Football analysis using spatio-temporal tools

Computers, Environment and Urban Systems

Volumn 47, Issue , 2014, Pages 16-27

  Gudmundsson, Joachim ^a,b   Wolle, Thomas ^c  

^a UNIVERSITY OF SYDNEY   (Australia)

^b CSIRO   (Australia)

^c Arclight Investments Pty Ltd   (Australia)

Author keywords

Algorithms; Data structures; Football; Movement analysis; Spatio temporal analysis

Indexed keywords

ALGORITHMS; DATA STRUCTURES; ECOLOGY;

ADVANCED ANALYSIS; CURRENT TECHNOLOGY; FOOTBALL; FOOTBALL PLAYERS; HIGH RESOLUTION; MOVEMENT ANALYSIS; NON-TRIVIAL TASKS; SPATIOTEMPORAL ANALYSIS;

SPORTS;

ALGORITHM; MOVEMENT; SPATIOTEMPORAL ANALYSIS; SPORT;

EID: 84904793309     PISSN: 01989715     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compenvurbsys.2013.09.004     Document Type: Article

References (40)

1. Alt H., Godau M. Computing the Fréchet distance between two polygonal curves. International Journal of Computational Geometry and Applications 1995, 5(12):75-91.
2. Alt H., Knauer C., Wenk C. Comparison of distance measures for planar curves. Algorithmica 2004, 38(2):45-58.
3. Amisco. <>. Accessed 23.01.13. http://www.amisco.eu.
4. Aurenhammer F. Voronoi diagrams a survey of a fundamental geometric data structure. ACM Computing Surveys 1991, 23(3):345-405.
5. Beetz M., Kirchlechner B., Lames M. Computerized real-time analysis of football games. IEEE Pervasive Computing 2005, 4(3):1536-1568.
6. Buchin K., Buchin M., Gudmundsson J., Löffler M., Luo J. Detecting Commuting Patterns by Clustering Subtrajectories. International Journal on Computational Geometry and Applications 2011, 21(3):253-282.
7. de Berg M., Cheong O., van Kreveld M., Overmars M. Computational geometry: Algorithms and applications 2008, Springer-Verlag. 3rd ed.
8. Duch J., Waitzmanand J.S., Amaral L.A.N. PLoS ONE 2010, 5(6):e10937. (Public Library of Science).
9. Estephan, J. (2012) The good, the bad and the ok: The classification of the worth and risk of passes. Honours thesis. School of Electrical and Information Engineering, The University of Sydney.
10. Fréchet M. Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Mathematico di Palermo 1960, 22:1-74.
11. Fujimura A., Sugihara K. Geometric analysis and quantitative evaluation of sport teamwork. Systems and Computers in Japan 2005, 36(6):49-58.
12. Goff J.E., Carré M.J. Trajectory analysis of a soccer ball. American Journal of Physics 2009, 77(11):1020.
13. Grunz A., Memmert D., Perl J. Analysis and simulation of actions in games by means of special selforganizing maps. International Journal of Computer Science in Sport 2009, 8(1):22-36.
14. Grunz A., Memmert D., Perl J. Tactical pattern recognition in soccer games by means of special self-organizing maps. Human Movement Science 2012, 31(2):334-343.
15. Gudmundsson, J., & Valladares, N., (2012). A GPU approach to subtrajectory clustering using the Fréchet distance. In Proceedings of the international ACM conference on advances in geographic information systems (SIGSPATIAL) (pp. 259-268).
16. Gudmundsson J., Katajainen J., Merrick D., Ong C., Wolle T. Compressing spatio-temporal trajectories. Computational Geometry - Theory and Applications 2009, 42(9):825-841.
17. Halperin D., Sharir M. New bounds for lower envelopes in three dimensions, with applications to visibility in terrains. Discrete &Computational Geometry 1994, 12:313-326.
18. Han J., Cheng H., Xin D., Yan X. Frequent pattern mining: Current status and future directions. Data Mining Knowledge Discovery 2007, 15:55-86.
19. Hoff III, K. E., Culver, T., Keyser, J., Lin, M., & Manocha, D. (1999). Fast computation of generalized Voronoi diagrams using graphics hardware. In Proc. of the 26th conference on computer graphics and interactive techniques (SIGGRAPH) (pp. 277-286).
20. Kang, C.-H., Hwang, J.-R., & Li, K.-J. (2007). Trajectory analysis for soccer player. In Proceedings of the 6th IEEE international conference on data mining - Workshops (pp. 377-381).
21. Kim, H.-C., Kwon, O., & Li, K.-J. (2011). Spatial and spatiotemporal analysis of soccer. In Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 385-388).
22. Kruskal J.B. On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society 1956, 7(1):48-50.
23. Lee, J.-G., Han, J., & Whang, K.-Y. (2007). Trajectory clustering: A partition-and-group framework. in Proc. ACM SIGMOD international conference on management of data (pp. 593-604).
24. Luo, C., & Chung, S. (2005). Efficient mining of maximal sequential patterns using multiple samples. In Proceeding of the 2005 SIAM international conference on data mining (pp. 415-426).
25. McCreight E.M. A space-economical suffix tree construction algorithm. Journal of the ACM 1967, 23(2):262-272.
26. Nakanishi R., Maeno J., Murakami K., Naruse T. An approximate computation of the dominant region diagram for the real-time analysis of group behaviors. RoboCup 2009, 228-239.
27. Nanni M., Pedreschi D. Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems 2006, 27:267-289.
28. Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U. (2001). Multi-dimensional sequential pattern mining. In Proceeding of the 10th international conference on information and knowledge management (CIKM) (pp. 81-88).
29. ProZone. <>. Accessed 23.01.13. http://www.prozonesports.com.
30. Sharir M., Agarwal P.K. DavenportSchinzel sequences and their geometric applications 1995, Cambridge University Press.
31. SportsTec. <>. Accessed 23.01.13. http://www.sportstec.com.
32. Srikant, R., & Agrawal. R. (1995). Mining generalized association rules. In Proceeding of the 21st international conference on very large data bases (VLDB) (pp. 407-419).
33. Srikant, R.& Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. In Proceeding of the 5th international conference on extending database technology (pp. 3-17).
34. Taki, T., & Hasegawa, J. (2000). Visualization of dominant region in team games and its application to teamwork analysis. In Proceedings of the IEEE international conference on computer graphics (pp. 227-235).
35. Tracab. <>. Accessed 23.01.13. http://www.tracab.com.
36. Ukkonen E. On-line construction of suffix trees. Algorithmica 1995, 14(3):249-260.
37. Vlachos, M., Gunopoulos, D., & Kollios. G. (2002). Discovering similar multidimensional trajectories. In Proceedings of the 18th international conference on data engineering (ICDE) (pp. 673-682).
38. Weiner, P. (1973). Linear pattern matching algorithm. In Proceedings of the 14th IEEE symposium on switching and automata theory (pp. 111).
39. Wikipedia. 2012. Suffix tree - Wikipedia. The Free Encyclopedia. Accessed 23.01.13.
40. Zaki M. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 2001, 40:31-60.