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Cycling is the most important predictive split discipline in professional Ironman® 70.3 triathletes

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  • معلومة اضافية
    • بيانات النشر:
      Frontiers Research Foundation
    • الموضوع:
      2024
    • Collection:
      University of Zurich (UZH): ZORA (Zurich Open Repository and Archive
    • نبذة مختصرة :
      Introduction: Our study examined 16,611 records of professional triathletes from 163 Ironman® 70.3 races across 97 countries (2004-2020). The aim was to identify the most predictive discipline—swim, bike, or run—for overall race time. Methods: We used correlation matrices to compare the dependent variable “finish time” with independent variables “swim time,” “bike time,” and “run time.” This analysis was conducted separately for male and female athletes. Additionally, univariate and multiple linear regression models assessed the strength of these associations. Results: The results indicated that “bike time” had the strongest correlation with finish time (0.85), followed by “run time” (0.75 for females, 0.82 for males) and “swim time” (0.46 for females, 0.63 for males). Regression models confirmed “bike time” as the strongest predictor of overall race time (R² = 0.8), with “run time” and “swim time” being less predictive. Discussion. The study concludes that in Ironman 70.3 races, “bike time” is the most significant predictor of overall race performance for both sexes, suggesting a focus on cycling in training and competition strategies. It also highlights a smaller performance gap between genders in swimming than in cycling or running.
    • File Description:
      application/pdf
    • ISSN:
      2624-9367
    • Relation:
      https://www.zora.uzh.ch/id/eprint/256519/1/ZORA_pdf_isPublishedV2_False.pdf; urn:issn:2624-9367
    • الرقم المعرف:
      10.5167/uzh-256519
    • الرقم المعرف:
      10.3389/fspor.2024.1214929
    • Rights:
      info:eu-repo/semantics/openAccess ; Creative Commons: Attribution 4.0 International (CC BY 4.0) ; http://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.F7CA5D18