This project attempts to predict pitcher performance based on the quality of the pitches a pitcher throws. Statcast pitching data is used as the input features for predicting xwOBA based on the 2019 season. Models are created based on two different sets of input features. One set of features includes all pitchers that threw at least 100 pitches during the season, and the other set of features only includes pitchers that meet the requirements for being on Statcast Pitch Movement Leaderboard: “pitcher must have 3 pitches thrown per team game” and “must use it at least 5% of the time” https://baseballsavant.mlb.com/pitch-movement. Two machine learning methods were used for predicting xwOBA. Random Forest Regression was used for the first set of features, and Random Forest Regression and Linear Regression were used for the second set of features.