体育博彩的机器学习:模型选择应基于准确性还是校准?

Conor Walsh, Alok Joshi
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引用次数: 0

摘要

体育博彩最近在美国联邦合法化,这与机器学习的黄金时代不谋而合。如果投注者能利用数据可靠地预测结果的概率,他们就能识别博彩公司的赔率何时对他们有利。仅在美国,体育博彩就是一个价值数十亿美元的产业,因此发现这样的机会可能会带来巨大的利润。许多研究人员已将机器学习应用于体育比赛结果预测问题,通常使用准确率来评估预测模型的性能。我们假设,对于体育博彩问题,模型校准比准确性更重要。为了验证这一假设,我们对 NBA 几个赛季的数据进行了模型训练,并使用已公布的赔率对单个赛季进行了投注实验。我们的研究表明,使用校准而非准确性作为模型选择的基础,会带来更大的回报,平均而言(投资回报率为 +34.69% 对 -35.17%),在最佳情况下(投资回报率为 +36.93% 对 +5.56%)。这些发现表明,对于体育博彩(或任何概率决策问题)而言,校准是比准确性更重要的指标。因此,希望提高利润的体育投注者应该根据校准而不是准确性来选择预测模型。
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Machine learning for sports betting: Should model selection be based on accuracy or calibration?

Sports betting’s recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker’s odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that using calibration, rather than accuracy, as the basis for model selection leads to greater returns, on average (return on investment of +34.69% versus -35.17%) and in the best case (+36.93% versus +5.56%). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore select their predictive model based on calibration, rather than accuracy.

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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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审稿时长
98 days
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