Predicting Baseball Players’ Athletic Performance Utilizing Baseline Assessments of Vision

Project Summary

A team of students led by researchers from the Duke Human Performance Optimization Lab (OptiLab) and the Michael W. Krzyzewski Human Performance Laboratory (K-Lab) will develop an analytic and report generating application to test if baseline vision and movement screening measures are able to predict on-field baseball performance in a cohort of nearly 300 athletes who participated in the USA Baseball Prospect Development Pipeline (PDP).  Using machine learning and Bayesian hierarchical modeling, students will test data provided by USA baseball to identify relationships between baseline characteristics and performance in NCAA sanctioned and collegiate summer league games during the 2018 and 2019 seasons. The final deliverable will be both a report of the findings, and an analytic toolset that can be used within the PDP to provide direct feedback to the athletes about their future performance potential immediately following testing. As such, this program will provide valuable new information about the characteristics that predict successful athletic performance in demanding situations, and could be used to develop new approaches for talent identification within and beyond baseball. 

Project Leads: Greg Appelbaum, Marc Richard


Themes and Categories
Paul Bendich

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