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Machine Learning and Statistical Prediction of Pitching Arm Kinetics.

Kristen F Nicholson¹, Gary S Collins^2,3, Brian R Waterman¹

¹Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.

The American Journal of Sports Medicine

|November 15, 2021

Summary

Pitch velocity and specific pitching mechanics significantly influence elbow and shoulder stress in baseball pitchers. Machine learning models identified key variables to help prevent throwing-related injuries.

Keywords:

elbow injury machine learning pitching shoulder

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Area of Science:

Sports Medicine
Biomechanics
Orthopedics

Background:

Throwing-related injuries in baseball athletes are a significant concern.
Hundreds of variables in the kinetic chain, full body mechanics, and pitching cycle can influence arm health.
Limited research has evaluated the combined influence of multiple variables on throwing arm stress.

Purpose of the Study:

To identify key variables influencing elbow valgus torque and shoulder distraction force.
To utilize statistical and machine learning models for this identification.

Main Methods:

A retrospective review of 168 high school and collegiate pitchers was conducted.
Regression and four machine learning models were developed to predict elbow valgus torque and shoulder distraction force.
Predictor variables included pitch velocity and 17 pitching mechanics.

Main Results:

Gradient boosting machine learning models showed the best predictive performance for both elbow valgus torque and shoulder distraction force.
Pitch velocity was the most influential variable in both models.
Key pitching mechanics, such as maximum humeral rotation velocity and shoulder abduction at foot strike, also significantly influenced arm stress.

Conclusions:

Machine learning models effectively predict elbow and shoulder stress in pitchers.
Pitch velocity and specific biomechanical factors are critical determinants of arm stress.
Findings can inform strategies to mitigate injury risk in baseball athletes.