RidwanHaque/AI-ML-CV-NBA-Basketball-Analytics-System-Interface
A computer vision pipeline built with PyTorch for advanced NBA analytics. The system uses YOLO for player/ball detection, multi-object tracking, and a zero-shot classifier for team affiliation, and court key point detection to create a tactical top-down view and calculate real-world metrics like speed, distance, and passes.
This system helps NBA analysts and coaches gain deeper insights from game footage by automatically tracking players and the ball. It takes raw video of a basketball game as input and outputs a processed video with identified players, ball movement, team assignments, and calculated metrics like player speed and distance. Sports analysts, scouts, and coaching staff can use this to understand team tactics and individual player performance more objectively.
Use this if you need to automatically extract detailed tactical and physical performance data from NBA game videos for analysis without manual frame-by-frame annotation.
Not ideal if you need to analyze sports other than basketball, or require real-time, in-game analytics rather than post-game video processing.
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Jupyter Notebook
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Last pushed
Nov 02, 2025
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