ShuttleFlow: Learning the Distribution of Subsequent Badminton Shots Using Normalizing Flows

Abstract

This paper introduces ShuttleFlow, a simple yet effective model designed to forecast badminton shot types and shuttle positions. This tool could be invaluable for coaches, enabling them to identify opponents’ weaknesses and devise effective strategies accordingly. Given the inherent unpredictability of player behaviors, our model leverages conditional normalizing flow to generate the distributions of shot types and shuttle positions. This is achieved by considering the players and their preceding shots on the court. To augment the performance of our model, especially in predicting outcomes for players who have not previously competed against each other, we incorporate a novel regularization term. Additionally, we utilize Poisson disk sampling to reduce sample redundancy when generating the distributions. Compared to state-of-the-art techniques, our results underscore ShuttleFlow’s effectiveness in forecasting shot types and shuttle positions.