Forecasting the Olympic medal distribution – A socioeconomic machine learning model

Christoph Schlembach, Sascha L. Schmidt, Dominik Schreyer and Linus Wunderlich

Technological Forecasting and Social Change, 2022, vol. 175, issue C

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. We apply machine learning, more specifically a two-staged Random Forest, to a dataset containing socioeconomic variables of 206 countries (1991–2020). For the first time, we outperform the more traditional naïve forecast for four consecutive Olympics between 2008 and 2020.

Keywords: Olympic games; Medals; Sports; Forecasting; Machine learning; random forest (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521007459

DOI: 10.1016/j.techfore.2021.121314

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