Modeling the complexity of basketball games using marked mutually exciting point processes

References

• Berger, J., and D. Pope. 2011. Can losing lead to winning? Management Science 57 (5):817–27. doi:10.1287/mnsc.1110.1328.
   Web of Science ®Google Scholar
• Boshnakov, G., T. Kharrat, and I. G. McHale. 2017. A bivariate Weibull count model for forecasting association football scores. International Journal of Forecasting 33 (2):458–66. doi:10.1016/j.ijforecast.2016.11.006.
   Web of Science ®Google Scholar
• Cattelan, M., C. Varin, and D. Firth. 2013. Dynamic Bradley–Terry modelling of sports tournaments. Journal of the Royal Statistical Society Series C: Applied Statistics 62 (1):135–50. doi:10.1111/j.1467-9876.2012.01046.x.
   Web of Science ®Google Scholar
• Cervone, D., A. D’Amour, L. Bornn, and K. Goldsberry. 2016. A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association 111 (514):585–99. doi:10.1080/01621459.2016.1141685.
   Web of Science ®Google Scholar
• Chen, F., and W. H. Tan. 2018. Marked self-exciting point process modelling of information diffusion on Twitter. The Annals of Applied Statistics 12 (4):2175–196. doi:10.1214/18-AOAS1148.
   Web of Science ®Google Scholar
• Chen, T., and C. Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, United States. p. 785–94.
   Google Scholar
• Chen, T., T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, K. Chen, R. Mitchell, I. Cano, T. Zhou, et al. 2023. xgboost: Extreme gradient boosting. R package version 1.7.5.1. https://CRAN.R-project.org/package=xgboost.
   Google Scholar
• Colley, W. 2002. Colley’s bias free college football ranking method: The Colley Matrix Explained. Princeton: Princeton University.
   Google Scholar
• De Saá Guerra, Y., J. M. Martín Gonzalez, S. Sarmiento Montesdeoca, D. Rodriguez Ruiz, N. Arjonilla López, and J. M. García-Manso. 2013. Basketball scoring in NBA games: An example of complexity. Journal of Systems Science and Complexity 26 (1):94–103. doi:10.1007/s11424-013-2282-3.
   Web of Science ®Google Scholar
• Elo, A. E. 1978. The rating of chessplayers, past and present. New York, United States: Arco Publishing, Inc.
   Google Scholar
• Fox, E. W., M. B. Short, F. P. Schoenberg, K. D. Coronges, and A. L. Bertozzi. 2016. Modeling e-mail networks and inferring leadership using self-exciting point processes. Journal of the American Statistical Association 111 (514):564–84. doi:10.1080/01621459.2015.1135802.
   Web of Science ®Google Scholar
• Fryda, T., E. LeDell, N. Gill, S. Aiello, A. Fu, A. Candel, C. Click, T. Kraljevic, T. Nykodym, P. Aboyoun, et al. 2024. h2o: R interface for the ‘H2O’ scalable machine learning platform. R package version 3.44.0.3. https://CRAN.R-project.org/package=h2o.
   Google Scholar
• Gabel, A., and S. Redner. 2012. Random walk picture of basketball scoring. Journal of Quantitative Analysis in Sports 8 (1):1–20. doi:10.1515/1559-0410.1416.
   Google Scholar
• Günther, F., and S. Fritsch. 2010. Neuralnet: Training of neural networks. The R Journal 2 (1):30. doi:10.32614/RJ-2010-006.
   Google Scholar
• Halberstadt, J. B., and G. M. Levine. 1999. Effects of reasons analysis on the accuracy of predicting basketball games 1. Journal of Applied Social Psychology 29 (3):517–30. doi:10.1111/j.1559-1816.1999.tb01399.x.
   Web of Science ®Google Scholar
• Harville, D. A., and M. H. Smith. 1994. The home-court advantage: How large is it, and does it vary from team to team? The American Statistician 48 (1):22–8. doi:10.2307/2685080.
   Web of Science ®Google Scholar
• Hawkes, A. G. 1971a. Point spectra of some mutually exciting point processes. Journal of the Royal Statistical Society Series B: Statistical Methodology 33 (3):438–43. doi:10.1111/j.2517-6161.1971.tb01530.x.
