Augmenting Sports Videos with VisCommentator
Chen Zhu-Tian, Shuainan Ye, Xiangtong Chu, Haijun Xia, Hui Zhang,, Huamin Qu, Yingcai Wu

TL;DR
This paper introduces VisCommentator, a tool that simplifies creating augmented sports videos by combining a systematic design space with machine learning, enabling analysts to efficiently visualize data insights in sports videos.
Contribution
It presents a comprehensive design space for augmented sports videos and a prototype tool that streamlines the creation process using machine learning and visualization recommendations.
Findings
High user satisfaction with VisCommentator.
Participants can quickly reproduce augmented videos.
The system is adaptable to other racket sports.
Abstract
Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level (what the constituents are) and clip-level (how those constituents are organized). We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of…
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