Advertisement

Outplaying opponents—a differential perspective on passes using position data

  • Silvan SteinerEmail author
  • Stephan Rauh
  • Martin Rumo
  • Karin Sonderegger
  • Roland Seiler
Main Article
  • 8 Downloads

Abstract

In recent years, the availability of new tracking technologies has enabled new perspectives on passes played in football. One such perspective includes measuring the number of opponents that are outplayed by a pass (NOO). Various studies substantiate that this measure qualifies as one aspect of the passes’ offensive quality. Given the latent consensus that high-NOO passes indicate clever passing decisions, one aim of this study was to analyze how athletes prioritize contextually embedded passing options when playing passes that differ with regard to their NOO. Another aim was to determine the contributions of pass receivers to completing passes with different NOOs. To this end, position- and speed-related features of 12,411 passing options from 1,379 passing situations tracked during championship matches were analyzed. Overall, the findings indicate that decisions to play high NOO passes differ from decisions to play low NOO passes with regard to how contextually embedded passing options are prioritized. The passes’ NOOs increased as the decision-makers’ tendency to pass to loosely defended team members with open passing lanes and positions near the ball carrier decreased. Furthermore, higher physical contributions on the part of the pass receivers were observed when pass receivers completed passes with higher NOOs. Based on the findings, passes with a high NOO could be considered risky passes. The presented approach could be adopted to further analyze the circumstances that allow athletes to play such passes compared with those that absolutely do not, which could represent an important step concerning educational programs in football.

Keywords

Decision-making Packing Soccer Contextual information Environment 

Gegner aus dem Spiel nehmen – eine differenzielle Betrachtung von Pässen mittels Positionsdaten

Zusammenfassung

Das Aufkommen neuer Tracking-Technologien hat in den letzten Jahren neue Perspektiven auf Passspiele im Fußball eröffnet. Eine dieser Perspektiven beinhaltet das Bestimmen der Anzahl von Gegnern, die mit einem Pass überspielt werden („number of outplayed opponents“ [NOO]). Gemäß verschiedenen Studien kann dieses Maß als eine Kennzahl der offensiven Qualität eines Passes betrachtet werden und es existiert latenter Konsens, dass Pässen mit hoher NOO clevere Passentscheidungen zugrunde liegen. Vor diesem Hintergrund war ein Ziel dieser Studie, zu analysieren, wie Athleten kontextuell eingebettete Passoptionen bei Zuspielen mit unterschiedlicher NOO priorisieren. Ein weiteres Ziel war, die Beiträge von Passempfängern zur gelingenden Komplettierung von Pässen mit unterschiedlicher NOO zu bestimmen. Zu diesem Zweck wurden positions- und geschwindigkeitsbezogene Merkmale von 12.411 Passoptionen aus 1379 während Meisterschaftsspielen aufgezeichneten Passsituationen analysiert. Die Ergebnisse deuten darauf hin, dass kontextuell eingebettete Passoptionen bei Pässen mit hoher bzw. tiefer NOO unterschiedlich priorisiert werden. Mit abnehmender Tendenz der Passspieler, Pässe zu nahe positionierten, lose verteidigten Mitspielern mit offenen Passwegen zu spielen, erhöhte sich die Anzahl überspielter Gegner. Zudem zeigte sich, dass Ballempfänger für die Komplettierung von Pässen mit hoher NOO größere physische Beiträge leisteten als bei der Komplettierung von Pässen mit tiefer NOO. Basierend auf diesen Ergebnissen können Pässe mit hoher NOO als riskante Pässe interpretiert werden. In weiterführenden Untersuchungen könnten mit dem dargestellten methodischen Zugang diejenigen situativen Umstände bestimmt werden, unter welchen das Spielen riskanter Pässe eine Option sein könnte bzw. eher unterlassen werden sollte. Entsprechende Befunde wären im Hinblick auf fußballspezifische Ausbildungsprogramme interessant.

Schlüsselwörter

Entscheidung Packing Fußball Kontextuelle Information Umwelt 

Notes

Acknowledgements

We thank Nicolas Emery for his help in programming MATLAB.

