We consider the construction of the smallest ball B * enclosing a set Xn formed by n points in R d. We show that any probability measure on Xn, with mean c and variance matrix V , provides a lower bound b on the distance to c of any point on the boundary of B * , with b having a simple expression in terms of c and V. This inequality permits to remove inessential points from Xn, which do not participate to the definition of B * , and can be used to accelerate algorithms for the construction of B *. We show that this inequality is, in some sense, the best possible. A series of numerical examples indicates that, when d is reasonable small (d ≤ 10, say) and n is large (up to 10 5), the elimination of inessential points by a suitable two-point measure, followed by a direct (exact) solution by quadratic programming, outperforms iterative methods that compute an approximate solution by solving the dual problem.