In order to improve tactical analysis and decision-making, this study investigates the use of neural networks for identifying and categorizing player formations in sports. To precisely identify player positions on the field, high-resolution match film is gathered, carefully labeled, and then subjected to sophisticated object detection algorithms. To identify different formations, a convolutional neural network (CNN) is created and trained. It recognizes tactical settings in a variety of scenarios with remarkable accuracy. The outcomes demonstrate how well the approach works to give analysts and coaches insightful information. In order to improve strategic planning and team performance in competitive sports, future work will concentrate on improving the model's performance, growing the dataset, and incorporating real-time analysis capabilities