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

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.