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Scientific Reports|July 17, 2023
Predicting overall performance in Ironman 70.3 age group triathletes through split disciplinesPantelis Theodoros Nikolaidis, David Valero, Katja Weiss, et al.
Sports Medicine - Open|September 21, 2023
Comparing the Performance Gap Between Males and Females in the Older Age Groups in IRONMAN® 70.3: An Internet-Based Cross-Sectional Study of More Than 800,000 Race RecordsBeat Knechtle, David Valero, Elias Villiger, et al.
Frontiers in Physiology|May 31, 2021
Trends in Weather Conditions and Performance by Age Groups Over the History of the Berlin MarathonBeat Knechtle, David Valero, Elias Villiger, et al.
Plos One|October 7, 2024
Sex difference in IRONMAN age group triathletesBeat Knechtle, David Valero, Elias Villiger, et al.
Scientific Reports|January 21, 2025
The association of origin and environmental conditions with performance in professional IRONMAN triathletesBeat Knechtle, Mabliny Thuany, David Valero, et al.
Scientific Reports|January 6, 2025
The fastest nonprofessional age group IRONMAN triathletes in the world originate from EuropeBeat Knechtle, Elias Villiger, Katja Weiss, et al.
Sports Medicine - Open|November 25, 2025
Modeling Pe2rformance in IRONMAN<sup>®</sup> 70.3 Age Group TriathletesMabliny Thuany, David Valero, Elias Villiger, et al.
Sports Medicine - Open|March 28, 2025
Cycling and Running are More Predictive of Overall Race Finish Time than Swimming in IRONMAN<sup>®</sup> Age Group TriathletesBeat Knechtle, David Valero, Elias Villiger, et al.
Plos One|August 22, 2024
Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing eventsBeat Knechtle, Katja Weiss, David Valero, et al.
International Journal of Environmental Research and Public Health|February 25, 2023
A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in Ironman<sup>®</sup> 70.3 Races between 2004 and 2020Mabliny Thuany, David Valero, Elias Villiger, et al.
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