Knechtle Beat, Weiss Katja, Valero David, Scheer Volker, Villiger Elias, Nikolaidis Pantelis T, Andrade Marilia, Cuk Ivan, Gajda Robert, 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 Jul 11;15(1):25176. doi: 10.1038/s41598-025-09502-0.
The 100-km ultra-marathon is one of the most popular ultra-marathon distances. While we have a lot of scientific knowledge, no data exist about the influence of race course characteristics and other geographical aspects, on race performance. Therefore, the aims of this study were (i) to investigate where the fastest 100-km races are held and where the fastest runners originate from, (ii) to evaluate a potential influence of specific race characteristics (i.e., influence of elevation and race course characteristics) on performance, and (iii) to assess the influence of individual athlete performance against the other investigated factors. A total of 858,544 race records (732,748 from men and 125,796 from women) from 317,312 unique runners originating from 103 different countries and participating in 2,648 100-km races held in 80 different countries worldwide between 1892 and 2022 were analyzed using several descriptive, inferential and predictive methods, including a machine learning XG Boost Regression model. We evaluated the influence on the average running speed (in km/h) of factors such as gender of the athlete, age group, country of origin of the athlete, country where the race was held, course characteristics (i.e. mountain, trail, road, or track race) and elevation (i.e. flat or hilly course). The relative effect of the individual athlete performance was also investigated through a Mixed Effects Linear model. Discounting the fact that individual athlete performance is between 3 and 4 times ahead in race speed influence compared to the other factors, the model rated elevation (0.85) as the most important variable ahead of the country where the race was held (0.07), gender (0.02), age group (0.02), the country of origin of the runner (0.02) and the course characteristics (0.02). Running on a track (9.32 km/h) was the fastest ahead of road running (8.11 km/h), trail running (6.21 km/h) and mountain running (5.74 km/h). Flat running (8.85 km/h) was faster than running on a hilly course (6.57 km/h). The fastest athletes originated from African and Eastern European countries, with Swaziland (13.15 ± 0.88 km/h), Botswana (11.61 ± 2.22 km/h), Belarus (11.10 ± 2.29 km/h), Kazakhstan (10.74 ± 3.78 km/h), and Cape Verde (10.49 ± 2.26 km/h) in the top five. Africa, the Middle East, and Europe hold the fastest 100 km races, with Botswana (12.23 ± 1.35 km/h), Qatar (12.10 ± 1.63 km/h), Belarus (11.24 ± 1.27 km/h), Jordania (11.05 ± 1.58 km/h), and Montenegro (10.63 ± 1.90 km/h) in the top five. In summary, elevation was the most important variable in 100-km ultra-marathon running ahead of the country where the race was held, gender, age group, country of origin of the runner and course characteristics. Running on a track was the fastest ahead of road, trail and mountain running. Flat running was faster than running on a hilly course. Africa, the Middle East, and Europe hold the fastest 100 km races. Common for the fastest 100-km race courses was the fact that they were mainly indoor races and/or Championships. The fastest runners originated mainly from former republics of the dissolved Soviet Union. Future studies might select the fastest 100-km race courses.
100公里超级马拉松是最受欢迎的超级马拉松距离赛事之一。虽然我们掌握了大量科学知识,但尚无关于赛道特征和其他地理因素对比赛成绩影响的数据。因此,本研究的目的是:(i)调查最快的100公里比赛在何处举办以及最快的选手来自哪里;(ii)评估特定比赛特征(即海拔和赛道特征)对成绩的潜在影响;(iii)评估个体运动员成绩相对于其他调查因素的影响。使用多种描述性、推断性和预测性方法,包括机器学习XG Boost回归模型,对来自103个不同国家、参加了1892年至2022年间在全球80个不同国家举行的2648场100公里比赛的317312名独特选手的858544条比赛记录(男性732748条,女性125796条)进行了分析。我们评估了运动员性别、年龄组、运动员原籍国、比赛举办国、赛道特征(即山地、小径、公路或跑道比赛)和海拔(即平坦或起伏赛道)等因素对平均跑步速度(公里/小时)的影响。还通过混合效应线性模型研究了个体运动员成绩的相对影响。不考虑个体运动员成绩在比赛速度影响方面比其他因素领先3到4倍这一事实,该模型将海拔(0.85)列为最重要的变量,领先于比赛举办国(0.07)、性别(0.02)、年龄组(0.02)、选手原籍国(0.02)和赛道特征(0.02)。在跑道上跑步(9.32公里/小时)是最快的,领先于公路跑步(8.11公里/小时)、小径跑步(6.21公里/小时)和山地跑步(5.74公里/小时)。平坦赛道跑步(8.85公里/小时)比起伏赛道跑步(6.57公里/小时)更快。最快的运动员来自非洲和东欧国家,斯威士兰(13.15±0.88公里/小时)、博茨瓦纳(11.61±2.22公里/小时)、白俄罗斯(11.10±2.29公里/小时)、哈萨克斯坦(10.74±3.78公里/小时)和佛得角(10.49±2.26公里/小时)位列前五。非洲、中东和欧洲举办的100公里比赛速度最快,博茨瓦纳(12.23±1.35公里/小时)、卡塔尔(12.10±1.63公里/小时)、白俄罗斯(11.24±1.27公里/小时)、约旦(11.05±1.58公里/小时)和黑山(10.63±1.90公里/小时)位列前五。总之,在100公里超级马拉松比赛中,海拔是比比赛举办国、性别、年龄组、选手原籍国和赛道特征更重要的变量。在跑道上跑步比公路、小径和山地跑步更快。平坦赛道跑步比起伏赛道跑步更快。非洲、中东和欧洲举办的100公里比赛速度最快。最快的100公里比赛赛道的共同特点是它们主要是室内比赛和/或锦标赛。最快的选手主要来自解体后的苏联各加盟共和国。未来的研究可能会选择最快的100公里比赛赛道。