We introduce the Rule Violation Score (RVS), a metric that evaluates how well predictive models adhere to logical rules. RVS distinguishes between hard and soft rules, works with any relational dataset and model, and can be computed via SQL queries for Horn rules. Evaluation on multiple benchmarks shows that models with similar predictive accuracy can differ greatly in logical compliance, highlighting RVS's ability to reveal behaviors missed by standard metrics.