A Fuzzy Inference System for Players Evaluation in Multi-Player Sports: The Football Study Case

Abstract

Decision support systems often involve taking into account many factors that influence the choice of existing options. Besides, given the expert’s uncertainty on how to express the relationships between the collected data, it is not easy to define how to choose optimal solutions. Such problems also arise in sport, where coaches or players have many variants to choose from when conducting training or selecting the composition of players for competitions. In this paper, an objective fuzzy inference system based on fuzzy logic to evaluate players in team sports is proposed on the example of football. Based on the Characteristic Objects Method (COMET), a multi-criteria model has been developed to evaluate players on the positions of forwards based on their match statistics. The study has shown that this method can be used effectively in assessing players based on their performance. The COMET method was chosen because of its unique properties. It is one of the few methods that allow identifying the model without giving weightings of decision criteria. Symmetrical and asymmetrical fuzzy triangular numbers were used in model identification. Using the calculated derivatives in the point, it turned out that the criteria weights change in the problem state space. This prevents the use of other multi-criteria decision analysis (MCDA) methods. However, we compare the obtained model with the Technique of Order Preference Similarity (TOPSIS) method in order to better show the advantage of the proposed approach. The results from the objectified COMET model were compared with subjective rankings such as Golden Ball and player value.

A Dimension Reduction Approach to Player Rankings in European Football

“…These comparisons are kept separate for readability purposes. COMET has already been applied to Football [42] and Basketball [43] however the application to Football is restricted to comparing 6 players, therefore the methodology must be applied to our dataset for comparison purposes.…”

“…In this equation CO i represents the i th characteristic object andC ji is the fuzzy number for criteria. For details on the decision process, please refer to [42]. This expert function allows us to parallelize calculation of MEJ and removes the need for hierarchical modelling used in original paper.…”

“…They aggregate player contributions to scoring to arrive at a ranking of players. Sałabun et al [42] Football…”

A Multi-Criteria Decision Process for EV Charging Stations’ Deployment: Findings from Greece

“…Finally, AHP kept on evolving to new directions, such as the application of fuzzy based information and modelling [33], or with symmetric projection group approach [34], etc. The AHP was selected among other multi-criteria methods such as TOPSIS, PROMETHEE II, VIKOR, COPRAS, Best-Worst, and COMET methods due to its clarity, simplicity, and efficiency [35][36][37], as well as compared to the analytic network process (ANP) [38].…”

Effect of Criteria Range on the Similarity of Results in the COMET Method

“…The preferences for the set of alternatives are calculated using the rule base, which is obtained in the process of the pairwise comparison for the Characteristic Objects (COs) [30], [31], [32]. The main assumptions of the COMET method are shortly recalled below following [33]. Additionally, Fig.…”