Evaluation of tactical performance in invasion team sports: a systematic review

Development and Validation of an Observational Game Analysis Tool with Artificial Intelligence for Handball: Handball.ai

Performance analysis based on artificial intelligence together with game-related statistical models aims to provide relevant information before, during and after a competition. Due to the evaluation of handball performance focusing mainly on the result and not on the analysis of the dynamics of the game pace through artificial intelligence, the aim of this study was to design and validate a specific handball instrument based on real-time observational methodology capable of identifying, quantifying, classifying and relating individual and collective tactical behaviours during the game. First, an instrument validation by an expert panel was performed. Ten experts answered a questionnaire regarding the relevance and appropriateness of each variable presented. Subsequently, data were validated by two observers (1.5 and 2 years of handball observational analysis experience) recruited to analyse a Champions League match. Instrument validity showed a high accordance degree among experts (Cohen's kappa index (k) = 0.889). For both automatic and manual variables, a very good intra- ((automatic: Cronbach's alpha (α) = 0.984; intra-class correlation coefficient (ICC) = 0.970; k = 0.917) (manual: α = 0.959; ICC = 0.923; k = 0.858)) and inter-observer ((automatic: α = 0.976; ICC = 0.961; k = 0.874) (manual: α = 0.959; ICC = 0.923; k = 0.831) consistency and reliability was found. These results show a high degree of instrument validity, reliability and accuracy providing handball coaches, analysts, and researchers a novel tool to improve handball performance.

Analysis of the Steps Cycle in the Action of Throwing in Competition in Men’s Elite Handball

The aim of this article was to analyze the step cycle that precedes the throwing action in elite men’s handball and its relationship with different factors, such as throwing distance, throwing technics, throw speed, whether it occurs in jump or standing, the last step, and efficiency. Twenty-four complete matches were analyzed, with a total of 1013 throws in three international elite men’s handball championships. The results show that the most used step cycle is the one performed with two steps. There is a significant relationship between the step cycle and the throw distance, with two-step throws being the most used from outside 9 m (29.8%, p < 0.001), three-step throws at a distance between 6 and 9 m (35.9%, p < 0.001), and zero- and one-step throws from 6 m (30.5%, p < 0.001). Likewise, the last step with natural footing is the most used (93.7%, p < 0.001) with a complete cycle of steps, followed by false foot with more than three steps (27.3%, p < 0.001), and two feet with zero steps (12.6%, p < 0.001). In conclusion, the skill to take advantage of the dynamic improvement offered by the mastery of the step cycle, adapting to the different situations of the game, could be an essential characteristic of the player to effectively complete the throwing action.

Determinant Factors for Throwing in Competition in Male Elite Handball

The aims of this study were to define determinant factors that affected throwing actions used to score goals in handball, and to study the effectiveness of throwing actions and their relationships with different factors during their use, such as: the throwing distance, type of arm build, type of throw, whether it is done while jumping or while supported, the last supporting foot and the previous step cycle, as well as their relationship with the final score of the game in elite male handball competitions. A total of 1049 shots from 24 matches in the 2018/19/20 International Men’s Handball Championship were analysed. The results show that distance is relevant in the effectiveness of throwing, setting the build up, throwing while supported or jumping, the type of throwing, the type of steps cycle used and the supporting foot. This seems to indicate that the determining factor that affects a throw is distance.