Sports Action Analytics Using Artificial Intelligence and Quantum Networks

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Author(s): B. Rushidharan (Sathyabama Institute of Science and Technology, India), K. Dhanalakshmi (Sathyabama Institute of Science and Technology, India)and S. Karthikeyan (Sathyabama Institute of Science and Technology, India)
Copyright: 2025
Pages: 14
Source title: Multidisciplinary Applications of AI and Quantum Networking
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)and Jose Anand (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-9336-9.ch021

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Quantum Information & Computing

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Abstract

This study presents a novel approach to sports shot classification utilizing deep learning techniques including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and elements of artificial intelligence (AI), alongside foundational techniques from quantum networks. By analyzing video footage from diverse sports such as soccer, basketball, hockey, and volleyball, the developed models achieve real-time classification of shots such as goals, assists, blocks, and rebounds. By utilizing CNNs and similar architectures, the system extracts complex patterns and features from sports imagery, providing insights into player performance and strategic dynamics. This initiative has significant potential to transform sports analytics and broadcasting, offering instant shot identification for an enhanced viewing experience and assisting coaches and analysts in understanding sports gameplay intricacies.

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