Expert-oriented modelling of a 1vs1-situation in football

Leser, Roland; Moser, Bernhard; Hoch, Thomas; Stögerer, Johannes; Kellermayr, Gernot; Reinsch, Stephan; Baca, Arnold

doi:10.1080/24748668.2015.11868843

Cited by 12 publications

(13 citation statements)

References 3 publications

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“…The modeling process will eventually identify a set of variables and/or events to observe, and a quantitative scheme to aggregate and/or compare the measures. We distinguish two main approaches to guide the modeling: Domain-specific modeling is based on theories and concepts from Sport Science [79,80], which generalize relationships between actions and outcomes in the respective sport domain. On the other hand, data-driven or explorative modeling does typically not assume previous knowledge about the domain, but is guided by dependencies found in the data directly.…”

Section: Data Modelingmentioning

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: Data Modelingmentioning

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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“…Leser et al 87 Soccer Dyad Olthof et al 22 Soccer Young GC, dyad and area GPS/GNSS: Global Position System/Global Navigation Satellite System; LPS: local position system. aType of variable: 7 geometrical centre (GC), dyads, and area.…”

Section: Discussionmentioning

confidence: 99%

Past, present, and future of the technological tracking methods to assess tactical variables in team sports: A systematic review

Rico-González

Pino-Ortega

Nakamura

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part P:

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The main aim of this work was to review the use of technological tracking methods to assess collective spatial-positioning variables in team sports. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and PICO design for systematic reviews, study identification was performed in four databases (PubMed, SPORTDiscus, ProQuest Central, and Web of Science). Articles were selected if they focused on player position and technological tracking methods. After duplicate removal, 2194 articles were identified based on the established search criteria, of which 72 articles were selected and analysed. Semi-automatic optic-based systems, Global Positioning System/Global Navigation Satellite Systems, and local positioning systems were used in 60%, 33% and 7% of the studies, respectively. All studies that measured tactical variables by local positioning system technology in team sports used local position measurement technology. Optic-based systems were used more often in the early years to analyse collective tactical behaviour during competition. Later, Global Positioning System/Global Navigation Satellite Systems became more frequent to measure behaviour in team sports during the training process. The possibility of using the same system during competition and training will facilitate the assessment of collective tactical behaviour in team sports.

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“…The data was processed according to the approach explained by Hoch et al (2017) and as outlined in Figure 2. Based on the expert knowledge gathered by interviews (Leser, et al, 2015) the 3-vs-2 SSG was temporally segmented into a sequence of phases defined by certain events (e.g., shot, pass). In order to reduce complexity the time instant of the shot taken (T 0 ) and the time instant of the last action before the shot (T 1 ) were defined as the most crucial moments determining the outcome of a 3-vs-2 game by an expert panel.…”

Section: Data Processingmentioning

confidence: 99%

“…In order to reduce complexity the time instant of the shot taken (T 0 ) and the time instant of the last action before the shot (T 1 ) were defined as the most crucial moments determining the outcome of a 3-vs-2 game by an expert panel. All in all, 30 kinematic parameters (e.g., distances between players, movement speeds, accelerations), which were determined as relevant by experts, were calculated for these time instants and phases (for more details regarding the selection of experts and parameters, see Leser, et al, 2015). The statistical analysis comprised methods for feature reduction and pattern recognition.…”

Section: Data Processingmentioning

confidence: 99%

“…This proceeding might also raise the acceptance of practitioners towards scientific results and help to close the existing gap between science and practice (Mackenzie & Cushion, 2013). Leser et al (2015) analysed behaviour of two soccer players (an attacker and a defender) in a 1-versus-1 situation by means of position data, using expert knowledge for the underlying kinematic model. However, the players' tactical behaviour was only evaluated by means of isolated performance indicators, such as distances, speeds and accelerations at specific moments.…”

Section: Introductionmentioning

confidence: 99%

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Finding efficient strategies in 3-versus-2 small-sided games of youth soccer players

Leser

Hoch

Tan

et al. 2019

Kinesiology (Zagreb, Online)

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Most existing game analysis attempts do not consider situational context information sufficiently for the assessments of tactical behaviour. The purpose of this case study was to apply a new analysis approach, so far only used for 1-versus-1 analyses in soccer, to small-sided games and to identify and analyse therewith the efficiency of tactical patterns in 3-versus-2 game play. Therefore, 279 games, performed by an elite youth soccer team, were recorded by video and a position tracking system. The data was processed by the new approach, including unsupervised machine learning routines and artificial intelligence methods. Two crucial moments were used to analyse the occurring tactical patterns for the specific game: T0 (time instant of the shot) and T1 (time instant of the last action before the shot). For both moments typical tactical patterns, considering the spatio-temporal context of the situations, were found. By combining the patterns of both moments, hidden tactical strategies were identified and their efficiencies calculated. The most significant result is that offence attempts have the highest scoring probability when played via the wings near to the goal line and the final pass, before the shot, is given against the playing direction into the centre of the field.

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Expert-oriented modelling of a 1vs1-situation in football

Cited by 12 publications

References 3 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

Past, present, and future of the technological tracking methods to assess tactical variables in team sports: A systematic review

Finding efficient strategies in 3-versus-2 small-sided games of youth soccer players

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