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Communications in Statistics - Simulation and Computation Volume 50, 2021 - Issue 11
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Original Articles

A discrete-time and finite-state Markov chain based in-play prediction model for NBA basketball matches

Jian Shia Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;b School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
&
Kai Songa Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;b School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
Pages 3768-3776 | Received 13 Sep 2018, Accepted 13 Jun 2019, Published online: 27 Jun 2019

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Read on this site (2)

Mitchell F. Aarons, Chris M. Young, Lyndell Bruce & Dan B. Dwyer. (2023) Real time prediction of match outcomes in Australian football. Journal of Sports Sciences 41:11, pages 1115-1125.
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Xin-Yu Tian & Jian Shi. Modeling the complexity of basketball games using marked mutually exciting point processes. Communications in Statistics - Simulation and Computation 0:0, pages 1-24.
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Articles from other publishers (3)

Álvaro Briz-Redón. (2024) A doubly self-exciting Poisson model for describing scoring levels in NBA basketball. Journal of the Royal Statistical Society Series C: Applied Statistics.
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Dulani Jayasuriya, Jizhi Jacky Liu & Kevin E. Dow. (2024) Predicting United States National Basketball Game Spreads Using Machine Learning Techniques. SSRN Electronic Journal.
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Jason T. Maddox, Ryan Sides & Jane L. Harvill. (2022) Bayesian estimation of in-game home team win probability for college basketball. Journal of Quantitative Analysis in Sports 18:3, pages 201-213.
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