A Learning Framework for Predicting Badminton Players Performance: Focusing Leadership, Mental and Fitness Attributes

Abstract

This study examines the impact of physical fitness, psychological traits, leadership characteristics, and their collective influence on badminton performance in the context of Pakistan. The primary objective is to identify key variables affecting athletic performance and to develop a machine learning classifier. Key factors such as endurance strength, height, weight, leadership, goal-setting, and imagery were considered as intermediate variables influencing performance. Statistical analyses, including t-tests, correlation analysis, and Principal Component Analysis (PCA), were performed. The findings revealed significant relationships, such as the positive correlation between goal-setting and decision-making under pressure, and between height and smash power (r = 0.8). Endurance was strongly linked to match-play stamina. A Random Forest machine learning model, with a classification accuracy of 92%, was used to predict player performance based on physical indices (e.g., high jump, push-ups) and psychological factors (e.g., imagery, decision-making skills). The implications of these findings underscore the importance of comprehensive training programs addressing physical, psychological, and leadership domains. The results suggest that coaches should design personalized fitness regimes and incorporate mental resilience training, such as emotion regulation and imagery techniques, to enhance decision-making under pressure. This research provides a foundation for future studies on badminton performance and offers practical insights for athlete development and coaching. Future work should integrate longitudinal data and real-world performance to refine predictive models and optimize training approaches.