Origin and modifications of the geometrical centre to assess team behaviour in team sportsa systematic review

  1. Rico-González, Markel 1
  2. Pino-Ortega, José 2
  3. Nakamura, Fabio Yuzo 3
  4. Arruda-Moura, Felipe 4
  5. Los Arcos, Asier 5
  1. 1 Department of Physical Education and Sport, University of the Basque Country, UPV/EHU. Lasarte 71, 01007 Vitoria-Gasteiz, Spain.
  2. 2 Faculty of Sports Sciences. University of Murcia
  3. 3 Associate Graduate Programme in Physical Education UPE/UFPB, João Pessoa, Paraíba, Brazil.
  4. 4 Laboratory of Applied Biomechanics, Sports Sciences Department, State University of Londrina
  5. 5 Society, Sports and Physical Exercise Research Group (GIKAFIT). Department of Physical Education and Sport. Faculty of Education and Sport. University of the Basque Country (UPV/EHU), Vitoria-Gasteiz
Erakutsi afiliazioak +
Aldizkaria:
RICYDE. Revista Internacional de Ciencias del Deporte

ISSN: 1885-3137

Argitalpen urtea: 2020

Zenbakien izenburua: Julio

Alea: 16

Zenbakia: 61

Orrialdeak: 318-329

Mota: Artikulua

DOI: 10.5232/RICYDE2020.06106 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Beste argitalpen batzuk: RICYDE. Revista Internacional de Ciencias del Deporte

Laburpena

El objetivo de este estudio fue revisar sistemáticamente el origen y las modificaciones del centro geométrico (GC) en la evaluación del comportamiento táctico colectivo en los deportes de equipo. La identificación de los estudios se llevó a cabo en cuatro bases de datos (PubMed, SPORTDiscus, ProQuest Central, and Web of Sciences) siguiendo la guía PRISMA y el diseño PICO para revisiones sistemáticas. Un total de 3,973 documentos fueron inicialmente recuperados, de los cuales 1,779 eran duplicados. Después de analizar 2,178 artículos, otros 36 fueron añadidos tras ser rescatados de las referencias bibliográficas. 72 artículos cumplieron los criterios de inclusión, de los cuales 7 sugirieron variables tácticas originales relacionadas con el posicionamiento del GC. Dos cálculos diferentes han sido propuestos para medir el GC en los deportes de equipo, siendo la media [X, Y] de varios o todos los jugadores del equipo el más utilizado. El primer cálculo del GC fue propuesto en fútbol y consideró al portero, pero este jugador especial no suele ser incluido en la medición. La ubicación de los jugadores con respecto a la diana no ha sido considerada para valorar el GC en deportes de equipo como el fútbol. Por lo tanto, las variables tácticas complementarias, como por ejemplo la distancia entre el portero o la portería y el GC, podrían asociarse con el GC para evaluar la posición relativa de varios jugadores en el espacio de juego. Dos técnicas distintas (i.e. la transformación de Hilbert y el cluster analyses) han sido aplicadas para analizar la sincronización (i.e. la fase relativa) y el average mutual information (AMI) para evaluar la complejidad y regularidad o previsibilidad del GC en los deportes de equipo.

