Zhengyang He¹ and Rodrigo Onate², ¹USA, ²California State University, USA

This research presents an AI-powered basketball shot analysis system that integrates YOLO object detection and pose estimation to evaluate shooting mechanics [1]. The system detects key components such as basketball, hoop, and player movement, while tracking elbow, shoulder, and knee angles to assess shot accuracy and provide actionable feedback. The backend processes upload videos, detecting whether a shot was made and analyzing player movements, while the frontend displays AI-generated insights and stores feedback in Firebase for progress tracking [2]. Two experiments were conducted to evaluate system performance. The shot detection accuracy test showed an 89% overall accuracy, correctly identifying 86% of made shots and 92% of missed shots. The pose estimation test measured a mean absolute error of 4.2° for elbow angles, 5.1° for shoulder angles, and 4.8° for knee angles, confirming high reliability. However, low-light conditions and extreme camera angles introduced detection errors, suggesting improvements through data augmentation, real-time processing, and optimized model training. By providing automated, AI-driven shooting feedback, this system offers a cost-effective alternative to personal coaching, making basketball training more accessible, efficient, and data-driven for players at all skill levels.

Basketball Shot Analysis, YOLO Object Detection, Pose Estimation, AI-Powered Sports Training, Automated Shooting Feedback