Predicting Badminton Player Performance: Integrating Physical, Psychological, and Technical Factors Using Machine Learning

Abstract

Performance of athletes in competitive sports is thus influenced by physical, psychological as well as technical attributes. The nature of badminton, which is a rapidly progressive sport, is not easy to manage, requires high speed, endurance, spirit and combinatorial skills and perfect strategies. Historical approaches like exploration statistical look at performances and regression analysis prediction do not capture team and time dependencies. With the help of modern machine learning (ML) approaches, intra-and inter-sport analytics have become enriched with powerful tools for multivariate patterns analysis in high-dimensional data. Therefore, this paper engages an exploratory study of the efficacy of ML, focusing specifically on neural networks, as a means by which to predict performance by synthesizing physical fitness, psychological, and skills-based data in a single model. However, some gaps have been identified in the integration process of the said factors while adopting different configurations that point to the need for frameworks. The results of the study emphasize the need to incorporate modern machine learning techniques in the administration of training, in specifying game plans and in the scouting for talents in sports such as badminton among others.

Keywords:  Badminton Player Performance; Machine Learning; Physical, Psychological, and Technical Factors; Performance Prediction; Sports Analytics