A fuzzy framework to evaluate players' performance in handball - Romero, F. P.; Angulo, E.; Serrano-Guerrero, J.; Olivas, J. A. (International Journal of Computational Intelligence Systems, 2020)
The evaluation of the players' performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that best represent the handball player's performance in a match and for setting their relevance weights. Our approach consists of a fuzzy model to aggregate expert judgments. This methodology overcomes some drawbacks of classical systems, including the definition of the relevance of each criteria using linguistic labels. A preliminary evaluation analyzes handball players' performance indicators and their application to a short tournament. Considering the obtained results, we can conclude that the proposal is relevant and provides useful insights regarding player performance in different matches. The proposed methodology has also been compared with a basic plus-minus rating methodology. This comparison illustrates the feasibility of our approach. Results suggest that plus-minus rating is not the best solution to represent the performance of specialized players who only play when their team attack or defense. Our approach demonstrates being more appropriate for sports such as handball because it includes the valuation of a full set of positive actions in defense and attack.