Int J Sports Med 2012; 33(05): 395-401
DOI: 10.1055/s-0031-1301320
Behavioural Sciences
© Georg Thieme Verlag KG Stuttgart · New York

Measuring Tactical Behaviour in Football

J. Sampaio
1   Sports Sciences, Exercise and Health, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
2   Research Center for Sports Sciences, Health and Human Development, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
,
V. Maçãs
1   Sports Sciences, Exercise and Health, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
› Author Affiliations
Further Information

Publication History



accepted after revision 14 December 2011

Publication Date:
29 February 2012 (online)

Abstract

The present study explored how football players’ dynamic positional data can be used to assess tactical behaviour by measuring movement patterns and inter-player coordination. A pre post-test design was used to assess the effects of a 13-week constructivist and cognitivist training program by measuring behaviour in a 5×5 football small-sided game, played on a 60×40 m outdoor natural turf pitch. Data was captured at 5 Hz by GPS devices (SPI Pro, GPSports, Canberra, Australia) and analysed with non-linear signal processing methods. Approximate entropy values were lower in post-test situations suggesting that these time series became more regular with increasing expertise in football. Relative phase post-test values showed frequent periods with a clear trend to moving in anti-phase, as measured by players’ distance to the centre of the team. These advances may open new research topics under the tactical scope and allow narrowing the gap between sports sciences and sports coaching.

