In this study, we examine the predictive power of seven key bat tracking variables in determining Expected Weighted On-Base Average (xwOBA), a widely accepted measure of hitter success. xwOBA is considered a more reliable indicator of a player’s skill than traditional Weighted On-Base Average (wOBA), as it removes defensive factors from its calculation. Unlike wOBA, which depends on actual outcomes, xwOBA estimates a hitter’s performance based on batted ball characteristics—exit velocity, launch angle, and other relevant factors—assigning probabilities for singles, doubles, triples, and home runs based on contact quality. To identify the most predictive variables for xwOBA, we utilize multiple linear regression (employing forward and full-subset selection) alongside decision tree models, including random forests and boosted trees. Model performance is evaluated via Root Mean Square Error (RMSE), and we identify the most influential metrics in predicting hitter success. These findings offer valuable insights for player evaluation and development.

Authors: Braedyn Jacobson and Nick Sheridan, Faculty Advisor/Mentor: Dr. Jakub Michel, jmichel2@highpoint.edu https://baseballsavant.mlb.com/ https://thehittingvault.com/advanced-baseball-analytics-to-measure-a-great-hitter/