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A machine learning approach to triathlon component analysis

conference contribution
posted on 2011-01-01, 00:00 authored by Bahadorreza OfoghiBahadorreza Ofoghi, J Zeleznikow, C MacMahon
This paper presents work on using machine learning approaches for analyzing triathlon components for talent identification and predicting performance patterns of medalists. Triathlon is a high profile multi-component sport which requires strategic understanding and planning with respect to each of the three sports: swimming, cycling, and running, to achieve the best overall standing. We carried out machine learning decision tree-based analysis and probabilistic modeling on triathlon historical data since 1989 to investigate what minimum time gap behind the lead athlete (for each event) still results in a high likelihood of medaling. We found the minimum time differentials from the leader in each event, as well as the overall differential time that still results in a large chance of medal winning.

History

Location

Shanghai, China

Start date

2011-09-21

End date

2011-09-24

ISBN-13

9781846260872

Language

eng

Publication classification

EN.1 Other conference paper

Copyright notice

2011, World Academic Union

Editor/Contributor(s)

Jiang Y

Title of proceedings

Proceedings of the 8th International Symposium on Computer Science in Sport

Event

Computer Science in Sport. Symposium (8th : 2011 : Shanghai, China)

Publisher

World Academic Union

Place of publication

Shanghai, China

Series

Computer Science in Sport Symposium

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