  Hundreds of years after its creation, the game of chess is still widely
played worldwide. Opening Theory is one of the pillars of chess and requires
years of study to be mastered. Here we exploit the "wisdom of the crowd" in an
online chess platform to answer questions that, traditionally, only chess
experts could tackle. We first define the relatedness network of chess openings
that quantifies how similar two openings are to play. In this network, we spot
communities of nodes corresponding to the most common opening choices and their
mutual relationships, information which is hard to obtain from the existing
classification of openings. Moreover, we use the relatedness network to
forecast the future openings players will start to play and we back-test these
predictions, obtaining performances considerably higher than those of a random
predictor. Finally, we use the Economic Fitness and Complexity algorithm to
measure how difficult to play openings are and how skilled in openings players
are. This study not only gives a new perspective on chess analysis but also
opens the possibility of suggesting personalized opening recommendations using
complex network theory.
