Trading Players in the National Basketball Association: For Better or Worse?
David J. Berri and Stacey L. Brook
Neoclassical theory assumes that a firm's personnel decisions are made on the basis of maximizing behavior. Firm decide to add or subtract a worker by comparing the worker's wage with the worker's marginal revenue product (MRP).However, for most industries, emperical tests of this hypothesis are difficult because data on worker productivity are scarce.
In contrast, economists have demonstrated that MRP estimates can be made in professional sports industries, where extensive data are tabulated to measure worker productivity. For example, the National Basketball Association (NBA) allocates considerable resources to compile a variety of statistics that can be employed in player evaluation. This chapter proposes that such statistics can be utilized to measure a player's value, and therefore the Pareto optimality of player(s)-for-player(s) trade in the NBA can be objectively tested.
Neoclassical theory would predict that when a team trades a player(s), the new player(s) should contribute a greater or equal value, in terms of wins and/or rents, 1 to the team than the player(s) lost. If this is true for both teams, then the trade is Pareto optimal. At the onset of this study, however, it is suspected that neoclassical theory will not adequately explain the empirical evidence. In essence, we believe that general managers (GMs) have perfect information about a player's performance from statistics such as points, rebounds, and turnovers. However, we hypothesize that the mapping of the input vectors (individual performance statistics) to the output vector (number of team wins) is uncertain. Therefore, given a player's statistical production, GMs can arrive at erroneous valuations regarding the impact these inputs have on wins.
The purpose of this chapter is as follows: 1) present a game-theoretic model of the NBA decision-making process with respect to player(s)-for-player(s) trades, 2)