   Web of Science ®Google Scholar
• Hawkes, A. G. 1971b. Spectra of some self-exciting and mutually exciting point processes. Biometrika 58 (1):83–90. doi:10.2307/2334319.
   Web of Science ®Google Scholar
• Hawkes, A. G. 2018. Hawkes processes and their applications to finance: A review. Quantitative Finance 18 (2):193–8. doi:10.1080/14697688.2017.1403131.
   Web of Science ®Google Scholar
• Hawkes, A. G., and D. Oakes. 1974. A cluster process representation of a self-exciting process. Journal of Applied Probability 11 (3):493–503. doi:10.2307/3212693.
   Web of Science ®Google Scholar
• Keener, J. P. 1993. The Perron-Frobenius theorem and the ranking of football teams. SIAM Review 35 (1):80–93. doi:10.1137/1035004.
   Web of Science ®Google Scholar
• Kharrat, T., I. G. McHale, and J. L. Peña. 2020. Plus–minus player ratings for soccer. European Journal of Operational Research 283 (2):726–36. doi:10.1016/j.ejor.2019.11.026.
   Web of Science ®Google Scholar
• Kubatko, J., D. Oliver, K. Pelton, and D. T. Rosenbaum. 2007. A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports 3 (3):1–24. doi:10.2202/1559-0410.1070.
   Google Scholar
• Kvam, P., and J. S. Sokol. 2006. A logistic regression/Markov chain model for NCAA basketball. Naval Research Logistics (NRL) 53 (8):788–803. doi:10.1002/nav.20170.
   Web of Science ®Google Scholar
• Lewis, P. A. W, and G. S. Shedler. 1979. Simulation of nonhomogeneous poisson processes by thinning. Naval Research Logistics Quarterly 26 (3):403–13. doi:10.1002/nav.3800260304.
   Google Scholar
• Li, T., and H. Han. 2020. A high-performance basketball game forecast using magic feature extraction. In Recent advances in data science, ed. H. Han, T. Wei, W. Liu, and F. Han, 35–50. Singapore: Springer Singapore.
   Google Scholar
• Li, Y., L. Wang, and F. Li. 2021. A data-driven prediction approach for sports team performance and its application to National Basketball Association. Omega 98:102123. doi:10.1016/j.omega.2019.102123.
   Web of Science ®Google Scholar
• Li, Z., L. Cui, and J. Chen. 2018. Traffic accident modelling via self-exciting point processes. Reliability Engineering & System Safety 180:312–20. doi:10.1016/j.ress.2018.07.035.
   Web of Science ®Google Scholar
• Lopez, M. J., and G. J. Matthews. 2015. Building an NCAA men’s basketball predictive model and quantifying its success. Journal of Quantitative Analysis in Sports 11 (1):5–12. doi:10.1515/jqas-2014-0058.
   Web of Science ®Google Scholar
• Lorig, M., Z. Zhou, and B. Zou. 2021. Optimal bookmaking. European Journal of Operational Research 295 (2):560–74. doi:10.1016/j.ejor.2021.03.005.
   Web of Science ®Google Scholar
• Martín-González, J. M., Y. de Saá Guerra, J. M. García-Manso, E. Arriaza, and T. Valverde-Estévez. 2016. The poisson model limits in NBA basketball: Complexity in team sports. Physica A: Statistical Mechanics and Its Applications 464:182–90. doi:10.1016/j.physa.2016.07.028.
   Web of Science ®Google Scholar
• Massey, K. 1997. Statistical models applied to the rating of sports teams. Bluefield, Virginia, United States: Bluefield College.
   Google Scholar
• Merritt, S., and A. Clauset. 2014. Scoring dynamics across professional team sports: Tempo, balance and predictability. EPJ Data Science 3 (1):21. doi:10.1140/epjds29.
   Web of Science ®Google Scholar
• Meyer, S. 2018. Self-exciting point processes: Infections and implementations. Statistical Science 33 (3):327–29. doi:10.1214/18-STS653.
   Web of Science ®Google Scholar
• Miller, J. B., and A. Sanjurjo. 2018. Surprised by the hot hand fallacy? A truth in the law of small numbers. Econometrica 86 (6):2019–47. doi:10.3982/ECTA14943.
