A systematic approach for ranking football players within an integrated DEA‐OWA framework

Abstract

The ranking of players has always been an important input for key decisions in the football industry. This paper introduces a systematic methodology for ranking football players through objective performance factors within a framework that integrates data envelopment analysis and ordered weighted averaging operators. Players from European Premier League Football clubs are selected and ranked under a decision setting that involves the players' desirable and undesirable performance factors alongside the attitude of the decision maker. The robustness of the proposed methodology is evaluated using different levels of optimism of the decision maker. Although ranking players has received considerable attention in other sports, the problem has hardly been addressed in

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

“…Another avenue that also deserves to be explored is generating the PR weights by implicating more formally the CPs into the process through DEA cross‐efficiency (e.g., Oukil, ; Oukil & Amin, ). See Hassan and Oukil () and Oukil and Govindaluri () for recent applications. Theoretical developments relating to PR weights can be found in Oukil () and Oukil ().…”

Gangless cross-evaluation in DEA: an application to stock selection

“…Due to this desirable property, cross-efficiency evaluation is widely used in the DEA literature. Recent applications can be found in stock market portfolio selection [11], resource allocation [7], faculty academic performance evaluation [17], supply chain management [13], supplier selection under uncertainty [5], ranking of football players [20], and many others. In the meantime, the existence of alternate optimal solutions in DEA cross-evaluation motivated a lot of research on developing alternative secondary goal models (e.g., [6,16].…”

On the orness of Bonferroni mean and its variants

“…One of the appealing points of the OWA operator is the notion of orness . The orness measure reflects the and‐like or or‐like aggregation result of an OWA operator, which is very important both in theory and practical applications . Liu proposed a general optimization model to obtain the consistency of OWA operator family by changing the orness level; however, one important observation regarding Yager's orness is the lack of categorization of orness and andness.…”