Predictive athlete performance modeling with machine learning and biometric data integration.
Journal:
Scientific reports
PMID:
40348828
Abstract
The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. By merging physiological signals i.e., Heart rate variability, oxygen consumption, muscle activation patterns, with psychological signals i.e., mental toughness, athlete engagement, group cohesion along with contextual training data, we create a hybrid model that performs superiorly as compared to traditional unidimensional models. Our exercisers were trained using a gradient boosting and neural network to learn the very complex non-linear relationships that exist between the physical and the psychological performance drivers. With a rich sample set of 480 athletes from different sports, the proposed model achieved 90% accuracy (R = 0.90) in predicting performance outcomes and outdid the conventional methods with statistical approaches R = 0.77) and machine learning based methods (R = 0.77) And machine whiz methods. The robust results achieved from the model (over 90%) as compared to conventional nor statistical methods. From the analysis of the features' importance, the strongest predictor of performance are the Provided Dedicated Athletes' scores of the Functional Movement Screening (13.7%), athlete dedication (11.5%), maximum acceleration capabilities (10.2%), which verify the relationship along biomechanical, preconceived explosive power and psychological commitment. This reflects the finding whereby deep categorized athletic talent prediction requires a multi-dimensional approach by sophisticated fusion techniques. The framework is useful to coaches and sports scientists because it allows for the individualized design of injury risk mitigation and physiologically and psychologically-focused interpersonal help. This approach integrates multiple factors and constitutes an important progress in sports analytics by providing a comprehensive perspective on the complex realities which influence elite athletic performance.