Spatio-Temporal Analysis of Team Sports

Gudmundsson, Joachim; Horton, Michael

doi:10.1145/3054132

Cited by 240 publications

(174 citation statements)

References 72 publications

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“…Rugby is another example for team sport being analyzed nowadays as shown by Cintia et al [39]. Gudmundsson et al [40] published recently a comprehensive survey covering several aspects in team sports data. Our paper is aimed at both a survey and a concept paper, and our main goal is to widen the scope of considerations for team sports analysis as compared to previous surveys.…”

Section: Abstracting the Data Spacementioning

confidence: 99%

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

Stein

Janetzko

Seebacher

et al. 2017

Data

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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.

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Section: Abstracting the Data Spacementioning

confidence: 99%

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

Stein

Janetzko

Seebacher

et al. 2017

Data

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“…As the starting point, a collective behavior is captured through the identification of non-accidental patterns of individual behaviors, as observed at the global scale. These patterns can be captured by various tools, such as those well-developed for spatiotemporal pattern identification (Gudmundsson and Horton, 2016). However, an identifiable patterned behavioral co-ordination is not enough to consider that a collective behavior has emerged from interaction of its constituent individual parts; it also is required that the given interactors actively regulate the interpersonal co-ordination dynamics at the level of their local couplings.…”

Section: Hypothesis 2: Emergent Collective Behaviors Are Supported Bymentioning

confidence: 99%

How do Co-agents Actively Regulate their Collective Behavior States?

Bourbousson

Fortes-Bourbousson

2016

Front. Psychol.

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“…Sports analysis is an important research field, supporting a growing interest in data for statistical analysis of performance [1]. From recreational athletes wishing to track their own activities to professional teams, risking millions of dollars by losing a game, the interest in reliable performance measures is huge.…”

Section: Introductionmentioning

confidence: 99%

Constrained multi-target tracking for team sports activities

Gade

Moeslund

2018

IPSJ T Comput Vis Appl

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In sports analysis, player tracking is essential to the extraction of statistics such as speed, distance and direction of motion. Simultaneous tracking of multiple people is still a very challenging computer vision problem to which there is no satisfactory solution. This is especially true for sports activities, for which people often wear similar uniforms, move quickly and erratically, and have close interactions with each other. In this paper, we introduce a multi-target tracking algorithm suitable for team sports activities. We extend an existing algorithm by including an automatic estimation of the occupancy of the observed field and the duration of stable periods without people entering or leaving the field. This information is included as a constraint to the existing offline tracking algorithm in order to construct more reliable trajectories. On data from two challenging sports scenarios-an indoor soccer game captured with thermal cameras and an outdoor soccer training session captured with RGB camera-we show that the tracking performance is improved on all sequences. Compared to the original offline tracking algorithm, we obtain improvements of 3-7% in accuracy. Furthermore, the method outperforms two state-of-the-art trackers.

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Spatio-Temporal Analysis of Team Sports

Cited by 240 publications

References 72 publications

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

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

How do Co-agents Actively Regulate their Collective Behavior States?

Constrained multi-target tracking for team sports activities

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