Classification of playing position in elite junior Australian football using technical skill indicators

Author:

Woods Carl T.1,Veale James2,Fransen Job3,Robertson Sam4ORCID,Collier Neil French5

Affiliation:

1. Discipline of Sport and Exercise Science, James Cook University, Townsville, Queensland, Australia

2. Talent Pathway, Australian Football League, Melbourne, Victoria

3. Faculty of Health, University of Technology Sydney, New South Wales, Sydney, Australia

4. Institute of Sport, Exercise & Active Living (ISEAL), Victoria University, Melbourne, Australia

5. Faculty of Sustainability, Leuphana University, Luneburg, Germany

Publisher

Informa UK Limited

Subject

Physical Therapy, Sports Therapy and Rehabilitation,Orthopedics and Sports Medicine

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2. Artificial intelligence applications in the football codes: A systematic review;Journal of Sports Sciences;2024-07-02

3. Elite Rugby League Players’ Signature Movement Patterns and Position Prediction;Communications in Computer and Information Science;2024

4. Machine learning Model for footballer position classification;2023 3rd International Conference on Information Technology and Contemporary Sports (TCS);2023-12-22

5. PREDICTION OF SPORTS TALENT IN YOUNG THROWERS USING MACHINE LEARNING;REV INT MED CIENC AC;2023

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