Erreferentzia bibliografikoak

  • Araújo, D., & Davids, K. (2016). Team Synergies in Sport: Theory and Measures. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01449
  • Bourbousson, J.; Sève, C., & McGarry, T. (2010a). Space-time coordination dynamics in basketball: Part 1. Intraand inter-couplings among player dyads. Journal of Sports Sciences, 28(3), 339-347. https://doi.org/10.1080/02640410903503632
  • Bourbousson, J.; Sève, C., & McGarry, T. (2010b). Space-time coordination dynamics in basketball: Part 2. The interaction between the two teams. Journal of Sports Sciences, 28(3), 349-358. https://doi.org/10.1080/02640410903503640
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educ Psychol Meas. 20(1), 37-46. https://doi.org/10.1177/001316446002000104
  • Cover, T. M., & Thomas, J. A. (2005). Entropy, Relative Entropy, and Mutual Information. In Elements of Information Theory (pp. 13-55). John Wiley & Sons, Inc. https://doi.org/10.1002/047174882X.ch2
  • Duarte, R.; Araújo, D.; Correia, V.; Davids, K.; Marques, P., & Richardson, M. J. (2013). Competing together: Assessing the dynamics of team-team and player-team synchrony in professional association football. Human Movement Science, 32(4), 555-566. https://doi.org/10.1016/j.humov.2013.01.011
  • Duarte, R.; Araújo, D.; Freire, L.; Folgado, H.; Fernandes, O., & Davids, K. (2012). Intraand inter-group coordination patterns reveal collective behaviors of football players near the scoring zone. Human Movement Science, 31(6), 1639-1651. https://doi.org/10.1016/j.humov.2012.03.001
  • Frank, T. D., & Richardson, M. J. (2010). On a test statistic for the Kuramoto order parameter of synchronization: An illustration for group synchronization during rocking chairs. Physica D: Nonlinear Phenomena, 239(23-24), 2084-2092. https://doi.org/10.1016/j.physd.2010.07.015
  • Frencken, W., & Lemmink, K. (2009). Team kinematics of small-sided soccer games: A systematic approach. In: Reilly, T, and F. Korkusuz (Eds.), Science and Football VI. In Science and Football VI (pp. 161-166).
  • Frencken, W.; Lemmink, K.; Delleman, N., & Visscher, C. (2011). Oscillations of centroid position and surface area of soccer teams in small-sided games. European Journal of Sport Science, 11(4), 215-223. https://doi.org/10.1080/17461391.2010.499967
  • Gonçalves, B. V.; Figueira, B. E.; Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of Sports Sciences, 32(2), 191-199. https://doi.org/10.1080/02640414.2013.816761
  • Graham R. (1972). An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters. North-Holland publishing company, 132-133. https://doi.org/10.1016/0020-0190(72)90045-2
  • Grehaigne, J.-F.; Bouthier, D., & David, B. (1997). Dynamic-system analysis of opponent relationships in collective actions in soccer. Journal of Sports Sciences, 15(2), 137-149. https://doi.org/10.1080/026404197367416
  • Kuramoto, Y. (1984). Chemical Oscillations, Waves, and Turbulence. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69689-3
  • Lames, M.; Ertmer, J., & Walter, F., L. (2010). Oscillations in football-Order and disorder in spatial interactions between the two teams. International Journal of Sport Psychology, 41(4), 85.
  • Low, B.; Coutinho, D.; Gonçalves, B.; Rein, R.; Memmert, D., & Sampaio, J. (2020). A Systematic Review of Collective Tactical Behaviours in Football Using Positional Data. Sport Med., 50, 343-385. https://doi.org/10.1007/s40279-019-01194-7
  • Memmert, D.; Lemmink, K. A. P. M., & Sampaio, J. (2017). Current Approaches to Tactical Performance Analyses in Soccer Using Position Data. Sports Medicine, 47(1), 1-10. https://doi.org/10.1007/s40279-016-0562-5
  • Moher, D.; Liberati, A.; Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336-341. https://doi.org/10.1016/j.ijsu.2010.02.007
  • Moura, F. A.; Santana, J. E.; Marche, A. L.; Aguiar, H., & Cunha, S. A. (2011). Quantitative analysis of the futsal players'organization on the court. Portuguese Journal of Sport Sciences, 11(Suppl 2), 105-108.
  • Palut, Y., & Zanone, P.-G. (2005). A dynamical analysis of tennis: Concepts and data. Journal of Sports Sciences, 23(10), 1021-1032. https://doi.org/10.1080/02640410400021682
