Understanding Career Progression in Baseball Through Machine Learning

Abstract-- Professional baseball players are increasingly guaranteed expensive long-term contracts, with over 70 deals signed in excess of $90 million, mostly in the last decade. These are substantial sums compared to a typical franchise valuation of $1-2 billion. Hence, the players to whom a team chooses to give such a contract can have an enormous impact on both competitiveness and profit. Despite this, most published approaches examining career progression in baseball are fairly simplistic. We applied four machine learning algorithms to the problem and soundly improved upon existing approaches, particularly for batting data. I. INTRODUCTION The typical mode of entry for a player into baseball is through the first-year player draft. Players usually enter the draft immediately after high school or college and then spend several years in the drafting team's minor league system. When deemed ready, the drafting team can promote the player to the Major Leagues.