Affiliation:
1. Kyunggi University, Graduate School of Sports Science, Suwon City, Korea
2. School of Physical Education and Training, Shanghai University of Sport, Shanghai, China
Abstract
Decision-making is a vital aspect of any sport and competitive success, in particular open, strong, competitive team sports like soccer, volleyball, basketball, and rugby. However, in the notational analysis, it has largely ignored. To successfully win in any game and compete in every sport for individuals and teams at the national and international level, all success factors must be reconsidered to ensure a better winning decision. Hence, in this paper, Exploratory Hybridized Structural Equation Modeling Framework (HSEMF) has been proposed for decision making on functional training competitive sports training. The training environment will allow competitors to weigh up their options, determine, and mistake. However, the main feature of the environment is that athletes must be informed if they make mistakes to ensure that they do not take part in the future. The feedback should be provided to improve the performance of the athlete. The decision accuracy and performance indicators have been evaluated. Overall, the results did not show a causal relationship between changes in decision making after implementing the learning modules. However, it has provided moderate evidence of improved reaction time due to the learning modules for decision-making.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Cited by
1 articles.
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