  We perform a quantitative analysis of extensive chess databases and show that
the frequencies of opening moves are distributed according to a power-law with
an exponent that increases linearly with the game depth, whereas the pooled
distribution of all opening weights follows Zipf's law with universal exponent.
We propose a simple stochastic process that is able to capture the observed
playing statistics and show that the Zipf law arises from the self-similar
nature of the game tree of chess. Thus, in the case of hierarchical
fragmentation the scaling is truly universal and independent of a particular
generating mechanism. Our findings are of relevance in general processes with
composite decisions.
