Bio-inspired algorithms for the characterization of excellent performance in handball players: A data-driven methodology

Bio-inspired algorithms have been successfully applied to solve complex optimization problems. They are also widely used to train and optimize machine learning and data-driven models, providing competitive results. This study presents a novel data-driven approach to identify and quantify the factors that characterize the excellent performance of handball players, depending on the specific position in which they play. This will give us the most important characteristics that differentiate the most excellent players in their positions. Based on bio-inspired algorithms, this research delves into the complex optimization problems inherent in sports analytics. The study utilizes data from the Women’s European Handball Championship, employing seven distinct algorithms, of which six are different bio-inspired algorithms - including a hybrid bio-inspired algorithm - and one Consensus-Based Aggregation algorithm to analyze and assign weights to each player’s actions during a match. This approach is further validated by comparing the findings against the top five players in each position as recognized by the European Handball Federation (EHF). Subsequently, the established model’s robustness and applicability are tested using data from the Women’s World Handball Championships. • A data-driven methodology to analyze the sporting performance of handball players. • Bio-inspired algorithms are employed to characterize excellent performance. • Real data from international women championships are used to test the methodology. • Explainable Artificial Intelligence characterizes the best players on the court. • Identify the most distinguishable characteristics of the best high-performance handball players.