Technical Efficiency in Major League Baseball
John Ruggiero, Lawrence Hadley, and Elizabeth Gustafson
Major league baseball (MLB) is a uniquely-suited industry for empirical analyses of production and technical efficiency. Unlike most industries, the productivity of labor inputs can be directly measured. Industry output is also easily observable as the winning percentage of each team. Management serves various functions in the production of team wins. However, the ultimate objective of the field manager is to use all available information to make decisions that maximize the probability of winning. This is achieved by using the available player talent to score more runs than does the opposition. This process of converting player talents into games won can be considered a production process. Players' abilities are inputs and team winning percent is the output.
The manager is the central decision maker in the production process, and it is possible that some are more efficient at transforming a given set of inputs into winning percentages. Equivalently, some managers are relatively inefficient in converting their inputs into winning percentages. Given data on inputs and outputs, it is possible to measure Farrell ( 1957) efficiency in baseball production. There are three alternative approaches for measuring this efficiency. 1 First, a deterministic parametric approach based on minimization of squared or absolute residuals can be used. This technique is problematic because it attributes all deviations from the production frontier to inefficiency, and hence, does not allow for measurement error. Further, the approach is restrictive because it requires a priori functional form specification.
Data Envelopment Analysis (DEA), developed by Charnes, Cooper, and Rhodes ( 1978) and extended in Färe, Grosskopf, and Lovell ( 1985), is another approach for the measurement of Farrell efficiency. DEA is nonparametric and allows for multiple outputs. This approach, used by Fizel and D'itri ( 1995) to