  From sports to science, the recent availability of large-scale data has
allowed to gain insights on the drivers of human innovation and success in a
variety of domains. Here we quantify human performance in the popular game of
chess by leveraging a very large dataset comprising of over 120 million games
between almost 1 million players. We find that individuals encounter hot
streaks of repeated success, longer for beginners than for expert players, and
even longer cold streaks of unsatisfying performance. Skilled players can be
distinguished from the others based on their gaming behaviour. Differences
appear from the very first moves of the game, with experts tending to
specialize and repeat the same openings while beginners explore and diversify
more. However, experts experience a broader response repertoire, and display a
deeper understanding of different variations within the same line. Over time,
the opening diversity of a player tends to decrease, hinting at the development
of individual playing styles. Nevertheless, we find that players are often not
able to recognize their most successful openings. Overall, our work contributes
to quantifying human performance in competitive settings, providing a first
large-scale quantitative analysis of individual careers in chess, helping
unveil the determinants separating elite from beginner performance.
