Advertisement

Deep Learning from Spatial Relations for Soccer Pass Prediction

  • Ondřej HubáčekEmail author
  • Gustav Šourek
  • Filip Železný
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11330)

Abstract

We propose a convolutional architecture for learning representations over spatial relations in the game of soccer, with the goal to predict individual passes between players, as a submission to the prediction challenge organized for the 5th Workshop on Machine Learning and Data Mining for Sports Analytics. The goal of the challenge was to predict the receiver of a pass given location of the sender and all other players. From each soccer situation, we extract spatial relations between the players and a few key locations on the field, which are then hierarchically aggregated within the neural architecture designed to extract possibly complex gameplay patterns stemming from these simple relations. The use of convolutions then allows to efficiently capture the various regularities that are inherent to the game. In the experiments, we show very promising performance of the method.

Notes

Acknowledgements

Authors acknowledge support by “Deep Relational Learning” project no. 17-26999S granted by the Czech Science Foundation. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”.

References

  1. 1.
    Brooks, J., Kerr, M., Guttag, J.: Using machine learning to draw inferences from pass location data in soccer. Stat. Anal. Data Min. ASA Data Sci. J. 9(5), 338–349 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, J., Cohn, A.G., Liu, D., Wang, S., Ouyang, J., Yu, Q.: A survey of qualitative spatial representations. Knowl. Eng. Rev. 30(1), 106–136 (2015)CrossRefGoogle Scholar
  3. 3.
    Lucey, P., Bialkowski, A., Monfort, M., Carr, P., Matthews, I.: Quality vs quantity: improved shot prediction in soccer using strategic features from spatiotemporal data. In: Proceedings 8th Annual MIT Sloan Sports Analytics Conference, pp. 1–9 (2014)Google Scholar
  4. 4.
    Lucey, P., Oliver, D., Carr, P., Roth, J., Matthews, I.: Assessing team strategy using spatiotemporal data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1366–1374. ACM (2013)Google Scholar
  5. 5.
    Spearman, W., Basye, A., Dick, G., Hotovy, R., Pop, P.: Physics-based modeling of pass probabilities in soccer. In: Proceeding of the 11th MIT Sloan Sports Analytics Conference (2017)Google Scholar
  6. 6.
    Van Haaren, J., Dzyuba, V., Hannosset, S., Davis, J.: Automatically discovering offensive patterns in soccer match data. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds.) IDA 2015. LNCS, vol. 9385, pp. 286–297. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24465-5_25CrossRefGoogle Scholar
  7. 7.
    Vercruyssen, V., De Raedt, L., Davis, J.: Qualitative spatial reasoning for soccer pass prediction. In: Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2016 Workshop (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ondřej Hubáček
    • 1
    Email author
  • Gustav Šourek
    • 1
  • Filip Železný
    • 1
  1. 1.Czech Technical UniversityPragueCzech Republic

Personalised recommendations