by John Bush

Non_PPR Ranks Risk Skew Week 3

Non_PPR Ranks Risk and Skew Week 3 present my rankings, which are not simply numbers on a page or column. I included my metric based risk assessments at the player and team levels as well a new metric based range skew analysis that I have developed this summer.

I have also included two stand-alone figures that expand the concepts of Risk as I use in fantasy football and the idea of a Ranking Range Skew number. I present the Team landscapes of risk numbers and risk analysis of the positions within the teams.

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Let’s begin with Risk. I use that term as a measure of possibilities. Each ranking in this world is a number usually an average. In 100 games played under these same conditions, a player will average at their rank (50 percentile). What about the rare games where they scored much higher or lower? Thus a range of possibilities can exist. You use to understand my rankings; I wanted to declare my view of the player range of possible outcomes.

High Risk means a large set of possibilities, Mid Risk means a narrower set of outcomes and Low Risk implies a very tight range of outcomes. See Figure 1 for a visual description.

Figure 1. Fantasy Football Risk Metric and Analysis.

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Figure 2. Outcome Range Skew

I have divided the positions into groups and present my PPR based rankings with risk and Range Skew from high to low rankings (Green to Red).

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Week 3 Team Risk Landscape

As you stroll into my rankings, later on, I predicted that the team’s risk levels can be used to modify your rankings based lineups or bargain hunting in DFS situations. In Figure 3, I simply present for each team a count of their ranked players who are in the week 3, High, Mid or Low-risk players.

For example, this week WAS has well over half its ranked players at high risk (63%)! My rankings must be considered a best case. ATL, CHI, DEN, and NO have only 2 players at predicted high risk! Use this data to evolve your views.

FYI to finish my week’s picture

• I consider game scripts, DAPS, Risk, Range Skew and rankings.
• I use all the data predictions to move to a holistic opinion!
• Blindly following numbers is not the best way!

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Figure 3 to 5. Week 3 Team Risk Landscape

Figure 3 shows each team’s Player Counts for Risk Categories. I present an Area View of the Tabular Data. and Finally a focused view of the Team’s with High to Low Counts of Risky players.

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Non_PPR Ranks, Risk, Skew Week 3

I use the ranking as a best case and modify my DFS and Lineups based on the other factors of game scripts, DAPs, Risk, and Range Skew. Good Luck!

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Defense and Special Teams

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Kickers

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Quarterbacks

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Non_PPR RBs

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Non_PPR TEs

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Non_PPR WRs

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