ArticleScientific reports2023
Quantifying the complexity and similarity of chess openings using online chess community data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Emergent complexity in the decision-making process of chess players.Scientific reports · 2025Article
- Mapping job fitness and skill coherence into wages: an economic complexity analysis.Scientific reports · 2024Article
- Quantifying human performance in chess.Scientific reports · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Chess is a centuries-old game that continues to be widely played worldwide. Opening Theory is one of the pillars of chess and requires years of study to be mastered. In this paper, we use the games played in an online chess platform to exploit the "wisdom of the crowd" and answer questions traditionally tackled only by chess experts. We first define a relatedness network of chess openings that quantifies how similar two openings are to play. Using this network, we identify communities of nodes corresponding to the most common opening choices and their mutual relationships. Furthermore, we demonstrate how the relatedness network can be used to forecast future openings players will start to play, with back-tested predictions outperforming a random predictor. We then apply the Economic Fitness and Complexity algorithm to measure the difficulty of openings and players' skill levels. Our study not only provides a new perspective on chess analysis but also opens the possibility of suggesting personalized opening recommendations using complex network theory.
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Registered trials
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