SPRING 2026

AUTHOR

Davis Meng, The Groton School, Groton, Massachusetts

ABSTRACT

Modern basketball increasingly demands the skill of precise shooting. However, accessible tools for analyzing shooting mechanics remain limited due to the lack of resources and reliability. This study addresses this gap by developing and comparing two machine learning approaches for real-time basketball jumpshot detection on consumer iOS devices. Building upon previous MediaPipe-based research that achieved basic success, this work transitions to Apple’s native Vision framework. It leverages hardware-optimized pose detection to enable practical on-device analysis.

Through systematic collection and annotation of basketball shooting footage, biomechanical features were extracted from body pose landmarks. These features capture the essential mechanics of any given jumpshot. Then, two contrasting machine learning architectures were developed and evaluated. Linear Regression was chosen for computational efficiency versus Random Forest for classification accuracy. They were evaluated for both predictive performance and real-world computational feasibility on mobile hardware.

This research establishes that sophisticated basketball shot analysis can operate entirely on smartphones without specialized equipment. This could potentially democratize access to personalized coaching feedback for athletes at all levels.