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

机译:基于模糊的个人体育分支机构评估的性能模型:网球运动员的案例研究

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摘要

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.
机译:性能测量是可持续评估的重要阶段。为了采购准确的绩效评估,重要的是根据可用数据考虑相对于测量标准的所有替代方案是至关重要的。在这种情况下,多标准决策(MCDM)方法可以考虑作为这种问题环境的最合适方法之一。由于性能评估不仅包括基于基于主题信息而且基于决策者的经验,可以将其分类为语言信息,因此经典的MCDM方法无法反映数据的最大程度。因此,通过利用模糊逻辑方法而不是经典的MCDM方法,可以更合适和准确地评估网球运动员的性能。在这项研究中,基于模糊的MCDM模型由Delphi,分析层次处理(AHP)和通过相似性与理想解决方案(Topsis)的顺序偏好组成,以便适当地进行毕达哥拉斯模糊集(PFSS)的性能评估评估过程。通过使用Delphi方法确定标准和替代方案。通过使用PF AHP获得标准的权重,并且通过使用PF TOPSIS来确定玩家的最终排名。此外,进行一次性敏感性分析以检查所获得的结果的灵活性。所获得的结果表明,而不是仅使用统计数据,这是一种可以代表统计和语言数据的模糊的MCDM方法更适合于性能评估。

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