Upper Extremity Injury Prediction in Elite Youth Baseball Pitchers using Classification Methods

Upper Extremity Injury Prediction in Elite Youth Baseball Pitchers using Classification Methods

Sengalrayan, Patrick (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Biomechanical Engineering)

van Trigt, B. (mentor)
Veeger, H.E.J. (graduation committee)
Söhl, J. (graduation committee)

Delft University of Technology

Mechanical Engineering

2020-10-19

In baseball, pitchers have a high rate of throwing arm injury, which could lead to disability and less training time. This paper aims at investigating whether upcoming injuries of youth baseball pitchers can be detected before the athlete experiences injury symptoms. A total of 118 elite youth baseball pitchers from the Dutch national baseball team and six Dutch academies were followed over three years. Promising variables like Range of Motion, Muscle Force, Ball Speed and Training Time were included for use in a supervised classification problem. Prediction accuracy performance was then measured for different algorithms in the form of F1 and F2 scores. Results showed deficient performance for injury prediction using single-point-in-time measurements for all examined algorithms, with scores of both F1 and F2 reaching maximums of 0.5. The results, however, revealed the importance of measuring variables like hip force and hip range of motion for shoulder injury prediction, and force in the hip and shoulder together with the total rotational motion (TRM) of the shoulder for elbow injury prediction. Ball speed and training time contributed less for the tested models. Higher frequency data is needed for better injury prediction performance. Future studies are recommended to measure data with a time between measurements of one to two weeks. This high frequency makes it possible to use time-series analysis to detect slight asymptomatic pathology developments progressing over time, to help youth baseball pitchers avoid injuries and keep their performance ready for top-level play.

shoulder injury
elbow injury
range of motion
maximum contraction force
ball speed
training time
decision tree
logistic regression

http://resolver.tudelft.nl/uuid:707fe827-e93b-44ad-901e-835615c66411

Student theses

master thesis

© 2020 Patrick Sengalrayan