Here we present the conception, implementation and application of a virtual environment simulator dedicated to the assessment and training of slap shot performance in hockey. The simulator is based on human-avatar interaction, namely a real shooter and a virtual goalkeeper whose behavior is dependent on that of the shooter. The synthesis of the virtual goalkeeper relied on a high-quality model and realistic, motion-captured movements. A regression model based on Kriging was used to predict in real-time the shooter's behavior in order to trigger the blocking moves of the virtual goalkeeper at the right time. Our model provided accurate predicted values as well as an estimation of the reliability of these values, which allowed us to optimize the behavioral animation of the virtual goalkeeper. We then ran a validation experiment testing the effectiveness of our simulator. The simulator proved very useful both to assess the initial performance of the players and to train and improve this performance. In particular, training as little as 3 hours with our simulator gave rise to substantial and significant improvements (up to 22 percent) of the redirection threshold, i.e., the minimum time required to successfully redirect a shot during movement execution when the outcome is imperiled. Importantly, in comparison with ‘classical’ training methods, our simulator better triggers (precisely and timely) the movements of the goalkeeper based on the movements of the shooter.