-2025-0035
Primary Topic
Sports Analytics and Performance
Type
article
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article

Investigating experiential effects in online chess using a hierarchical Bayesian analysis

Adam Gee, Sydney Seese, Owen G. Ward, James P. Curley
Journal of Quantitative Analysis in Sports
Sports Analytics and Performance
article

Investigating experiential effects in online chess using a hierarchical Bayesian analysis

Adam Gee, Sydney Seese, Owen G. Ward, James P. Curley
article en

Abstract

Abstract The presence or absence of winner-loser effects is a widely discussed phenomenon across both sports and psychology research. Investigation of such effects is often hampered by the limited availability of data. Online chess has exploded in popularity in recent years and provides vast amounts of data which can be used to explore this question. With a hierarchical Bayesian regression model, we carefully investigate the presence of such experiential effects in online chess. Using a large quantity of online chess data, we see little evidence for experiential effects that are consistent across all players, with some individual players showing some evidence for such effects. Given the challenging temporal nature of this data, we discuss several methods for assessing the suitability of our model and carefully check its validity.

Journal of Quantitative Analysis in Sports
Simon Fraser University (CA), The University of Texas at Austin (US)
Openalex Percentile: Top 66%
Sports Analytics and Performance
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