From Movement to Events: Improving Soccer Match Annotations

Abstract

Match analysis has become an important task in everyday work at professional soccer clubs in order to improve team performance. Video analysts regularly spend up to several days analyzing and summarizing matches based on tracked and annotated match data. Although there already exists extensive capabilities to track the movement of players and the ball from multimedia data sources such as video recordings, there is no capability to sufficiently detect dynamic and complex events within these data. As a consequence, analysts have to rely on manually created annotations, which are very time-consuming and expensive to create. We propose a novel method for the semi-automatic definition and detection of events based entirely on movement data of players and the ball. Incorporating Allen's interval algebra into a visual analytics system, we enable analysts to visually define as well as search for complex, hierarchical events. We demonstrate the usefulness of our approach by quantitatively comparing our automatically detected events with manually annotated events from a professional data provider as well as several expert interviews. The results of our evaluation show that the required annotation time for complete matches by using our system can be reduced to a few seconds while achieving a similar level of performance.

Sport action mining: Dribbling recognition in soccer

“…Finally, the study of [49] proposed a novel method for the semi-automatic definition and detection of events based entirely on the movement data of players and the ball. Using Allen's interval algebra integrated with a visual analytics system, the authors enable analysts to visually define, as well as search for complex and hierarchical events.…”

MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales

“…Though, these aforementioned approaches focus on event sequences and not unlabeled time series data. In contrast, Stein et al [63] consider time-annotated trajectories and present a VQL for the hierarchical definition of events. However, their approach only focuses on movement data and does not entail multi-scale segmentation.…”

Toward Automatically Labeling Situations in Soccer

“…There are several recent studies aiming to detect basic events directly out of video footage (Ekin et al, 2003 ; Wickramaratna et al, 2005 ; Kolekar and Palaniappan, 2009 ) or positional data (Zheng and Kudenko, 2010 ; Motoi et al, 2012 ; Richly et al, 2016 ; Stein et al, 2019 ) and others focus on the identification of sophisticated tactical patterns (Hobbs et al, 2018 ; Andrienko et al, 2019 ; Shaw and Sudarshan, 2020 ; Anzer et al, 2021 ; Bauer and Anzer, 2021 ). The proposed approaches provide useful solutions for their respective tasks.…”