Knechtle Beat, Villiger Elias, Valero David, Braschler Lorin, Weiss Katja, Vancini Rodrigo Luiz, Andrade Marilia S, Scheer Volker, Nikolaidis Pantelis T, Cuk Ivan, Rosemann Thomas, Thuany Mabliny
Medbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001, St. Gallen, Switzerland.
Institute of Primary Care, University of Zurich, Zurich, Switzerland.
BMC Res Notes. 2024 Dec 19;17(1):372. doi: 10.1186/s13104-024-07028-8.
Ultra-marathon running races are held as distance-limited or time-limited events, ranging from 6 h to 10 days. Only a few runners compete in 10-day events, and so far, we have little knowledge about the athletes' origins, performance, and event characteristics. The aim of the present study was to investigate the origin and performance of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. The model explainability tools were then used to investigate how each independent variable would influence the predicted running speed.
The model rated the origin of the athlete as the most important predictor, followed by age group, running on dirt path, gender, running on asphalt, and event location. Running on dirt path led to a significant reduction of running speed, while running on asphalt showed faster running speeds compared to other surfaces. Most athletes came from USA, followed by Russia, Germany, Ukraine, the Czech Republic, and Slovakia. Most of the runners competed in USA. The fastest 10-day runners were from Finland and Israel. The fastest 10-day races were held in Greece.
Most 10-day runners originated from USA, but the fastest runners originate from Finland and Israel. The fastest race courses were in Greece. Running on dirt paths leads to a significant reduction in running speed while running on asphalt leads to faster running speeds.
超级马拉松比赛分为限时或限距赛事，时长从6小时到10天不等。只有少数选手参加为期10天的赛事，到目前为止，我们对这些运动员的出身、表现和赛事特点了解甚少。本研究的目的是调查这些选手的出身和表现以及最快的比赛地点。构建了一个基于XG Boost算法的机器学习模型，用于根据运动员的年龄、性别、原籍国、比赛举办国、比赛类型和跑道类型预测跑步速度。然后使用模型可解释性工具来研究每个自变量如何影响预测的跑步速度。
该模型将运动员的出身列为最重要的预测因素，其次是年龄组、在土路上跑步、性别、在柏油路上跑步和赛事地点。在土路上跑步会导致跑步速度显著降低，而在柏油路上跑步与其他路面相比速度更快。大多数运动员来自美国，其次是俄罗斯、德国、乌克兰、捷克共和国和斯洛伐克。大多数选手在美国参赛。最快的为期10天的比赛选手来自芬兰和以色列。最快的为期10天的比赛在希腊举行。
大多数参加为期10天比赛的选手来自美国，但最快的选手来自芬兰和以色列。最快的比赛路线在希腊。在土路上跑步会导致跑步速度显著降低，而在柏油路上跑步会使速度更快。
