Abstract
Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of handball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.
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References
Bransen, L., Van Haaren, J.: Measuring football players’ on-the-ball contributions from passes during games. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) MLSA 2018. LNCS (LNAI), vol. 11330, pp. 3–15. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17274-9_1
Cervone, D., D’amour, A., Bornn, L., Goldsberry, K.: POINTWISE: predicting points and valuing decisions in real time with NBA optical tracking data: a new microeconomics for the NBA. In: Proceedings of the 8th MIT Sloan Sports Analytics Conference (2014)
Cervone, D., D’Amour, A., Bornn, L., Goldsberry, K.: A multiresolution stochastic process model for predicting basketball possession outcomes. J. Am. Stat. Assoc. 111(514), 585–599 (2016)
Decroos, T., Bransen, L., Van Haaren, J., Davis, J.: Actions speak louder than goals. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1851–1861 (2019)
Fernández, J., Bornn, L., Cervone, D.: A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions. Mach. Learn. 110(6), 1389–1427 (2021). https://doi.org/10.1007/s10994-021-05989-6
Fernandez, J., Bornn, L., Cervone, D.: Decomposing the immeasurable sport: a deep learning expected possession value framework for soccer. In: Proceedings of the 13th MIT Sloan Sports Analytics Conference (2019)
Hamill, T.M., Juras, J.: Measuring forecast skill: is it real skill or is it the varying climatology? Q. J. Roy. Meteorol. Soc. 132(621C), 2905–2923 (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–780 (1997)
Müller, R., Kornblith, S., Hinton, G.: When does label smoothing help? In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Power, P., Ruiz, H., Wei, X., Lucey, P.: Not all passes are created equal. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1605–1613 (2017)
Dal Pozzolo, A., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence, pp. 159–166 (2015)
Sicilia, A., Pelechrinis, K., Goldsberry, K.: Deep-Hoops: evaluating micro-actions in basketball using deep feature representations of spatio-temporal data. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2096–2104 (2019)
Spearman, W.: Beyond expected goals. In: Proceedings of the 12th MIT Sloan Sports Analytics Conference (2018)
Sun, Y., Wong, A.K.C., Kamel, M.S.: Classification of imbalanced data: a review. Int. J. Pattern Recognit. Artif. Intell. 23(04), 687–719 (2009)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: A strong baseline. In: 2017 International Joint Conference on Neural Networks, pp. 1578–1585 (2017)
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A Transformer-based Framework for Multivariate Time Series Representation Learning. arXiv preprint (2020). https://arxiv.org/abs/2010.02803
Zeuthen, K.: Team Handball: It’s Not What You Think It Is, February 2002. https://www.washingtonpost.com/archive/lifestyle/2002/02/15/team-handball-its-not-what-you-think-it-is/09cee01c-9daa-4fdb-a17b-bb23fc60fd11/
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Müller, O., Caron, M., Döring, M., Heuwinkel, T., Baumeister, J. (2022). PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2021. Communications in Computer and Information Science, vol 1571. Springer, Cham. https://doi.org/10.1007/978-3-031-02044-5_10
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