Routine Inspection: A Playbook for Corner Kicks

Shaw, Laurie; Gopaladesikan, Sudarshan

doi:10.1007/978-3-030-64912-8_1

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Two methods for formation detection were tested in this study; however, other approaches, such as Gaussian mixture models [ 53 ] and non-negative matrix factorization [ 4 ], have been used for similar problems in other sports and may be appropriate for future research in AF.…”

Section: Discussionmentioning

confidence: 99%

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League

Seakins,

Gastin,

Jackson

et al. 2023

Sensors

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The extent of player formation usage and the characteristics of player arrangements are not well understood in Australian football, unlike other team-based invasion sports. Using player location data from all centre bounces in the 2021 Australian Football League season; this study described the spatial characteristics and roles of players in the forward line. Summary metrics indicated that teams differed in how spread out their forward players were (deviation away from the goal-to-goal axis and convex hull area) but were similar with regard to the centroid of player locations. Cluster analysis, along with visual inspection of player densities, clearly showed the presence of different repeated structures or formations used by teams. Teams also differed in their choice of player role combinations in forward lines at centre bounces. New terminology was proposed to describe the characteristics of forward line formations used in professional Australian Football.

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Section: Discussionmentioning

confidence: 99%

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League

Seakins,

Gastin,

Jackson

et al. 2023

Sensors

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“…Consequently, storing and computing with tracking data is significantly more resource-intensive than with event data -a football game contains roughly 3 million tracking data points. In the recent years, tracking data has been extensively used to perform football analytics, and its proliferation has given rise to a number of advanced metrics, for instance the quantification of team tactics, pitch control or expected possession value to name a few [40,37,20,33,47,46,43,38,22,30,26,29,48].…”

Section: Introductionmentioning

confidence: 99%

Automatic event detection in football using tracking data

Vidal-Codina¹,

Evans²,

Fakir³

et al. 2022

Preprint

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One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a computational framework to automatically extract football events using tracking data, namely the coordinates of all players and the ball. Our approach consists of two models: (1) the possession model evaluates which player was in possession of the ball at each time, as well as the distinct player configurations in the time intervals where the ball is not in play; (2) the event detection model relies on the changes in ball possession to determine in-game events, namely passes, shots, crosses, saves, receptions and interceptions, as well as set pieces. First, analyze the accuracy of tracking data for determining ball possession, as well as the accuracy of the time annotations for the manually collected events. Then, we benchmark the auto-detected events with a dataset of manually annotated events to show that in most categories the proposed method achieves +90% detection rate. Lastly, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Toward Automatically Labeling Situations in Soccer

Fassmeyer

Anzer

Bauer

et al. 2021

Front. Sports Act. Living

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We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. Unfortunately, non-trivially annotated situations in soccer games are scarce, expensive and almost always require human experts; a fully supervised approach appears infeasible. Hence, we split the problem into two parts and learn (i) a meaningful feature representation using variational autoencoders on unlabeled data at large scales and (ii) a large-margin classifier acting in this feature space but utilize only a few (manually) annotated examples of the situation of interest. We propose four different architectures of the variational autoencoder and empirically study the detection of corner kicks, crosses and counterattacks. We observe high predictive accuracies above 90% AUC irrespectively of the task.

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Routine Inspection: A Playbook for Corner Kicks

Cited by 9 publications

References 9 publications

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League

Automatic event detection in football using tracking data

Toward Automatically Labeling Situations in Soccer

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