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

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2017
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Gold
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Data. 2017, 2(1), 2. eISSN 2306-5729. Available under: doi: 10.3390/data2010002
Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
sport analytics; visual analytics; high frequency spatio-temporal data
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690STEIN, Manuel, Halldor JANETZKO, Daniel SEEBACHER, Alexander JÄGER, Manuel NAGEL, Jürgen HÖLSCH, Sven KOSUB, Tobias SCHRECK, Daniel A. KEIM, Michael GROSSNIKLAUS, 2017. How to Make Sense of Team Sport Data : From Acquisition to Data Modeling and Research Aspects. In: Data. 2017, 2(1), 2. eISSN 2306-5729. Available under: doi: 10.3390/data2010002
BibTex
@article{Stein2017-03Sense-38112,
  year={2017},
  doi={10.3390/data2010002},
  title={How to Make Sense of Team Sport Data : From Acquisition to Data Modeling and Research Aspects},
  number={1},
  volume={2},
  journal={Data},
  author={Stein, Manuel and Janetzko, Halldor and Seebacher, Daniel and Jäger, Alexander and Nagel, Manuel and Hölsch, Jürgen and Kosub, Sven and Schreck, Tobias and Keim, Daniel A. and Grossniklaus, Michael},
  note={Article Number: 2}
}
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/123456789/38112">
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/38112/3/Stein_0-390361.pdf"/>
    <dc:creator>Nagel, Manuel</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:contributor>Stein, Manuel</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-23T09:17:25Z</dc:date>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dc:creator>Jäger, Alexander</dc:creator>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dc:contributor>Janetzko, Halldor</dc:contributor>
    <dc:creator>Hölsch, Jürgen</dc:creator>
    <dc:contributor>Grossniklaus, Michael</dc:contributor>
    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-23T09:17:25Z</dcterms:available>
    <dc:contributor>Nagel, Manuel</dc:contributor>
    <dc:contributor>Seebacher, Daniel</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/38112"/>
    <dc:creator>Janetzko, Halldor</dc:creator>
    <dc:contributor>Jäger, Alexander</dc:contributor>
    <dc:contributor>Kosub, Sven</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/38112/3/Stein_0-390361.pdf"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Hölsch, Jürgen</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Seebacher, Daniel</dc:creator>
    <dc:creator>Stein, Manuel</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>How to Make Sense of Team Sport Data : From Acquisition to Data Modeling and Research Aspects</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2017-03</dcterms:issued>
    <dc:creator>Grossniklaus, Michael</dc:creator>
    <dc:creator>Kosub, Sven</dc:creator>
    <dc:language>eng</dc:language>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen