Knechtle Beat, Valero David, Villiger Elias, Weiss Katja, Nikolaidis Pantelis T, Braschler Lorin, Vancini Rodrigo Luiz, Andrade Marilia Santos, 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.
Sci Rep. 2025 Mar 29;15(1):10901. doi: 10.1038/s41598-025-94402-6.
Ultra-marathon running - where races are held in distance-limited (50 km, 50 miles, 100 km, 100 miles, etc.), time-limited (6 h, 12 h, 24 h, 48 h, 72 h, etc.), and multi-stage races - is gaining in popularity. However, we have no knowledge of where the fastest 48-hour runners originate and where the fastest 48-hour races are held. This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). Model explainability tools were then used to investigate how each independent variable would influence the predicted result. A sample of 16,233 race records from 7,075 unique runners originating from 60 different countries and participating in races held in 36 different countries between 1980 and 2022 was analyzed. Participation was spread across many countries, with USA, France, Germany, and Australia at the top of the participants' rankings. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. The XG Boost model showed that elevation of the course (flat course) and the running surface (track) were the variables that had a larger influence on the running speed. The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. Men were ~ 0.5 km/h faster than women. Most finishers were 45-49 years old, and runners in this age group achieved the fastest running speeds. In summary, elevation of the course (flat course) and the running surface (track) were the most important variables for fast 48-hour races, whilst the country of origin of the athlete and the country where the event was hold would lead to the broadest difference in the predicted running speed range. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. Any athlete intending to achieve a personal best performance in this race format can benefit from these findings by selecting the most appropriate race course.
超级马拉松比赛——包括距离限定(50公里、50英里、100公里、100英里等)、时间限定(6小时、12小时、24小时、48小时、72小时等)以及多阶段赛事——正越来越受欢迎。然而,我们并不清楚最快的48小时跑者来自哪里,以及最快的48小时比赛在哪里举办。本研究试图确定最快的48小时跑者的来源,以及与48小时超级马拉松表现相关的预测因素,如年龄、性别、赛事举办国家、运动员原籍国以及赛道的特定特征。基于XG Boost算法构建了一个机器学习(ML)模型,以根据运动员的年龄、性别、原籍国、比赛举办地以及赛道特征(如海拔高度(平坦或多山)和路面状况(沥青、水泥、花岗岩、草地、砾石、沙地、跑道或小径))来预测跑步速度。然后使用模型可解释性工具来研究每个自变量将如何影响预测结果。分析了1980年至2022年间来自60个不同国家的7075名独特跑者的16233条比赛记录样本,这些跑者参加了在36个不同国家举办的比赛。参赛分布在许多国家,美国、法国、德国和澳大利亚在参赛人数排名中位居前列。来自日本、以色列和冰岛的运动员平均跑步速度最快。最快的比赛在日本、法国、英国、荷兰和埃及举办。XG Boost模型表明,赛道海拔(平坦赛道)和跑步路面(跑道)是对跑步速度影响较大的变量。通过SHAP分析,运动员的原籍国和赛事举办国家是最重要的特征,产生了更广泛的模型输出范围。男性比女性快约0.5公里/小时。大多数完赛者年龄在45 - 49岁之间,这个年龄组的跑者跑步速度最快。总之,赛道海拔(平坦赛道)和跑步路面(跑道)是48小时快速比赛中最重要的变量,而运动员的原籍国和赛事举办国家会导致预测跑步速度范围的最大差异。来自日本、以色列和冰岛的运动员平均跑步速度最快。最快的比赛在日本、法国、英国、荷兰和埃及举办。任何打算在这种比赛形式中取得个人最佳成绩的运动员都可以通过选择最合适的赛道从这些发现中受益。