Summary: Working with Markov kernels (conditional distributions) and right-hand derivatives \(D^+A\) of Pickands dependence functions \(A\) we study the way two-dimensional extreme-value copulas (EVCs) \(C_A\) distribute mass. Underlining the usefulness of working directly with \(D^+A\), we give first an alternative simple proof of the fact that EVCs with piecewise linear \(A\) can be expressed as weighted geometric mean of some EVCs whose dependence functions \(A\) have at most two edges and present a generalization of this result. After showing that the discrete component of the Markov kernel of \(C_A\) concentrates its mass on the graphs of some increasing homeomorphisms \(f_t\), we determine which EVC assigns maximum mass to the union of the graphs of \(f_{t_{1}},\dots ,f_{t_{N}}\), derive the absolutely continuous component of an arbitrary EVC \(C_A\) and deduce that the minimum copula \(M\) is the only (purely) singular EVC. Additionally, we prove the existence of EVCs \(C_A\) which, despite their simple analytic form, exhibit the following surprisingly singular behavior: the discrete, the absolutely continuous and the singular component of the Lebesgue decomposition of the Markov kernel \(K_{C_{A}}(x,\cdot)\) of \(C_A\) have full support \([0,1]\) for every \(x\in [0,1]\).