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BACKGROUND: Over the past decade, research has attempted to elucidate the cause of throwing-related injuries in the baseball athlete. However, when considering the entire kinetic chain, full body mechanics, and pitching cycle sequencing, there are hundreds of variables that could influence throwing arm health, and there is a lack of quality investigations evaluating the relationship and influence of multiple variables on arm stress. PURPOSE: To identify which variables have the most influence on elbow valgus torque and shoulder distraction force using a statistical model and a machine learning approach. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: A retrospective review was performed on baseball pitchers who underwent biomechanical evaluation at the university biomechanics laboratory. Regression models and 4 machine learning models were created for both elbow valgus torque and shoulder distraction force. All models utilized the same predictor variables, which included pitch velocity and 17 pitching mechanics. RESULTS: The analysis included a total of 168 high school and collegiate pitchers with a mean age of 16.7 years (SD, 3.2 years) and BMI of 24.4 (SD, 1.2). For both elbow valgus torque and shoulder distraction force, the gradient boosting machine models demonstrated the smallest root mean square errors and the most precise calibrations compared with all other models. The gradient boosting model for elbow valgus torque reported the highest influence for pitch velocity (relative influence, 28.4), with 5 mechanical variables also having significant influence. The gradient boosting model for shoulder distraction force reported the highest influence for pitch velocity (relative influence, 20.4), with 6 mechanical variables also having significant influence. CONCLUSION: The gradient boosting machine learning model demonstrated the best overall predictive performance for both elbow valgus torque and shoulder distraction force. Pitch velocity was the most influential variable in both models. However, both models also revealed that pitching mechanics, including maximum humeral rotation velocity, shoulder abduction at foot strike, and maximum shoulder external rotation, significantly influenced both elbow and shoulder stress. CLINICAL RELEVANCE: The results of this study can be used to inform players, coaches, and clinicians on specific mechanical variables that may be optimized to mitigate elbow or shoulder stress that could lead to throwing-related injury.

Original publication

DOI

10.1177/03635465211054506

Type

Journal article

Journal

Am j sports med

Publication Date

15/11/2021

Keywords

elbow, injury, machine learning, pitching, shoulder