How to Play Fantasy Sports Strategically (and Win)

Problem formulation

Haugh wanted to use statistics to produce a Fantasy football team that would score in the top 20th percentile.

Modeling Opponents

Haugh also looked to predict DFS opponents’ behaviors to inform what players his model chose. This is a defining factor between his method and previous Fantasy research.

Constructing an Optimal Team

Our maximization equation above can be restated as:

Modeling Opponents

Modeling opponents’ DFS teams is important because the benchmark G(r) depends on opponents’ entries Wop. Opponents’ entries could add up to half a million of those 300-dimensional team vectors.

Numerical Results

Though Haugh presented on double-up Fantasy competitions where all teams that score above a certain benchmark receive the same reward, he tested his algorithm in the 2017–18 NFL season with a focus on top-heavy competitions. In these competitions, different reward amounts are dispersed to winners based on how well they do. Very few people win anything, but those who do win get a substantial reward.



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