TY  - GEN
A1  - Richly, Keven
T1  - Leveraging spatio-temporal soccer data to define a graphical query language for game recordings
T2  - IEEE International Conference on Big Data (Big Data)
N2  - For professional soccer clubs, performance and video analysis are an integral part of the preparation and post-processing of games. Coaches, scouts, and video analysts extract information about strengths and weaknesses of their team as well as opponents by manually analyzing video recordings of past games. Since video recordings are an unstructured data source, it is a complex and time-intensive task to find specific game situations and identify similar patterns. In this paper, we present a novel approach to detect patterns and situations (e.g., playmaking and ball passing of midfielders) based on trajectory data. The application uses the metaphor of a tactic board to offer a graphical query language. With this interactive tactic board, the user can model a game situation or mark a specific situation in the video recording for which all matching occurrences in various games are immediately displayed, and the user can directly jump to the corresponding game scene. Through the additional visualization of key performance indicators (e.g.,the physical load of the players), the user can get a better overall assessment of situations. With the capabilities to find specific game situations and complex patterns in video recordings, the interactive tactic board serves as a useful tool to improve the video analysis process of professional sports teams.
KW  - Spatio-temporal data analysis
KW  - soccer analytics
KW  - graphical query language
Y1  - 2019
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/54004
SN  - 978-1-5386-5035-6
SN  - 2639-1589
SP  - 3456
EP  - 3463
PB  - IEEE
CY  - New York
ER  -