Using Machine Learning to Predict Salaries of Major League Baseball Players

Abstract

Major League Baseball is one of the most watched sports in the world. In recent years, in addition to focusing on the performance of a player and his team, a player’s salary has also been a focus of fan discussion, always generating discussion and beginning to examine whether a player’s performance really matches his worth. Therefore, how to evaluate the salary of players has always been a hot topic. The most direct basis is the performance of players in the game. In addition to the statistical performance of players on the field, many scholars have also proposed some new variables that may affect the salary of players. At present, there have been many studies on the salary of major league baseball, and there are many reasons for the influence of salary. Some scholars even divide the players into pitcher and hitter for analysis. Therefore, this study focused on the players into the compensation to the annual salary increase do interval, using machine learning methods, such as limit gradient (XGBoost) to do a classification prediction model, From the research results, it can be concluded that the new variables are helpful for the increase of accuracy.