Analysis of the 72-h ultramarathon using a predictive XG Boost model

Author:

Knechtle BeatORCID,Villiger EliasORCID,Weiss KatjaORCID,Valero DavidORCID,Gajda RobertORCID,Scheer VolkerORCID,de Lira Claudio Andre BarbosaORCID,Braschler LorinORCID,Nikolaidis Pantelis T.ORCID,Vancini Rodrigo LuizORCID,Cuk IvanORCID,Rosemann ThomasORCID,Thuany MablinyORCID

Abstract

Abstract Background Ultramarathon running enjoys unwavering popularity. This includes the 72-h run, the longest time-limited ultramarathon based on hours and not days, yet this specific race format remains understudied. In particular, we are still determining where the fastest 72-h ultra-marathoners originate or where the fastest races are held. The aim of the present study was to investigate the origins of the best performers and the locations of the fastest races. Methods A machine learning model based on the XG Boost algorithm was built to predict running speed based on the athlete´s gender, age group, country of origin, the country where the race was held, the kind of race course (road, trail, track), and the elevation (flat, hilly). Model explainability tools were then used to investigate how each independent variable would influence the predicted running speed. Results A total of 2,857 race records from 1,870 unique runners from 36 different countries participating in 55 races held in 22 countries between 1989 and 2022 were analyzed. Athletes from the USA account for more than 2/3 of the sample size. Also, more than 3/4 of the participants competed in USA-based races. Athletes from Ireland, Japan, and Ukraine were the fastest. In respect of the fastest races, they were held in Ukraine, The Netherlands, and Japan. The model rated the country of event as the most important predictor followed by the race characteristics of elevation and race course, athlete country of origin, age group, and gender. On average, men were 0.33 km/h faster than women. The fastest running speeds were achieved by runners in age group 45–49 years. Conclusions The country of the event was found to be the most important predictor in the 72-h run. Despite the dominance of runners from USA and the predominance of courses in the USA in terms of participation, athletes from Ireland, Japan, and Ukraine achieved the fastest times, while Ukraine, The Netherlands, and Japan were found to host the fastest courses.

Funder

Open access funding provided by University of Zurich

University of Zurich

Publisher

Springer Science and Business Media LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3