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Muacho, H., Ribeiro, R. & Lopes, R. J. (2022). The elusive features of success in soccer passes: A machine learning perspective. In Capelli, C., Verhagen, E., Pezarat-Correia, P., Vilas-Boas, J., and Cabri, J. (Ed.), Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support - icSPORTS. (pp. 110-116). Valletta, Malta: SCITEPRESS – Science and Technology Publications, Lda.

H. Muacho et al.,  "The elusive features of success in soccer passes: A machine learning perspective", in Proc. of the 10th Int. Conf. on Sport Sciences Research and Technology Support - icSPORTS, Capelli, C., Verhagen, E., Pezarat-Correia, P., Vilas-Boas, J., and Cabri, J., Ed., Valletta, Malta, SCITEPRESS – Science and Technology Publications, Lda, 2022, vol. 1, pp. 110-116

@inproceedings{muacho2022_1715431023067,
	author = "Muacho, H. and Ribeiro, R. and Lopes, R. J.",
	title = "The elusive features of success in soccer passes: A machine learning perspective",
	booktitle = "Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support - icSPORTS",
	year = "2022",
	editor = "Capelli, C., Verhagen, E., Pezarat-Correia, P., Vilas-Boas, J., and Cabri, J.",
	volume = "1",
	number = "",
	series = "",
	doi = "10.5220/0011541700003321",
	pages = "110-116",
	publisher = "SCITEPRESS – Science and Technology Publications, Lda",
	address = "Valletta, Malta",
	organization = "",
	url = "https://www.scitepress.org/ProceedingsDetails.aspx?ID=FhMEa6tJrsI=&t=1"
}

TY  - CPAPER
TI  - The elusive features of success in soccer passes: A machine learning perspective
T2  - Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support - icSPORTS
VL  - 1
AU  - Muacho, H.
AU  - Ribeiro, R.
AU  - Lopes, R. J.
PY  - 2022
SP  - 110-116
DO  - 10.5220/0011541700003321
CY  - Valletta, Malta
UR  - https://www.scitepress.org/ProceedingsDetails.aspx?ID=FhMEa6tJrsI=&t=1
AB  - Machine learning has in recent years been increasingly used in the soccer realm. This paper focuses on investigating the factors influencing pass success, a chief element in team performance. Decision tree techniques are used aiming to identify which features are the most important in pass success. This process is applied to a data set of 13 matches of the men’s French “Ligue 1”. Two experiments are conducted using different feature sets: one containing the positional data and Voronoi area off all players, the second considering only the ball carrier and closest teammates and opponents. The results obtained with the first feature set indicate that the relative importance of features is match dependent and somehow related to teams’ formation and players’ tactical mission. The second feature set, being more directly related to the passing process, provided a more consistent ranking of features. Features related to the interaction with the opponent standout. Low precision and recall val ues show that the features and factors leading to pass success are in fact elusive.
ER  -