Inside the Matchup Matrix: Week 14

Kevin Cole goes inside the numbers to identify attackable and avoidable DFS matchups. He brings a scientific approach coupled with years of NFL knowledge to the table in identifying noteworthy situations on a weekly basis.

Week 14

This is the Matchup Matrix, where I simulate every game on the main Sunday slate in order to find stacks with the highest upside. You can find last week’s results here.

In my Find Your Edge article from Week 1, I went through the last two years’ worth of DraftKings tournament contest results and identified a number of stacks that tournament winners (top-0.1% of entries) are using more than the field.

Three of the commonly rostered stacks with the greatest leverage, or winner usage over the field, were QB/WR, QB/WR/TE and QB/WR/WR. Those are the stacks I’m focusing on in my simulations.

Methodology

Rather than layer tons of assumptions for projected scoring, scoring distributions, and player correlations, I decided to use actual historical results from similar games.

I calculate the profile of each week’s matchup by using the trailing scoring for the QB, RB1, WR1, WR2, TE1, and the games’ spreads and over/unders. I then match those profiles to thousands of games from the last ten seasons to find the 150 that are the most similar.

Once I have the most similar games identified, I look at the actual fantasy points scored from each game, then calculate the combined fantasy points for the relevant stacks.

While I also include DraftKings and FanDuel salaries for each stack in the analysis, we also have fantastic resources for determining which stacks will be chalkier and which contrarian. Our ownership projections (pOWN%) and info from Buzzscore on who’s being hyped up are available in the premium section of LineupHQ.

QB/WR Stacks

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About the Author

  • Kevin Cole (colekev)

  • Kevin Cole previously worked on Wall Street as an equity and credit analyst before transitioning to data-related sports analysis. For a number of years, Kevin has specialized in creating predictive sports models that includes published work at Pro Football Focus, Rotoworld, numberFire, and RotoViz. He is now the Director of Data and Analytics at RotoGrinders.

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