Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model
This research provides improved Olympic medal forecasting accuracy for sports betting companies, sponsors, media companies, and sports politicians/managers, representing an incremental improvement in prediction methodology.
This paper developed a two-staged Random Forest model to forecast Olympic medal counts for nations, outperforming traditional naive forecasts for the 2008, 2012, and 2016 Olympics. For the Tokyo 2020 Games, the model predicts the United States will win 120 medals, China 87, and Great Britain 74, with the COVID-19 pandemic having no significant impact on medal distribution.
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). 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).