CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics

Polk, Tom; Jäckle, Dominik; Häußler, Johannes; Yang, Jing

doi:10.1109/tvcg.2019.2934243

Cited by 22 publications

(16 citation statements)

References 30 publications

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“…Consistently, here, the visualizations stay still; for example, in VisCommentator [17] visualizations are embedded on static video screenshots. Motion is visible in cases where users can interact through zoom, pan, or rotation [69], [108], [110], [112], or when animated traces are replayed in the tools [4], [68], [90], [101]. However, in contrast to our work, this literature does not discuss how to design moving visualizations and most visualizations are not embedded with a data referent.…”

Section: Moving Visualization Stationary Viewermentioning

confidence: 85%

“…In contrast to these moving visualizations, several static sports visualizations have been developed in research as well. These tools add statistical analysis visualizations for sports such as table tennis [17], [101], [109], soccer [4], [90], [110], basketball [8], [96], cycling [48], badminton [68], [112], or tennis [69]. Consistently, here, the visualizations stay still; for example, in VisCommentator [17] visualizations are embedded on static video screenshots.…”

Section: Moving Visualization Stationary Viewermentioning

confidence: 98%

See 1 more Smart Citation

Visualization in Motion: A Research Agenda and Two Evaluations

Yao

Bezerianos

Vuillemot

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Moving visualization & stationary viewer. Stationary visualization & moving viewer. Moving visualization & moving viewer.Fig. 1: Visualization scenarios that involve different types of relative movement between viewers and visualization: (a): 0 A.D. game characters with attached health meters, (b): an augmented basketball match from the tool Clipper CourtVision. (c): a walkable visualization of the general organization of scholars at ENAC in France [75], [76]. (d): an on-street bar chart that can be driven or walked by created by the Respect New Haven activist group. (e): a runner looking at her fitness data. (f): a person checking financial charts on her phone while walking to a meeting. Image permissions are listed in the acknowledgments.

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Section: Moving Visualization Stationary Viewermentioning

confidence: 85%

Section: Moving Visualization Stationary Viewermentioning

confidence: 98%

Visualization in Motion: A Research Agenda and Two Evaluations

Yao

Bezerianos

Vuillemot

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…In tennis, which has a much larger court and a larger ball than table tennis and badminton, managing the physical energy by predicting the ball position and moving in advance is critical to tennis players. Therefore, the analysis often emphasizes player movement and its correlation with ball position [17], in order to understand the spatio-temporal shot patterns [36,37] and how players use various techniques [61] to mobilize their opponents to move.…”

Section: Expert Interviewmentioning

confidence: 99%

EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos

Deng

Wang

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.

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“…In the first case, the video is used to complement other types of data. For instance, Polk et al [ 7 ] used video to reinforce learning outcomes after analyzing tabular data, as one coach noted during their evaluation, “ seeing is believing. ” In the second case, the video is actively used and is considered as the main data input.…”

Section: Related Workmentioning

confidence: 99%

Stabilization and visual analysis of video-recorded sailing sessions

Reichert

Pieras

Marroquim

et al. 2021

Vis. Comput. Ind. Biomed. Art

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One common way to aid coaching and seek to improve athletes’ performance is by recording training sessions for posterior analysis. In the case of sailing, coaches record videos from another boat, but usually rely on handheld devices, which may lead to issues with the footage and missing important moments. On the other hand, by autonomously recording the entire session with a fixed camera, the analysis becomes challenging owing to the length of the video and possible stabilization issues. In this work, we aim to facilitate the analysis of such full-session videos by automatically extracting maneuvers and providing a visualization framework to readily locate interesting moments. Moreover, we address issues related to image stability. Finally, an evaluation of the framework points to the benefits of video stabilization in this scenario and an appropriate accuracy of the maneuver detection method.

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CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics

Cited by 22 publications

References 30 publications

Visualization in Motion: A Research Agenda and Two Evaluations

Visualization in Motion: A Research Agenda and Two Evaluations

EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos

Stabilization and visual analysis of video-recorded sailing sessions

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