News & Analysis

Premium Content

DFS data dive: The highest fantasy ceilings of Week 4

By Kevin Cole
Sep 27, 2019

Fantasy Featured Tools

  • Sort through our expert fantasy player rankings by analyst and league type.

  • Sort projected player stats and fantasy points by position, week, and category.

  • Research past fantasy performance with sortable player stats including PFF-exclusives like aDOT and fantasy points per opportunity.

  • Run your fantasy season here. Import your league settings to access Season GM, Waiver-Wire, and Player Comparison in your scoring system.

PFF Edge
Unlock Player Grades, Fantasy & NFL Draft
Learn More
$39.99 / yr
$9.99 / mo
PFF Elite
Unlock Premium Stats, PFF Greenline & DFS
Learn More
$199.99 / yr
$34.99 / mo
Nov 12, 2017; Los Angeles, CA, USA; Los Angeles Rams wide receiver Robert Woods (17) celebrates with the crowd after a touchdown against the Houston Texans during the third quarter at Los Angeles Memorial Coliseum. Mandatory Credit: Kelvin Kuo-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.

Methodology

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.

Quarterbacks

Pos Highest % Player Team DK Fpts DK Salary FD Fpts FD Salary
QB 13.36 Patrick Mahomes KC 25.6 7,500 24.0 9,200
QB 8.88 Lamar Jackson BLT 23.3 6,900 21.9 8,300
QB 8.26 Russell Wilson SEA 23.5 6,100 22.0 7,800
QB 8.25 Jared Goff LA 22.2 6,300 20.4 7,700
QB 6.57 Philip Rivers LAC 19.3 6,200 17.8 7,800
QB 6.16 Jameis Winston TB 18.5 5,700 17.0 7,500
QB 5.67 Kyler Murray ARZ 19.7 6,000 18.7 7,600
QB 5.45 Daniel Jones NYG 17.7 5,300 16.8 7,300
QB 5.23 Kyle Allen CAR 18.2 5,200 17.0 6,800
QB 4.66 Deshaun Watson HST 24.1 6,400 22.7 8,200
QB 4.08 Matthew Stafford DET 20.4 5,500 18.6 6,900
QB 3.89 Tom Brady NE 17.6 6,600 16.3 7,800
QB 3.58 Jacoby Brissett IND 20.9 5,400 19.8 7,300
QB 2.76 Matt Ryan ATL 20.8 5,900 19.2 7,900
QB 2.67 Kirk Cousins MIN 16.6 5,000 15.6 6,900
QB 2.63 Marcus Mariota TEN 18.0 5,100 17.2 6,900
QB 1.98 Derek Carr OAK 18.3 5,300 16.8 6,800
QB 1.88 Gardner Minshew JAX 15.6 5,200 15.0 6,700
QB 1.38 Case Keenum WAS 17.0 4,900 15.9 7,200
QB 1.04 Josh Allen BUF 16.7 5,600 15.9 7,400
QB 0.93 Baker Mayfield CLV 17.7 5,800 16.5 7,500
QB 0.73 Josh Rosen MIA 13.5 4,500 12.8 6,200
QB 0.59 Joe Flacco DEN 16.1 4,600 15.1 6,400
QB 0.13 Mitchell Trubisky CHI 16.2 5,000 15.4 6,800

Read More PFF Analysis

Subscribe to PFF Edge or Elite to continue reading

Already have a subscription? Sign In

PFF Edge
PFF Elite