非互斥投注的凯利准则和分数凯利策略

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of Quantitative Analysis in Sports Pub Date : 2023-01-17 DOI:10.1515/jqas-2020-0122

Benjamin P. Jacot, Paul V. Mochkovitch

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引用次数: 1

摘要

摘要本文研究了凯利标准，一种通过知情投注最大化资本预期对数增长的策略，如何应用于非互斥投注。这些赌注与游戏的可能结果之间没有一对一的对应关系。这种情况在赛马中很常见，在一场比赛中有多种类型的赌注。本文首先提供了凯利投注策略的理论概述，然后讨论了如何将其扩展到非互斥投注。对于这种类型的情况，还提出了分数凯利策略的新公式，该策略涉及投注凯利标准所建议金额的固定分数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Kelly criterion and fractional Kelly strategy for non-mutually exclusive bets

Abstract This paper examines how the Kelly criterion, a strategy for maximizing the expected log-growth of capital through informed betting, can be applied to non-mutually exclusive bets. These are bets where there is no one-to-one correspondence between the bets and the possible outcomes of the game. This type of situation is common in horse racing, where multiple types of bets are available for a single race. The paper begins by providing a theoretical overview of the Kelly betting strategy and then discusses how it can be extended to non-mutually exclusive bets. A new formulation of the fractional Kelly strategy, which involves betting a fixed fraction of the amount suggested by the Kelly criterion, is also presented for this type of scenario.

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来源期刊

Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

2.00

自引率

12.50%

发文量

期刊介绍： The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.