Evidence map›Paper›PMID 36746974›Full record

ArticleScientific reports2023

Quantifying human performance in chess.

Sandeep Chowdhary, Iacopo Iacopini, Federico Battiston

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Individual and team performance in cricket.Royal Society open science · 2024
    Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Sandeep ChowdharyDepartment of Network and Data Science, Central European University, 1100, Vienna, Austria.
Iacopo IacopiniDepartment of Network and Data Science, Central European University, 1100, Vienna, Austria.
Federico BattistonDepartment of Network and Data Science, Central European University, 1100, Vienna, Austria. battistonf@ceu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

SportsAchievementHumans

Identifiers

PMID36746974
PMCPMC9902564

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.