Understanding Career Progression in Baseball Through Machine Learning
This work addresses the high-stakes financial and competitive impact of contract decisions for baseball teams, though it is incremental in applying standard machine learning methods.
The authors tackled the problem of predicting career progression in baseball to improve contract decisions, achieving significant improvements over existing approaches, particularly for batting data.
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.