Stein, Manuel
[Verfasser:in];
Häußler, Johannes
[Verfasser:in];
Jäckle, Dominik
[Verfasser:in];
Janetzko, Halldor
[Verfasser:in];
Schreck, Tobias
[Verfasser:in];
Keim, Daniel A.
[Verfasser:in]
Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction
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Medientyp:
Sonstige Veröffentlichung;
E-Artikel
Titel:
Visual Soccer Analytics : Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction
Beteiligte:
Stein, Manuel
[Verfasser:in];
Häußler, Johannes
[Verfasser:in];
Jäckle, Dominik
[Verfasser:in];
Janetzko, Halldor
[Verfasser:in];
Schreck, Tobias
[Verfasser:in];
Keim, Daniel A.
[Verfasser:in]
Erschienen:
KOPS - The Institutional Repository of the University of Konstanz, 2015
Erschienen in:ISPRS International Journal of Geo-Information. 2015, 4(4), pp. 2159-2184. eISSN 2220-9964. Available under: doi:10.3390/ijgi4042159
Sprache:
Englisch
DOI:
https://doi.org/10.3390/ijgi4042159
Entstehung:
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
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. ; published ; published