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      Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data

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      PLoS ONE
      Public Library of Science

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          Abstract

          This study describes an approach to quantification of attacking performance in football. Our procedure determines a quantitative representation of the probability of a goal being scored for every point in time at which a player is in possession of the ball–we refer to this as dangerousity. The calculation is based on the spatial constellation of the player and the ball, and comprises four components: (1) Zone describes the danger of a goal being scored from the position of the player on the ball, (2) Control stands for the extent to which the player can implement his tactical intention on the basis of the ball dynamics, (3) Pressure represents the possibility that the defending team prevent the player from completing an action with the ball and (4) Density is the chance of being able to defend the ball after the action. Other metrics can be derived from dangerousity by means of which questions relating to analysis of the play can be answered. Action Value represents the extent to which the player can make a situation more dangerous through his possession of the ball. Performance quantifies the number and quality of the attacks by a team over a period of time, while Dominance describes the difference in performance between teams. The evaluation uses the correlation between probability of winning the match (derived from betting odds) and performance indicators, and indicates that among Goal difference (r = .55), difference in Shots on Goal (r = .58), difference in Passing Accuracy (r = .56), Tackling Rate (r = .24) Ball Possession (r = .71) and Dominance (r = .82), the latter makes the largest contribution to explaining the skill of teams. We use these metrics to analyse individual actions in a match, to describe passages of play, and to characterise the performance and efficiency of teams over the season. For future studies, they provide a criterion that does not depend on chance or results to investigate the influence of central events in a match, various playing systems or tactical group concepts on success.

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          Most cited references8

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          Quantifying the Performance of Individual Players in a Team Activity

          Background Teamwork is a fundamental aspect of many human activities, from business to art and from sports to science. Recent research suggest that team work is of crucial importance to cutting-edge scientific research, but little is known about how teamwork leads to greater creativity. Indeed, for many team activities, it is not even clear how to assign credit to individual team members. Remarkably, at least in the context of sports, there is usually a broad consensus on who are the top performers and on what qualifies as an outstanding performance. Methodology/Principal Findings In order to determine how individual features can be quantified, and as a test bed for other team-based human activities, we analyze the performance of players in the European Cup 2008 soccer tournament. We develop a network approach that provides a powerful quantification of the contributions of individual players and of overall team performance. Conclusions/Significance We hypothesize that generalizations of our approach could be useful in other contexts where quantification of the contributions of individual team members is important.
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            Performance analysis in football: a critical review and implications for future research.

            This paper critically reviews existing literature relating to performance analysis (PA) in football, arguing that an alternative approach is warranted. The paper considers the applicability of variables analysed along with research findings in the context of their implications for professional practice. This includes a review of methodological approaches commonly adopted throughout PA research, including a consideration of the nature and size of the samples used in relation to generalisability. Definitions and classifications of variables used within performance analysis are discussed in the context of reliability and validity. The contribution of PA findings to the field is reviewed. The review identifies an overemphasis on researching predictive and performance controlling variables. A different approach is proposed that works with and from performance analysis information to develop research investigating athlete and coach learning, thus adding to applied practice. Future research should pay attention to the social and cultural influences that impact PA delivery and athlete learning in applied settings.
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              The Use of Match Statistics that Discriminate Between Successful and Unsuccessful Soccer Teams

              Three soccer World Cups were analysed with the aim of identifying the match statistics which best discriminated between winning, drawing and losing teams. The analysis was based on 177 matches played during the three most recent World Cup tournaments: Korea/Japan 2002 (59), Germany 2006 (59) and South Africa 2010 (59). Two categories of variables were studied: 1) those related to attacking play: goals scored, total shots, shots on target, shots off target, ball possession, number of off-sides committed, fouls received and corners; and 2) those related to defence: total shots received, shots on target received, shots off target received, off-sides received, fouls committed, corners against, yellow cards and red cards. Discriminant analysis of these matches revealed the following: (a) the variables related to attacking play that best differentiated between winning, drawing and losing teams were total shots, shots on target and ball possession; and (b) the most discriminating variables related to defence were total shots received and shots on target received. These results suggest that winning, drawing and losing national teams may be discriminated from one another on the basis of variables such as ball possession and the effectiveness of their attacking play. This information may be of benefit to both coaches and players, adding to their knowledge about soccer performance indicators and helping to guide the training process.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 December 2016
                2016
                : 11
                : 12
                : e0168768
                Affiliations
                [001]Department of Exercise Science and Sport Informatics, Technical University of Munich, Munich. Germany
                Northwestern University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: DL.

                • Data curation: DL SL PS.

                • Formal analysis: DL SL PS.

                • Funding acquisition: DL.

                • Investigation: DL SL PS.

                • Methodology: DL SL PS.

                • Project administration: DL.

                • Resources: DL.

                • Software: DL SL PS.

                • Supervision: DL.

                • Validation: DL.

                • Visualization: DL SL PS.

                • Writing – original draft: DL.

                • Writing – review & editing: DL.

                Author information
                http://orcid.org/0000-0003-3606-2986
                Article
                PONE-D-16-27998
                10.1371/journal.pone.0168768
                5201291
                28036407
                1fe8632e-3bba-4710-a007-ef49f0ddcd63
                © 2016 Link et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 July 2016
                : 5 December 2016
                Page count
                Figures: 9, Tables: 1, Pages: 16
                Funding
                Funded by: This work was supported by the German Research Foundation (DFG) and the Technische Universität München within the funding programme Open Access Publishing
                Award Recipient :
                This work was supported by the German Research Foundation (DFG) and the Technische Universität München within the funding programme Open Access Publishing. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Behavior
                Recreation
                Sports
                Biology and Life Sciences
                Sports Science
                Sports
                Biology and Life Sciences
                Behavior
                Recreation
                Games
                Biology and Life Sciences
                Psychology
                Collective Human Behavior
                Team Behavior
                Social Sciences
                Psychology
                Collective Human Behavior
                Team Behavior
                Physical Sciences
                Physics
                Classical Mechanics
                Pressure
                High Pressure
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Density
                Computer and Information Sciences
                Network Analysis
                Centrality
                Social Sciences
                Sociology
                Communications
                Mass Media
                Biology and Life Sciences
                Sports Science
                Custom metadata
                Data are from Deutsche Fußball Liga (DFL). To request the data, readers may contact: DFL Deutsche Fußball Liga GmbH, Guiollettstraße 44-46, 60325 Frankfurt/Main, info@ 123456bundesliga.de .

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