We show how to obtain theoretical and numerical estimates of correlation dimension and phase space contraction by using the extreme value theory. Maxima of suitable observables sampled along the trajectory of a chaotic dynamical system converge asymptotically to classical extreme value laws where: i) the inverse of the scale parameter gives the correlation dimension, ii) the extremal index is associated to the rate of phase space contraction for backward iteration, which in dimension 1 and 2 is closely related to the positive Lyapunov exponent and in higher dimensions is related to the metric entropy. We call it the Dynamical Extremal Index. Numerical estimates are straightforward to obtain as they imply just a simple fit to an univariate distribution. Numerical tests range from low dimensional maps, to generalized Henon maps and climate data. The estimates of the indicators are particularly robust even with relatively short time series.