Integrating Expert Hesitation and Performance Data Using Intuitionistic Fuzzy Sets for Badminton Talent Identification

Abstract

Traditional youth badminton talent selection often fails to capture the uncertainty and subjectivity of expert evaluations. This research proposes a hybrid intuitionistic fuzzy (IF) multi-attribute decision-making framework in which the IF analytic hierarchy process is applied to criterion weights, and the IF technique for order preference by similarity to ideal solution (IF-TOPSIS) is applied to athlete ranking. The framework was evaluated on 40 elite junior badminton athletes. Validation using Wilcoxon signed-rank, Friedman, Pearson correlation, and Kendall's concordance tests demonstrated that the proposed IF multi-attribute decision-making model achieved strong agreement with expert rankings. The model accuracy was 0.91, the ranking stability was 97.4%, and the closeness coefficient variance was 0.0079, indicating high performance under uncertainty. The comparative analysis demonstrated that the proposed method achieved better ranking consistency and uncertainty handling than the analytic hierarchy process, TOPSIS, and fuzzy TOPSIS.