Date of Completion

Spring 5-1-2023

Thesis Advisor(s)

Stephen Camilli

Honors Major

Mathematics/Actuarial Science


Applied Statistics | Design of Experiments and Sample Surveys | Discrete Mathematics and Combinatorics | Programming Languages and Compilers | Statistical Models | Statistical Theory


Challenging conventional wisdom is at the very core of baseball analytics. Using data and statistical analysis, the sets of rules by which coaches make decisions can be justified, or possibly refuted. One of those sets of rules relates to the construction of a batting order. Through data collection, data adjustment, the construction of a baseball simulator, and the use of a Monte Carlo Simulation, I have assessed thousands of possible batting orders to determine the roster-specific strategies that lead to optimal run production for the 2023 UConn baseball team. This paper details a repeatable process in which basic player statistics (available to almost any baseball program) can be used to evaluate and select batting orders that have never before been tested in live action.

Thesis_R_Code_Gavin_Rublewski.R (60 kB)
Monte Carlo Simulation and Analysis Code