Small slate DFS requires a completely different approach than the traditional regular season main slate with around 10 different game options. The DFS sites broke up the wild card round into featured slates and also provided a three-day structure with all six games.
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The various DFS models in this article will have applications and plays for any of the sites that span the weekend contests. On shorter slates, don’t be afraid to press on the game stack, with the expectation that identifying the right game provides value when it far outshines the others.
SALARY-ADJUSTED FANTASY PERFORMANCE
One of the best ways to find value opportunities on a given slate is to start with the salary-adjusted expectation for all players. We have a worthwhile dataset of player salaries and their resulting fantasy performances for DraftKings since it has been around for over six years. By figuring out the expectation based on each player’s salary and position, we can compare their salary-expected fantasy performance to PFF’s fantasy projections. Doing this highlights the best value plays based on salary.
Player | Position | Team | DK Salary | Salary Expectation | Proj. Fantasy Pts | Proj. Above Salary Expectation |
Leonard Fournette | HB | TB | 5900 | 12.96 | 17.91 | 4.95 |
Tyler Johnson | WR | TB | 4000 | 6.96 | 11.55 | 4.59 |
Cooper Kupp | WR | LA | 9000 | 22.69 | 26.92 | 4.23 |
Jakobi Meyers | WR | NE | 4500 | 8.53 | 12.74 | 4.21 |
Antoine Wesley | WR | ARZ | 3300 | 4.75 | 8.47 | 3.72 |
Odell Beckham Jr. | WR | LA | 5100 | 10.42 | 14.07 | 3.65 |
Tee Higgins | WR | CIN | 6300 | 14.20 | 17.77 | 3.57 |
C.J. Uzomah | TE | CIN | 3200 | 5.11 | 8.63 | 3.52 |
Tyler Boyd | WR | CIN | 5000 | 10.10 | 13.14 | 3.04 |
Emmanuel Sanders | WR | BUF | 4000 | 6.96 | 9.92 | 2.96 |
Brandon Aiyuk | WR | SF | 5400 | 11.36 | 13.96 | 2.60 |
Hunter Henry | TE | NE | 3700 | 6.68 | 9.21 | 2.53 |
Ja'Marr Chase | WR | CIN | 7400 | 17.66 | 20.14 | 2.48 |
Cole Beasley | WR | BUF | 4300 | 7.90 | 10.24 | 2.34 |
DeVonta Smith | WR | PHI | 5500 | 11.68 | 14.02 | 2.34 |
PASS-CATCHING BLOWUP MODEL
Utilizing specific variables, we can build a model tuned to predict performances where a pass-catcher goes over a certain site-specific scoring threshold. For DraftKings, this is set at 25 fantasy points.
This model provides the percentage chance each receiver will meet or exceed our fantasy-point threshold for the site given their historical opportunity. The model won’t be right on every player, but it is useful in identifying the blow-up performances we want to unearth in our DFS lineups.