Pitch-classifier model for professional pitchers utilizing 3D motion capture and machine learning algorithms

Abstract

IntroductionA pitcher's ability to achieve pitch location precision after a complex series of motions is of paramount importance. Kinematics have been used in analyzing performance benefits like ball velocity, as well as injury risk profile; however, prior utilization of such data for pitch location metrics is limited. ObjectiveTo develop a pitch classifier model utilizing machine learning algorithms to explore the potential relationships between kinematic variables and a pitcher's ability to throw a strike or ball. MethodsThis was a descriptive laboratory study involving professional baseball pitchers (n = 318) performing pitching tests under the setting of 3D motion-capture (480 Hz). Main outcome measures included accuracy, sensitivity, specificity, F1 score, positive predictive value (PPV), and negative predictive value (NPV) of the random forest model. ResultsThe optimized random forest model resulted in an accuracy of 70.0 %, sensitivity of 70.3 %, specificity of 48.5 %, F1 equal to 80.6 %, PPV of 94.3 %, and a NPV of 11.6 %. Classification accuracy for predicting strikes and balls achieved an area under the curve of 0.67. Kinematics that derived the highest % increase in mean square error included: trunk flexion excursion(4.06 %), pelvis obliquity at foot contact(4.03 %), and trunk rotation at hand separation(3.94 %). Pitchers who threw strikes had significantly less trunk rotation at hand separation(p = 0.004) and less trunk flexion at ball release(p = 0.003) compared to balls. The positive predictive value for determining a strike was within an acceptable range, while the negative predictive value suggests if a pitch was determined as a ball, the model was not adequate in its prediction. ConclusionsKinematic measures of pelvis and trunk were crucial determinants for the pitch classifier sequence, suggesting pitcher kinematics at the proximal body segments may be useful in determining final pitch location.