Forecasting the Olympic medal distribution during a pandemic: A socio-economic machine learning model ∗ Christoph Schlembach, Sascha L. Schmidt, Dominik Schreyer, and Linus Wunderlich First Version: December 09, 2020 This Version: June 21, 2021 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 naïve 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). Keywords: Olympic Games, medals, sports, forecasting, machine learning, random forest JEL: C53; Z20 Running head: Forecasting the Olympic medal distribution ∗ Schlembach: WHU – Otto Beisheim School of Management, Erkrather Str. 224a, 40233, Düsseldorf, Germany (e-mail:
[email protected]); Schmidt: WHU – Otto Beisheim School of Management, Erkrather Str. 224a, 40233, Düsseldorf, Germany, and CREMA – Center for Research in Economics, Management and the Arts, Switzerland, and LISH – Lab of Innovation Science at Harvard, 175 N. Harvard Street Suite 1350, Boston, MA 02134, USA (e-mail:
[email protected]); Schreyer: WHU – Otto Beisheim School of Management, Erkrather Str. 224a, 40233, Düsseldorf, Germany (e-mail:
[email protected]); Wunderlich: School of Mathematical Sciences, Queen Mary University London, Mile End Road, London E1 4NS (e-mail:
[email protected]).