A Starting Point for Navigating the World of Daily Fantasy Basketball

Charles South, Ryan Elmore, Andrew Clarage, Rob Sickorez, Jing Cao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 K-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 K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)179-185
Number of pages7
JournalAmerican Statistician
Issue number2
StatePublished - Apr 3 2019


  • Bayesian statistics
  • K-Nearest neighbors
  • Lasso
  • NBA
  • Random effects

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Statistics, Probability and Uncertainty


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