ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton
  Tactical Analysis
Wei-Yao Wang Yung-Chang Huang Tsi-Ui Ik Wen-Chih Peng
With the recent progress in sports analytics, deep learning approaches have
demonstrated the effectiveness of mining insights into players' tactics for
improving performance quality and fan engagement. This is attributed to the
availability of public ground-truth datasets. While there are a few available
datasets for turn-based sports for action detection, these datasets severely
lack structured source data and stroke-level records since these require
high-cost labeling efforts from domain experts and are hard to detect using
automatic techniques. Consequently, the development of artificial intelligence
approaches is significantly hindered when existing models are applied to more
challenging structured turn-based sequences. In this paper, we present
ShuttleSet, the largest publicly-available badminton singles dataset with
annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492
strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles
and women's singles players. ShuttleSet is manually annotated with a
computer-aided labeling tool to increase the labeling efficiency and
effectiveness of selecting the shot type with a choice of 18 distinct classes,
the corresponding hitting locations, and the locations of both players at each
stroke. In the experiments, we provide multiple benchmarks (i.e., stroke
influence, stroke forecasting, and movement forecasting) with baselines to
illustrate the practicability of using ShuttleSet for turn-based analytics,
which is expected to stimulate both academic and sports communities. Over the
past two years, a visualization platform has been deployed to illustrate the
variability of analysis cases from ShuttleSet for coaches to delve into
players' tactical preferences with human-interactive interfaces, which was also
used by national badminton teams during multiple international high-ranking
matches.