title: Evaluating a computer based skills acquisition trainer to classify badminton players.
abstract: The aim of the present study was to compare the statistical ability of both neural networks and discriminant function analysis on the newly developed SATB program. Using these statistical tools, we identified the accuracy of the SATB in classifying badminton players into different skill level groups. Forty-one participants, classified as advanced, intermediate, or beginner skilled level, participated in this study. Results indicated neural networks are more effective in predicting group membership, and displayed higher predictive validity when compared to discriminant analysis. Using these outcomes, in conjunction with the physiological and biomechanical variables of the participants, we assessed the authenticity and accuracy of the SATB and commented on the overall effectiveness of the visual based training approach to training badminton athletes. Key pointsNeural networks are more effective in predicting group membership and displayed higher predictive validity when compared to discriminant analysis.These results provide implications for coaches and trainers of badminton to implement visual based training methods into their own training program.Predicting shot type was more successful that predicting location placement. This suggests implications for training badminton player's judgement of shuttlecock trajectory.
authors.affiliation: RMIT University , Melbourne, Australia.
authors.forename: Minh Vu
authors.initials: MV
authors.lastname: Huynh
authors.forename: Anthony
authors.initials: A
authors.lastname: Bedford
keywords: Skills acquisition; badminton; discriminant analysis; neural networks