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Self-selection in tournaments: The case of chess players

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  • Laurent Linnemer

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

  • Michael Visser

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

Abstract

We consider a simple tournament model in which individuals auto-select into the contest on the basis of their commonly known strength levels, and privately observed strength-shocks (reflecting temporary deviations from observed levels). The model predicts that the participation rate should increase with the player's observed strength, and the total awarded prize amount. Furthermore, under certain conditions self-selection implies that participants with high observed strength levels have smaller expected strength-shocks than those with low levels. Consequently, the latter should play better than predicted and the former worse (given their observed strength). These predictions are confronted with data from a large and high-prize chess tournament held in the USA. This tournament is divided into different sections, with players being able to play in the section to which their current chess rating (observed strength) belongs. As predicted, we find that within each section the participation probability increases with chess rating and prize amounts, and players with a relatively low (resp. high) rating are indeed the ones who have a better (resp. worse) relative performance.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Laurent Linnemer & Michael Visser, 2016. "Self-selection in tournaments: The case of chess players," Post-Print hal-01629749, HAL.
    • Handle: RePEc:hal:journl:hal-01629749
    DOI: 10.1016/j.jebo.2016.03.007
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    Cited by:

    1. Dainis Zegners & Uwe Sunde & Anthony Strittmatter, 2020. "Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach," Papers 2005.12638, arXiv.org, revised Dec 2020.
    2. Zak, Uri & Avrahami, Judith & Kareev, Yaakov, 2019. "The lions–foxes dilemma: The case of chess tournaments," Journal of Economic Psychology, Elsevier, vol. 75(PB).
    3. Dilmaghani, Maryam, 2021. "The gender gap in competitive chess across countries: Commanding queens in command economies," Journal of Comparative Economics, Elsevier, vol. 49(2), pages 425-441.
    4. Bilen, Eren & Matros, Alexander, 2023. "The Queen's Gambit: Explaining the superstar effect using evidence from chess," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 307-324.
    5. L'aszl'o Csat'o, 2024. "Most Swiss-system tournaments are unfair: Evidence from chess," Papers 2410.19333, arXiv.org, revised Jul 2025.
    6. Wei-Torng Juang & Guang-Zhen Sun & Kuo-Chih Yuan, 2020. "A model of parallel contests," International Journal of Game Theory, Springer;Game Theory Society, vol. 49(2), pages 651-672, June.
    7. Noemí Herranz-Zarzoso & Gerardo Sabater-Grande, 2020. "Self-selection bias in a field experiment: Recruiting subjects under different payment schemes," Working Papers 2020/13, Economics Department, Universitat Jaume I, Castellón (Spain).
    8. Agnieszka Szczepańska & Rafał Kaźmierczak, 2022. "The Theoretical Model of Decision-Making Behaviour Geospatial Analysis Using Data Obtained from the Games of Chess," IJERPH, MDPI, vol. 19(19), pages 1-25, September.
    9. Anthony Strittmatter & Uwe Sunde & Dainis Zegners, 2020. "Life cycle patterns of cognitive performance over the long run," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(44), pages 27255-27261, November.
    10. Csató, László & Boros, Réka & Czakó, Adrienn, 2025. "Igazságtalanul rendezik a svájci rendszerű sakkversenyeket?. Egy empirikus bizonyíték [Are Swiss-system chess tournaments unfair?. Empirical evidence]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 596-607.
    11. Deutscher, Christian & Neuberg, Lena & Thiem, Stefan, 2023. "Who’s afraid of the GOATs? - Shadow effects of tennis superstars," Journal of Economic Psychology, Elsevier, vol. 99(C).
    12. Johannes Carow & Niklas M. Witzig, 2024. "Time Pressure and Strategic Risk-Taking in Professional Chess," Working Papers 2404, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    13. Pastoriza, David & Alegre, Inés & Canela, Miguel A., 2021. "Conditioning the effect of prize on tournament self-selection," Journal of Economic Psychology, Elsevier, vol. 86(C).
    14. Jose De Sousa, "undated". "Peer competition: Evidence from 5- to 95-year-olds," French Stata Users' Group Meetings 2022 03, Stata Users Group.
    15. Ala Avoyan & Robizon Khubulashvili & Giorgi Mekerishvili, 2020. "Call It a Day: History Dependent Stopping Behavior," CESifo Working Paper Series 8603, CESifo.

    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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