The core of the classical block maxima method in (multivariate) extreme value statistics consists of fitting an extreme value distribution to a sample of maxima over blocks extracted from an underlying time series. Traditionally, the maxima are taken over disjoint blocks of observations of a fixed size. Alternatively, the blocks can be chosen to be of varying size and to slide through the observation period, yielding a larger number of overlapping blocks. Nonparametric estimation of extreme value copulas based on sliding blocks is found to be more efficient than estimation based on disjoint blocks.