Machine Learning

, Volume 108, Issue 1, pp 127–147 | Cite as

Probabilistic movement models and zones of control

  • Ulf Brefeld
  • Jan LasekEmail author
  • Sebastian Mair
Part of the following topical collections:
  1. Special Issue on Machine Learning for Soccer


Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is prone to errors. As a remedy, we propose to estimate individual movement models from positional data and show how to turn these estimates into accurate and realistic zones of control. Our approach accounts for characteristic traits of players, scales with large amounts of data, and can be efficiently computed in a distributed fashion. We report on empirical results.


Positional data Movement models Zones of control Soccer 



The authors would like to thank Hendrik Weber and Deutsche Fußball Liga (DFL) and Sportcast GmbH for providing positional data.


  1. Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., & Havinga, P. (2010). Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In 23th International conference on architecture of computing systems 2010, pp. 1–10.Google Scholar
  2. Barris, S., & Button, C. (2008). A review of vision-based motion analysis in sport. Sports Medicine, 38(12), 1025–1043.CrossRefGoogle Scholar
  3. Brooks, J., Kerr, M., & Guttag, J. (2016). Using machine learning to draw inferences from pass location data in soccer. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5), 338–349.MathSciNetCrossRefGoogle Scholar
  4. Byrne, M., Parry, T., Isola, R., & Dawson, A. (2013). Identifying road defect information from smartphones. Road & Transport Research, 22(1), 39–50.Google Scholar
  5. Coutts, A. J., Quinn, J., Hocking, J., Castagna, C., & Rampinini, E. (2010). Match running performance in elite Australian rules football. Journal of Science and Medicine in Sport, 13(5), 543–548.CrossRefGoogle Scholar
  6. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.CrossRefGoogle Scholar
  7. D’Orazio, T., & Leo, M. (2010). A review of vision-based systems for soccer video analysis. Pattern Recognition, 43(8), 2911–2926.CrossRefGoogle Scholar
  8. Fonseca, S., Milho, J., Travassos, B., & Araújo, D. (2012). Spatial dynamics of team sports exposed by voronoi diagrams. Human Movement Science, 31(6), 1652–1659.CrossRefGoogle Scholar
  9. Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015). Characterizing the spatial structure of defensive skill in professional basketball. The Annals of Applied Statistics, 9(1), 94–121.MathSciNetCrossRefzbMATHGoogle Scholar
  10. Fujimura, A., & Sugihara, K. (2005). Geometric analysis and quantitative evaluation of sport teamwork. Systems and Computers in Japan, 36(6), 49–58.CrossRefGoogle Scholar
  11. Gottfried, B. (2008). Representing short-term observations of moving objects by a simple visual language. Journal of Visual Languages & Computing, 19(3), 321–342.CrossRefGoogle Scholar
  12. Gottfried, B. (2011). Interpreting motion events of pairs of moving objects. GeoInformatica, 15(2), 247–271.CrossRefGoogle Scholar
  13. Grün, Tvd, Franke, N., Wolf, D., Witt, N., & Eidloth, A. (2011). A real-time tracking system for football match and training analysis (pp. 199–212). Berlin Heidelberg: Springer.Google Scholar
  14. Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys, 50(2), 22:1–22:34.CrossRefGoogle Scholar
  15. Gudmundsson, J., & Wolle, T. (2014). Football analysis using spatio-temporal tools. Computers, Environment and Urban Systems, 47, 16–27.CrossRefGoogle Scholar
  16. Haase, J. & Brefeld, U. (2014). Mining positional data streams. In International workshop on new frontiers in mining complex patterns, pp. 102–116. Springer.Google Scholar
  17. Harmon, M., Lucey, P., & Klabjan, D. (2016). Predicting shot making in basketball learnt from adversarial multiagent trajectories. ArXiv e-prints.Google Scholar
  18. Horton, M., Gudmundsson, J., Chawla, S., & Estephan, J. (2015). Automated classification of passing in football. In Pacific-Asia conference on knowledge discovery and data mining, pp. 319–330. Springer.Google Scholar
  19. Janetzko, H., Sacha, D., Stein, M., Schreck, T., Keim, D. A., & Deussen, O. (2014). Feature-driven visual analytics of soccer data. In 2014 IEEE conference on visual analytics science and technology (VAST), pp. 13–22.Google Scholar
  20. Knauf, K., Memmert, D., & Brefeld, U. (2016). Spatio-temporal convolution kernels. Machine Learning, 102(2), 247–273.MathSciNetCrossRefzbMATHGoogle Scholar
  21. Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Domínguez, E. (2009). Analysis of work-rate in soccer according to playing positions. International Journal of Performance Analysis in Sport, 9(2), 218–227.CrossRefGoogle Scholar
  22. Lasek, J. & Gagolewski, M. (2015). The winning solution to the AAIA’15 data mining competition: Tagging firefighter activities at a fire scene. In 2015 Federated conference on computer science and information systems (FedCSIS), pages 375–380.Google Scholar
