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A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes

Cervone, Daniel, D’Amour, Alex, Bornn, Luke, Goldsberry, Kirk·2016·Journal of the American Statistical Association
DOI 10.1080/01621459.2016.1141685
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  • Cervone et al. (2014, 2016) introduce expected possession value (EPV) as a continuous-time estimate of the expected number of points for the offensive team on a given possession using player and ball locations.

    “In basketball, Cervone et al. (2014) and Cervone et al. (2016) introduce expected possession value (EPV) as a means toward an assessment of a player's on-court performance.This metric is a continuous-time estimate of the expected number of points for the offensive team on a given possession using player and ball locations.”

    From Big ideas in sports analytics and statistical tools for their investigation (2023) · cites this paper as Cervone et al. (2016)

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Research works printed but not linked: 22

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  3. Cox, D. R. (1975a), "A Note on Partially Bayes Inference and the Linear Model," Biometrika, 62(3), 651-654.
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  5. Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015), "Characterizing the Spatial Structure of Defensive Skill in Professional Basketball," Annals of Applied Statistics, .
  6. Goldner, K. (2012), "A Markov Model of Football: Using Stochastic Processes to Model a Football Drive," Journal of Quantitative Analysis in Sports [online], 8(1).
  7. Higdon, D. (2002), "Space and Space-Time Modeling Using Process Convolutions," in Quan- titative Methods for Current Environmental Issues, New York, NY: Springer, pp. 37-56.
  8. Ihler, A., Hutchins, J., & Smyth, P. (2006), "Adaptive Event Detection with Time-Varying Poisson Processes," in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY: ACM, pp. 207-216.
  9. Kemeny, J. G., & Snell, J. L. (1976), Finite Markov chains: with a new appendix" General- ization of a fundamental matrix" Springer.
  10. Lindgren, F., Rue, H., & Lindström, J. (2011), "An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: the Stochastic Partial Differential Equation Ap- proach," Journal of the Royal Statistical Society: Series B (Methodological), 73(4), 423- 498.
  11. Lock, D., & Nettleton, D. (2014), "Using random forests to estimate win probability before each play of an NFL game," Journal of Quantitative Analysis in Sports, 10(2), 197-205.
  12. Miller, A., Bornn, L., Adams, R., & Goldsberry, K. (2013), "Factorized Point Process In- tensities: A Spatial Analysis of Professional Basketball," in Proceedings of the 31st International Conference on Machine Learning, pp. 235-243.
  13. Omidiran, D. (2011), "A New Look at Adjusted Plus/Minus for Basketball Analysis," MIT Sloan Sports Analytics Conference [online], 2011.
  14. Prentice, R. L., Kalbfleisch, J. D., Peterson Jr, A. V., Flournoy, N., Farewell, V., & Bres- low, N. (1978), "The Analysis of Failure Times in the Presence of Competing Risks," Biometrics, pp. 541-554.
  15. Quiñonero-Candela, J., & Rasmussen, C. E. (2005), "A Unifying View of Sparse Approximate Gaussian Process Regression," The Journal of Machine Learning Research, 6, 1939- 1959.
  16. Rasmussen, C. E. (2006), Gaussian Processes for Machine Learning, Cambridge, MA: MIT Press.
  17. Rue, H. (2001), "Fast sampling of Gaussian Markov random fields," Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 325-338.
  18. Rue, H., Martino, S., & Chopin, N. (2009), "Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations," Journal of the Royal Statistical Society: Series B (Methodological), 71(2), 319-392.
  19. Shao, X., & Li, L. (2011), "Data-Driven Multi-Touch Attribution Models," in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY: ACM, pp. 258-264.
  20. Thomas, A., Ventura, S. L., Jensen, S. T., & Ma, S. (2013), "Competing Process Hazard Function Models for Player Ratings in Ice Hockey," The Annals of Applied Statistics, 7(3), 1497-1524.
  21. Wong, W. H. (1986), "Theory of Partial Likelihood," The Annals of Statistics, pp. 88-123.
  22. Yang, T. Y., & Swartz, T. (2004), "A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball," Journal of Data Science, 2(1), 61-73.

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Authors
Cervone, D., D’Amour, A., Bornn, L., & Goldsberry, K.
Venue
Journal of the American Statistical Association
Publisher
Informa UK Limited
Published
2016-04-02
Volume
111
Issue
514
Pages
585-599
ISSN
  • 0162-1459
  • 1537-274X
DOI
10.1080/01621459.2016.1141685
Cited by
149
References
1
Language
en
Update notices
correction (2017-05-03)

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