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Open AccessArticle

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

1
MAP5—Paris Descartes University, IRMES-INSEP, 75012 Paris, France
2
French Swimming Federation, 92583 Paris, France
3
MAP5—Paris Descartes University, 75006 Paris, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(10), 1766; https://doi.org/10.3390/app8101766
Received: 27 August 2018 / Revised: 24 September 2018 / Accepted: 26 September 2018 / Published: 30 September 2018
(This article belongs to the Special Issue Computer Science in Sport)
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. View Full-Text
Keywords: curve clustering; functional data analysis; swimming; sport; detection curve clustering; functional data analysis; swimming; sport; detection
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MDPI and ACS Style

Leroy, A.; MARC, A.; DUPAS, O.; REY, J.L.; Gey, S. Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering. Appl. Sci. 2018, 8, 1766.

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