TenniVis: Visualization for Tennis Match Analysis

Polk, Tom; Yang, Jing; Hu, Yueqi; Zhao, Ye

doi:10.1109/tvcg.2014.2346445

Cited by 77 publications

(49 citation statements)

References 17 publications

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“…Polk et al [41] describe an analysis approach for tennis, based on meta-data as result, length of rallies, and service. Their visual method includes a comprehensive overview of the overall game and highlights strength and weaknesses of players based on pure statistics.…”

Section: Sports Analysismentioning

confidence: 99%

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Stein

Janetzko

Lamprecht

et al. 2018

IEEE Trans. Visual. Comput. Graphics

132

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach. Permanent repository link

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Section: Sports Analysismentioning

confidence: 99%

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Stein

Janetzko

Lamprecht

et al. 2018

IEEE Trans. Visual. Comput. Graphics

132

View full text Add to dashboard Cite

show abstract

“…There are many works to visualize sports features. One class of methods focuses on visualizing match features like events or scores with only two examples being tennis scores [9] or table tennis scores [10]. Another class of techniques considers especially movement-oriented features in sport matches, with soccer being a prominent application due to research and public interest.…”

Section: B Visual Analysis Of Sports and Soccermentioning

confidence: 99%

Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis

Stein

Breitkreutz

Häußler

et al. 2018

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)

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The analysis of invasive team sports often concentrates on cooperative and competitive aspects of collective movement behavior. A main goal is the identification and explanation of strategies, and eventually the development of new strategies. In visual sports analytics, a range of different visual-interactive analysis techniques have been proposed, e.g., based on visualization using for example trajectories, graphs, heatmaps, and animations. Identifying suitable visualizations for a specific situation is key to a successful analysis. Existing systems enable the interactive selection of different visualization facets to support the analysis process. However, an interactive selection of appropriate visualizations is a difficult, complex, and timeconsuming task. In this paper, we propose a four-step analytics conceptual workflow for an automatic selection of appropriate views for key situations in soccer games. Our concept covers classification, specification, explanation, and alteration of match situations, effectively enabling the analysts to focus on important game situations and the determination of alternative moves. Combining abstract visualizations with real world video recordings by Immersive Visual Analytics and descriptive storylines, we support domain experts in understanding key situations. We demonstrate the usefulness of our proposed conceptual workflow via two proofs of concept and evaluate our system by comparing our results to manual video annotations by domain experts. Initial expert feedback shows that our proposed concept improves the understanding of competitive sports and leads to a more efficient data analysis.

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“…Some notable works include examinations of data in soccer, 5 ice hockey, 6 and tennis. 7 Despite this growth in both academia and practice in recent years, many opportunities remain. The ubiquity, diversity, and relative accessibility of sports data makes it a particularly attractive domain for a range of visualization researchers.…”

Section: Rahul C Basole ■ Georgia Institute Of Technologymentioning

confidence: 99%