Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Princ...

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Main Authors:	Arthur Leroy, Andy MARC, Olivier DUPAS, Jean Lionel REY, Servane Gey
Format:	Article
Language:	English
Published:	MDPI AG 2018-09-01
Series:	Applied Sciences
Subjects:	curve clustering functional data analysis swimming sport detection
Online Access:	http://www.mdpi.com/2076-3417/8/10/1766

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spelling	doaj-b1f3cac450ea47d1b5e15bb59ab370822020-11-25T00:40:16ZengMDPI AGApplied Sciences2076-34172018-09-01810176610.3390/app8101766app8101766Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves ClusteringArthur Leroy0Andy MARC1Olivier DUPAS2Jean Lionel REY3Servane Gey4MAP5—Paris Descartes University, IRMES-INSEP, 75012 Paris, FranceMAP5—Paris Descartes University, IRMES-INSEP, 75012 Paris, FranceFrench Swimming Federation, 92583 Paris, FranceFrench Swimming Federation, 92583 Paris, FranceMAP5—Paris Descartes University, 75006 Paris, FranceMany data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.http://www.mdpi.com/2076-3417/8/10/1766curve clusteringfunctional data analysisswimmingsportdetection
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Arthur Leroy Andy MARC Olivier DUPAS Jean Lionel REY Servane Gey
spellingShingle	Arthur Leroy Andy MARC Olivier DUPAS Jean Lionel REY Servane Gey Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering Applied Sciences curve clustering functional data analysis swimming sport detection
author_facet	Arthur Leroy Andy MARC Olivier DUPAS Jean Lionel REY Servane Gey
author_sort	Arthur Leroy
title	Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering
title_short	Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering
title_full	Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering
title_fullStr	Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering
title_full_unstemmed	Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering
title_sort	functional data analysis in sport science: example of swimmers’ progression curves clustering
publisher	MDPI AG
series	Applied Sciences
issn	2076-3417
publishDate	2018-09-01
description	Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.
topic	curve clustering functional data analysis swimming sport detection
url	http://www.mdpi.com/2076-3417/8/10/1766
work_keys_str_mv	AT arthurleroy functionaldataanalysisinsportscienceexampleofswimmersprogressioncurvesclustering AT andymarc functionaldataanalysisinsportscienceexampleofswimmersprogressioncurvesclustering AT olivierdupas functionaldataanalysisinsportscienceexampleofswimmersprogressioncurvesclustering AT jeanlionelrey functionaldataanalysisinsportscienceexampleofswimmersprogressioncurvesclustering AT servanegey functionaldataanalysisinsportscienceexampleofswimmersprogressioncurvesclustering
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Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

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