  Traditional NBA player evaluation metrics are based on scoring differential
or some pace-adjusted linear combination of box score statistics like points,
rebounds, assists, etc. These measures treat performances with the outcome of
the game still in question (e.g. tie score with five minutes left) in exactly
the same way as they treat performances with the outcome virtually decided
(e.g. when one team leads by 30 points with one minute left). Because they
ignore the context in which players perform, these measures can result in
misleading estimates of how players help their teams win. We instead use a win
probability framework for evaluating the impact NBA players have on their
teams' chances of winning. We propose a Bayesian linear regression model to
estimate an individual player's impact, after controlling for the other players
on the court. We introduce several posterior summaries to derive rank-orderings
of players within their team and across the league. This allows us to identify
highly paid players with low impact relative to their teammates, as well as
players whose high impact is not captured by existing metrics.
