一个花名册建设决策工具,为MLS扩张队

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
Zachary J. Smith, J. Bickel
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引用次数: 0

摘要

摘要本文提出了美国职业足球大联盟(MLS)扩军球队阵容构建的数学建模框架。该模型寻求在联盟工资规则下构建最佳阵容,同时平衡现值,潜在价值和未来薪金灵活性。玩家获取决策,以及工资分配,目标分配资金(TAM),一般分配资金(GAM)和指定的玩家插槽,都是由混合整数规划模型同时确定的。我们在构造一个假设的扩展花名册时证明了该模型的功能,并提出了一些扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A roster construction decision tool for MLS expansion teams
Abstract We present a mathematical modeling framework for roster construction of a Major League Soccer (MLS) expansion team. The model seeks to construct the best squad feasible under league salary rules, while balancing present value, potential value, and future cap flexibility. Player acquisition decisions, as well as allocation of salary, targeted allocation money (TAM), general allocation money (GAM), and designated player slots, are determined simultaneously by a mixed-integer programming model. We demonstrate the model’s functionality in constructing a hypothetical expansion roster and propose a number of extensions.
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来源期刊
Journal of Quantitative Analysis in Sports
Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
2.00
自引率
12.50%
发文量
15
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.
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