This project aims to predict how a Major League Baseball batter would perform against a specific pitcher matchup using the intricate Statcast data in an attempt to improve management’s ability to build an ideal roster and to help optimize a manager’s in-game decisions. A profile for each pitcher was developed by averaging their pitch characteristics to simulate a normal plate appearance. The objectives were to determine which data science model-building techniques and validation processes were appropriate to create the models to perform this task, evaluate these methods against one another, and, finally, apply the pitcher profiles to the final model and investigate the most important factors that best describe batter performance.