Quantifying human performance in chess

TL;DR

This study analyzes a large dataset of chess games to quantify human performance, revealing patterns of success, behavior differences between skill levels, and insights into the development of playing styles.

Contribution

It provides the first large-scale quantitative analysis of individual chess careers, identifying behavioral and strategic differences between beginners and experts.

Findings

Hot streaks last longer for beginners than experts.

Experts tend to specialize in specific openings and understand variations better.

Players often cannot recognize their most successful openings.

Abstract

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.