Analyzing Players Performance Using PSO-CNNModel

In the modern technological era, the applications of Artificial Intelligence (AI) have been growing exponentially. The sports industry has also experienced an increased demand for AI and Machine Learning (ML) techniques to enhance both the sports business and athletic performance. By making use of deep learning models and other ML techniques, it is now possible to significantly boost an athlete's performance. These methods offer improved accuracy and efficiency compared to traditional manual coaching where traditional athlete performance analysis is obstructed by subjectivity, limited data, and a lack of real-time feedback. It overlooks detailed insights, offers little personalization, and focuses on past performances rather than predicting future trends. Although there are recent techniques such as GWO-CNN which are implemented, they lack simplicity and data quality. This research aims to implement the most effective optimization algorithms and deep learning techniques to enhance the performance of individual athletes as well as teams. The Particle Swarm Optimization (PSO) algorithm-based Machine Learning model has been identified as an optimal solution for predicting athletic performance. This study also seeks to provide a deeper understanding of how ML methods can be employed to further improve player performance.