  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).
