Robust Baseball Spin Rate Measurement using a Smartphone with Outlier Correction

Abstract

Spin rate is a critical performance indicator in baseball pitching. However, professional-grade systems are expensive, and even more affordable alternatives, such as sensor-equipped balls, still pose barriers to widespread implementation. This study aims to develop and validate a method for easily measuring baseball spin rate using a standard smartphone camera and machine learning techniques. The proposed method utilizes YOLOv8 for ball detection and cropping, followed by a CNN model to classify the ball’s orientation into four discrete angles (0◦, 45◦, 90◦, 135◦) by leveraging the seam-pattern symmetry. To address the quantization noise and errors inherent in discrete classification, we introduce two key techniques: 1) an outlier correction algorithm based on temporal continuity, and 2) a cumulative linear regression method that estimates rotation speed from phase-unwrapped angles. Experimental results on 15 test videos demonstrated that the proposed method significantly outperformed the baseline angular difference method. Using a success criterion of error ≤ 8.9%, the method achieved a success rate of 90% with an average error of 5.7%, verifying that our approach effectively mitigates discretization artifacts and provides accessible, high-precision measurement.