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Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line

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  • Song, Kai
  • Gao, Yiran
  • Shi, Jian

Abstract

The paper presents a gamma process based model for the total points processes of NBA basketball matches. This model obtains a useful formula for the in-play prediction. What is more, we employ the bookmaker’s betting line to adjust the original gamma process model. The out-of-sample forecasting performances are evaluated, and more profoundly, this model can produce a positive return on the over–under betting market. Besides, our model has an application in monitoring the betting market, which may be useful to bettors.

Suggested Citation

  • Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    • Handle: RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437120301618
    DOI: 10.1016/j.physa.2020.124411
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    References listed on IDEAS

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    Cited by:

    1. Jeon, Gyuhyeon & Park, Juyong, 2021. "Characterizing patterns of scoring and ties in competitive sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).

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