Advancement in real time football analysis using fuzzy based decision-making of the WASPAS method

In order to provide workable solutions for improving football players' functional strength training, this study focuses on assessing the effectiveness of intelligent picture-processing approaches employing deep learning algorithms in the context of football. Numerous mathematicians have developed various fuzzy mathematical aggregation operators (AOs). This article explores a potent approach to the circular q-rung orthopair fuzzy set (Crq-ROFS) that is used to mitigate uncertainty and vagueness in human judgments. The discussed fuzzy framework is a broader and extended version of an intuitionistic fuzzy set and q-rung orthopair fuzzy set. Besides the theoretical concepts of circular q-rung orthopair fuzzy information, we modify power aggregation operators to integrate expert's opinions without any external weights of criteria. Besides the concepts of Crq-ROFSs, a family of Dombi power aggregation operators is also initiated, such as the circular q-rung orthopair fuzzy Dombi power-weighted averaging (Crq-ROFDPWA) and circular q-rung orthopair fuzzy Dombi power-weighted geometric (Crq-ROFDPWG) operators. To show the robustness and applicability of derived aggregation operators, some appropriate properties are also discussed. An intelligent decision algorithm for the weighted aggregated sum product assessment (WASPAS) method is established to resolve complex real-life applications under multi-attribute group decision-making (MAGDM) problems. The WASPAS method is also applied to investigate the rank of alternatives under different criteria and human opinions. Furthermore, an application related to advancements in real-time football analysis is discussed with the help of numerical examples and mathematical methodologies. A comparison technique is also adopted to reveal the superiority and effectiveness of pioneering approaches with previously developed mathematical algorithms.