JOURNAL ARTICLE

STOCHASTIC OPTIMIZATION APPROACH FOR PREDICTING HORSE RACING OUTCOMES.

  • Published In: Journal of Applied & Numerical Optimization, 2026, v. 8, n. 1. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Li, Yizhou; URYASEV, STAN 3 of 3

Abstract

This paper uses the stochastic programming model by Peng and Uryasev (2025) to forecast horse racing outcomes. The model captures the nonlinear relationship between explanatory factors and horse running time. This random running time is characterized by quantile functions, allowing for considerable flexibility in shaping the conditional distribution as a function of factor data. The proposed model demonstrates good numerical efficiency and adaptability, making it well-suited for predicting various horse racing outcomes. The case study compares the proposed model with alternative approaches for predicting racing outcomes. The suggested model achieves comparable or superior performance while maintaining computational efficiency across various race scenarios. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Applied & Numerical Optimization. 2026/01, Vol. 8, Issue 1, p1
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2026
  • ISSN:25625527
  • DOI:10.23952/jano.8.2026.1.01
  • Accession Number:193187286
  • Copyright Statement:Copyright of Journal of Applied & Numerical Optimization is the property of Biemdas Academic Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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