A Fuzzy Based Performance Model for the Assessment of Individual Sport Branches: A Case Study for Tennis Players


ÖZKAN B. , KARAŞAN A. , KAYA İ.

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, vol.37, pp.27-51, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37
  • Publication Date: 2021
  • Title of Journal : JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
  • Page Numbers: pp.27-51

Abstract

Performance measurement is a vital phase for a sustainable assessment. To procure an accurate performance evaluation, it is crucial to consider all of the alternatives with respect to the measurement criteria based on the available data. In this context, multi-criteria decision-making (MCDM) methods can be consider as one of the most appropriate approaches such a problem environment. Since the performance evaluations do not only consist of cardinal information but also are based on decision makers' experience which can be categorized as linguistic information, the classical MCDM methods are not capable to reflect the fullest extent of the data. As a result of that, it would be more appropriate and accurate to evaluate performances of tennis players by utilizing fuzzy logic approach instead of classical MCDM methods. In this study, a fuzzy based MCDM model consists of Delphi, Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed for performance evaluation extended with Pythagorean fuzzy sets (PFSs), for an appropriate assessment process. The criteria and alternatives have been determined by using Delphi method. The weights of the criteria are obtained by using PF AHP and final ranking of the players are determined by using PF TOPSIS. Also, one-at-a-time sensitivity analysis is conducted to check the flexibility of the obtained results. The obtained results showed that instead of using only the statistical data, a fuzzy based MCDM methodology which can represent both statistical and linguistic data is more suitable for the performance evaluation.