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A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in Ironman ® 70.3 Races between 2004 and 2020

Author

Listed:
  • Mabliny Thuany

    (Faculty of Sports, University of Porto, 4200-450 Porto, Portugal)

  • David Valero

    (Ultra Sports Science Foundation, 69310 Pierre-Benite, France)

  • Elias Villiger

    (Klinik für Allgemeine Innere Medizin, Kantonsspital St. Gallen, 9000 St. Gallen, Switzerland)

  • Pedro Forte

    (CI-ISCE, Higher Institute of Educational Sciences of the Douro, 4560-708 Penafiel, Portugal
    Research Center in Sports, Health and Human Development, 6201-001 Covilhã, Portugal
    Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal)

  • Katja Weiss

    (Institute of Primary Care, University Hospital Zurich, 8091 Zurich, Switzerland)

  • Pantelis T. Nikolaidis

    (School of Health and Caring Sciences, University of West Attica, 12243 Athens, Greece)

  • Marília Santos Andrade

    (Department of Physiology, Federal University of São Paulo, São Paulo 04021-001, Brazil)

  • Ivan Cuk

    (Faculty of Sport and Physical Education, University of Belgrade, 11000 Belgrade, Serbia)

  • Caio Victor Sousa

    (Health and Human Sciences, Loyola Marymount University, Los Angeles, CA 90045, USA)

  • Beat Knechtle

    (Institute of Primary Care, University Hospital Zurich, 8091 Zurich, Switzerland
    Medbase St. Gallen Am Vadianplatz, 9001 St. Gallen, Switzerland)

Abstract

Our purpose was to find the fastest race courses for elite Ironman ® 70.3 athletes, using machine learning (ML) algorithms. We collected the data of all professional triathletes competing between 2004 and 2020 in Ironman 70.3 races held worldwide. A sample of 16,611 professional athletes originating from 97 different countries and competing in 163 different races was thus obtained. Four different ML regression models were built, with gender, country of origin, and event location considered as independent variables to predict the final race time. For all the models, gender was the most important variable in predicting finish times. Attending to the single decision tree model, the fastest race times in the Ironman ® 70.3 World Championship of around ~4 h 03 min would be achieved by men from Austria, Australia, Belgium, Brazil, Switzerland, Germany, France, the United Kingdom, South Africa, Canada, and New Zealand. Considering the World Championship is the target event for most professional athletes, it is expected that training is planned so that they attain their best performance in this event.

Suggested Citation

  • Mabliny Thuany & David Valero & Elias Villiger & Pedro Forte & Katja Weiss & Pantelis T. Nikolaidis & Marília Santos Andrade & Ivan Cuk & Caio Victor Sousa & Beat Knechtle, 2023. "A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in Ironman ® 70.3 Races between 2004 and 2020," IJERPH, MDPI, vol. 20(4), pages 1-12, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3619-:d:1072549
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    References listed on IDEAS

    as
    1. Beat Knechtle & Thomas Rosemann & Pantelis Theo Nikolaidis, 2020. "The Role of Nationality in Ultra-Endurance Sports: The Paradigm of Cross-Country Skiing and Long-Distance Running," IJERPH, MDPI, vol. 17(7), pages 1-9, April.
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