Article: Harnessing deep learning and advanced analytics to revolutionise badminton performance Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.10 pp.61 - 79 Abstract: Badminton, the combination of agility, precision, and strategic thinking, is a unique challenge for optimisation. In this study, we combine advanced analytics and deep learning for biomechanics and prevention of injuries, as well as to refine tactics. Purchased computer vision models (93% accuracy) and identified and corrected movement inefficiencies, resulting in a 15% increase in shuttling speed and a 12% increase in agility. Wearable sensor data predictive models for injury risk forecasting had 90% accuracy, which led to a 25% reduction in injuries via proactive training. Reinforcement learning uncovered gameplay patterns - e.g., detecting 68% of cross-court smashes in an extended rally improved defence by 20%. These results confirm that training with data-based real-time feedback is superior to typical training. This approach provides evidence-based athletic development with accessible data and implementable methods, which leads to scalable solutions for badminton and beyond. Inderscience Publishers - linking academia, business and industry through research

Title: Harnessing deep learning and advanced analytics to revolutionise badminton performance

Authors: Enge Chen; Yunfeng Zheng

Addresses: Sports Training, Chongqing City Vocational College, Chongqing, 402160, China ' Sports Training, Chongqing University of Arts and Sciences, Chongqing, 402160, China

Abstract: Badminton, the combination of agility, precision, and strategic thinking, is a unique challenge for optimisation. In this study, we combine advanced analytics and deep learning for biomechanics and prevention of injuries, as well as to refine tactics. Purchased computer vision models (93% accuracy) and identified and corrected movement inefficiencies, resulting in a 15% increase in shuttling speed and a 12% increase in agility. Wearable sensor data predictive models for injury risk forecasting had 90% accuracy, which led to a 25% reduction in injuries via proactive training. Reinforcement learning uncovered gameplay patterns - e.g., detecting 68% of cross-court smashes in an extended rally improved defence by 20%. These results confirm that training with data-based real-time feedback is superior to typical training. This approach provides evidence-based athletic development with accessible data and implementable methods, which leads to scalable solutions for badminton and beyond.

Keywords: badminton performance optimisation; deep learning in sports; advanced sports analytics; biomechanics and injury prevention; tactical decision making in badminton.

DOI: 10.1504/IJICT.2025.146097

International Journal of Information and Communication Technology, 2025 Vol.26 No.10, pp.61 - 79

Received: 19 Feb 2025
Accepted: 05 Mar 2025
Published online: 06 May 2025 *

Title: Harnessing deep learning and advanced analytics to revolutionise badminton performance

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