We introduce a new metric--the classification power--to examine the effectiveness of postsecondary mathematics placement policies. This metric addresses the methodological challenges of contextualizing the effectiveness of a single placement policy and comparing the effectiveness of multiple placement policies across different student populations. We leverage an information-theoretic approach to construct the classification power as a measure of the improvement of the implemented policy over a hypothetical policy that places students by a Bernoulli coin-flip process. We expect an effective policy to perform significantly better than this hypothetical weighted coinflip placement. We illustrate the utility of this metric by applying this methodology to institutional data from our four-year institution. We find the classification power provides more information about the quality of placement compared to the previously defined severe error rate metric.