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Forecasting National Medal Totals at the Summer Olympic Games Reconsidered

Author

Listed:
  • Nicolas Scelles
  • Wladimir Andreff
  • Liliane Bonnal
  • Madeleine Andreff
  • Pascal Favard

Abstract

Objective 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. Method Econometric testing not only resorts to a Tobit model as usual but also to a Hurdle model. Results Most variables have a significant impact on national team medal totals; it appears to be negative for most regions other than North America except Western Europe and Oceania (not significant). Then, two models (Tobit and Hurdle) are implemented to forecast national medal totals at the 2016 and 2020 Summer Olympics. Conclusion Both models are complementary for the 2016 forecast. The 2020 forecast is consistent with Olympic Medals Predictions, although some striking differences are found.

Suggested Citation

  • Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 697-711, March.
    • Handle: RePEc:bla:socsci:v:101:y:2020:i:2:p:697-711
    DOI: 10.1111/ssqu.12782
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    References listed on IDEAS

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    1. 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.
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    1. 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).
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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