Use predictive analytics and big data technologies to enhance badminton game strategy development and performance indicators

Abstract

The increasing integration of data-driven approaches and machine learning (ML) in sports presents a significant opportunity to optimize performance, predict outcomes, and refine strategies, especially in badminton. Despite its promise, challenges such as the lack of comprehensive datasets, limited use of advanced ML techniques, insufficient focus on tactical decision-making, and the underutilization of predictive analytics in training remain prevalent. To develop a predictive model that analyzes technical and tactical decisions in badminton, enhancing strategy development and performance evaluation for competitive gameplay, the proposed method, Puffer Fish Algorithm-tuned Intelligent Support Vector Machine (PFA-INT-SVM), combines the benefits of Pufferfish mutation with INT-SVM to improve prediction accuracy and classification tasks. The model utilizes data that encompasses player performance metrics, shot types, match context, opponent behavior, physical conditions, environmental factors, and tactical decisions. One-hot encoding is applied to categorical features, while normalization standardizes numerical data, and Linear Discriminant Analysis (LDA) is employed for dimensionality reduction and feature extraction. Experimental results demonstrate that PFA-INT-SVM significantly outperforms traditional methods in terms of accuracy and efficiency. This model effectively predicts player performance and match outcomes, showing promising potential for enhancing badminton strategy development and analysis. The findings highlight the future of integrating machine learning techniques for advanced sports analytics and practical training applications.