Tracking Multiple Handball Players using Multi-Commodity Network Flow for Assessing Tactical Behavior

The aim of this work is to present an approach that can help to characterize teams’ and players’ tactical behavior using two techniques to aid handball coaches to assess tactical procedures when using spatial measures derived from players position data. Results suggest that it is possible to identify tactical spatial differences between fast-break and fast throw-off. The approach presented in this work may be useful to reduce the time spent in game analysis and to improve coaches’ assessment of tactical performance during the training sessions.

[1]  Pascal Fua,et al.  Tracking multiple people under global appearance constraints , 2011, 2011 International Conference on Computer Vision.

[2]  T. McGarry Applied and theoretical perspectives of performance analysis in sport: Scientific issues and challenges , 2009 .

[3]  Keith Davids,et al.  Proximity-to-goal as a constraint on patterns of behaviour in attacker–defender dyads in team games , 2012, Journal of sports sciences.

[4]  Nenad Rogulj,et al.  THE EFFICIENCY OF ELEMENTS OF COLLECTIVE ATTACK TACTICS IN HANDBALL UČINKOVITOST ELEMENTOV KOLEKTIVNE TAKTIKE NAPADA V ROKOMETU , 2011 .

[5]  Pascal Fua,et al.  Multi-Commodity Network Flow for Tracking Multiple People , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  William J. McDermott,et al.  A nonlinear dynamics approach to human movement , 2004 .

[7]  Tim McGarry,et al.  Space–time coordination dynamics in basketball: Part 2. The interaction between the two teams , 2010, Journal of sports sciences.

[8]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Ioannis A. Bayios,et al.  A multivariate assessment of offensive performance indicators in Men’s Handball: Trends and differences in the World Championships , 2011 .

[10]  Paula Louro,et al.  Optical Filter Design Using Background Wavelength Processing Techniques , 2012, Plasmonics.

[11]  Oleguer Camerino,et al.  Mixed methods research in the movement sciences : case studies in sport, physical education and dance , 2012 .

[12]  S. Kim,et al.  Voronoi Analysis of a Soccer Game , 2004 .

[13]  Bruno Travassos,et al.  Measuring spatial interaction behavior in team sports using superimposed Voronoi diagrams , 2013 .

[14]  Mike D Hughes,et al.  The use of performance indicators in performance analysis , 2002, Journal of sports sciences.

[15]  Keith Davids,et al.  Dynamical systems theory: a relevant framework for performance-oriented sports biomechanics research , 2003 .

[16]  J. L. López,et al.  Diseños Observacionales: Ajuste y aplicación en psicología del deporte , 2011 .

[17]  Roger Bartlett,et al.  Analysing Team Coordination Patterns from Player Movement Trajectories in Soccer: Methodological Considerations , 2012 .

[18]  Tsuyoshi TAKI,et al.  QUANTITATIVE MEASUREMENT OF TEAMWORK IN BALL GAMES USING DOMINANT REGION , 2011 .

[19]  Pascal Fua,et al.  Tracking Multiple Players using a Single Camera , 2013, MVA 2013.

[20]  Arnold Baca,et al.  Local Positioning Systems in (Game) Sports , 2011, Sensors.

[21]  Mike Hughes,et al.  An Exploration of Team Sport as a Dynamical System. , 2006 .

[22]  Bruno Travassos,et al.  Eco-Dynamics Approach to the study of Team Sports Performance , 2014 .

[23]  Keith Davids,et al.  Interpersonal Pattern Dynamics and Adaptive Behavior in Multiagent Neurobiological Systems: Conceptual Model and Data , 2009, Journal of motor behavior.

[24]  Ian M. Franks,et al.  Notational Analysis Of Sport Systems For Better Coaching And Performance In Sport , 2004 .