Labor Market Efficiency and Team Transactions in Major League Baseball

Abstract

This dissertation is comprised of three separate papers related to the player labor market and team decision making process in Major League Baseball (MLB).

The first paper focuses on the MLB labor market after the publication of the book Moneyball (Lewis, 2003). The book claimed that data analytics enabled savvy operators to exploit inefficiencies in the market for baseball players. The economic analysis of Hakes and Sauer (2006) appeared to show that the publication of Moneyball represented a watershed, after which inefficiencies had been competed away. In both cases analysis focused on composite statistics such as on-base percentage (OBP) and slugging percentage (SLG). This paper relies on a more structural approach, associated with the statistical analysis of Lindsey (1963) which identifies the run value of each individual event in a game. Using a dataset of every event in every game from 1996 to 2015, we show that the run value of each event can be accurately calculated, as can the run value contribution of each player. We show that the compensation of free agents reliably reflects the run value contribution of each player, regardless of the source of those contributions (walks, singles, and home runs). We find this was true both before and after the publication of Moneyball, suggesting that the labor market for batters in Major League Baseball operated efficiently across our entire sample period.

The second paper proposes novel approaches to measuring team productivity and evaluating trading efficiency in MLB from 1994-2016 through an application of portfolio theory. The performance of individual players is again measured using a structural approach relating player outcomes to team runs as developed by Lindsey (1963). Using a portfolio theory framework, we treat MLB teams as a portfolio of players (assets), each of which can be defined by an expected contribution of runs per game and the variance of this measure. It is found that both the expected value and variance have a positive impact on team runs scored. Given our definition of teams characterized by their expected values and variances, we evaluate trading efficiency between teams given their pre-trade expected values and variances and the acquired player’s pre-trade expected value and variance. We find that trade efficiency has improved in recent years, consistent with the growth in data-driven decision making used in MLB front offices.

The third and final paper investigates the impact of employer role assignments for entry-level positions on the career paths of their entry-level employees. Specifically, we focus on the impact of a pitcher's entry-level role on the pitcher's survivability in MLB. In professional baseball, pitchers are generally categorized into one of two pitcher types: starting pitcher or relief pitcher. These pitcher types have distinct roles and different levels of labor supply associated with them. These roles are also generally assigned to the pitcher by his team. Using Kaplan-Meier estimators and a Cox-Proportional Hazard model, we find that players assigned entry-level reliever roles face a significant survival disadvantage compared to entry-level starting pitchers and that differences in quality between pitcher groups do not explain this disadvantage. These results illustrate how the early-career decisions made by employers on behalf of their employees can ultimately have career altering implications for workers.