ArticleScientific reports2023
A macro to micro analysis to understand performance in 100-mile ultra-marathons worldwide.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Modeling Pe2rformance in IRONMANSports medicine - open · 2025Article
- Race course characteristics are the most important predictors in 48 h ultramarathon running.Scientific reports · 2025Article
- Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model.Scientific reports · 2025Article
- Article
- An analysis of the 6-h ultra-marathon race using a machine learning approach.Frontiers in sports and active living · 2025Article
- Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons.Scientific reports · 2024Article
- Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events.PloS one · 2024Article
- Elderly female ultra-marathoners reduced the gap to male ultra-marathoners in Swiss running races.Scientific reports · 2023Article
- A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in IronmanInternational journal of environmental research and public health · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purposes of this study were (i) to describe differences in participation in 100-mile ultra-marathons by continent; (ii) to investigate differences in performance between continents; and (iii) to identify the fastest runners by continent and country. Data from 148,169 athletes (119,408 men), aged 18-81 years, and finishers in a 100-miles ultra-marathon during 1870-2020 were investigated. Information about age, gender, origin, performance level (top three, top 10, top 100) was obtained. Kruskal-Wallis tests and linear regressions were performed. Athletes were mostly from America and Europe. A macro-analysis showed that the fastest men runners were from Africa, while the fastest women runners were from Europe and Africa. Women from Sweden, Hungary and Russia presented the best performances in the top three, top 10 and top 100. Men from Brazil, Russia and Lithuania were the fastest. The lowest performance and participation were observed for runners from Asia. In summary, in 100-miles ultra-marathon running, the majority of athletes were from America, but for both sexes and performance levels, the fastest runners were from Africa. On a country level, the fastest women were from Sweden, Hungary and Russia, while the fastest men were from Brazil, Russia and Lithuania.
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