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Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

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STEIN, Manuel, Johannes HÄUSSLER, Dominik JÄCKLE, Halldór JANETZKO, Tobias SCHRECK, Daniel A. KEIM, 2015. Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction. In: ISPRS International Journal of Geo-Information. 4(4), pp. 2159-2184. eISSN 2220-9964

@article{Stein2015Visua-32476, title={Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction}, year={2015}, doi={10.3390/ijgi4042159}, number={4}, volume={4}, journal={ISPRS International Journal of Geo-Information}, pages={2159--2184}, author={Stein, Manuel and Häußler, Johannes and Jäckle, Dominik and Janetzko, Halldór and Schreck, Tobias and Keim, Daniel A.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/32476"> <dc:creator>Jäckle, Dominik</dc:creator> <dc:contributor>Schreck, Tobias</dc:contributor> <dc:contributor>Jäckle, Dominik</dc:contributor> <dcterms:abstract xml:lang="eng">With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. Capturing and analyzing complex movement patterns and interdependencies between the players with respect to various characteristics is challenging. So far, soccer experts manually post-analyze game situations and depict certain patterns with respect to their experience. We propose a visual analysis system for interactive identification of soccer patterns and situations being of interest to the analyst. Our approach builds on a preliminary system, which is enhanced by semantic features defined together with a soccer domain expert. The system includes a range of useful visualizations to show the ranking of features over time and plots the change of game play situations, both helping the analyst to interpret complex game situations. A novel workflow includes improving the analysis process by a learning stage, taking into account user feedback. We evaluate our approach by analyzing real-world soccer matches, illustrate several use cases and collect additional expert feedback. The resulting findings are discussed with subject matter experts.</dcterms:abstract> <dcterms:rights rdf:resource="http://nbn-resolving.de/urn:nbn:de:bsz:352-20150914100631302-4485392-8"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/32476"/> <dc:creator>Stein, Manuel</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-12-18T09:42:57Z</dcterms:available> <dc:creator>Keim, Daniel A.</dc:creator> <dc:contributor>Janetzko, Halldór</dc:contributor> <dc:contributor>Häußler, Johannes</dc:contributor> <dc:creator>Janetzko, Halldór</dc:creator> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:contributor>Stein, Manuel</dc:contributor> <dcterms:title>Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction</dcterms:title> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-12-18T09:42:57Z</dc:date> <dc:creator>Häußler, Johannes</dc:creator> <dcterms:issued>2015</dcterms:issued> <dc:creator>Schreck, Tobias</dc:creator> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 18.12.2015 (Informationen über die Zugriffsstatistik)

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