Understanding Career Progression in Baseball Through Machine Learning

TL;DR

This paper applies advanced machine learning algorithms to analyze and improve the understanding of career progression in professional baseball, focusing on predicting player performance and contract outcomes.

Contribution

It introduces the use of four machine learning algorithms to enhance analysis of baseball career progression, especially for batting data, surpassing previous simplistic methods.

Findings

Improved prediction accuracy for player performance.

Enhanced understanding of factors influencing career progression.

Demonstrated effectiveness of machine learning in sports analytics.

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.