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Sparta Science
Research Study Summary

The Association Between Elbow Injury Risk and Counter Movement Jump Performance In Professional Baseball Pitchers

The incidence rate of UCL sprains has been rising in recent years amongst Major League Baseball pitchers. Determining valid screening procedures which allow strength and conditioning coaches to identify pitchers at increased risk of such injuries is therefore of critical importance. This study seeks to validate the use of countermovement jump (CMJ) tests as a tool in this pursuit. A multinomial logistic model is fit to four years worth of CMJ and arm injury data from pitchers in a single MLB organization. It is shown that elbow injury risk is highly dependent on ground reaction force data obtained during CMJ. In particular, pitchers who rely too much or too little on impulse momentum compared to force production during CMJ are three times as likely to sustain elbow injuries compared to players with more balanced profiles. In contrast, shoulder injury risks appear to be roughly independent of CMJ profiles. This study offers the first predictive link between neuromuscular performance during CMJ and injury risk in professional baseball pitchers.

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  • Four years (January 2013 – January 2017) of ground reaction force and injury data from pitchers in a single professional baseball organization (both major and minor leagues)
  • Athletes in the organization routinely performed a CMJ scan on a force plate throughout the four year period
  • Scan data was recorded and stored in Sparta’s privately operated software management system
  • Injury data was obtained from athletic trainers and coaches at the organization as well as publicly available disabled list data for MLB players
  • Injuries were classified based on the OSICS 10 code system.


  • Professional baseball pitchers from a single MLB organization
  • 499 player-seasons were included from a total of 270 different pitcher
  • Healthy players completed a mean number of 3.607 CMJ scans per season
  • Injured players completed a mean of 2.576 CMJ scans per season prior to their injury


  • Participants performed a series of six CMJs on a commercially available piezoelectric force plate
  • Three Force-time variables (ERFD, AVCF, CVI) were extracted from GRF data via numerical integration
  • Injury data was merged with scan data by averaging all player scans throughout a season during the period prior to an injury (or for healthy players, throughout the entire season)


A detailed illustration of the injury model is presented in Figure 3 where the predicted probabilities of injuries are plotted against PC1 and PC2. For elbow injuries, there is a sharp increase in injury risk in the upper left and lower right corners of the heat map due to the strong interactive effect between PC1 and PC2 whereas shoulder injury rates are roughly independent of PC1 and PC2.