Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset

TL;DR

This paper introduces ShuttleSet22, a comprehensive badminton dataset with detailed stroke-level data, and benchmarks forecasting future strokes in rallies to advance AI research in badminton analytics.

Contribution

It provides a new large-scale, detailed badminton dataset and establishes a benchmark challenge for stroke forecasting, fostering research in AI-driven badminton analysis.

Findings

Baseline models achieve moderate forecasting accuracy.

The dataset enables diverse research on player and match analysis.

Innovative approaches can significantly improve stroke prediction.

Abstract

In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used by researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set, with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023, to encourage researchers to tackle this real-world problem through innovative approaches and to summarize insights between the state-of-the-art baseline and improved techniques, exchanging inspiring ideas. The baseline codes and the dataset are made available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.