Stokastic's NFL DFS Boom-Bust Tool
Below you can see Stokastic’s NFL Boom/Bust Probability Tool for this week's DraftKings and FanDuel slate. This data gives the user an idea how likely a player is to meet their ceiling and floor, relative to their median projections. You're seeing a PREVIEW of our industry-leading NFL DFS data for today's DraftKings slate. The preview does not contain all the data for today's slate. Join for the full list of our NFL DFS booms and busts!
Name | Team | Salary | Position | Projection | Std Dev | Ceiling | Floor | Bust% | Boom% | Slate | Own% | Optimal% | Leverage |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Patrick Mahomes | KC | 11,000 | QB | 21.7 | 8.7 | 27.5 | 15.8 | 85.9 | 0.3 | Main | 20.5 | ||
Jerick McKinnon | KC | 6,800 | RB | 10.0 | 5.4 | 13.7 | 6.4 | 78.5 | 0.1 | Main | 40.5 | ||
Isiah Pacheco | KC | 7,200 | RB | 10.2 | 5.1 | 13.7 | 6.8 | 83.7 | 0.0 | Main | 21.6 | ||
Ronald Jones II | KC | 200 | RB | 0.2 | 0.6 | 0.6 | -0.2 | 0.3 | 0.0 | Main | |||
Kadarius Toney | KC | 4,400 | WR | 7.2 | 5.1 | 10.7 | 3.8 | 60.3 | 0.3 | Main | 17.6 | ||
Marquez Valdes-Scantling | KC | 6,200 | WR | 7.6 | 5.7 | 11.5 | 3.8 | 81.9 | 0.1 | Main | 13.5 | ||
JuJu Smith-Schuster | KC | 5,600 | WR | 9.3 | 6.3 | 13.5 | 5.1 | 63.3 | 0.8 | Main | 24.0 | ||
Justin Watson | KC | 2,000 | WR | 3.8 | 4.0 | 6.5 | 1.2 | 39.6 | 0.1 | Main | |||
Skyy Moore | KC | 3,800 | WR | 3.0 | 3.4 | 5.3 | 0.6 | 88.7 | 0.0 | Main | |||
Travis Kelce | KC | 10,600 | TE | 20.5 | 9.6 | 27.0 | 14.0 | 62.0 | 7.3 | Main | 50.5 | ||
Jody Fortson | KC | 200 | TE | 0.7 | 1.8 | 1.9 | -0.5 | 10.6 | 0.0 | Main | |||
Noah Gray | KC | 1,200 | TE | 3.9 | 4.0 | 6.6 | 1.2 | 22.7 | 2.3 | Main | 8.9 | ||
Harrison Butker | KC | 4,000 | K | 8.9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Main | |||
Chiefs | KC | 3,400 | DST | 5.8 | 4.8 | 9.1 | 2.6 | 52.8 | 1.5 | Main | 27.5 | ||
Jalen Hurts | PHI | 11,200 | QB | 24.7 | 9.1 | 30.8 | 18.6 | 77.6 | 0.9 | Main | 32.2 | ||
Boston Scott | PHI | 3,000 | RB | 2.6 | 2.6 | 4.4 | 0.9 | 84.1 | 0.0 | Main | |||
Miles Sanders | PHI | 7,800 | RB | 9.0 | 4.9 | 12.3 | 5.7 | 94.2 | 0.0 | Main | 29.0 | ||
Kenneth Gainwell | PHI | 5,000 | RB | 5.7 | 4.0 | 8.4 | 3.0 | 86.0 | 0.0 | Main | 9.8 | ||
A.J. Brown | PHI | 9,200 | WR | 15.9 | 8.9 | 22.0 | 9.9 | 67.8 | 2.8 | Main | 35.5 | ||
DeVonta Smith | PHI | 8,600 | WR | 15.3 | 8.4 | 21.0 | 9.7 | 65.3 | 2.6 | Main | 32.6 | ||
Zach Pascal | PHI | 800 | WR | 2.7 | 3.4 | 5.1 | 0.4 | 20.7 | 0.2 | Main | |||
Quez Watkins | PHI | 1,400 | WR | 4.6 | 4.4 | 7.6 | 1.6 | 23.3 | 1.4 | Main | 8.1 | ||
Dallas Goedert | PHI | 6,400 | TE | 13.0 | 7.6 | 18.1 | 7.9 | 51.7 | 6.7 | Main | 40.0 | ||
Grant Calcaterra | PHI | 200 | TE | 0.3 | 0.9 | 0.9 | -0.3 | 2.1 | 0.0 | Main | |||
Jack Stoll | PHI | 600 | TE | 0.9 | 2.0 | 2.2 | -0.4 | 22.8 | 0.0 | Main | |||
Jake Elliott | PHI | 4,200 | K | 8.5 | 5.1 | 11.9 | 5.0 | 46.9 | 0.7 | Main | |||
Eagles | PHI | 3,600 | DST | 6.9 | 5.2 | 10.4 | 3.4 | 48.1 | 3.0 | Main | 29.0 |
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About Our NFL DFS Boom-Bust Tool
In all DFS sports, being able to compare the strength of a player each day versus their popularity is key to success. For each player, I calculate the standard deviation based on a variety of factors and use that to calculate the ceiling (75th-percentile performance) the floor (25th-percentile performance) and the probability that they will bust (not meeting value) or boom (large field GPP winning player). Combining these metrics, you will be able to gain an edge on everyone by identifying under-owned or over-owned players.
The Boom and Bust percentages are determined by the probability that each player will reach a target score determined by position and salary. For instance on DK the average points/$1k for QB's is about 3x while other positions about 2.4x. On top of that on a full slate your team has to overperform by about 100 points, which is divvied up across the 9 roster spots.
How To Use Our NFL DFS Boom-Bust Tool
View the video below to see how our NFL DFS booms and busts can be used to build successful lineups.
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