An analysis of the 6-h ultra-marathon race using a machine learning approach

Abstract

Background: Ultra-marathon running popularity is increasing, with the 6-h run being the shortest time-limited ultra-marathon. Since very little is known regarding the country from which the fastest 6-h runners originate, the fastest age group, and where the fastest 6-h race courses are located, this study aims to close this gap. Methods: A machine learning model based on the XG Boost algorithm was built to predict running speed based on the athletés age, gender, country of origin, and the country where the race takes place. Model explainability tools were used to investigate how each independent variable would influence the predicted running speed. To assess the impact of individual performance against the other variables under study, a Mixed Effects Linear Model was also built. Results: A total of 117,882 race records from 51,018 unique runners from 65 countries participating in races held in 56 different countries were analyzed. Participation is spread across a wide range of countries, with a high correlation between the country of origin and the country of the event. Most runners originated from Germany, Italy, France, the USA, and Sweden, with Europe (Belgium, Russia, Spain, Poland, Romania, and Lithuania), being the fastest. Most athletes competed in Italy, Germany, France, the USA, and The Netherlands. The fastest average running speeds were also achieved in European countries (Russia, Belgium, Poland, Netherlands, Romania, Croatia, and Lithuania). Conclusions: For athletes competing in a 6-h ultramarathon, gender was the most important predictor, followed by the origin of the athlete, the age, and the race location. The 6-h running event seems to be dominated by European athletes regarding both participation and performance.