Neutrosophic MCDM Approach for Performance Evaluation and Recommendation of Best Players in Sports League

Abstract

In this era of the commercialization of sports, various sports leagues are organized across the globe. At the end of the Series, players are awarded for their performances. These awards are decided by human experts or are based on just one performance indicator. However, human decisions are subjective and error-prone, and decisions based on just one criterion are incomplete and inconsistent. This paper identifies the decision-making problem in sports. It proposes a Neutrosophic TOPSIS approach for performance evaluation and recommendation of the best batsman and bowler of the Series. The approach is well-structured, robust, and efficient in handling vagueness, inconsistency, indeterminacy, and imprecision in real-life problems. We present a case study using the data of IPL 2021. In the case study, we calculate the ranks of the players using neutrosophic TOPSIS with two objective weight calculation methods. Then we evaluate and compare the obtained rank lists using Kendal Tau (). The values of for bowling-ranked lists is 0.83 and for batting-ranked lists is 0.72, which are impressive and prove the efficiency and effectiveness of the proposed approach. We believe that the proposed approach can be applied to identify and recommend the best resources in other domains of life.