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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026)

by
Aldo Seffrin
1,
Pantelis Theodoros Nikolaidis
2,
Marilia Santos Andrade
3,
Elias Villiger
4,
Thomas Rosemann
4,
Katja Weiss
4,
Daniel Ferreira
1 and
Beat Knechtle
4,5,*
1
Nova O2 Sports Science, São Paulo 12246-020, Brazil
2
School of Health and Caring Sciences, University of West Attica, 12243 Athens, Greece
3
Department of Physiology, Federal University of São Paulo (UNIFESP), São Paulo 04023-062, Brazil
4
Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland
5
Medbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001 St. Gallen, Switzerland
*
Author to whom correspondence should be addressed.
Sci 2026, 8(9), 265; https://doi.org/10.3390/sci8090265 (registering DOI)
Submission received: 25 July 2026 / Revised: 10 September 2026 / Accepted: 15 September 2026 / Published: 19 September 2026
(This article belongs to the Section Sports Science and Medicine)
Review Reports Versions Notes

Abstract

Long-distance triathlon has become a major model for studying endurance performance. Research has nonetheless been constrained by distance-specific datasets that separate full-distance from half-distance events and largely omit transition times. Our aim was to assemble, validate, and describe an openly available dataset that removes both constraints. We compiled 2,706,922 athlete-race records from more than 1500 IRONMAN® and IRONMAN® 70.3 events held between 2002 and 2026, the final season partial, drawn from the official IRONMAN® results platform (75.4%) and a third-party aggregator covering event series absent from it (24.6%). Each record contains swimming, first transition (T1), cycling, second transition (T2), running, and overall times in seconds, with finish status, age group, gender, country, and qualification points. The dataset showed close correspondence between sources and high internal consistency. Split times were identical to the second for 98.8% of 4,732,776 athlete–discipline pairs across all 559 race-years present in both sources, summed splits matched reported overall times within one second for 98.3% of records, and separated T1 and T2 times were available for 84.2% and 84.1% of records—to our knowledge, the first population-level provision of this information. Openly deposited with reproducible collection scripts, the dataset supports research on pacing, transition efficiency, sex differences, non-completion, and qualification equity.
Keywords: triathlon; IRONMAN®; open dataset; transition times; data validation; endurance performance; race results; reproducibility triathlon; IRONMAN®; open dataset; transition times; data validation; endurance performance; race results; reproducibility

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MDPI and ACS Style

Seffrin, A.; Nikolaidis, P.T.; Andrade, M.S.; Villiger, E.; Rosemann, T.; Weiss, K.; Ferreira, D.; Knechtle, B. A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026). Sci 2026, 8, 265. https://doi.org/10.3390/sci8090265

AMA Style

Seffrin A, Nikolaidis PT, Andrade MS, Villiger E, Rosemann T, Weiss K, Ferreira D, Knechtle B. A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026). Sci. 2026; 8(9):265. https://doi.org/10.3390/sci8090265

Chicago/Turabian Style

Seffrin, Aldo, Pantelis Theodoros Nikolaidis, Marilia Santos Andrade, Elias Villiger, Thomas Rosemann, Katja Weiss, Daniel Ferreira, and Beat Knechtle. 2026. "A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026)" Sci 8, no. 9: 265. https://doi.org/10.3390/sci8090265

APA Style

Seffrin, A., Nikolaidis, P. T., Andrade, M. S., Villiger, E., Rosemann, T., Weiss, K., Ferreira, D., & Knechtle, B. (2026). A Validated, Population-Scale Dataset of 2.7 Million IRONMAN® Triathlon Records with Separated Transition Times (2002–2026). Sci, 8(9), 265. https://doi.org/10.3390/sci8090265

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