Collective team behaviour of Australian Rules football during phases of match play

ABSTRACT Using the spatiotemporal characteristics of players, the primary aim of this study was to determine whether differences in collective team behaviour exist in Australian Rules football during different phases of match play. The secondary aim was to determine the extent to which collective team behaviour differed between competing teams and match half. Data was collected via 10 Hz global positioning system devices from a professional club during a 2 × 20 min, 15-v-15-match simulation drill. Five spatiotemporal variables from each team (x centroid, y centroid, length, width, and surface area) were collected and analysed during offensive, defensive, and contested phases. A multivariate analysis of variance comparing phase of match play (offensive, defensive, contested), Team (A & B), and Half (1 & 2) revealed that x-axis centroid and y-axis centroid showed considerable variation during all phases of match play. Length, width, and surface area were typically greater during the offensive phase comparative to defensive and contested phases. Clear differences were observed between teams with large differences recorded for length, width, and surface area during all phases of match play. Spatiotemporal variables that describe collective team behaviour may be used to understand team tactics and styles of play.

[1]  Jaime Sampaio,et al.  Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup , 2015, Journal of sports sciences.

[2]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[3]  A. Figueiredo,et al.  Measuring Collective Behaviour in Football Teams: Inspecting the impact of each half of the match on ball possession , 2013 .

[4]  Wouter Frencken,et al.  Oscillations of centroid position and surface area of soccer teams in small-sided games , 2011 .

[5]  J. Castellano,et al.  What are the differences between first and second divisions of Spanish football teams? , 2015 .

[6]  Keith Davids,et al.  Sports Teams as Superorganisms , 2012, Sports Medicine.

[7]  Carlos Lago,et al.  The influence of match location, quality of opposition, and match status on possession strategies in professional association football , 2009, Journal of sports sciences.

[8]  supFilipe M. Clemente,et al.  An Online Tactical Metrics Applied to Football Game , 2013 .

[9]  M. Akritas,et al.  Heteroscedastic One-Way ANOVA and Lack-of-Fit Tests , 2004 .

[10]  Daniel Memmert,et al.  Current Approaches to Tactical Performance Analyses in Soccer Using Position Data , 2016, Sports Medicine.

[11]  Joachim Mester,et al.  Mathematical Analysis of a Soccer Game. Part I: Individual and Collective Behaviors , 2008 .

[12]  Yisong Yue,et al.  “Win at Home and Draw Away”: Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors , 2014 .

[13]  Keith Davids,et al.  Performance analysis in team sports: Advances from an Ecological Dynamics approach , 2013 .

[14]  Micael S. Couceiro,et al.  Measuring Tactical Behaviour Using Technological Metrics: Case Study of a Football Game , 2013 .

[15]  Mike Hughes,et al.  Analysis of passing sequences, shots and goals in soccer , 2005, Journal of sports sciences.

[16]  Jaime Sampaio,et al.  Identifying the effects from the quality of opposition in a Football team positioning strategy , 2013 .

[17]  Matthew C. Varley,et al.  Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion , 2012, Journal of sports sciences.

[18]  J F Gréhaigne,et al.  Dynamic-system analysis of opponent relationships in collective actions in soccer. , 1997, Journal of sports sciences.

[19]  Daniel Memmert,et al.  Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science , 2016, SpringerPlus.

[20]  Keith Davids,et al.  The Role of Ecological Dynamics in Analysing Performance in Team Sports , 2012, Sports Medicine.

[21]  Adrian J. Gray,et al.  Match Analysis and the Physiological Demands of Australian Football , 2010, Sports medicine.

[22]  Stephen J Kelly,et al.  Validity and Interunit Reliability of 10 Hz and 15 Hz GPS Units for Assessing Athlete Movement Demands , 2014, Journal of strength and conditioning research.

[23]  Wouter Frencken,et al.  Length, width and centroid distance as measures of teams tactical performance in youth football , 2014, European journal of sport science.

[24]  Keith Davids,et al.  Sports teams as superorganisms: implications of sociobiological models of behaviour for research and practice in team sports performance analysis. , 2012, Sports medicine.

[25]  Martin Vogelbein,et al.  Defensive transition in soccer – are prompt possession regains a measure of success? A quantitative analysis of German Fußball-Bundesliga 2010/2011 , 2014, Journal of sports sciences.

[26]  Koen A P M Lemmink,et al.  The older, the wider: On-field tactical behavior of elite-standard youth soccer players in small-sided games. , 2015, Human movement science.

[27]  Keith Davids,et al.  Science of winning soccer: Emergent pattern-forming dynamics in association football , 2013, Journal of Systems Science and Complexity.