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Neural Computing and Applications
Published in: Neural Computing and Applications 15/2020

18-07-2018 | S.I.: India Intl. Congress on Computational Intelligence 2017

Development of a framework for modeling preference times in triathlon

Authors: Iztok Fister Jr., Andres Iglesias, Suash Deb, Dušan Fister, Iztok Fister

Published in: Neural Computing and Applications | Issue 15/2020

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Abstract

Preference time in a triathlon denotes the time that is planned to be achieved by an athlete in a particular competition. Usually, the preference time is calculated some days, weeks, or even months before the competition. Mostly, trainers calculate the proposed preference time according to the current form, body performances of athletes, psychological abilities and their health state. They also take course specifications into account in order to make their proposal as exact as possible. However, until recently, this prediction was performed manually. This paper presents an automatic framework for modeling preference times based on previous results of athletes on a particular racecourse and particle swarm optimization. Indeed, the framework observed the problem as optimization, where the goal is to find such preference time that is as much as possible correlated with past data. Practical experiments with different scenarios reveal that the proposed solution is promising.
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Footnotes
2
Data from 2017 editions are not included in dataset edition of 2016.
 
3
Swarm plots were created by seaborn python package: https://​github.​com/​mwaskom/​seaborn.
 
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Metadata
Title
Development of a framework for modeling preference times in triathlon
Authors
Iztok Fister Jr.
Andres Iglesias
Suash Deb
Dušan Fister
Iztok Fister
Publication date
18-07-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 15/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3632-9

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