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

(Ergänzung der subjektiven durch objektive Daten bei der Analyse von Fachwissen: ein auf Badminton angewandter maschineller Lernansatz)

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.
© Copyright 2020 Journal of Sports Sciences. Taylor & Francis. Alle Rechte vorbehalten.

Bibliographische Detailangaben
Schlagworte:
Notationen:Spielsportarten Naturwissenschaften und Technik
Tagging:maschinelles Lernen data mining
Veröffentlicht in:Journal of Sports Sciences
Sprache:Englisch
Veröffentlicht: 2020
Online-Zugang:https://doi.org/10.1080/02640414.2020.1764812
Jahrgang:38
Heft:17
Seiten:1943-1952
Dokumentenarten:Artikel
Level:hoch