How to Build an AI Basketball Shot Evaluator

Aarnav Shah describes a local, computer-vision-based basketball shot tracker that analyzes video to record makes and misses alongside release velocity, trajectory arc, release height, body positioning, and joint angles. The system combines a custom RF-DETR-small detector trained to identify basketballs and rims with a zero-shot RF-DETR keypoint model for tracking a shooter’s posture, then uses a stateful rules engine and physics-based calculations to detect releases, project trajectories, classify shot outcomes, and produce annotated video and JSON event logs. Physical measurements are calibrated from the known 18-inch rim diameter, while false shot detections are reduced by requiring the ball to separate from the wrist and follow an arc toward the hoop. The project addresses practical video challenges such as net occlusion, rim bounces, and ambiguous single-camera perspectives by analyzing post-rim ball speed, identifying rattled outcomes, and flagging uncertain calls. Available through a GitHub repository and optional FastAPI dashboard, the tracker runs on a laptop without ongoing cloud costs, although its accuracy remains limited by two-dimensional camera views and could be improved with a custom rim-keypoint model.

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