  23. Laube, P., Imfeld, S., & Weibel, R. (2005). Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science, 19(6), 639–668.CrossRefGoogle Scholar
  24. Le, H. M., Carr, P., Yue, Y., & Lucey, P. (2017). Data-driven ghosting using deep imitation learning. In MIT sloan sports analytics conference.Google Scholar
  25. Link, D., Lang, S., & Seidenschwarz, P. (2016). Real time quantification of dangerousity in football using spatiotemporal tracking data. PLoS ONE, 11(12), 1–16.CrossRefGoogle Scholar
  26. Lucey, P., Bialkowski, A., Carr, P., Foote, E., & Matthews, I. (2012). Characterizing multi-agent team behavior from partial team tracings: Evidence from the English Premier League. InProceedings of the twenty-sixth AAAI conference on artificial intelligence, AAAI’12, pp. 1387–1393. AAAI Press.Google Scholar
  27. Mazimpaka, J. D., & Timpf, S. (2016). Trajectory data mining: A review of methods and applications. Journal of Spatial Information Science, 2016(13), 61–99.Google Scholar
  28. Memmert, D., Lemmink, K. A. P. M., & Sampaio, J. (2016). Current approaches to tactical performance analyses in soccer using position data. Sports Medicine, 47, 1–10.CrossRefGoogle Scholar
  29. Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008). Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM conference on embedded network sensor systems, SenSys ’08, pp. 323–336. ACM.Google Scholar
  30. Mutschler, C., Ziekow, H., & Jerzak, Z. (2013). The DEBS 2013 grand challenge. In Proceedings of the 7th ACM international conference on distributed event-based systems, DEBS ’13, pp. 289–294, New York, NY: ACM.Google Scholar
  31. Nakanishi, R., Maeno, J., Murakami, K., & Naruse, T. (2009). An approximate computation of the dominant region diagram for the real-time analysis of group behaviors. In Robot soccer world cup, pp. 228–239. Springer.Google Scholar
  32. Narizuka, T., Yamamoto, K., & Yamazaki, Y. (2014). Statistical properties of position-dependent ball-passing networks in football games. Physica A: Statistical Mechanics and its Applications, 412, 157–168.MathSciNetCrossRefzbMATHGoogle Scholar
  33. Paefgen, J., Michahelles, F., & Staake, T. (2011). GPS trajectory feature extraction for driver risk profiling. In Proceedings of the 2011 international workshop on trajectory data mining and analysis, TDMA ’11, pp. 53–56, New York, NY: ACM.Google Scholar
  34. Rossi, A., Pappalardo, L., Cintia, P., Fernandez, J., Iaia, F. M., & Medina, D. (2017). Who is going to get hurt? Predicting injuries in professional soccer. In Proceedings the machine learning and data mining for sports analytics workshop (MLSA’17), ECML/PKDD, CGI ’00, pp. 227–235.Google Scholar
  35. Son, L. H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87–104.CrossRefGoogle Scholar
  36. Sprado, J., & Gottfried, B. (2009). What motion patterns tell ss about soccer teams (pp. 614–625). Heidelberg: Springer.Google Scholar
  37. Taki, T. & Hasegawa, J. (2000). Visualization of dominant region in team games and its application to teamwork analysis. In Proceedings of the international conference on computer graphics, CGI ’00, pp. 227–235, Washington, DC: IEEE Computer Society.Google Scholar
  38. Taki, T., Hasegawa, J., & Fukumura, T. (1996). Development of motion analysis system for quantitative evaluation of teamwork in soccer games. In Proceedings of 3rd IEEE international conference on image processing, vol. 3, pp. 815–818.Google Scholar
  39. Turlach, B. A. (1993). Bandwidth selection in kernel density estimation: A review. In CORE and institut de statistique.Google Scholar
  40. Ueda, F., Masaaki, H., & Hiroyuki, H. (2014). The causal relationship between dominant region and offense-defense performance—Focusing on the time of ball acquisition. Football Science, 11, 1–17.Google Scholar
  41. Voronoi, G. (1908). Nouvelles applications des paramètres continus à la théorie des formes quadratiques. premier mémoire. Sur quelques propriétés des formes quadratiques positives parfaites. Journal für die reine und angewandte Mathematik, 133, 97–178.MathSciNetzbMATHGoogle Scholar
  42. Zhang, P., Beernaerts, J., Zhang, L., & de Weghe, N. V. (2016). Visual exploration of match performance based on football movement data using the continuous triangular model. Applied Geography, 76(Supplement C), 1–13.CrossRefGoogle Scholar
  43. Zhao, Y., Yin, F., Gunnarsson, F., Hultkratz, F., & Fagerlind, J. (2016). Gaussian processes for flow modeling and prediction of positioned trajectories evaluated with sports data. In 2016 19th international conference on information fusion (FUSION), pp. 1461–1468.Google Scholar
  44. Zheng, S., Yue, Y., & Hobbs, J. (2016). Generating long-term trajectories using deep hierarchical networks. In Advances in Neural Information Processing Systems, 29, 1543–1551.Google Scholar
  45. Zheng, Y. (2015). Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology, 6(3), 29:1–29:41.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Leuphana UniversityLüneburgGermany
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

Personalised recommendations