The generalized shapley measure for ranking players in basketball: applications and future directions

A wide range of measures have been proposed to quantify a player’s marginal contribution to a team. We contributed to this strand of research by propos- ing, specifically for basketball, a new measure based on a combination of the Shapley value from game theory and the logistic regression, which is based on considering the utility of a player in every single lineup. Some applications where the measure can be useful are presented, such as ranking players, forming lineups, and predicting a remunerative new contract for free agent players. We also discuss possible ideas for future research developments.