  I introduce a model of predictive scoring. A receiver wants to predict a
sender's quality. An intermediary observes multiple features of the sender and
aggregates them into a score. Based on the score, the receiver makes a
decision. The sender prefers "higher" decisions, and she can distort each
feature at a privately known cost. I characterize the scoring rule that
maximizes decision accuracy. This rule underweights some features to deter
sender distortion, and overweights other features so that the score is correct
on average. The receiver prefers this scoring rule to full disclosure because
it mitigates his commitment problem.