  • Parlebas. (2002). Elementary mathematic modelization of games and sports. Bridging the gap between empirical sciences and theoretical research in the social sciences. In The Explanatory Power of Models (197-228). Kluwer Academic. https://doi.org/10.1007/978-1-4020-4676-6_11
  • Passos, P.; Araújo, D.; Davids, K.; Gouveia, L., Serpa, S.: Milho, J., & Fonseca, S. (2009). Interpersonal Pattern Dynamics and Adaptive Behavior in Multiagent Neurobiological Systems: Conceptual Model and Data. Journal of Motor Behavior, 41(5), 445-459. https://doi.org/10.3200/35-08-061
  • Pincus, S. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297-2301. https://doi.org/10.1073/pnas.88.6.2297
  • Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3108-2
  • Rico-González, M.; Los Arcos, A.; Nakamura, F. Y.; Moura, F. A., & Pino-Ortega, J. (2020a). The use of technology and sampling frequency to measure variables of tactical positioning in team sports: A systematic review. Research in Sports Medicine, 28(2), 279-292. https://doi.org/10.1080/15438627.2019.1660879
  • Rico-González, M.; Pino-Ortega, J.; Nakamura, F. Y.; Moura, F. A., & Arcos, A. L. (2020b). Identification, Computational Examination, Critical Assessment and Future Considerations of Distance Variables to Assess Collective Tactical Behaviour in Team Invasion Sports by Positional Data: A Systematic Review. Int. J. Environ. Res. Public Health, 14. https://doi.org/10.3390/ijerph17061952
  • Sampaio, J., & Maçãs, V. (2012). Measuring Tactical Behaviour in Football. International Journal of Sports Medicine, 33(05), 395-401. https://doi.org/10.1055/s-0031-1301320
  • Sarmento, H.; Anguera, M. T.; Pereira, A., & Araújo, D. (2018). Talent Identification and Development in Male Football: A Systematic Review. Sports Med, 48(4), 907-931. https://doi.org/10.1007/s40279-017-0851-7
  • Sarmento, H.; Clemente, F. M.; Araújo, D.; Davids, K.; McRobert, A., & Figueiredo, A. (2018). What Performance Analysts Need to Know About Research Trends in Association Football (2012-2016): A Systematic Review. Sports Medicine, 48(4), 799-836. https://doi.org/10.1007/s40279-017-0836-6
  • Schmidt, R. C.; O' Brien, B., & Sysko, R. (1999). Self organization of between person cooperative tasks and possible applications to sport. Int. J. Sport Psychol, 30, 558-579.
  • Schöllhorn W. (2003). Coordination Dynamics and its Consequences on Sports. Int. J. Comp. Sci. Sport, 2(2), 40-46.
  • Silva, P.; Chung, D.; Carvalho, T.; Cardoso, T.; Davids, K.; Araújo, D., & Garganta, J. (2016). Practice effects on intra-team synergies in football teams. Human Movement Science, 46, 39-51. https://doi.org/10.1016/j.humov.2015.11.017
  • Silva, P.; Duarte, R.; Esteves, P.; Travassos, B., & Vilar, L. (2016). Application of entropy measures to analysis of performance in team sports. International Journal of Performance Analysis in Sport, 16(2), 753-768. https://doi.org/10.1080/24748668.2016.11868921
  • Silva, P.; Duarte, R.; Sampaio, J.; Aguiar, P.; Davids, K.; Araújo, D., & Garganta, J. (2014). Field dimension and skill level constrain team tactical behaviours in small-sided and conditioned games in football. Journal of Sports Sciences, 32(20), 1888-1896. https://doi.org/10.1080/02640414.2014.961950
  • Silva, P.; Travassos, B.; Vilar, L.; Aguiar, P.; Davids, K.; Araújo, D., & Garganta, J. (2014). Numerical Relations and Skill Level Constrain Co-Adaptive Behaviors of Agents in Sports Teams. PLoS ONE, 9(9), e107112. https://doi.org/10.1371/journal.pone.0107112
  • Travassos, B.; Araújo, D.; Duarte, R., & McGarry, T. (2012). Spatiotemporal coordination patterns in futsal (indoor football) are guided by informational game constraints. Human Movement Science, 31(4), 932-945. https://doi.org/10.1016/j.humov.2011.10.004
  • Travassos, B.; Davids, K.; Araújo, D., & Esteves, T. P. (2013). Performance analysis in team sports: Advances from an Ecological Dynamics approach. International Journal of Performance Analysis in Sport, 13(1), 83-95. https://doi.org/10.1080/24748668.2013.11868633
  • Yue, Z.; Broich, H.; Seifriz, F., & Mester, J. (2008). Mathematical Analysis of a Soccer Game. Part I: Individual and Collective Behaviors. Studies in Applied Mathematics, 121(3), 223-243. https://doi.org/10.1111/j.1467-9590.2008.00413.x
Datuen iturria: Dialnet