Measuring players’ importance in basketball using the generalized Shapley value

Abstract Measuring players’ importance in team sports to help coaches and staff with the aim of winning game is gaining relevance, mainly because advent new data advanced technologies. In this paper we evaluate each player’s - for first time basketball as his/her average marginal contribution utility an ordered subset players, through a generalized version Shapley value, where value assumed by characteristic function coalitional expressed terms probability certain lineup has win game. turn, such estimated applying logistic regression model which response represented outcome Dean’s factors are used explanatory features. Then, estimate values associated bootstrap confidence intervals. A novelty, allowed explicitly considering single lineups, possibility forming best lineups based on conditional specific constraints, injury or “a-priori” coach’s decision. comparison our proposed approach industry-standard counterparts shows strong linear relation. We show application method seventeen full NBA seasons (from 2004/2005 2020/21). eventually Utah Jazz players how be form lineups.