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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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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