We introduce $k$-variance, a generalization of variance built on the machinery random bipartite matchings. $K$-variance measures expected cost matching two sets $k$ samples from distribution to each other, capturing local rather than global information about measure as increases; it is easily approximated stochastically using sampling and linear programming. In addition defining $k$-variance pr...