Abstract
A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. In this paper we consider the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. In addition, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.
J. Gudmundsson—Joachim Gudmundsson was supported by the Australian Research Council (project numbers DP150101134 and FT100100755).
S. Chawla—Sanjay Chawla’s research was supported by ARC Discovery and Linkage grants.
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References
de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational geometry. Springer (2008)
Borrie, A., Jonsson, G.K., Magnusson, M.S.: Temporal pattern analysis and its applicability in sport: an explanation and exemplar data. Journal of Sports Sciences 20(10), 845–852 (2002)
ChyronHego Corporation, : Tracab player tracking system. http://chyronhego.com/sports-data/player-tracking (2014)
Franks, I.M., Miller, G.: Eyewitness testimony in sport. Journal of Sport Behavior (1986)
Gudmundsson, J., Wolle, T.: Towards automated football analysis: Algorithms and data structures. In: Proc. 10th Australasian Conf. on Mathematics and Computers in Sport, Citeseer (2010)
Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Computers, Environment and Urban Systems (2013)
Horton, M., Gudmundsson, J., Chawla, S., Estephan, J.: Classification of passes in football matches using spatiotemporal data. arXiv preprint arXiv:1407.5093 (2014)
Horton, M., Gudmundsson, J., Chawla, S., Estephan, J.: Feature descriptions for pass classifier. http://bit.ly/1ynhNm5 (2014)
Jenatton, R., Audibert, J.Y., Bach, F.: Structured variable selection with sparsity-inducing norms. The Journal of Machine Learning Research 12, 2777–2824 (2011)
Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society 7(1), 48–50 (1956)
Leo, M., Mosca, N., Spagnolo, P., Mazzeo, P.L., D’Orazio, T., Distante, A.: Real-time multiview analysis of soccer matches for understanding interactions between ball and players. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, pp. 525–534. ACM, 1386419 (2008)
Liu, W., Chawla, S.: A quadratic mean based supervised learning model for managing data skewness. In: SDM, pp. 188–198. SIAM (2011)
Nakanishi, R., Maeno, J., Murakami, K., Naruse, T.: An Approximate Computation of the Dominant Region Diagram for the Real-Time Analysis of Group Behaviors. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS, vol. 5949, pp. 228–239. Springer, Heidelberg (2010)
Prozone Sports Ltd: Prozone: Prozone sports - prozone 3 - sports performance analysis. http://www.prozonesports.com/product/prozone3/ (2013)
Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Rusboost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(1), 185–197 (2010)
Taki, T., Hasegawa, J.: Visualization of dominant region in team games and its application to teamwork analysis. In: Proceedings of the Computer Graphics International, pp. 227–235 (2000)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc. (1995)
Wilson, J.: Inverting the pyramid: The history of football tactics. Hachette UK (2010)
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Horton, M., Gudmundsson, J., Chawla, S., Estephan, J. (2015). Automated Classification of Passing in Football. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_25
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DOI: https://doi.org/10.1007/978-3-319-18032-8_25
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