Research the role of artificial intelligence in developing personalized training plans to maximize the potential of basketball players

Abstract

Basketball players can maximize their potential and enhance their skills, strength, and overall performance with the help of customized training routines. Players in games must quickly adapt to changing court circumstances, often adjusting tactics, but identifying the best course of action in real-time is challenging due to the complexity of handling data signals. This research explores the use of artificial intelligence (AI) in creating personalized training plans to improve basketball players’ abilities. Specifically, a novel Intelligent Cheetah Optimizer with Flexible Recurrent Neural Networks (ICO-FRNN) was proposed to generate training plans by identifying individual player strengths and areas for improvement. To get information from sensors during practice and competition, monitor physical performance indicators such as heart rate, speed, jump height, endurance, and biomechanical movements. The collected data undergoes preprocessing to address missing values, normalize data formats, and remove outliers by using Z-score normalization and linear discriminant analysis (LDA) is used for feature extraction. The findings show that the ICO-RNN approach enables more intelligent, player-specific training plans, facilitating improved decision-making, skill improvement, and injury avoidance. Findings indicate that AI-driven personalized training plans result in notable performance gains when compared to conventional training regimens. The performance metrics are accuracy (0.9680), recall (0.9680), F1 score (0.9681), and precision (0.9700). The result demonstrates that AI can revolutionize basketball coaching techniques by creating data-driven, dynamic training programs that optimize players’ potential.