This paper revisits the notion of case base competence in the light of recent advances in the modeling of analogical reasoning, based on the idea of similarity transfer from a situation space to an outcome space. For that we consider the CoAT indicator, that measures the compatibility between two similarity measures on a case base, and use it to define an intrinsic measure of competence of a case base with respect to a reference set. Initial experimental results show that the proposed competence measure correlates with the performance of the CoAT prediction algorithm. In fact, our preliminary results seem to indicate that, under some initial conditions, our competence based model can fit any classification boundary. We then revisit the notions of case competence and locality, and show that some source cases may degrade the overall case base competence while others may improve it, and that a given source case may have disparate influence on different regions of the case space.