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

Joseph E Manzi1, Brittany Dowling2, Spencer Krichevsky3

  • 1Department of Orthopaedic Surgery, Northwell Health, New York, NY, USA.

Journal of Orthopaedics
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict pitch location using pitcher kinematics. Trunk and pelvis movements are key, but models struggle to accurately predict balls, indicating room for improvement in baseball performance analysis.

Keywords:
KinematicsMotion-capturePitch locationPredictive modelStrike zone

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Area of Science:

  • Biomechanics in sports
  • Machine learning applications in athletics
  • Baseball performance analysis

Background:

  • Pitch location precision is critical for baseball pitchers.
  • Kinematic analysis aids in understanding pitching performance and injury risk.
  • Limited research exists on using kinematic data for pitch location prediction.

Purpose of the Study:

  • Develop a machine learning pitch classifier.
  • Investigate relationships between kinematic variables and pitch outcomes (strike/ball).

Main Methods:

  • Descriptive laboratory study with 318 professional pitchers.
  • Utilized 3D motion-capture technology (480 Hz).
  • Employed a random forest model to classify pitch outcomes.

Main Results:

  • The random forest model achieved 70.0% accuracy and 80.6% F1 score.
  • Key kinematic factors included trunk flexion, pelvis obliquity, and trunk rotation.
  • Pitchers throwing strikes showed less trunk rotation and flexion compared to those throwing balls.

Conclusions:

  • Pelvic and trunk kinematics are significant predictors of pitch location.
  • Proximal body segment movements are valuable for determining final pitch outcomes.
  • While strike prediction was acceptable, ball prediction accuracy needs enhancement.