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

Abstract

In this article, a discrete-time and finite-state Markov chain model is developed to fit the NBA basketball data. It can be used to produce in-play prediction for basketball matches. An iterative algorithm is designed to calculate probabilities of the final score difference. Empirical study shows that the proposed model performs well, and more profoundly it can have positive return when we bet with the market.

Keywords:

• Betting
• In-play predictions
• Markov chain
• Positive returns
• Uncertainty analysis

Acknowledgments

We would like to thank the editor and anonymous referees for their constructive comments and suggestions which have considerably improved this paper. We also thank the Beijing StausWin Lottery Operations Technology Ltd for providing the data.