Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton.
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 ty...
| Publicado en: | Journal of Sports Sciences Vol. 38; no. 17; pp. 1943 - 1953 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145413863&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145413863 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640414 5BV jtl: Journal of Sports Sciences issn: 02640414 maglogo: Y pubinfo: dt: Sep2020 vid: 38 iid: 17 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 145413863 145413863 145601814 145413863 10.1080/02640414.2020.1764812 145413863 ppf: 1943 ppct: 10 formats: tig: atl: Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton. aug: au: Dieu, Olivier Schnitzler, Christophe Llena, Clément Potdevin, François affil: Univ. Littoral Côte d'Opale, Univ. Lille, Univ. Artois, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, F-59140 Dunkerque, France sug: subj: Machine Learning Racquet Sports Task Performance and Analysis Athletic Performance Human Coaches, Athletic Accelerometry Factor Analysis Random Forest Algorithms Descriptive Statistics Videorecording ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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