Compliance with ethical guidelines

Conflict of interest

S. Steiner, S. Rauh, M. Rumo, K. Sonderegger and R. Seiler declare that they have no competing interests.

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Aquino, R., Puggina, E. F., Alves, I. S., & Garganta, J. (2017). Skill-related performance in soccer: A systematic review. Human Movement, 18(5), 3–24.  https://doi.org/10.1515/humo-2017-0042.CrossRefGoogle Scholar
  2. Araújo, D., Davids, K., & Hristovski, R. (2006). The ecological dynamics of decision making in sport. Psychology of Sport and Exercise, 7, 653–676.CrossRefGoogle Scholar
  3. Bar-Eli, M., Plessner, H., & Raab, M. (2011). Judgment, decision-making and success in sport. Chichester: John Wiley & Sons.  https://doi.org/10.1002/9781119977032.ch3.CrossRefGoogle Scholar
  4. Bourbousson, J., & Fortes-Bourbousson, M. (2016). How do co-agents actively regulate their collective states? Frontiers in Psychology, 7, 1732.  https://doi.org/10.3389/fpsyg.2016.01732.CrossRefGoogle Scholar
  5. Chawla, S., Estephan, J., Gudmundsson, J., & Horton, M. (2017). Classification of passes in football matches using spatiotemporal data. ACM Transaction of Spatial Algorithms and Systems, 3(2)  https://doi.org/10.1145/3105576.Google Scholar
  6. Clemente, M. F., Martins, F. M. L., Couceiro, S. M., Mendes, S. R., & Figueiredo, A. J. (2014). Evaluating the offensive definition zone in football: A case study. South African Journal for Research in Sport, Physical Education and Recreation, 36(3), 25–37.Google Scholar
  7. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.  https://doi.org/10.1177/001316446002000104.CrossRefGoogle Scholar
  8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edn.). Hillsdale: Erlbaum.Google Scholar
  9. Eid, M., Gollwitzer, M., & Schmitt, M. (2015). Statistik und Forschungsmethoden (4th edn.). Basel: Beltz.Google Scholar
  10. Ensum, J., Pollard, R., & Taylor, S. (2003). Applications of logistic regression to shots at goal at association football. In T. Reilly, J. Cabri & D. Araújo (Eds.), The Proceedings of the fifth World Congress on Science and Football. Science and football, (Vol. V, pp. 214–221). Abingdon: Routledge.Google Scholar
  11. Evangelos, B., Aristotelis, G., Ioannis, G., Stergios, K., & Foteini, A. (2014). Winners and losers in top level soccer. How do they differ? Journal of Physical Education and Sport, 14, 398–405.Google Scholar
  12. Frencken, W. G. P., Lemmink, K. A. P. M., & Delleman, N. J. (2010). Soccer-specific accuracy and validity of the local position measurement (LPM) system. Journal of Science and Medicine in Sport, 13, 641–645.CrossRefGoogle Scholar
  13. Gonzalez-Rodenas, J., Lopez-Bondia, I., Calabuig, F., James, N., & Aranda, R. (2015). Association between playing tactics and creating scoring opportunities in elite football. A case study in Spanish Football National Team. Journal of Human Sport and Exercise, 10(1), 65–80.  https://doi.org/10.14198/jhse.2015.101.14.CrossRefGoogle Scholar
  14. Heck, R. H., Thomas, S. L., & Tabata, L. N. (2014). Multilevel and longitudinal modeling with IBM SPSS (2nd edn.). New York: Routledge.Google Scholar
  15. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd edn.). New York: Wiley.CrossRefGoogle Scholar
  16. Hughes, M., & Franks, I. M. (2004). Notational analysis of sport: Systems for better coaching and performance in sport. London: Routledge.Google Scholar
  17. Lago-Ballesteros, J., Lago-Peñas, C., & Rey, E. (2012). The effect of playing tactics and situational variables on achieving score-box possessions in a professional soccer team. Journal of Sports Sciences, 30, 1455–1461.CrossRefGoogle Scholar
