Director's Cut: Analysis and Annotation of Soccer Matches

Stein, Manuel; Janetzko, Halldór; Breitkreutz, Thorsten; Seebacher, Daniel; Schreck, Tobias; Grossniklaus, Michael; Couzin, Iain D.; Keim, Daniel A.

doi:10.1109/mcg.2016.102

Cited by 50 publications

(48 citation statements)

References 4 publications

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“…We give an example of a set of potentially relevant events for soccer analysis in Figure 3. This list of events derived from our previous works on a continuously expanded system for feature-and event-based soccer analysis [21][22][23]. Although arguably it may be extensible, and is not tailored towards a specific model from Sport Science, we believe it is a practical starting point for reasoning about the types of events potentially useful for analysis.…”

Section: Event Datamentioning

confidence: 99%

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Stein

Janetzko

Seebacher

et al. 2017

Data

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Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially high commercial and research interest. The analysis of team ball games can serve many goals, e.g., in coaching to understand effects of strategies and tactics, or to derive insights improving performance. Also, it is often decisive to trainers and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors are. We consider team sport as group movement including collaboration and competition of individuals following specific rule sets. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. We identify important components of team sport data, exemplified by the soccer case, and explain how to analyze team sport data in general. We identify challenges arising when facing these data sets and we propose a multi-facet view and analysis including pattern detection, context-aware analysis, and visual explanation. We also present applicable methods and technologies covering the heterogeneous aspects in team sport data.

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Section: Event Datamentioning

confidence: 99%

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Stein

Janetzko

Seebacher

et al. 2017

Data

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“…For example, one analysis task might be to represent which team or player was dominating which part of the pitch under specific conditions. Two recently proposed techniques include so-called interaction spaces and free spaces [47]. Interaction spaces visualize the surrounding area that each player directly aims to control.…”

Section: Region-based Analysismentioning

confidence: 99%

“…Interaction spaces visualize the surrounding area that each player directly aims to control. They can be computed as the area that can be reached by a player, modeled as a function of velocity and heading [47]. Intersecting interaction spaces between players of opposing teams are identified as duel areas.…”

Section: Region-based Analysismentioning

confidence: 99%

“…Players need to take many factors into account when deciding for low-risk or more insecure passes to gain space. Recently, an analysis technique calculating pass alternatives for a player in a given game situation was introduced [47]. The overall goal was not to assess the quality of a pass but rather determine the risk involved for various passing alternatives.…”

Section: Event-based Analysismentioning

confidence: 99%

“…So far, arrows pointing towards potential receiving players on an abstract pitch are employed to visualize possible pass alternatives. While integrating the pass visualization into the video, we extended the original two-dimensional version [47] by additionally highlighting players, for example, with a floating colored diamond-shape over their heads. In Figure 7 (a), we show the previous two-dimensional visualization on an abstract rendered pitch in comparison to our enhanced version visualized on the pitch in Figure 7 (b).…”

Section: Event-based Analysismentioning

confidence: 99%

See 2 more Smart Citations

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

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139

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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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Breaking the Curse of Visual Analytics: Accommodating Virtual Reality in the Visualization Pipeline

Kraus

Miller

Buchmüller

et al. 2020

Communications in Computer and Information Science

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mennatallah . el-assady}©uni-konstanz.de https://www.vis.uni-konstanz.de/ A bstract. Previous research has exposed the discrepancy between the subject of analysis (real world) and the actual data on which the analysis is performed ( data world) as a crit ical weak spot in visual analysis pipelines . In this paper, we demonstrate how V irtual Reality (VR) can help to verify the correspondence of both worlds in the context of Information Visualization (InfoVis) and Visual Analytics (VA). Immersion allows the analyst to

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Director's Cut: Analysis and Annotation of Soccer Matches

Cited by 50 publications

References 4 publications

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

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

Breaking the Curse of Visual Analytics: Accommodating Virtual Reality in the Visualization Pipeline

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