Abstract: Daily Fantasy Sports (DFS) is a multi-billion dollar industry with millions of annual users and widespread appeal among sports fans across a broad range of popular sports. Building on the recent work of Hunter, Vielma and Zaman (2016), we provide a coherent framework for constructing DFS portfolios where we explicitly model the behavior of other DFS players. We formulate an optimization problem that accurately describes the DFS problem for a risk-neutral decision-maker in both double-up and top-heavy payoff settings. Our formulation maximizes the expected reward subject to feasibility constraints and we relate this formulation to the literature on mean-variance optimization and the out-performance of stochastic benchmarks. Using this connection, we show how the problem can be reduced to the problem of solving a series of binary quadratic programs. One of the contributions of our work is the introduction of a Dirichlet-multinomial data generating process for modeling opponents’ team selections and we estimate the parameters of this model via Dirichlet regressions. A further benefit to modeling opponents’ team selections is that it enables us to estimate the value in a DFS setting of (i) insider trading and (ii) collusion whereby a number of DFS players combine to construct a single portfolio of entries to a given contest. We demonstrate the value of our framework by applying it to both double-up and top-heavy DFS contests in the 2017 NFL season. (Joint work with Raghav Singal)
Bio: Martin Haugh has been a faculty member at Imperial College Business School since 2017. Prior to that he spent more than 10 years in the Department of Industrial Engineering & Operations Research at Columbia University as well as 4 years working as a "quant" in the hedge fund industry in New York and London. He obtained his PhD in Operations Research from MIT in 2001 and has MS degrees in Mathematics and Applied Statistics from Cork and Oxford, respectively. His research interests are in computational finance and risk management, dynamic programming, and data-driven analytics.