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Forecasting National Medal Totals at the Summer Olympic Games Reconsidered

Author

Listed:
  • Nicolas Scelles

    (MMU - Manchester Metropolitan University)

  • Wladimir Andreff

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Liliane Bonnal

    (Université de Poitiers - Faculté de Sciences économiques - UP - Université de Poitiers = University of Poitiers, CRIEF [Poitiers] - Centre de recherche sur l'intégration économique et financière - UP - Université de Poitiers = University of Poitiers)

  • Madeleine Andreff

    (Université Gustave Eiffel)

  • Pascal Favard

    (IRJI - Institut de recherche juridique interdisciplinaire - UT - Université de Tours)

Abstract

This article aims at explaining national medal totals at the 1992–2016 Summer Olympic Games (n = 1,289 observations) and forecasting them in 2016 (based on 1992–2012 data) and 2020 with a set of variables similar to previous studies, as well as a regional (subcontinents) variable not tested previously in the literature in English.

Suggested Citation

  • Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Post-Print hal-03206951, HAL.
    • Handle: RePEc:hal:journl:hal-03206951
    DOI: 10.1111/ssqu.12782
    Note: View the original document on HAL open archive server: https://hal.science/hal-03206951
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    References listed on IDEAS

    as
    1. Vagenas, George & Vlachokyriakou, Eleni, 2012. "Olympic medals and demo-economic factors: Novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited," Sport Management Review, Elsevier, vol. 15(2), pages 211-217.
    2. Marcus Noland & Kevin Stahler, 2016. "What Goes into a Medal: Women's Inclusion and Success at the Olympic Games," Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 177-196, June.
    3. Leeds Eva Marikova & Leeds Michael A., 2012. "Gold, Silver, and Bronze: Determining National Success in Men’s and Women’s Summer Olympic Events," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(3), pages 279-292, June.
    4. Pravin K. Trivedi & David M. Zimmer, 2014. "Success at the Summer Olympics: How Much Do Economic Factors Explain?," Econometrics, MDPI, vol. 2(4), pages 1-34, December.
    5. Aaron Lowen & Robert O. Deaner & Erika Schmitt, 2016. "Guys and Gals Going for Gold," Journal of Sports Economics, , vol. 17(3), pages 260-285, April.
    6. Andrew B. Bernard & Meghan R. Busse, 2004. "Who Wins the Olympic Games: Economic Resources and Medal Totals," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 413-417, February.
    7. Forrest, David & Sanz, Ismael & Tena, J.D., 2010. "Forecasting national team medal totals at the Summer Olympic Games," International Journal of Forecasting, Elsevier, vol. 26(3), pages 576-588, July.
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    Cited by:

    1. David Forrest & J. D. Tena & Carlos Varela-Quintana, 2023. "The influence of schooling on performance in chess and at the Olympics," Empirical Economics, Springer, vol. 64(2), pages 959-982, February.
    2. Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
    3. Carl Singleton & J. James Reade & Johan Rewilak & Dominik Schreyer, 2021. "How big is home advantage at the Olympic Games?," Economics Discussion Papers em-dp2021-13, Department of Economics, University of Reading.
    4. Franklin G. Mixon Jr. & Richard J. Cebula, 2022. "Property Rights Freedom and Innovation: Eponymous Skills in Women's Gymnastics," Journal of Sports Economics, , vol. 23(4), pages 407-430, May.
    5. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Emilio Gómez-Déniz & Nancy Dávila-Cárdenes & Alejandro Leiva-Arcas & María J. Martínez-Patiño, 2021. "Measuring Efficiency in the Summer Olympic Games Disciplines: The Case of the Spanish Athletes," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
    7. David Forrest & J.D Tena & Carlos Varela-Quintana, 2020. "Who wins at the Chess Olympics? The role of resources and education capital," Working Papers 202013, University of Liverpool, Department of Economics.

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