Machine learning applications to sports event prediction and detection of match-fixing in sports betting markets

机器学习在体育赛事预测和体育博彩市场假球检测中的应用

• 批准号: 2894958
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2894958
• 关键词: Machine; learning; applications; sports; event

Machine learning is successfully used for many classification and prediction purposes, but state-of-the-art sports prediction is reluctant to properly embrace modern machine learning methods such as deep neural networks. Instead, statistical models are generally favoured for sports prediction, and have often been found to be superior. For example, the point-based Bayesian hierarchical model proposed by Ingram [1] for tennis, and the seminal paper by Dixon and Coles [2] that proposed a Poisson regression model for football. Despite this, there are recent advancements that demonstrate how machine learning can be successful in sports prediction. A 2021 review by Wilkens [3] summarizes the machine learning prediction methods in the case of tennis and argues for further research since many of these strategies are still flawed and inaccurate, illustrated by their unprofitability in betting applications.

机器学习已成功用于许多分类和预测目的，但最先进的体育预测不愿意正确采用现代机器学习方法，例如深度神经网络。相反，统计模型通常更适合体育预测，并且经常被发现更优越。例如，Ingram [1] 提出的针对网球的基于点的贝叶斯分层模型，以及 Dixon 和 Coles [2] 提出的针对足球的泊松回归模型的开创性论文。尽管如此，最近的进展证明了机器学习如何在体育预测中取得成功。 Wilkens [3] 在 2021 年发表的一篇评论总结了网球领域的机器学习预测方法，并主张进行进一步研究，因为其中许多策略仍然存在缺陷和不准确，它们在博彩应用中的无利可图就说明了这一点。