(In-)Effciency scores of ex-post production activities are generally measured relative to an estimated production frontier. Data Envelopment Analysis (DEA) is a well-established non-parametric method to get such information for the activities of some comparable Decision-Making-Units (DMUs). It is well-known that this technique can be applied under the assumption of constant returns to scale (CRS) or variable returns to scale (VRS), respectively. In fact, if it is done under CRS, the returns to scale (RTS) for each activity equals one. More precisely: an input change results in an equiproportional change of outputs. However, in case of variable returns to scale the situation might be different; now the respective input variation can lead to a proportionally less or even higher output change. But often it becomes clear that the determined RTS-value is - from a decision-maker’s point of view - unrealistically high (or low) and thus impracticable. Therefore, a new approach to treat this issue will be presented. In a nutshell: We identify a new kind of mavericks - we call them RTS-mavericks; and in order to cope with this problem, we propose a median-based outlier test that leads to a new type of constraints in DEA - named scale restrictions.