Legislative drafters use plain language drafting techniques to increase the readability of statutes in several Anglo-American jurisdictions. Existing readability metrics, such as Flesch-Kincaid, however, are a poor proxy for how effectively drafters incorporate these guidelines. This paper proposes a rules-based operationalization of the literature’s readability measures and tests them on legislation that underwent plain language rewriting. The results suggest that our readability metrics provide a more holistic representation of a statute’s readability compared to traditional techniques. Future machine-learning classifications promise to further improve the detection of complex features, such as nominalizations.