Application of DEA to Voting for the Baseball Hall of Fame

This paper applies Data Envelopment Analysis (DEA) to voting for the Baseball Hall of Fame. The approach interprets a player's career statistics as inputs, and the percentage of votes he received for the HOF as the output. A constructed frontier based on past voting defines the maximum number of votes that a player should receive based on his statistical profile. Our results suggest that about a third of current members of the HOF (excluding Negro League players, managers, umpires, and executives) should be replaced by more deserving players. Our conclusions, however, do not account for those aspects of a player's career (both positive and negative) not captured by statistics.