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

TL;DR

This paper introduces a socio-economic machine learning model using a two-staged Random Forest to forecast Olympic medal distributions, outperforming traditional methods and suggesting pandemic impacts are limited.

Contribution

The paper presents a novel two-staged Random Forest approach for Olympic medal forecasting, incorporating socio-economic factors and pandemic data, improving accuracy over naive models.

Findings

US predicted to lead with 120 medals

Pandemic impact on medal counts is limited

Model outperforms traditional forecasting methods

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).