Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction
Dateien
Datum
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
STEIN, Manuel, Johannes HÄUSSLER, Dominik JÄCKLE, Halldor 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. 2015, 4(4), pp. 2159-2184. eISSN 2220-9964. Available under: doi: 10.3390/ijgi4042159BibTex
@article{Stein2015Visua-32476, year={2015}, doi={10.3390/ijgi4042159}, title={Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction}, 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, Halldor and Schreck, Tobias and Keim, Daniel A.} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/32476"> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Janetzko, Halldor</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:contributor>Schreck, Tobias</dc:contributor> <dc:creator>Schreck, Tobias</dc:creator> <dc:creator>Stein, Manuel</dc:creator> <dc:creator>Häußler, Johannes</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32476/1/Stein_0-309987.pdf"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/32476"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-12-18T09:42:57Z</dcterms:available> <dcterms:title>Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction</dcterms:title> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:creator>Jäckle, Dominik</dc:creator> <dc:contributor>Häußler, Johannes</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32476/1/Stein_0-309987.pdf"/> <dc:creator>Janetzko, Halldor</dc:creator> <dc:contributor>Stein, Manuel</dc:contributor> <dcterms:issued>2015</dcterms:issued> <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>Keim, Daniel A.</dc:creator> <dc:rights>Attribution 4.0 International</dc:rights> <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> </rdf:Description> </rdf:RDF>