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DFS data dive: The highest fantasy ceilings of Week 7

Orchard Park, NY, USA; Buffalo Bills running back Frank Gore (20) runs off the field during the third quarter against the New England Patriots at New Era Field. Mandatory Credit: Douglas DeFelice-USA TODAY Sports

One of the advantages of being a “data scientist” is that everyone assumes you know what you’re talking about. While that has its privileges, more important to the craft is the ability to leverage statistical programming packages like R and Python and apply them to almost any questions that can be answered through data.

Recently I started publishing analyses on the single-game DraftKings showdown slates that use the combination of current projections, historical game results and similarity algorithms to simulate an upcoming game by looking back at the most similar historical matchups. In this analysis I’m taking the same outline and applying it instead to the DFS main slate by projecting the likelihood each individual QB, RB, WR, TE, and DST option will be the highest-scoring of the slate. In doing this, we can find the unlikely tournament plays who may not have been on your radar.

British statistician George E.P. Box once said, “All models are wrong, but some are useful,” and that’s the mindframe you should bring to this analysis. The numbers below are not “right” in the traditional sense of maximizing accuracy, which would only mimic the recommendations you’ll find throughout the DFS toutersphere. The numbers below are harnessing the unexpected connections and reactions between players that have actually happened over the past several years, and thereby points to under/overvalued players that won’t be identified through traditional projections and logical deduction.


For each game on the DFS Sunday main slate, I looked through thousands of NFL matchups from 2012-2018 and found the closest analogies according to the following parameters: Betting spread, over/under, average fantasy points scoring for the top-ranked positional players of both rosters (QB1, RB1, WR1, TE1).

Once I find the 75 most similar matchups for each upcoming game, I then simulate the main slate 10K times by randomly choosing one of the 75 matchups for each game and then find the highest-scoring QB, RB, WR, TE and DST on the entire slate.

Every match of historical and current games is not perfect, but by matching 75 different matchups to each game and simulating 10K times we can smooth out the bumps and get a strong picture of how a slate of similar games would have played out.

The last step is totaling up the number of times that a particular team shows up as the top option for the slate at each position and divide by the total simulations. That number is what I call “highest-scoring %” in the bar charts below labeled by team. Below the bar charts by team, I join the highest projected player on that team for the position and list his projected fantasy points and salaries for DraftKings and FanDuel.


Pos Highest % Player Team DK Fpts DK Salary FD Fpts FD Salary
QB 8.95 Russell Wilson SEA 22.2 6,600 21.0 8,500
QB 7.57 Jacoby Brissett IND 19.3 5,600 18.2 7,300
QB 7.11 Gardner Minshew JAX 16.5 5,400 15.7 6,900
QB 6.93 Jared Goff LA 22.3 6,200 20.4 7,800
QB 6.27 Matt Ryan ATL 20.9 6,300 19.4 7,900
QB 6.20 Derek Carr OAK 15.5 5,000 14.5 6,500
QB 6.18 Kirk Cousins MIN 20.2 5,800 18.9 7,300
QB 5.91 Daniel Jones NYG 17.6 6,100 16.7 7,200
QB 5.40 Matthew Stafford DET 18.9 5,200 17.2 7,300
QB 4.92 Deshaun Watson HST 23.0 7,000 21.5 8,300
QB 4.89 Kyler Murray ARZ 20.2 6,700 19.4 7,700
QB 4.73 Lamar Jackson BLT 23.6 6,800 22.1 8,400
QB 4.11 Josh Allen BUF 19.4 6,500 18.6 7,700
QB 3.90 Jimmy Garoppolo SF 18.6 6,000 17.6 7,500
QB 3.74 Aaron Rodgers GB 20.8 6,400 19.7 7,600
QB 3.38 Andy Dalton CIN 18.6 5,400 17.4 6,900
QB 3.16 Teddy Bridgewater NO 12.8 5,300 12.2 7,000
QB 2.70 Ryan Tannehill TEN 14.1 4,800 13.5 6,400
QB 2.25 Mitchell Trubisky CHI 16.7 5,100 15.9 6,800
QB 2.19 Philip Rivers LAC 16.6 5,500 15.3 7,600
QB 0.58 Ryan Fitzpatrick MIA 13.8 4,500 13.1 6,400
QB 0.53 Case Keenum WAS 15.6 4,600 14.6 6,600

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