An approach for enhancing physical education analytics for college sports using AHP based MCDM algorithm integrated with MEREC method

In today's data-driven era, the ability to make a well-informed and smart decision has never been more essential than ever. In this respect, physical education analytics has become a crucial tool for coaches, managers, and instructors that assists in assessing player growth and managing resources, and performances with great efficiency by tracking physical fitness, injury prevention, and the overall effectiveness of a program. Traditional approaches frequently overlook the significant aspects of physical education essential for long-term sports success, such as motor skills, teamwork dynamics, and mental toughness. So, there is a need to integrate the data into a decision-making framework that enhances the performance of athletes while optimizing the resources. By considering it, this paper introduces a multi-criteria decision-making (MCDM) framework that enhances physical education analytics by integrating the decision-making model, i.e., analytical hierarchy process (AHP) and (method on the removal effect of criteria) MEREC within the spherical fuzzy (SF) context. The proposed approach provides a robust and efficient framework that evaluates the pairwise comparison of each factor and analyzes the relative importance, which assists decision-makers in making more well-informed and comprehensive decisions. Moreover, the spherical fuzzy set (SFS) offers flexibility in describing human opinions by utilizing satisfaction, abstinence, and dissatisfaction levels. So, by leveraging this framework, colleges can make more efficient and reliable decisions that boost athletic performances and promote sustainable development.