Abstract
Modern basketball players depend on decision-making techniques to enhance their position orientation and performance results. The way a player positions, together with their effectiveness and gameplay strategy, changes based on what they have learned and the amount of experience they have gained. A new intelligent decision-making model has been created to evaluate basketball player orientation while enhancing their performance through assessments. The proposed evaluation method uses an advanced decision algorithm based on circular q-rung orthopair fuzzy sets (Cq-ROFS), representing an evolved extension of fuzzy set theory to handle uncertain and complex data from player evaluation. This study introduced four innovative aggregation operators, which include circular q-rung orthopair fuzzy muirhead mean (Cq-ROFMM), circular q-rung orthopair fuzzy dual muirhead mean (Cq-ROFDMM), circular q-rung orthopair fuzzy weighted muirhead mean (Cq-ROFWMM) and circular q-rung orthopair fuzzy dual weighted muirhead mean (Cq-ROFDWMM). The decision-making accuracy improves when these operators maintain criterion relationships because they produce unbiased and dependable evaluations of player abilities and expertise. The development includes constructing key theoretical properties while creating a reliable multi-criteria decision-making (MCDM) algorithm that enables efficient computations. A case study of basketball player orientation implements the proposed methodology to prove its practical success and demonstrates its ability to handle uncertainties. The proposed method outperforms existing techniques according to sensitivity and comparative analysis tests because it demonstrates robustness, flexibility, and enhanced decision-making precision. The proposed model demonstrates emerging value as an intelligent sports analytics tool and a broader MCDM applications solution.