  Chess is an emblematic sport that stands out because of its age, popularity
and complexity. It has served to study human behavior from the perspective of a
wide number of disciplines, from cognitive skills such as memory and learning,
to aspects like innovation and decision making. Given that an extensive
documentation of chess games played throughout history is available, it is
possible to perform detailed and statistically significant studies about this
sport. Here we use one of the most extensive chess databases in the world to
construct two networks of chess players. One of the networks includes games
that were played over-the-board and the other contains games played on the
Internet. We study the main topological characteristics of the networks, such
as degree distribution and correlations, transitivity and community structure.
We complement the structural analysis by incorporating players' level of play
as node metadata. Although both networks are topologically different, we show
that in both cases players gather in communities according to their expertise
and that an emergent rich-club structure, composed by the top-rated players, is
also present.
