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Related Experiment Video

Updated: Dec 18, 2025

Biomechanical Analysis Methods to Assess Professional Badminton Players' Lunge Performance

Biomechanical Analysis Methods to Assess Professional Badminton Players' Lunge Performance

Published on: June 11, 2019

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

Olivier Dieu¹, Christophe Schnitzler², Clément Llena³

¹Univ. Littoral Côte d'Opale, Univ. Lille, Univ. Artois, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société , F-59140 Dunkerque, France.

Journal of Sports Sciences

|June 18, 2020

Summary

Combining subjective coach assessments with objective data like rally duration and player acceleration can effectively identify expertise levels in sports. This approach aids in designing tailored training programs for athletes.

Keywords:

Performance analysis accelerometry conative data mining

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Area of Science:

Sports Science
Performance Analysis
Motor Learning

Background:

Identifying athlete expertise is crucial for effective training program design.
Traditional methods often rely solely on subjective coach evaluations.
Objective, empirical data can provide a more comprehensive understanding of player skill.

Purpose of the Study:

To determine the optimal combination of subjective and empirical data for identifying sport expertise levels.
To validate a 5-stage conative typology for classifying player expertise.
To explore the predictive power of objective performance metrics on expertise classification.

Main Methods:

Expert coaches (n=10) classified 40 participants into 5 expertise levels using video analysis.
Empirical data collected included rally duration (video) and player involvement intensity (tri-axial accelerometers).
Principal Component Analysis (PCA) and Random Forest algorithms were used to analyze data relationships and predictive accuracy.

Main Results:

PCA revealed two key dimensions explaining 54.9% of variance, linking conative stages and rally parameters (duration, intensity) to one axis, and inter-rally data to another.
Random Forest analysis demonstrated that acceleration, rally duration, and time between rallies could predict conative stages with accuracy exceeding chance.
Subjective coach classifications correlated significantly with objective performance metrics.

Conclusions:

Integrating subjective coach insights with objective performance data enhances the accuracy of expertise level identification.
Objective measures like acceleration and rally duration are valuable predictors of an athlete's conative stage.
This combined approach facilitates the development of more precise and individualized athlete training plans.