  In team sports, traditional ranking statistics do not allow for the
simultaneous evaluation of both individuals and combinations of players.
Metrics for individual player rankings often fail to consider the interaction
effects between groups of players, while methods for assessing full lineups
cannot be used to identify the value of lower-order combinations of players
(pairs, trios, etc.). Given that player and lineup rankings are inherently
dependent on each other, these limitations may affect the accuracy of
performance evaluations. To address this, we propose a novel adjusted box score
plus-minus (APM) approach that allows for the simultaneous ranking of
individual players, lower-order combinations of players, and full lineups. The
method adjusts for the complete dependency structure and is motivated by the
connection between APM and the hypergraph representation of a team. We discuss
the similarities of our approach to other advanced metrics, demonstrate it
using NBA data from 2012-2022, and suggest potential directions for future
work.
