publication . Preprint . 2019

DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data

Sicilia, Anthony; Pelechrinis, Konstantinos; Goldsberry, Kirk;
Open Access English
  • Published: 21 Feb 2019
Abstract
How much is an on-ball screen worth? How much is a backdoor cut away from the ball worth? Basketball is one of a number of sports which, within the past decade, have seen an explosion in quantitative metrics and methods for evaluating players and teams. However, it is still challenging to evaluate individual off-ball events in terms of how they contribute to the success of a possession. In this study, we develop an end-to-end deep learning architecture DeepHoops to process a unique dataset composed of spatio-temporal tracking data from NBA games in order to generate a running stream of predictions on the expected points to be scored as a possession progresses. W...
Subjects
free text keywords: Statistics - Applications
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34 references, page 1 of 3

[1] Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, and Iain Matthews. 2014. Large-scale analysis of soccer matches using spatiotemporal tracking data. In IEEE ICDM.

[2] Glenn W Brier. 1950. Verification of forecasts expressed in terms of probability. Monthey Weather Review 78, 1 (1950), 1-3.

[3] Dan Cervone, Luke Bornn, and Kirk Goldsberry. 2016. NBA Court Realty. In 10th MIT Sloan Sports Analytics Conference.

[4] Daniel Cervone, Alex D'Amour, Luke Bornn, and Kirk Goldsberry. 2016. A multiresolution stochastic process model for predicting basketball possession outcomes. J. Amer. Statist. Assoc. 111, 514 (2016), 585-599.

[5] Nitesh V Chawla. 2009. Data mining for imbalanced datasets: An overview. In Data mining and knowledge discovery handbook. Springer, 875-886.

[6] François Chollet et al. 2015. Keras. https://keras.io.

[7] Daniel Daly-Grafstein and Luke Bornn. 2018. Rao-Blackwellizing Field Goal Percentage. arXiv preprint arXiv:1808.04871 (2018).

[8] Alexander D'Amour, Daniel Cervone, Luke Bornn, and Kirk Goldsberry. 2015. Move or Die: How Ball Movement Creates Open Shots in the NBA.

[9] Alexander Franks, Andrew Miller, Luke Bornn, and Kirk Goldsberry. 2015. Counterpoints: Advanced defensive metrics for nba basketball. 9th Annual MIT Sloan Sports Analytics Conference.

[10] Alexander Franks, Andrew Miller, Luke Bornn, Kirk Goldsberry, et al. 2015. Characterizing the spatial structure of defensive skill in professional basketball. The Annals of Applied Statistics 9, 1 (2015), 94-121.

[11] Yarin Gal and Zoubin Ghahramani. 2016. A theoretically grounded application of dropout in recurrent neural networks. In NIPS.

[12] Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. 1999. Learning to forget: Continual prediction with LSTM. (1999).

[13] Tilmann Gneiting and Adrian E Raftery. 2007. Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102, 477 (2007), 359-378. [OpenAIRE]

[14] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.

[15] Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 6645-6649.

34 references, page 1 of 3
Abstract
How much is an on-ball screen worth? How much is a backdoor cut away from the ball worth? Basketball is one of a number of sports which, within the past decade, have seen an explosion in quantitative metrics and methods for evaluating players and teams. However, it is still challenging to evaluate individual off-ball events in terms of how they contribute to the success of a possession. In this study, we develop an end-to-end deep learning architecture DeepHoops to process a unique dataset composed of spatio-temporal tracking data from NBA games in order to generate a running stream of predictions on the expected points to be scored as a possession progresses. W...
Subjects
free text keywords: Statistics - Applications
Download from
34 references, page 1 of 3

[1] Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, and Iain Matthews. 2014. Large-scale analysis of soccer matches using spatiotemporal tracking data. In IEEE ICDM.

[2] Glenn W Brier. 1950. Verification of forecasts expressed in terms of probability. Monthey Weather Review 78, 1 (1950), 1-3.

[3] Dan Cervone, Luke Bornn, and Kirk Goldsberry. 2016. NBA Court Realty. In 10th MIT Sloan Sports Analytics Conference.

[4] Daniel Cervone, Alex D'Amour, Luke Bornn, and Kirk Goldsberry. 2016. A multiresolution stochastic process model for predicting basketball possession outcomes. J. Amer. Statist. Assoc. 111, 514 (2016), 585-599.

[5] Nitesh V Chawla. 2009. Data mining for imbalanced datasets: An overview. In Data mining and knowledge discovery handbook. Springer, 875-886.

[6] François Chollet et al. 2015. Keras. https://keras.io.

[7] Daniel Daly-Grafstein and Luke Bornn. 2018. Rao-Blackwellizing Field Goal Percentage. arXiv preprint arXiv:1808.04871 (2018).

[8] Alexander D'Amour, Daniel Cervone, Luke Bornn, and Kirk Goldsberry. 2015. Move or Die: How Ball Movement Creates Open Shots in the NBA.

[9] Alexander Franks, Andrew Miller, Luke Bornn, and Kirk Goldsberry. 2015. Counterpoints: Advanced defensive metrics for nba basketball. 9th Annual MIT Sloan Sports Analytics Conference.

[10] Alexander Franks, Andrew Miller, Luke Bornn, Kirk Goldsberry, et al. 2015. Characterizing the spatial structure of defensive skill in professional basketball. The Annals of Applied Statistics 9, 1 (2015), 94-121.

[11] Yarin Gal and Zoubin Ghahramani. 2016. A theoretically grounded application of dropout in recurrent neural networks. In NIPS.

[12] Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. 1999. Learning to forget: Continual prediction with LSTM. (1999).

[13] Tilmann Gneiting and Adrian E Raftery. 2007. Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102, 477 (2007), 359-378. [OpenAIRE]

[14] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.

[15] Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 6645-6649.

34 references, page 1 of 3
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