 
  • References

  • 1 Araujo D, Davids K, Hristovski R. The ecological dynamics of decision making in sport. Psychol Sport Exerc 2006; 7: 653-676
  • 2 Aughey RJ. Australian Football player work rate: evidence of fatigue and pacing?. Int J Sports Physiol Perform 2010; 5: 394-405
  • 3 Bangsbo J, Peitersen B. Soccer Systems and Strategies: Human Kinetics. Champaign IL: 2000
  • 4 Bourbousson J, Seve C, McGarry T. Space-time coordination dynamics in basketball: Part 2. The interaction between the two teams. J Sport Sci 2010; 28: 349-358
  • 5 Bourbousson J, Seve C, McGarry T. Space-time coordination dynamics in basketball: Part 1. Intra- and inter-couplings among player dyads. J Sport Sci 2010; 28: 339-347
  • 6 Coutts AJ, Duffield R. Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport 2010; 13: 133-135
  • 7 Coutts AJ, Quinn J, Hocking J, Castagna C, Rampinini E. Match running performance in elite Australian Rules Football. J Sci Med Sport 2010; 13: 543-548
  • 8 Duclos Y, Burnet H, Schmied A, Rossi-Durand C. Approximate entropy of motoneuron firing patterns during a motor preparation task. J Neurosci Meth 2008; 172: 231-235
  • 9 Duffield R, Reid M, Baker J, Spratford W. Accuracy and reliability of GPS devices for measurement of movement patterns in confined spaces for court-based sports. J Sci Med Sport 2010; 13: 523-525
  • 10 Dyson B, Griffin LL, Hastie P. Sport education, tactical games, and cooperative learning: Theoretical and pedagogical considerations. Quest 2004; 56: 226-240
  • 11 Glazier PS. Game, set and match? Substantive issues and future directions in performance analysis. Sports Med 2010; 40: 625-634
  • 12 GPSports. http://www.gpsports.com/ [accessed, 281208] 2008
  • 13 Gray AJ, Jenkins D, Andrews MH, Taaffe DR, Glover ML. Validity and reliability of GPS for measuring distance travelled in field-based team sports. J Sport Sci 2010; 28: 1319-1325
  • 14 Gray AJ, Jenkins DG. Match analysis and the physiological demands of Australian Football. Sports Med 2010; 40: 347-360
  • 15 Grehaigne JF, Godbout P. Tactical knowledge in team sports from a constructivist and cognitivist perspective. Quest 1995; 47: 490-505
  • 16 Grehaigne JF, Bouthier D, David B. Dynamic-system analysis of opponent relationships in collective actions in soccer. J Sport Sci 1997; 15: 137-149
  • 17 Harriss DJ, Atkinson G. Update – Ethical standards in sport and exercise science research. Int J Sports Med 2011; 32: 819-821
  • 18 Hill-Haas S, Coutts A, Rowsell G, Dawson B. Variability of acute physiological responses and performance profiles of youth soccer players in small-sided games. J Sci Med Sport 2008; 11: 487-490
  • 19 Hughes M, Dawkins N, David R, Mills J. The perturbation effect and goal opportunities in soccer. J Sport Sci 1997; 16: 20-21
  • 20 Issurin VB. New Horizons for the methodology and physiology of training periodization. Sports Med 2010; 40: 189-206
  • 21 Kannekens R, Elferink-Gemser MT, Visscher C. Positioning and deciding: key factors for talent development in soccer. Scand J Med Sci Sports 2010; DOI: 10.1111/j.1600-0838.2010.01104.x.
  • 22 Kurz MJ, Stergiou N. Applied dynamic systems theory for the analysis of movement. In: Stergiou N. (ed.) Innovative Analyses of Human Movement Human Kinetics. Champaign, Illinois: 2004
  • 23 Lames M, McGarry T. On the search for reliable performance indicators in game sports. Int J Perform Anal Sport 2007; 7: 16
  • 24 MacMahon C, Starkes JL, Deakin J. Differences in processing of game information in basketball players, coaches and referees. Int J Sport Psychol 2009; 40: 403-423
  • 25 McGarry T, Khan MA, Franks IM. On the presence and absence of behavioural traits in sport: An example from championship squash match-play. J Sport Sci 1999; 17: 297-311
  • 26 Mombaerts E. Football, de l´analyse du jeu à la formation du joueur. Éditions Action; Paris: 1991
  • 27 Palut Y, Zanone PG. A dynamical analysis of tennis: Concepts and data. J Sport Sci 2005; 23: 1021-1032
  • 28 Pincus SM. Approximate entropy as a measure of sSystem-complexity. Proc Natl Acad Sci USA 1991; 88: 2297-2301
  • 29 Pincus SM, Goldberger AL. Physiological time-series analysis – What does regularity quantify. Am J Physiol 1994; 266: H1643-H1656
  • 30 Poplu G, Ripoll H, Mavromatis S, Baratgin J. How do expert soccer players encode visual information to make decisions in simulated game situations?. Res Q Exerc Sport 2008; 79: 392-398
  • 31 Preatoni E, Ferrario M, Dona G, Hamill J, Rodano R. Motor variability in sports: A non-linear analysis of race walking. J Sport Sci 2010; 28: 1327-1336
  • 32 Randers MB, Mujika I, Hewitt A, Santisteban J, Bischoff R, Solano R, Zubillaga A, Peltola E, Krustrup P, Mohr M. Application of four different football match analysis systems: A comparative study. J Sport Sci 2010; 28: 171-182
  • 33 Schmidt R, Lee T. Motor Control and Learning: A Behavioral Emphasis. Champaign, IL: Human Kinetics; 1999
  • 34 Smith DJ. A framework for understanding the training process leading to elite performance. Sports Med 2003; 33: 1103-1126
  • 35 Stergiou N, Buzzi U, Kurz M, Heidel J. Nonlinear Tools in Human Movement. In: Stergiou N. (ed.) Innovative Analyses of Human Movement. Champaign, IL: Human Kinetics; 2004: 63-90
  • 36 Wade A. Principles of Team Play. Reedswain Inc; Spring City PA: 1996
  • 37 Wade A. Positional play: Midfield. Reedswain Inc; Spring City, PA: 1997
  • 38 Wisbey B, Montgomery PG, Pyne DB, Rattray B. Quantifying movement demands of AFL football using GPS tracking. J Sci Med Sport 2010; 13: 531-536
  • 39 Wright S, McNeill M, Fry JM. The tactical approach to teaching games from teaching, learning and mentoring perspectives. Sport Educ Soc 2009; 14: 223-244