References
- Besag, J. (1974), “Spatial Interaction and the Statistical Analysis of Lattice Systems,,” Journal of the Royal Statistical Society, Series B, 36, 192–236.
- Bukiet, B., Harold, E. R., and Palacios, J. L. (1997), “A Markov Chain Approach to Baseball,” Operations Research, 45, 14–23.
- Burke, B. (2010), “Win Probability Added (WPA) Explained,” available at www.advancedfootballanalytics.com.
- Cox, D. R. (1975a), “A Note on Partially Bayes Inference and the Linear Model,” Biometrika, 62, 651–654.
- Cox, D. R. (1975b), “Partial Likelihood,” Biometrika, 62, 269–276.
- Franks, A., Miller, A., Bornn, L., and Goldsberry, K. (2015), “Characterizing the Spatial Structure of Defensive Skill in Professional Basketball,” Annals of Applied Statistics, 9, 94–121.
- Gneiting, T., Balabdaoui, F., and Raftery, A. E. (2007), “Probabilistic Forecasts, Calibration and Sharpness,” Journal of the Royal Statistical Society, Series B, 69, 243–268.
- Goldner, K. (2012), “A Markov Model of Football: Using Stochastic Processes to Model a Football Drive,” Journal of Quantitative Analysis in Sports [online], 8.
- Higdon, D. (2002), “Space and Space–Time Modeling Using Process Convolutions,” in Quantitative Methods for Current Environmental Issues, New York: Springer, pp. 37–56.
- Hollinger, J. (2005), Pro Basketball Forecast, 2005–06, Washington, DC: Potomac Books.
- Ihler, A., Hutchins, J., and 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: ACM, pp. 207–216.
- Kemeny, J. G., and Snell, J. L. (1976), Finite Markov chains: With a New Appendix” Generalization of a Fundamental Matrix, New York: Springer.
- Lindgren, F., Rue, H., and Lindström, J. (2011), “An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach,” Journal of the Royal Statistical Society, Series B, 73, 423–498.
- Lock, D., and Nettleton, D. (2014), “Using Random Forests to Estimate Win Probability Before Each Play of an NFL Game,” Journal of Quantitative Analysis in Sports, 10, 197–205.
- Miller, A., Bornn, L., Adams, R., and Goldsberry, K. (2013), “Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball,” in Proceedings of the 31st International Conference on Machine Learning, pp. 235–243.
- Omidiran, D. (2011), “A New Look at Adjusted Plus/Minus for Basketball Analysis,” MIT Sloan Sports Analytics Conference [online].
- Prentice, R. L., Kalbfleisch, J. D., Peterson Jr, A. V., Flournoy, N., Farewell, V., and Breslow, N. (1978), “The Analysis of Failure Times in the Presence of Competing Risks,” Biometrics, 34, 541–554.
- Quiñonero-Candela, J., and Rasmussen, C. E. (2005), “A Unifying View of Sparse Approximate Gaussian Process Regression,” The Journal of Machine Learning Research, 6, 1939–1959.
- Rasmussen, C. E. (2006), Gaussian Processes for Machine Learning, Cambridge, MA: MIT Press.
- Rue, H., Martino, S., and Chopin, N. (2009), “Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations,” Journal of the Royal Statistical Society, Series B, 71, 319–392.
- Shao, X., and 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: ACM, pp. 258–264.
- Thomas, A., Ventura, S. L., Jensen, S. T., and Ma, S. (2013), “Competing Process Hazard Function Models for Player Ratings in Ice Hockey,” The Annals of Applied Statistics, 7, 1497–1524.
- Wong, W. H. (1986), “Theory of Partial Likelihood,” The Annals of Statistics, 14, 88–123.
- Yang, T. Y., and Swartz, T. (2004), “A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball,” Journal of Data Science, 2, 61–73.