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Quantifying human performance in chess
Physics and Society · Submitted 15 Jul 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s1 | 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. | contribution 0.66 | 0.94 | 0.05 | asserted 0.99 |
| s2 | 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. | result 1.00 | 0.96 | 0.20 | asserted 1.00 |
| s3 | Skilled players can be distinguished from the others based on their gaming behaviour. | result 1.00 | 0.87 | 0.17 | asserted 1.00 |
| s4 | 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. | result 1.00 | 0.91 | 0.08 | asserted 0.93 |
| s5 | However, experts experience a broader response repertoire, and display a deeper understanding of different variations within the same line. | result 0.99 | 0.91 | 0.07 | asserted 1.00 |
| s6 | Over time, the opening diversity of a player tends to decrease, hinting at the development of individual playing styles. | result 0.80 | 0.93 | 0.07 | hedged possibility 0.68 |
| s7 | Nevertheless, we find that players are often not able to recognize their most successful openings. | result 1.00 | 0.95 | 0.05 | asserted 0.99 |
| s8 | 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. | contribution 0.99 | 0.91 | 0.65 | asserted 0.99 |
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The paper
Original 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.
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