Date of Award


Degree Type


Degree Name

Honors Thesis



First Advisor

Mohammad Shaha Patwary

Second Advisor

John Herr


This exploration attempts to create statistical procedure capable of defining and comparing a Major League Baseball Manager’s performance in respect to player development. Using a supervised learning method of multiple linear regression, we examine how improvements or deterioration of certain player skills predict an increase in runs scored in a season. Major League Baseball teams (N = 30) over the span of eight seasons since the beginning of the Statcast era in 2015 were evaluated on their ability to improve their team’s batters’ patience in selecting hittable balls, or pitch selection, and quality of contact with the baseball, and graded on how their team’s variation predicts either runs scored, or additional, more traditional offensive metrics that already have well established statistics and research into their run predictiveness. Given the millions of dollars spent on team Manager contracts a year, much less the hundreds of millions spent on player assets said Manager is responsible for developing, more research and data is needed to create more understandable metrics and analytics to better judge a manager’s performance.