European Journal of Applied Physiology

, Volume 115, Issue 5, pp 1147–1154 | Cite as

Pacing strategies during the swim, cycle and run disciplines of sprint, Olympic and half-Ironman triathlons

  • Sam Shi Xuan WuEmail author
  • Jeremiah J. Peiffer
  • Jeanick Brisswalter
  • Kazunori Nosaka
  • Wing Yin Lau
  • Chris R. Abbiss
Original Article



This study investigated the influence of distance on self-selected pacing during the swim, cycle and run disciplines of sprint, Olympic and half-Ironman (HIM) distance triathlon races.


Eight trained male triathletes performed the three individual races in <2 months. Participants’ bikes were fitted with Schoberer Rad Meßtechnik to monitor speed, power output and heart rate during the cycle discipline. Global positioning system was worn to determine speed and heart rate during the swim and run disciplines.


An even swim pacing strategy was adopted across all distances. A more stochastic pacing was observed during the HIM cycle [standard deviation of exposure variation analysis (EVASD) = 3.21 ± 0.61] when compared with the sprint cycle discipline (EVASD = 3.84 ± 0.44, p = 0.018). Only 20.9 ± 4.1 % of the cycling time was spent more than 10 % above the mean power output in the HIM, compared with 43.8 ± 2.9 % (p = 0.002) and 37.7 ± 11.1 % (p = 0.039) during the sprint and Olympic distance triathlons, respectively. Conversely, 13.6 ± 5.1 % of the cycling time was spent 5–10 % below the mean power output during the HIM, compared with 5.9 ± 1.2 % (p = 0.034) and 8.0 ± 5.1 % (p = 0.045) during the sprint and Olympic distance triathlons, respectively. A negative pacing strategy was adopted during the sprint distance run, compared with positive pacing strategy during the Olympic and HIM.


Results of this study suggest that pacing strategies during triathlon are highly influenced by distance and discipline, and highlight the importance of developing pacing strategies based on distance, strengths and individual fitness.


Multisport Power output Endurance Ironman Exposure variation analysis 



Analysis of variance


Exposure variation analysis


Standard deviation of exposure variation analysis


Global positioning system




Maximal aerobic power


Power band


Relative humidity


Schoberer Rad Meßtechnik


Time band


Peak oxygen uptake



The authors would like to thank all who helped and participated in this study. At the time of this study, Sam SX Wu was the recipient of an Australian government funded Postgraduate Research Scholarship (Australian Postgraduate Award) and an additional University funded Scholarship (Edith Cowan University Research Excellence Award).

Conflict of interest

No conflict of interest was declared for all authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sam Shi Xuan Wu
    • 1
    Email author
  • Jeremiah J. Peiffer
    • 2
  • Jeanick Brisswalter
    • 3
  • Kazunori Nosaka
    • 1
  • Wing Yin Lau
    • 1
  • Chris R. Abbiss
    • 1
  1. 1.Centre for Exercise and Sport Science Research, School of Exercise and Health SciencesEdith Cowan UniversityJoondalupAustralia
  2. 2.School of Psychology and Exercise ScienceMurdoch UniversityPerthAustralia
  3. 3.Laboratory of Human Motricity, Education Sport and HealthUniversity of Nice Sophia AntipolisNiceFrance

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