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Performance analysis and prediction in triathlon
journal contribution
posted on 2016-01-01, 00:00 authored by Bahadorreza OfoghiBahadorreza Ofoghi, J Zeleznikow, C Macmahon, J Rehula, Dan DwyerDan DwyerPerformance 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.
History
Journal
Journal of sports sciencesVolume
34Issue
7Pagination
607 - 612Publisher
Taylor & FrancisLocation
Abingdon, Eng.Publisher DOI
ISSN
0264-0414eISSN
1466-447XLanguage
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, Taylor & FrancisUsage metrics
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