Sports Analysis: Analyzing Tennis Using Deep Learning and Keypoint Extraction

In the world of professional sports, sometimes split seconds between victory and defeat can be made by decisions taken at the right time. Managers and analysts have to go to work to interpret intricate game dynamic and player performance, which may mean hours and hours of video footage, leaving the final observing of physical activity and judgment purely subjective. That is to say, the old way of doing things is not only time-consuming and unreliable but also does not capitalize fully on the scope of available data in modern sports. That being said, the need is clear an advanced, automated system with seamless integration into the fast paced arena of sport, offering real time actionable insights derived from comprehensive data analysis. The system describes a new approach in tennis and football analysis which uses the powers of machine learning, object detection, and deep learning to revolutionize analyzing sports. It will have an automated tracking of ball movement, predict speeds, capture player distances, adapt to camera movements, and send accurate real time analytics. This will empower coaches and analysts to make data-driven decisions to hence enhance player performance as well as to gain an edge over others in the ever-changing arena of professional sports.