   Web of Science ®Google Scholar
• Ogata, Y. 1998. Space-time point-process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics 50 (2):379–402. doi:10.1023/A:1003403601725.
   Web of Science ®Google Scholar
• Olson, J. F., and K. M. Carley. 2013. Exact and approximate EM estimation of mutually exciting Hawkes processes. Statistical Inference for Stochastic Processes 16 (1):63–80. doi:10.1007/s11203-013-9074-1.
   Google Scholar
• Ozkan, I. A. 2020. A novel basketball result prediction model using a concurrent neuro-fuzzy system. Applied Artificial Intelligence 34 (13):1038–54. doi:10.1080/08839514.2020.1804229.
   Web of Science ®Google Scholar
• Pai, P. F., L. H. ChangLiao, and K. P. Lin. 2017. Analyzing basketball games by a support vector machines with decision tree model. Neural Computing and Applications 28 (12):4159–67. doi:10.1007/s00521-016-2321-9.
   Web of Science ®Google Scholar
• Park, J., and M. E. J. Newman. 2005. A network-based ranking system for US college football. Journal of Statistical Mechanics: Theory and Experiment 2005 (10):P10014. doi:10.1088/1742-5468/2005/10/P10014.
   Web of Science ®Google Scholar
• Ronning, G. 1989. Maximum likelihood estimation of Dirichlet distributions. Journal of Statistical Computation and Simulation 32 (4):215–21. doi:10.1080/00949658908811178.
   Google Scholar
• Shi, J., and K. Song. 2021. A discrete-time and finite-state Markov chain based in-play prediction model for NBA basketball matches. Communications in Statistics—Simulation and Computation 50 (11):3768–76. doi:10.1080/03610918.2019.1633351.
   Web of Science ®Google Scholar
• Shi, J., and X. Y. Tian. 2020. Learning to rank sports teams on a graph. Applied Sciences 10 (17):5833. doi:10.3390/app10175833.
   Google Scholar
• Silver, N., and R. Fischer-Baum. 2015. How we calculate NBA ELO ratings. http://fivethirtyeight.com/features/how-we-calculate-nba-elo-ratings.
   Google Scholar
• Song, K., and J. Shi. 2020. A gamma process based in-play prediction model for National Basketball Association games. European Journal of Operational Research 283 (2):706–13. doi:10.1016/j.ejor.2019.11.012.
   Web of Science ®Google Scholar
• Stekler, H., D. Sendor, and R. Verlander. 2010. Issues in sports forecasting. International Journal of Forecasting 26 (3):606–21. doi:10.1016/j.ijforecast.2010.01.003.
   Web of Science ®Google Scholar
• Stern, H. S. 1994. A Brownian motion model for the progress of sports scores. Journal of the American Statistical Association 89 (427):1128–34. doi:10.2307/2290943.
   Web of Science ®Google Scholar
• Štrumbelj, E., and P. Vračar. 2012. Simulating a basketball match with a homogeneous Markov model and forecasting the outcome. International Journal of Forecasting 28 (2):532–42. doi:10.1016/j.ijforecast.2011.01.004.
   Web of Science ®Google Scholar
• Terner, Z., and A. Franks. 2021. Modeling player and team performance in basketball. Annual Review of Statistics and Its Application 8 (1):1–23. doi:10.1146/annurev-statistics-040720-015536.
   Google Scholar
• Tian, X., Y. Gao, and J. Shi. 2022. Modeling basketball games by inverse Gaussian processes. Communications in Statistics—Simulation and Computation 51 (11):6246–56. doi:10.1080/03610918.2020.1798461.
   Web of Science ®Google Scholar
• Veen, A., and F. P. Schoenberg. 2008. Estimation of space–time branching process models in seismology using an EM–type algorithm. Journal of the American Statistical Association 103 (482):614–24. doi:10.1198/016214508000000148.
   Web of Science ®Google Scholar
• Vračar, P., E. Štrumbelj, and I. Kononenko. 2016. Modeling basketball play-by-play data. Expert Systems with Applications 44:58–66. doi:10.1016/j.eswa.2015.09.004.
   Web of Science ®Google Scholar
• Xu, L., J. A. Duan, and A. Whinston. 2014. Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Science 60 (6):1392–412. doi:10.1287/mnsc.2014.1952.
   Web of Science ®Google Scholar