Quantifying the complexity and similarity of chess openings using online chess community data
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-31658-w |
| _version_ | 1797853788291203072 |
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| author | Giordano De Marzo Vito D. P. Servedio |
| author_facet | Giordano De Marzo Vito D. P. Servedio |
| author_sort | Giordano De Marzo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T19:56:29Z |
| format | Article |
| id | doaj.art-71dea06abca54963b4163f54e6228183 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T19:56:29Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-71dea06abca54963b4163f54e62281832023-04-03T05:27:22ZengNature PortfolioScientific Reports2045-23222023-04-0113111210.1038/s41598-023-31658-wQuantifying the complexity and similarity of chess openings using online chess community dataGiordano De Marzo0Vito D. P. Servedio1Centro Ricerche Enrico FermiComplexity Science Hub ViennaAbstract 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.https://doi.org/10.1038/s41598-023-31658-w |
| spellingShingle | Giordano De Marzo Vito D. P. Servedio Quantifying the complexity and similarity of chess openings using online chess community data Scientific Reports |
| title | Quantifying the complexity and similarity of chess openings using online chess community data |
| title_full | Quantifying the complexity and similarity of chess openings using online chess community data |
| title_fullStr | Quantifying the complexity and similarity of chess openings using online chess community data |
| title_full_unstemmed | Quantifying the complexity and similarity of chess openings using online chess community data |
| title_short | Quantifying the complexity and similarity of chess openings using online chess community data |
| title_sort | quantifying the complexity and similarity of chess openings using online chess community data |
| url | https://doi.org/10.1038/s41598-023-31658-w |
| work_keys_str_mv | AT giordanodemarzo quantifyingthecomplexityandsimilarityofchessopeningsusingonlinechesscommunitydata AT vitodpservedio quantifyingthecomplexityandsimilarityofchessopeningsusingonlinechesscommunitydata |