A Predictive Model: The Key to Success for Badminton Servers and Receivers

Abstract

To explore the influence of technical actions on badminton competition outcomes, this study analyzes the frequency and impact of specific movements made by players, distinguishing between the server and receiver roles. Focusing on international competitions from 2019 to 2023, we collected data on 23 distinct technical actions (e.g., Net Front, Slice/Drop, Push) to construct a predictive model. The study distinguishes itself by employing a Random Forest algorithm to ascertain the significance of each technical action, utilizing forward stepwise selection and 5-fold cross-validation for feature refinement. SHAP value analysis further validated the pivotal roles of Net Front, Slice/Drop, and Push across both sexes, linking higher frequencies of Net Front with increased match-winning probabilities. Model validation on a test set demonstrated effective performance in both sexes, with the model based on male data exhibiting higher accuracy and predictive values, surpassing the performance of the female data model. This comprehensive examination, grounded in quantitative analysis, not only enhances our understanding of badminton gameplay dynamics but also offers valuable insights for coaching strategies and training methodologies.