A Study on VR Training of Baseball Athletes

Kincaid, Jack A.; Gong, Fengchen; Jia, Tianjie; Tan, Hong Z.; Kohr, Casey; Bertoline, Gary R.

doi:10.1007/978-3-030-78227-6_14

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…For example, a video assistant referee system has been deployed in football over 100 competitions including the 2022 FIFA World Cup to support the referee team to reduce misjudgments 1 . On the other hand, player evaluation and tactic investigation have improved the effectiveness in multiple sports, such as football [1,7,23,36], baseball [16,24], basketball [4,47], and badminton [3,43]. To train models for these applications, the datasets are mainly collected from either videos [9,50] or sensors [15,18,29] to extract the enormous variable metadata in the matches.…”

Section: Introductionmentioning

confidence: 99%

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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