title: Predictors of Ulnar Collateral Ligament Reconstruction in Major League Baseball Pitchers.
abstract: Ulnar collateral ligament (UCL) reconstruction surgeries in Major League Baseball (MLB) have increased significantly in recent decades. Although several risk factors have been proposed, a scientific consensus is yet to be reached, providing challenges to those tasked with preventing UCL injuries.
  
  
  
  To identify significant predictors of UCL reconstruction in MLB pitchers.
  
  
  
  Case control study; Level of evidence, 3.
  
  
  
  Demographic and pitching performance data were sourced from public databases for 104 MLB pitchers who underwent UCL reconstruction surgery and 104 age- and position-matched controls. These variables were compared between groups and inserted into a binary logistic regression to identify significant predictors of UCL reconstruction. Two machine learning models (naïve Bayes and support vector machine) were also employed to predict UCL reconstruction in this cohort.
  
  
  
  The binary linear regression model was statistically significant (χ(2)(12) = 33.592; P = .001), explained 19.9% of the variance in UCL reconstruction surgery, and correctly classified 66.8% of cases. According to this model, (1) fewer days between consecutive games, (2) a smaller repertoire of pitches, (3) a less pronounced horizontal release location, (4) a smaller stature, (5) greater mean pitch speed, and (6) greater mean pitch counts per game were all significant predictors of UCL reconstruction. More specifically, an increase in mean days between consecutive games (odds ratio [OR], 0.685; 95% CI, 0.542-0.865) or number of unique pitch types thrown (OR, 0.672; 95% CI, 0.492-0.917) was associated with a significantly smaller likelihood of UCL reconstruction. In contrast, an increase in mean pitch speed (OR, 1.381; 95% CI, 1.103-1.729) or mean pitches per game (OR, 1.020; 95% CI, 1.007-1.033) was associated with significantly higher odds of UCL reconstruction surgery. The naïve Bayes classifier predicted UCL reconstruction with an accuracy of 72% and the support vector machine classifier with an accuracy of 75%.
  
  
  
  This study identified 6 key performance factors that may present significant risk factors for UCL reconstruction in MLB pitchers. These findings could help to enhance the prevention of UCL reconstruction surgery in MLB pitchers and shape the direction of future research in this domain.
authors.affiliation: School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA Game Insight Group, Tennis Australia, Melbourne, Australia Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia david.whiteside@gmail.com.
authors.forename: David
authors.initials: D
authors.lastname: Whiteside
authors.affiliation: Department of Neurology, School of Medicine, Oregon Health and Science University, Portland, Oregon, USA.
authors.forename: Douglas N
authors.initials: DN
authors.lastname: Martini
authors.affiliation: Department of Kinesiology, University of Connecticut, Storrs, Connecticut, USA.
authors.forename: Adam S
authors.initials: AS
authors.lastname: Lepley
authors.affiliation: School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, Michigan, USA Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Ronald F
authors.initials: RF
authors.lastname: Zernicke
authors.affiliation: School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Grant C
authors.initials: GC
authors.lastname: Goulet
mesh_terms: D000328:Adult; D001484:Baseball / Q000293:injuries*; D001499:Bayes Theorem; D016022:Case-Control Studies; D000070637:Collateral Ligament, Ulnar / Q000601:surgery*; D006801:Humans; D016014:Linear Models; D008297:Male; D012307:Risk Factors; D000070638:Ulnar Collateral Ligament Reconstruction / Q000379:methods*; D055815:Young Adult; D000092464:Elbow Injuries
keywords: Tommy John; elbow; injury; machine learning; pitch count; surgery