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An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein

ZhiXin Fang1, XinLi Yao2, Man Song3

  • 1Physical Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

Scientific Reports
|July 22, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing football player positioning using a mathematical model enhances team performance. This approach balances player skills and weaknesses for superior on-field strategy and outcomes.

Keywords:
COCOSO methodCircular intuitionistic fuzzy setsEinstein aggregation operatorsMovement pattern evaluationMulti-attribute decision-makingPlayer position analysisSports decision support system

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Area of Science:

  • Sports Science
  • Mathematical Modeling
  • Decision Support Systems

Background:

  • Team performance in football is highly sensitive to player positioning and individual skill sets.
  • Optimizing player placement is crucial for maximizing a team's overall effectiveness on the field.

Purpose of the Study:

  • To develop a mathematical model for optimizing football player positioning based on key attributes.
  • To integrate the Combined Composite Solution (COCOSO) and Circular Intuitionistic Fuzzy Set (CrIFS) for performance evaluation.
  • To introduce novel data aggregation operators (AOs) for enhanced decision-making in player placement.

Main Methods:

  • A mathematical model was developed incorporating player attributes: technical awareness, decision-making, stamina, ball control, passing, coordination, and communication.
  • The Combined Composite Solution (COCOSO) method was utilized alongside Circular Intuitionistic Fuzzy Sets (CrIFS) to assess player performance factors.
  • New data aggregation operators (AOs) were created using weighted averaging and geometric means with Einstein t-norm (ETN) and Einstein t-conorm (ETCN).

Main Results:

  • The integrated COCOSO and novel AOs successfully identified optimal player positions by balancing individual strengths and weaknesses.
  • The proposed AOs demonstrated superior performance compared to existing aggregation techniques in data integration.
  • The model provides a data-driven approach to enhance strategic player positioning in football.

Conclusions:

  • The developed mathematical model offers a significant advancement in optimizing player positioning for improved football team performance.
  • The novel aggregation operators provide a robust method for integrating diverse performance data.
  • This research highlights the practical applicability of advanced mathematical techniques in sports analytics.