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

Authors:	Olivier Dieu Christophe Schnitzler Clément Llena François Potdevin

Institution:	1. Univ. Littoral C?te d’Opale, Univ. Lille, Univ. Artois, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société , F-59140 Dunkerque, France olivierdieu@msn.comhttps://orcid.org/0000-0002-6936-5273;3. Université de Strasbourg, E3S UR 1342, Faculté des Sciences du Sport , F-67000, Strasbourg, France https://orcid.org/0000-0002-3801-8789;4. Univ. Lille, Univ. Artois, Univ. Littoral C?te d’Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société , F-59000 Lille, France https://orcid.org/0000-0002-9571-617X;5. Univ. Lille, Univ. Artois, Univ. Littoral C?te d’Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société , F-59000 Lille, France https://orcid.org/0000-0002-6060-1698

Abstract:	ABSTRACT 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.

Keywords:	Performance analysis data mining accelerometry conative

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