Using Data Analytics to Identify the Best Predictors of Successful Valparaiso University Baseball Players

Faculty Sponsor

Tiffany Kolba


Arts and Sciences



Presentation Type

Oral Presentation

Symposium Date

Spring 4-29-2021


The Valparaiso University baseball team, along with many other Division I programs, recruits baseball players from colleges in divisions lower than Division 1. The goal of this project is to identify a metric with which to assess the predicted success of a player at the Division 1 level based upon their performance at the lower division school. I specifically looked at the relationship between offensive performance in Division 1 and lower divisions using a multiple linear regression model. Using my general knowledge of the sport, I picked the initial components of the multiple linear regression model to be runs, hits, homeruns, RBIs, strikeouts, batting average, OPS, and XBH. The statistical software package R was used for data analysis to determine which of these initial choices for predictors are the most effective. The results of this project can be utilized by the Valparaiso University baseball team to evaluate potential recruits based upon the identified best predictors and predict how successful those players will be at the Division 1 level.

Biographical Information about Author(s)

I am a senior statistics major with a minor in computer science. I am from Indianapolis and hope to find a long-term career there as a data analyst. My love for sports and experience playing baseball for many years prior to coming to Valpo made this a very enjoyable project.

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