Matchings in a market may have varying degrees of compromise from efficiency, fairness, and or stability. A distance function allows to quantify such concepts or the (dis)similarity between any two matchings. There are a few attempts to propose such functions, however these are tailored for specific applications and ignore the individual preferences completely. In this paper, we construct a normative framework to quantify the difference between outcomes of market mechanisms in matching markets, while endogenizing the preferences of the individuals into the distance concept. Several conditions are introduced to capture natural and appealing behavior of such functions. We find a class of distance functions called \textit{scaled Borda distances}, which is the only class of distance functions that satisfies these conditions simultaneously.