We describe efficient methods to score structured hypotheses from threat detection technologies that fuse evidence from massive data streams to detect threat phenomena. The strongly object-oriented threat case representation summarizes only key object attributes. Pairing of hypothesized and reference cases exploits a directed acyclic case type graph to minimize case comparisons. Because case pairing is expensive, we expediently avoid it where possible. One global pairing operation suffices to develop: (1) Count-based metrics (precision, recall, F-value) that generalize the traditional versions to object-oriented versions that accommodate inexact matching over structured hypotheses with weighted attributes; (2) Area under the object-oriented precision-recall curve; (3) Cost-based metrics that address timely incremental evidence processing; (4) Statistical significance of computed scores. Many software parameters support customized experimentation.