Performance analysis and prediction in triathlon

Ofoghi, Bahadorreza, Zeleznikow, John, Macmahon, Clare, Rehula, Jan and Dwyer, Dan B. 2016, Performance analysis and prediction in triathlon, Journal of sports sciences, vol. 34, no. 7, pp. 607-612, doi: 10.1080/02640414.2015.1065341.

Attached Files
Name Description MIMEType Size Downloads

Title Performance analysis and prediction in triathlon
Author(s) Ofoghi, Bahadorreza
Zeleznikow, John
Macmahon, Clare
Rehula, Jan
Dwyer, Dan B.ORCID iD for Dwyer, Dan B.
Journal name Journal of sports sciences
Volume number 34
Issue number 7
Start page 607
End page 612
Total pages 6
Publisher Taylor & Francis
Place of publication Abingdon, Eng.
Publication date 2016
ISSN 0264-0414
Keyword(s) Bayesian networks
decision making
race strategy
race tactics
Summary Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008–2012). The analysis reveals patterns of performance in five components of triathlon (three race “legs” and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.
Language eng
DOI 10.1080/02640414.2015.1065341
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, Taylor & Francis
Persistent URL

Document type: Journal Article
Collections: Faculty of Health
School of Exercise and Nutrition Sciences
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 12 times in TR Web of Science
Scopus Citation Count Cited 11 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 617 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Tue, 22 Sep 2015, 14:41:10 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact