title: Performance analysis and prediction in triathlon.
abstract: 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.
authors.affiliation: a Department of Computing and Information Systems , The University of Melbourne , Parkville , Australia.
authors.forename: Bahadorreza
authors.initials: B
authors.lastname: Ofoghi
authors.affiliation: b College of Business , Victoria University , Melbourne , Australia.
authors.forename: John
authors.initials: J
authors.lastname: Zeleznikow
authors.affiliation: c Department of Biomedical and Health Sciences , Swinburne University of Technology , Melbourne , Australia.
authors.forename: Clare
authors.initials: C
authors.lastname: Macmahon
authors.affiliation: d Triathlon Australia and the Victorian Institute of Sport , Melbourne , Australia.
authors.forename: Jan
authors.initials: J
authors.lastname: Rehula
authors.affiliation: e Centre for Exercise & Sport Science, School of Exercise and Nutrition Sciences , Deakin University , Geelong , Australia.
authors.forename: Dan B
authors.initials: DB
authors.lastname: Dwyer
mesh_terms: D054874:Athletic Performance / Q000502:physiology* / Q000523:psychology; D001499:Bayes Theorem; D001642:Bicycling / Q000502:physiology*; D003163:Competitive Behavior / Q000502:physiology; D003657:Decision Making; D005260:Female; D006040:Goals; D006801:Humans; D008297:Male; D010806:Physical Education and Training; D012420:Running / Q000502:physiology*; D012737:Sex Factors; D013550:Swimming / Q000502:physiology*; D013647:Task Performance and Analysis; D013997:Time Factors
keywords: Bayesian networks; decision making; race strategy; race tactics