An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation
Abstract In the highly competitive world of modern football, each millisecond and centimeter can influence match outcomes. A team’s overall performance often hinges on the positioning and movement of even a single player. Thus, the positioning of each player according to his skill set is crucial to...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08605-y |
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| author | ZhiXin Fang XinLi Yao Man Song |
| author_facet | ZhiXin Fang XinLi Yao Man Song |
| author_sort | ZhiXin Fang |
| collection | DOAJ |
| description | Abstract In the highly competitive world of modern football, each millisecond and centimeter can influence match outcomes. A team’s overall performance often hinges on the positioning and movement of even a single player. Thus, the positioning of each player according to his skill set is crucial to enhance overall performance. This study develops a mathematical model that optimizes player positioning for a football team, considering key attributes such as technical awareness and decision-making, stamina and endurance, ball control and passing (technical ability), coordination, and communication. The combined composite solution (COCOSO) and circular intuitionistic fuzzy set (CrIFS) consider the factors affecting the player’s performance. Furthermore, a data aggregation model based on the weighted averaging mean and weighted geometric mean is developed using the Einstein t-norm (ETN) and the Einstein t-conorm (ETCN). The developed model aggregates the data collected, integrating the COCOSO method. The resulting aggregation operators (AOs) are examined for their fundamental properties and then used in conjunction with COCOSO to identify the most suitable player positions, balancing individual strengths and weaknesses. A comparative analysis confirms that the proposed AOs offer noticeable advantages over existing aggregation techniques, underscoring the practical significance of the model. |
| format | Article |
| id | doaj-art-7c5da9a60897472b95fa7f557e83c9ac |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-7c5da9a60897472b95fa7f557e83c9ac2025-08-20T03:05:23ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-08605-yAn intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregationZhiXin Fang0XinLi Yao1Man Song2Physical Education, Beijing University of Posts and TelecommunicationsCangzhou Normal CollegeEnglish Language and Literature, Beijing University of Posts and TelecommunicationsAbstract In the highly competitive world of modern football, each millisecond and centimeter can influence match outcomes. A team’s overall performance often hinges on the positioning and movement of even a single player. Thus, the positioning of each player according to his skill set is crucial to enhance overall performance. This study develops a mathematical model that optimizes player positioning for a football team, considering key attributes such as technical awareness and decision-making, stamina and endurance, ball control and passing (technical ability), coordination, and communication. The combined composite solution (COCOSO) and circular intuitionistic fuzzy set (CrIFS) consider the factors affecting the player’s performance. Furthermore, a data aggregation model based on the weighted averaging mean and weighted geometric mean is developed using the Einstein t-norm (ETN) and the Einstein t-conorm (ETCN). The developed model aggregates the data collected, integrating the COCOSO method. The resulting aggregation operators (AOs) are examined for their fundamental properties and then used in conjunction with COCOSO to identify the most suitable player positions, balancing individual strengths and weaknesses. A comparative analysis confirms that the proposed AOs offer noticeable advantages over existing aggregation techniques, underscoring the practical significance of the model.https://doi.org/10.1038/s41598-025-08605-yCircular intuitionistic fuzzy setsCOCOSO methodEinstein aggregation operatorsMulti-attribute decision-makingPlayer position analysisMovement pattern evaluation |
| spellingShingle | ZhiXin Fang XinLi Yao Man Song An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation Scientific Reports Circular intuitionistic fuzzy sets COCOSO method Einstein aggregation operators Multi-attribute decision-making Player position analysis Movement pattern evaluation |
| title | An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation |
| title_full | An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation |
| title_fullStr | An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation |
| title_full_unstemmed | An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation |
| title_short | An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation |
| title_sort | intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy cocoso with einstein aggregation |
| topic | Circular intuitionistic fuzzy sets COCOSO method Einstein aggregation operators Multi-attribute decision-making Player position analysis Movement pattern evaluation |
| url | https://doi.org/10.1038/s41598-025-08605-y |
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