The major difficulty in evaluating individual player performance in basketball is adjusting for interaction effects by teammates. With the advent of play-by-play data, the plus-minus statistic was created to address this issue . While variations on this statistic (ex: adjusted plus- minus ) do correct for some existing confounders, they struggle to gauge two aspects: the importance of a player’s contribution to his units or squads, and whether that contribution came as unexpected (i.e. over- or under-performed) as determined by a statistical model. We quantify both in this paper by adapting a network-based algorithm to estimate centrality scores and their corresponding statistical significances . Using four seasons of data , we construct a single network where the nodes are players and an edge exists between two players if they played in the same five-man unit. These edges are assigned weights that correspond to an aggregate sum of the two players’ performance during the time they played together. We determine the statistical contribution of a player in this network by the frequency with which that player is visited in a random walk on the network, and we implement bootstrap techniques on these original weights to produce reference distributions for testing significance.
The full paper can be found here
The conference poster can be found here
RESEARCH PAPER POSTER - NO PRESENTATION GIVEN