The classical branch-and-bound algorithm for the integer feasibility problem has exponential worst case complexity. We prove that it is surprisingly efficient on reformulations, in which the columns of the constraint matrix are short, and near orthogonal, i.e. a reduced basis of the generated lattice; when the entries of A (i.e. the dense part of the constraint matrix) are from {1, …, M} for a large enough M, branch-and-bound solves almost all reformulated instances at the rootnode. For all A matrices we prove an upper bound on the width of the reformulations along the last unit vector. The analysis builds on the ideas of Furst and Kannan to bound the number of integral matrices for which the shortest nonzero vectors of certain lattices are long, and also uses a bound on the size of the branch-and-bound tree based on the norms of the Gram-Schmidt vectors of the constraint matrix. We explore practical aspects of these results. First, we compute numerical values of M which guarantee that 90, and 99 percent of the reformulated problems solve at the root: these turn out to be surprisingly small when the problem size is moderate. Second, we confirm with a computational study that random integer programs become easier, as the coefficients grow.