Padel Analytics using Deep Learning

Padel, a dynamic racket sport similar to tennis and squash, is played on a compact court enclosed by walls and a net. Padel Analytics leverages computer vision and AI-driven techniques to extract meaningful insights from game recordings. Utilizing a deep learning model, the system processes video footage by analyzing frames per second and tracking objects to assess player movements and ball dynamics. It identifies key details such as player positioning, boundary detection, movement speed, ball trajectories, heatmaps, and error patterns. Additionally, it recognizes gestures like forehands, backhands, and smashes while predicting ball hits for strategic evaluation. The system produces a high-frame-rate analytical video with visualized gameplay, enabling coaches, analysts, and players to make data-driven decisions. This AI-powered approach enhances performance analysis, optimizes strategy, and deepens game understanding, revolutionizing Padel analytics.