  18. Lienert, G. A., & Raatz, U. (1998). Testaufbau und Testanalyse (6th edn.). Weinheim: Beltz.Google Scholar
  19. Link, D., & Hoernig, M. (2017). Individual ball possession in soccer. PLoS ONE, 12(7), e179953.  https://doi.org/10.1371/journal.pone.0179953.CrossRefGoogle Scholar
  20. Liu, H., Gómez, M. Á., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34, 509–518.  https://doi.org/10.1080/02640414.2015.1117121 CrossRefGoogle Scholar
  21. Memmert, D., & Raabe, D. (2017). Revolution im Profifußball. Mit Big Data zur Spielanalyse 4.0. Berlin: Springer.CrossRefGoogle Scholar
  22. Ogris, G., Leser, R., Horsak, B., Kornfeind, P., Heller, M., & Baca, A. (2012). Accuracy of the LPM tracking system considering dynamic position changes. Journal of Sports Sciences, 30, 1503–1511.CrossRefGoogle Scholar
  23. Reed, L. (2004). The official FA guide to basic team coaching. London: Hodder & Stoughton.Google Scholar
  24. Reedwood-Brown, A. (2008). Passing patterns before and after goal scoring in FA Premier League Soccer. International Journal of Performance Analysis in Sport, 8(3), 172–182.CrossRefGoogle Scholar
  25. Reep, C., & Benjamin, B. (1968). Skill and chances in association football. Journal of the Royal Statistical Society. Series A, 131, 581–585.CrossRefGoogle Scholar
  26. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. SpringerPlus, 5, 1410.  https://doi.org/10.1186/s40064-016-3108-2.CrossRefGoogle Scholar
  27. Rein, R., Raabe, D., & Memmert, D. (2017). “Which pass is better?” Novel approaches to assess passing effectiveness in elite soccer. Human Movement Science, 55, 172–181.CrossRefGoogle Scholar
  28. Siegle, M., Stevens, T., & Lames, M. (2013). Design of an accuracy study for position detection in football. Journal of Sports Sciences, 31, 166–172.CrossRefGoogle Scholar
  29. Steiner, S. (2018). Passing decisions in football: Introducing an empirical approach to estimating the effects of perceptual information and associative knowledge. Frontiers in Psychology, 9, 361.  https://doi.org/10.3389/fpsyg.2018.00361.CrossRefGoogle Scholar
  30. Steiner, S., Rauh, S., Rumo, M., Sonderegger, K., & Seiler, R. (2018). Using position data to estimate effects of contextual features on passing decisions in football. Current Issues in Sport Science, 3, 9.  https://doi.org/10.15203/CISS_2018.009.Google Scholar
  31. Stevens, T. G., de Ruiter, C. J., van Niel, C., van de Rhee, R., Beek, P. J., & Savelsbergh, G. J. (2014). Measuring acceleration and deceleration in soccer-specific movements using a local position measurement (LPM) system. International Journal of Sports Physiology and Performance, 9, 446–456.CrossRefGoogle Scholar
  32. Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics (6th edn.). Harlow: Pearson.Google Scholar
  33. Tenga, A., Holme, I., Ronglan, L. T., & Bahr, R. (2010). Effect of playing tactics on achieving score-box possessions in a random series of team possessions from Norwegian professional soccer matches. Journal of Sports Sciences, 28, 245–255.CrossRefGoogle Scholar
  34. Turvey, M. T., & Shaw, R. E. (1995). Toward an ecological physics and a physical psychology. In R. L. Solso & D. W. Massaro (Eds.), The science of the mind: 2001 and beyond (pp. 144–169). New York: Oxford University Press.Google Scholar
  35. Williams, A. M., Davids, K., & Williams, J. G. (2005). Visual perception and action in sport. London: Taylor & Francis.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Sport ScienceUniversity of BernBernSwitzerland
  2. 2.Swiss Federal Institute of SportMagglingen (SFISM)MagglingenSwitzerland

Personalised recommendations