S<sup>2</sup>-Labeling: Shot-By-Shot Microscopic Badminton Singles Tactical Dataset

Huang, Y. W.; Huang, Yung-Chang; Lee, Hao Syuan; İk, Tsì-Uí; Wang, Chih-Chuan

doi:10.23919/apnoms56106.2022.9919982

Cited by 3 publications

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“…The annotation process was carried out mainly with the badminton experts using the shot-by-shot (S 2 ) labeling tool [17]. It is a microscopic labeling system for badminton and has been demonstrated as having efficient and effective labeling quality by badminton experts.…”

Section: Annotation Processmentioning

confidence: 99%

“…In this paper, we present the ShuttleSet dataset annotated by domain experts with the efficient and precise S 2 -labeling tool [17] using the BLSR format [42]. Based on the labeling tool, ShuttleSet provides stroke-by-stroke human annotation ground truth records for 44 matches from 2018 to 2021 with 27 high-ranking players (16 men's singles and 11 women's singles), which consist of 104 sets, 3,685 rallies, and 36,492 strokes.…”

Section: Introductionmentioning

confidence: 99%

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ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis

Wang

Huang

İk

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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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 groundtruth datasets. While there are a few available datasets for turnbased sports for action detection, these datasets severely lack structured source data and stroke-level records since these require highcost 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 publiclyavailable 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.

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Section: Annotation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%