Using Statistical Classification Methods to Predict Hits Using Statcast

Expected Batting Average in Major League Baseball is currently calculated mainly by using two different factors for a batted ball: launch angle (the vertical angle at which the ball leaves a player\u27s bat after being struck) and exit velocity (speed of the baseball as it comes off the bat, immediately after a batter makes contact). However, other factors that may affect whether a ball results in a hit or an out, such as the stadium where the batted ball occurred, the defensive team at the time of the batted ball, and the distance the ball travels in the air, are not analyzed in the model. Therefore, three different statistical learning methods: logistic regression models, generalized additive models, and random forests will be applied to a dataset that contains launch angle, exit velocity, and various other possible factors for every batted ball in the 2022 season. From there, analysis will be done to determine whether models that contain other factors were more effective at predicting hits than the model with just launch angle and exit velocity