Evaluating athletic mental energy analysis: a novel approach using fuzzy-based Bayesian networks.

Abstract

Background: This paper focuses on the impact of physical and psychological conditions on athletes' mental energy, which is a serious concern in terms of performance and overall health and seeks to evaluate this problem by identifying its causes.

Method: A Fuzzy Bayesian Network (FBN) model is therefore created to provide an approach for determining the factors and weights to be considered by coaches and sports scientists when assessing the causes of mental fatigue in athletes.

Result: As a result of the research process, a model for evaluating the factors affecting athletic mental energy was developed, and probabilistic relationships between physical and psychological causes were established. The analysis of the model indicates that physical fitness (10.9%), fatigue (10.4%), and nutrient intake (10.2%) are the three most significant root causes affecting athletic mental energy. In addition, the most obvious finding to emerge from this study is that the factors affecting athletic mental energy are closely related to both physical state and psychological state processes. Taken together, the results indicate that physical condition variables are too important to be overlooked in effectively enhancing athletes' mental energy.

Conclusion: Consequently, In conclusion, the FBN model developed for this study is considered to provide a comprehensive probabilistic analysis of the interactions between physical and psychological factors, and to serve as an important guide in identifying new ways to enhance athletes' mental energy.