QB and RBs: PPR ADPs Converted to Fantasy Points Per Game
- Updated: July 22, 2019
Introduction
Welcome to my next multiple part series of articles based on PPR ADPs Converted to Fantasy Points Per Game. The metrics presented here are not my personal projections but those FP/G projections based on the Public’s PPR ADPs. Use these metrics to spot RB by committees and key QB tiers.
I used the last 5 years of preseason ADPs and compared to the end of season FP scored by those players. Next, I applied a mathematic data transformation to create conversion factors for each position. Finally, this transformation model was applied to the current PPR ADP based FP/Gs to create player data projections based on what the public is telling us.
Under uncertainty, one way to deal with that issue is to develop reference class forecasting. We remove our biased insider views and look instead from an outside view. Now using my methods, we are free to study and debate what the public is telling us really. 100 ADP vs 120 ADP is hard to understand but 10 FP/G vs 8 FP/G is much easier to determine the differences.
link – Reference Class Forecasting.
COM Score
COM SCORE- This is a simple percentage comparison between 2 players of the same position within a team.
I would use this score:
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Form an opinion of whether an RBBC exists
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How many players are thought to be within a small or large % of FP/G
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Strength of the public’s opinion of a player
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Potential injury handcuffs from the public’s viewpoint.
The QB and RB Landscape of the Entire Player’s FP/G as predicted by ADPs
This figure defines the 2019 Draft of FP/G by positions. I present each position along with the MAX (Highest), Median (50% Level) and MIN (Lowest) FP/G.
Quarterbacks
The key QBs will be between 20 and 16 FP/G. That is only a 20% decline from the top to median QB. Late QB drafting is certainly supported by analysis of this data. These represent the first 20 QBs. The first 10 QBs will be between the 89% to 100% level. I try to hit QBs within 6 to 10 area and then I am satisfied
Quarterbacks – FP-G Sorted High to Low
Takeaways
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The top 6 QBs are almost the same. No need to rush to grab a QB.
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An average difference or 1 to 2 FP/G can be made up by your other positions
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Tom Brady is the 16th QB and is going to get you within 3 FP/G of the top QBs
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Using the COM scores, the public suspects Daniel Jones will replace Eli and give you 97.7% of his FP/G
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FitzMagic will give you 97.7 % of Rosen
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Drew Lock gets you 94% of Flacco and Keenum 89.9% of Haskins.
The QB and RB Landscape of the Entire Player’s FP/G as predicted by ADPs
Running Backs
PPR ADPs Converted to Fantasy Points Per Game is essential for detecting RB committees and tiers based on FP/G. The MAX RB starts at 16.7 FP/G and drops to the 50% level at 8 FP/G. That data range should contain the major players for 2019. Here are the landmarks of RB percentages.
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RB1s exist between 100% to 77.8% of the top value FP/G.
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RB2s exist between 76% to 63% of the top value FP/G.
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RB3s exist between 62% to 53.8% of the top value FP/G
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RB4s exist between 53.2% to 46.7% of the top value FP/G
Running Backs Sorted High to Low with Position Type and Com Score.
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White as an RB-2 gets you 94.3% of Michel. He is still higher than 5 RB-1 types. NE is an RBBC.
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CHI is also an RBBC with Cohen delivering 94.6% of Montgomery’s value
Using the COM score we can say the public predicts RBBCs for :
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NE White and Michel 94% similarity
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CHI Cohen and Montgomery 94% similarity
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TB Barber and Jones 93% similarity
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SEA Penny and Carson 91.7% similarity
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SF McKinnon and Coleman 86.9% similarity
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HOU Miller and Foreman 88% similarity
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PHI Sanders and Howard 95% similarity
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DEN/BUF/WAS also have strong indications of RBBC as well
Running Backs Position Type Sorted
The figure below indicates the RB-1 for each team.
Some data suggest lower RB-1s can do better than thought.
The public may have a harder time choosing an RB-1 type from a mix of RBs
The Top COM scores suggest RBBC as previously discussed.
PHI/ CHI/ NE/ TB/ SEA/ HOU/ SF/ BUF/ DEN/ WAS
The RB-3 Com scores give insight into a 3 way RBBC Note SF KC WAS and NE are in this mix
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Interestingly, these data can set a line in the sand for your deeper decisions.
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If you can pick out the winners in the RBBC battles then you should profit.
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Finally, in best-ball leagues, these data can allow you to scoop all the players for control of a Team’s rushing points.
Fun Research in my textbook!
Winning Your Fantasy Football Drafts: A Comprehensive Textbook: June 2019 Edition [Print Replica] Kindle Edition
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