Visualization of dominant region in team games and its application to teamwork analysis

Taki, Tsuyoshi; Hasegawa, Jun‐ichi

doi:10.1109/cgi.2000.852338

Cited by 80 publications

(89 citation statements)

References 4 publications

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“…Taki and Hasegawa presents the dominant region as a dynamic area of influence that a player in a football match can exert dominance over, where dominance is defined as being the regions of the pitch that the player is able to reach before any other player [20]. We propose to use the dominant region as a measure to approximate the strategic position that a team at a given point in time in the match by constructing feature functions based on it.…”

Section: The Dominant Regionmentioning

confidence: 99%

“…Collecting positional data is possible using modern image capture and processing technology [20,12]. There are commercial companies with similar systems such as Prozone [17] and Opta [16] which are used in professional leagues.…”

Section: Related Workmentioning

confidence: 99%

“…Taki and Hasegawa defined a geometric subdivision called a dominant region [20], akin to a Voronoi region [3], that subdivides the pitch into cells owned by players such that the player can reach all areas of the cell before any other player. The dominant region considers the direction and velocity of the players [3].…”

Section: Related Workmentioning

confidence: 99%

“…The approach taken was to design predictor variables based on the dominant region construct proposed by [20], see Section 3.3.…”

Section: Feature Functionsmentioning

confidence: 99%

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Classification of Passes in Football Matches Using Spatiotemporal Data

Chawla

Estephan

Gudmundsson

et al. 2017

ACM Trans. Spatial Algorithms Syst.

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A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. We investigate the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. Furthermore, the learned classifiers are analysed to determine if there is a relationship between the complexity of the algorithm that computed the predictor variable and the importance of the variable to the classifier. Experimental results show that we are able to produce a classifier with 85.8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers. Finally, we show that the interrater agreement on pass classification between the machine classifier and a human observer is of similar magnitude to the agreement between two observers. Categories and Subject DescriptorsI.5 [Pattern Recognition]: Design Methodology-Feature evaluation and selection * A poster submission has also been made to the Large Scale Sports Analytics workshop at the KDD conference on this work. This submission covers the problem definition and solution framework described in this paper, and also includes preliminary experimental results on the accuracy of the framework.

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Section: The Dominant Regionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The approach taken was to design predictor variables based on the dominant region construct proposed by [20], see Section 3.3.…”

Section: Feature Functionsmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Passes in Football Matches Using Spatiotemporal Data

Chawla

Estephan

Gudmundsson

et al. 2017

ACM Trans. Spatial Algorithms Syst.

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“…The term "controlled space at t" in (1 b) and (1 c) is defined as follows: At any point in time t, each player controls his own Voronoi cell (Fonseca, Milho, Travassos, & Araújo, 2012;Taki & Hasegawa, 2000), which is the set of all points of the pitch he can reach faster than any other player. This approach uses equal speed values and optimal player orientations and is, therefore, of course, an idealising model.…”

Section: Methodsmentioning

confidence: 99%

A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics

Perl¹,

Memmert

2017

International Journal of Computer Science in Sport

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The intention of Key Performance Indicators (KPI) is to map complex systembehaviour to single values for scaling, rating and ranking systems or system components. Very often, however, this mapping only reduces important information about tactical behaviour or playing dynamics without replacing it by useful ones. The presented approach tries to bridge the gap between complex dynamics and numerical indicators in the case of offensive effectiveness in soccer in two steps. First, a model is developed which visualises offensive actions in a process-oriented way by using information units to represent offensive performance -i.e. Key Performance Indicators. Second, this model is organised in relation to time intervals, which enables to measure the effectiveness for a whole half-time as well as for arbitrary intervals of any desired lengths. This contribution is meant as an introduction to a new modelling idea, where examples are calculated as case studies to demonstrate how it works. Therefore, only two games have been exemplarily analysed yet: The first one, which is used to demonstrate the method, is an example for similar quantitative indicators but different dynamic behaviour. The last one is used to demonstrate the results in the case of teams with extreme different strengths.

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Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills

Harari,

Dias,

Kennedy-Metz

et al. 2024

JAMA Netw Open

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ImportanceAssessing nontechnical skills in operating rooms (ORs) is crucial for enhancing surgical performance and patient safety. However, automated and real-time evaluation of these skills remains challenging.ObjectiveTo explore the feasibility of using motion features extracted from surgical video recordings to automatically assess nontechnical skills during cardiac surgical procedures.Design, Setting, and ParticipantsThis cross-sectional study used video recordings of cardiac surgical procedures at a tertiary academic US hospital collected from January 2021 through May 2022. The OpenPose library was used to analyze videos to extract body pose estimations of team members and compute various team motion features. The Non-Technical Skills for Surgeons (NOTSS) assessment tool was employed for rating the OR team’s nontechnical skills by 3 expert raters.Main Outcomes and MeasuresNOTSS overall score, with motion features extracted from surgical videos as measures.ResultsA total of 30 complete cardiac surgery procedures were included: 26 (86.6%) were on-pump coronary artery bypass graft procedures and 4 (13.4%) were aortic valve replacement or repair procedures. All patients were male, and the mean (SD) age was 72 (6.3) years. All surgical teams were composed of 4 key roles (attending surgeon, attending anesthesiologist, primary perfusionist, and scrub nurse) with additional supporting roles. NOTSS scores correlated significantly with trajectory (r = 0.51, P = .005), acceleration (r = 0.48, P = .008), and entropy (r = −0.52, P = .004) of team displacement. Multiple linear regression, adjusted for patient factors, showed average team trajectory (adjusted R2 = 0.335; coefficient, 10.51 [95% CI, 8.81-12.21]; P = .004) and team displacement entropy (adjusted R2 = 0.304; coefficient, −12.64 [95% CI, −20.54 to −4.74]; P = .003) were associated with NOTSS scores.Conclusions and RelevanceThis study suggests a significant link between OR team movements and nontechnical skills ratings by NOTSS during cardiac surgical procedures, suggesting automated surgical video analysis could enhance nontechnical skills assessment. Further investigation across different hospitals and specialties is necessary to validate these findings.

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Visualization of dominant region in team games and its application to teamwork analysis

Cited by 80 publications

References 4 publications

Classification of Passes in Football Matches Using Spatiotemporal Data

Classification of Passes in Football Matches Using Spatiotemporal Data

A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics

Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills

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