Fuzzy DEA models for sports data analysis: The evaluation of the relative performances of professional (virtual) football teams

Pinto, Claudio (2020): Fuzzy DEA models for sports data analysis: The evaluation of the relative performances of professional (virtual) football teams.

Abstract

The measurement of sports performances both of individual athletes and of an entire sports team, now highly widespread thanks to the enormous availability of sports data, is a crucial moment for professional sports clubs as the their survival is increasingly linked both to the results in the field obtained by its athletes and/or the team/s and to the achievement of many other sporting objectives. We here propose the use of the DEA methodology adapted to fuzzy logic to measure relative performances in the presence of uncertainty of a virtual sample of professional football teams along two dimensions: efficiency and effectiveness. The results obtained are especially interesting from the point of view of policy indications for the organization and management of the teams on the soccer pitch. The work then develops a second stage analysis structured in order to investigate on the one hand with the help of an econometric model the influence that a set of external factors can have on the performances and on the other, by calculating the gini coefficient, evaluates for various attitudes on the part of managers on uncertainty the degree of inequality in the distribution of sports performances of the groups that have participated in an ideal tournament. In conclusion, the work aims to develop, to our knowledge, an innovative and original way for the reference literature, a framework for analyzing sports data (and in particular for professional football clubs) in order to provide policy indications for improve their sports performances.

• Item Type: MPRA Paper
• Language: English
• Keywords: relative performance, sports data,fuzzy DEA
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty; L - Industrial Organization > L2 - Firm Objectives, Organization, and Behavior > L25 - Firm Performance: Size, Diversification, and Scope

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