Title :

Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton

Author(s) :

Dieu, Olivier [Auteur]
Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369
Schnitzler, Christophe [Auteur]
Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369 - ULR 4488 [URePSSS]
Llena, Clément [Auteur]
Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369 - ULR 4488 [URePSSS]
Potdevin, Francois [Auteur]
Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369 - ULR 4488 [URePSSS]

Journal title :

Journal of Sports Sciences

Abbreviated title :

Journal of Sports Sciences

Volume number :

38

Pages :

1943-1952

Publisher :

Informa UK Limited

Publication date :

2020-06-17

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the ...
Show more >This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the video footage of the rallies. The expertise levels were determined using a typology based on a continuum of 5 conative stages: (1) structural, (2) functional, (3) technical, (4) contextual, and (5) expertise. The video allowed empirical measurement of the duration of the rallies, and tri-axial accelerometers measured the intensity of the player’s involvement. A principal component analysis showed that two dimensions explained 54.9% of the total variance in the data and that conative stage and empirical parameters during rallies (duration, intensity of the game) were correlated with axis 1, whereas duration and acceleration data between rallies were correlated with axis 2. A random forest algorithm showed that among the parameters considered, acceleration, duration of the rallies, and time between rallies could predict conative stages with a prediction accuracy above possibility. This study suggests that performance analysis benefits from the confrontation of subjective and objective data in order to design training plans according to the expertise level of the participants.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Non spécifiée

Popular science :

Non

Administrative institution(s) :

Université de Lille
Univ. Artois
Univ. Littoral Côte d’Opale

Collections :

• Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369

Research team(s) :

Activité Physique, Muscle, Santé (APMS)

Submission date :

2021-11-24T22:44:22Z
2021-12-01T10:08:20Z
2024-02-18T10:50:53Z
2024-02-27T13:27:01Z