Automated Classification of Baseball Pitching Phases Using Machine Learning and Artificial Intelligence-Based Posture Estimation

Abstract

High-precision analyses of baseball pitching have traditionally relied on optical motion capture systems, which, despite their accuracy, are complex and impractical for widespread use. Classifying sequential pitching phases, essential for biomechanical evaluation, conventionally requires manual expert labeling, a time-consuming and labor-intensive process. Accurate identification of phase boundaries is critical because they correspond to key temporal events related to pitching injuries. This study developed and validated a smartphone-based system for automatically classifying the five key pitching phases—wind-up, stride, arm-cocking, arm acceleration, and follow-through—using pose estimation artificial intelligence and machine learning. Slow-motion videos (240 frames per second, 1080p) of 500 healthy right-handed high school pitchers were recorded from the front using a single smartphone. Skeletal landmarks were extracted using MediaPipe, and 33 kinematic features, including joint angles and limb distances, were computed. Expert-annotated phase labels were used to train classification models. Among the models evaluated, Light Gradient Boosting Machine (LightGBM) achieved a classification accuracy of 99.7% and processed each video in a few seconds demonstrating feasibility for on-site analysis. This system enables high-accuracy phase classification directly from video without motion capture, supporting future tools to detect abnormal pitching mechanics, prevent throwing-related injuries, and broaden access to pitching analysis.