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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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 |
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