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. 2024 Feb 8:6:1214929.
doi: 10.3389/fspor.2024.1214929. eCollection 2024.

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

Katja Weiss  1 , David Valero  2 , Marilia Santos Andrade  3 , Elias Villiger  4 , Mabliny Thuany  5 , Beat Knechtle  1   6
Affiliations

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

Katja Weiss et al. Front Sports Act Living. .

Abstract

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.

Keywords: performance; prediction; race time; split discipline; triathlon.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Trends in professional (PRO) female and male participation across years with the male-to-female ratio.
Figure 2
Figure 2
Trends in average and best overall race times for both professional (PRO) female and male athletes.
Figure 3
Figure 3
Distribution (histograms) of finish and split times by sex of the female and male professional (PRO) triathletes (times in HH:MM:SS).
Figure 4
Figure 4
Pearson correlation matrix of finish and split times of the female and male professional triathletes.
Figure 5
Figure 5
Scatter plots of finish and split times of the female and male professional (PRO) triathletes (times in HH:MM:SS).
Figure 6
Figure 6
Univariate linear regressions for swimming, cycling and running (times in seconds) for both sexes together.
Figure 7
Figure 7
Univariate linear regressions for swimming, cycling, and running (times in seconds) for each sex.
Figure 8
Figure 8
Relationships between split times of the female (F) and male (M) professional (PRO) triathletes by sex (scatter plots) along with the distributions of each variable in the diagonal (times in seconds).
See this image and copyright information in PMC

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