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Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model

Author

Listed:
  • Christoph Schlembach
  • Sascha L. Schmidt
  • Dominik Schreyer
  • Linus Wunderlich

Abstract

Forecasting the number of Olympic medals for each nation is highly relevant for different stakeholders: Ex ante, sports betting companies can determine the odds while sponsors and media companies can allocate their resources to promising teams. Ex post, sports politicians and managers can benchmark the performance of their teams and evaluate the drivers of success. To significantly increase the Olympic medal forecasting accuracy, we apply machine learning, more specifically a two-staged Random Forest, thus outperforming more traditional na\"ive forecast for three previous Olympics held between 2008 and 2016 for the first time. Regarding the Tokyo 2020 Games in 2021, our model suggests that the United States will lead the Olympic medal table, winning 120 medals, followed by China (87) and Great Britain (74). Intriguingly, we predict that the current COVID-19 pandemic will not significantly alter the medal count as all countries suffer from the pandemic to some extent (data inherent) and limited historical data points on comparable diseases (model inherent).

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2012.04378
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).

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