@inproceedings{Stein2018-10Revea-44389,
  year={2018},
  doi={10.1109/BDVA.2018.8534022},
  title={Revealing the Invisible : Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis},
  isbn={978-1-5386-9194-6},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)},
  pages={148--156},
  author={Stein, Manuel and Breitkreutz, Thorsten and Häußler, Johannes and Seebacher, Daniel and Niederberger, Christoph and Schreck, Tobias and Grossniklaus, Michael and Keim, Daniel A. and Janetzko, Halldor}
}

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    <dc:language>eng</dc:language>
    <dcterms:title>Revealing the Invisible : Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis</dcterms:title>
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    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:creator>Grossniklaus, Michael</dc:creator>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Janetzko, Halldor</dc:contributor>
    <dc:creator>Niederberger, Christoph</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-12-19T13:43:25Z</dc:date>
    <dc:contributor>Breitkreutz, Thorsten</dc:contributor>
    <dc:contributor>Seebacher, Daniel</dc:contributor>
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    <dc:contributor>Grossniklaus, Michael</dc:contributor>
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    <dc:creator>Häußler, Johannes</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Janetzko, Halldor</dc:creator>
    <dc:creator>Schreck, Tobias</dc:creator>
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    <dc:contributor>Häußler, Johannes</dc:contributor>
    <dcterms:abstract xml:lang="eng">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 time-consuming 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.</dcterms:abstract>
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    <dc:contributor>Niederberger, Christoph</dc:contributor>
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    <dc:creator>Breitkreutz, Thorsten</dc:creator>
    <dc:creator>Seebacher, Daniel</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Stein, Manuel</dc:creator>
    <dc:contributor>Stein, Manuel</dc:contributor>
    <dcterms:issued>2018-10</dcterms:issued>
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