The goal of this study was to 1) investigate general body influence, hand-specific anthropometric parameters, and upper-limb power and strength on ball-throwing velocity in a standing position (V ball ); and 2) predict this velocity using the multi-regression analysis method. Forty-two skilled male handball players (age = 21 y±2.99; body height = 1.81 m±0.07; body mass =78.3 kg±11.3) participated in this study. We measured general anthropometric parameters (body height, body mass, lean mass, BMI) and handball-specific anthropometric parameters (hand size and arm span). Upper-limb dynamic strength was assessed using a medicine ball (2 kg MB) throwing test, and power through a maximal onerepetition bench-press test (1-RMBP). All the variables studied were correlated with Ball velocity. The 2 kg MB throwing performance was the best predictor (r=0.80). General anthropometric parameters were better predictors (r=0.55-0.70) than hand-specific ones (r=0.35-0.51). The best multiple regression model accounted for 74% of the total variance and included body mass, 2 kg MB performance and power output at the 20 kg-bench-press. The equation found could help trainers, athletes and professionals detect future talents or test athletes' current fitness levels.3

This study explores the cognitive management of a successful experienced elite handball coach during a competition. A coach's activity was examined based on a “single case” by analyzing the content of two types of interviews (semi-structured, stimulated recall) through deductive, then inductive approaches. Results showed: (a) in the planning phase, game plans intended for the competition were elaborated from situations that had long been mastered during training sessions: And (b) in the interactive phase on offense, the coach carried out adjustments through his players (especially the playmaker); on defense, changes were made using a basic knowledge of game systems. The coach's activity was organized into six prioritized tasks relating to: i) players' physical engagement; ii) management of collective duels; iii) management of individual duels; iv) refereeing; v) players' energy management; and vi) technical-tactical instruction to the substitutes. Cognition was principally distributed by interacting with the team's playmaker.

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International audienceThe purpose of this paper is to identify the dynamic adjustment of the cognitive control modes used by three professional head coaches during the defensive part of a handball match and, in particular, to highlight the contextual factors that have an effect on these modes. Two main aspects characterize cognitive control modes: the level of abstraction (symbolic/subsymbolic) and the origin (internal-anticipative/external-reactive) of the data used for control. Verbal communications between three coaches and their teams were recorded during 15 matches. The verbal meaningful units were encoded using a general cognitive method introduced by Amalberti and Hoc and coded using a predicate–argument format with MacSHAPA software. Analysis shows relationships between the mode of cognitive control used by coaches and the level of difficulty characterizing the situation. When the level of difficulty is low, coaches favor a reactive mode of control guided by external and subsymbolic data, whereas they favor a more abstract level of control and more internal data when facing a difficult situation. In the latter case, different coaching styles are in evidence

This study is based on Regulatory Focus Theory and investigates the links between the subjects' motivational orientations for promotion or prevention and their performance in handball penalty throws. In a real-world setting with high stakes, we analyzed 327 penalties taken from 68 closed games. The results of the 7-meter throws constitute the dependent variable (Goal vs Miss). The independent variables are the situational focus ([critical moment (ie, final minute) = pressure] vs [non-critical moment = no pressure]), the reward structure (ie, [thrower's team is behind = losses reward structure] vs [thrower's team is ahead or teams are tied = gains reward structure), and the game location (home vs away). Using logistic regression analysis, the results show that when there is a fit between the situational focus and the reward structure, the thrower's performance is higher than when there is a mismatch (OR = 2.21; P = .004). In the same way, the thrower's performance is higher when there is high fit between the situational focus and the reward structure (OR = 1.78), than when there is a high mismatch (OR = 0.54). These results globally support the Regulatory Fit Theory in an ecological context.

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This study was based on the naturalistic decision-making paradigm and regulatory focus theory. Its aim was to model coaches' decision-making processes for handball teams' defensive systems based on relevant cues of the reward structure, and to determine the weight of each of these cues. We collected raw data by video-recording 41 games that were selected using a simple random method. We considered the defensive strategy (DEF: aligned or staged) to be the dependent variable, and the three independent variables were (a) numerical difference between the teams; (b) score difference between the teams; and (c) game periods. We used a logistic regression design (logit model) and a multivariate logistic model to explain the link between DEF and the three category independent variables. Each factor was weighted differently during the decision-making process to select the defensive system, and combining these variables increased the impact on this process; for instance, a staged defense is 43 times more likely to be chosen during the final period in an unfavorable situation and in a man advantage. Finally, this shows that the coach's decision-making process could be based on a simple match or could require a diagnosis of the situation based on the relevant cues.