In the course of legal reasoning--whether for purposes of deciding an issue, justifying a decision, predicting how an issue will be decided, or arguing for how it should be decided--one often is required to reach (and assert) conclusions based on a balance of reasons that is not straightforwardly reducible to the application of rules. Recent AI and Law work has modeled reason-balancing, both within and across cases, with set-theoretic and rule- or value-ordering approaches. This article explores a way to model balancing in quantitative terms that may yield new questions, insights, and tools.