Feature-driven visual analytics of soccer data

No Thumbnail Available
Files
There are no files associated with this item.
Date
2014
Editors
Contact
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
DOI (citable link)
ArXiv-ID
International patent number
Link to the license
oops
EU project number
Project
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Contribution to a conference collection
Publication status
Published in
2014 IEEE Conference on Visual Analytics Science and Technology, Paris, France, 9-14 October 2014, Proceedings / Min Chen ... (ed.). - Piscataway, NJ : IEEE, 2014. - pp. 13-22. - ISBN 978-1-4799-6227-3
Abstract
Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Conference
IEEE Conference on Visual Analytics Science and Technology (VAST), 2014, Oct 9, 2014 - Oct 14, 2014, Paris
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690JANETZKO, Halldor, Dominik SACHA, Tobias SCHRECK, Daniel A. KEIM, Oliver DEUSSEN, 2014. Feature-driven visual analytics of soccer data. IEEE Conference on Visual Analytics Science and Technology (VAST), 2014. Paris, Oct 9, 2014 - Oct 14, 2014. In: MIN CHEN ..., , ed.. 2014 IEEE Conference on Visual Analytics Science and Technology, Paris, France, 9-14 October 2014, Proceedings. Piscataway, NJ:IEEE, pp. 13-22. ISBN 978-1-4799-6227-3. Available under: doi: 10.1109/VAST.2014.7042477
BibTex
@inproceedings{Janetzko2014Featu-30188,
  year={2014},
  doi={10.1109/VAST.2014.7042477},
  title={Feature-driven visual analytics of soccer data},
  isbn={978-1-4799-6227-3},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2014 IEEE Conference on Visual Analytics Science and Technology, Paris, France, 9-14 October 2014, Proceedings},
  pages={13--22},
  editor={Min Chen ...},
  author={Janetzko, Halldor and Sacha, Dominik and Schreck, Tobias and Keim, Daniel A. and Deussen, Oliver}
}
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/123456789/30188">
    <dc:creator>Sacha, Dominik</dc:creator>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dc:creator>Deussen, Oliver</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Deussen, Oliver</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2014</dcterms:issued>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T07:51:25Z</dc:date>
    <dcterms:abstract xml:lang="eng">Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach.</dcterms:abstract>
    <dcterms:title>Feature-driven visual analytics of soccer data</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T07:51:25Z</dcterms:available>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:contributor>Sacha, Dominik</dc:contributor>
    <dc:language>eng</dc:language>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/30188"/>
    <dc:creator>Janetzko, Halldor</dc:creator>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Janetzko, Halldor</dc:contributor>
  </rdf:Description>
</rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Contact
URL of original publication
Test date of URL
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed