Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks

Abstract

The interaction between teams behaviour is from high relevance for success in sports games. Since the analysis of this interaction is not well established, the present study attempts to model the interaction between opposing teams in team handball. Offensive and defensive playing patterns were determined by means of artificial neural networks from position data of 723 offensive action sequences and the corresponding defensive players, respectively. The most common combinations of these patterns were then analysed statistically. Pattern efficiency was assessed by scoring rate, distance between shooting position and nearest defensive player and distance to goal. No statistically significant relation between pattern combinations and efficiency was found. However, results revealed tendencies to higher efficiency of some tactical patterns. Furthermore, odds ratio analysis revealed advantageous defensive tactics against specific offensive behaviour. Summarizing, results indicate that artificial neural networks are appropriate to model the interaction between teams based on players' positions. ARTICLE HISTORY

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

“…The AI techniques or methods with the best model evaluation metrics were indicated to be applied (for details see Additional file 2: Table S2). Furthermore, 11 studies [22, 25, 26, 29, 37, 48, 49, 52, 53, 55, 69] did not report the evaluation metrics specific for the model. However, the authors of the latter studies recommended the application of AI techniques or methods tested in each manuscript.…”

“…The AI techniques or methods ( n = 2 studies) used to predict injury risk in professional handball (100%) and volleyball (50%) players were artificial neural network [65] and decision tree classifier [15]. For handball performance, all the studies were on the “technical and tactical analysis” area with youth academy based studies using artificial neural network ( n = 4 studies) [26–29]. For volleyball performance, most of the studies were on the “technical and tactical analysis” area too [33, 52, 53, 61] and only one, with professional players, was on “physical, technical, and tactical analysis” [51].…”

Analysis of the Influence of the Numerical Relation in Handball During an Organized Attack, Specifically the Tactical Behavior of the Center Back

“…Team sports entail complexity, opposition, and cooperation. They also involve continuous interaction of the players’ behaviors among themselves and with the context, be it in the place of the game, the zone of the field where the actions take place, the elapsed playing time, the partial score, or the numerical relation (Garganta, 1997; Tavares, 1999; Ribeiro and Silva, 2002; Prudente, 2006, unpublished; Garganta, 2009; Schrapf et al, 2017). In these sports, behaviors are usually emergent and can derive from the individual characteristics, the possibilities that the context offers, and the characteristics of the tasks performed by the players (Travassos et al, 2010, 2013; Rodriguez and Anguera, 2014).…”

The Application of Mathematical Modeling in Building of Social and Environmental Competence of Future Technical Specialists in the Health Care Area

“…Mathematical models are developed based on these patterns and they aimed at describing and assessing players' behavior [4,5]. Thus, artificial neural networks are used for analyzing the interaction of teams in handball [6].…”