A Starting Point for Navigating the World of Daily Fantasy Basketball

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and <i>K</i>-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the <i>K</i>-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.

梦幻体育（Fantasy Sports），尤以每日赛制——需每日重新构建参赛阵容——为代表，是一门高速增长的产业。每日梦幻体育领域的两家头部企业DraftKings与Fanduel，估值最高曾达20亿美元。本研究聚焦于开发一套完整的每日梦幻篮球赛事系统，涵盖球员表现预测与参赛阵容构建两大核心模块。首先，本研究采用贝叶斯随机效应模型（Bayesian random effects model）预测每日NBA球员的综合表现指标。随后基于模型输出的预测结果，结合赛事规则约束——通常包含虚拟薪资帽与球员位置限制——构建参赛阵容。本研究对比了基于置换法（Permutation-based method）与K近邻（K-nearest neighbors）两种方法在识别「高胜率阵容」——即基于历史数据可大概率具备竞争力的阵容——方面的表现。为验证所提系统的有效性，本研究将模型预测结果与某知名数据分析平台的预测结果进行对比，并基于2015–2016赛季的赛事数据开展每日赛事模拟。实验结果显示，采用K近邻方法时，初始投入500美元可获得约9000美元的预期收益，相较于仅采用置换法的收益提升了36%。本文补充材料可在线获取。