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Showing posts from August, 2016

NFC EAST__ Deep Look at 2015 Player PPR Based Performance by Week Sorted by Team and Position Numbers are a scaled based positional performance by week

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Deep Look at 2015 Player PPR 
Based Performance by Week Sorted by Team and Position Numbers are a scaled based positional performance by week 
(each week players were sorted by position and performance and the best was assigned a 100 score and downward from that mark.)

I suggest you use this data as a reference for this weeks drafts.  I give you TEAM by TEAM by position and players.
I color coded the weekly performance data Green             BEST Red                MIDDLE Yellow           BAD No Color -     WORST
Each Week The TOP at each Position in PPR scoring was assigned a 100. The Average was assigned a 50 and bottom was assigned less than 10.
I will add markers to highlight what I "see" is interesting. 
No text from this point. Its time for you to throw away the safety of pundits telling you whats whats. See the data that I use! Enjoy!
Can Dez and T Will survive Dak Attack? T WIll Weeks 1 to 5 vs 7 to 17!  Dez weeks 10 to 15!

Lots of fun nuggets buried here!




NFC South__ Deep Look at 2015 Player PPR Based Performance by Week Sorted by Team and Position Numbers are a scaled based positional performance by week

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Deep Look at 2015 Player PPR 
Based Performance by Week Sorted by Team and Position Numbers are a scaled based positional performance by week 
(each week players were sorted by position and performance and the best was assigned a 100 score and downward from that mark.)

I suggest you use this data as a reference for this weeks drafts.  I give you TEAM by TEAM by position and players.
I color coded the weekly performance data Green             BEST Red                MIDDLE Yellow           BAD No Color -     WORST
Each Week The TOP at each Position in PPR scoring was assigned a 100. The Average was assigned a 50 and bottom was assigned less than 10.
I will add markers to highlight what I "see" is interesting. 
No text from this point. Its time for you to throw away the safety of pundits telling you whats whats. See the data that I use! Enjoy!
Example did D Freemen Peak at Weeks 5 to 9? Why?

Lots of fun nuggets buried here!


NFC WEST Deep Look at 2015 Player PPR Based Performance by Week Sorted by Team and Position Numbers are a scaled based positional performance by week

Image
Deep Look at 2015 Player PPR 
Based Performance by Week Sorted by Team and Position Numbers are a scaled based positional performance by week 
(each week players were sorted by position and performance and the best was assigned a 100 score and downward from that mark.)

I suggest you use this data as a reference for this weeks drafts.  I give you TEAM by TEAM by position and players.
I color coded the weekly performance data Green             BEST Red                MIDDLE Yellow           BAD No Color -     WORST
Each Week The TOP at each Position in PPR scoring was assigned a 100. The Average was assigned a 50 and bottom was assigned less than 10.
I will add markers to highlight what I "see" is interesting. 
No text from this point. Its time for you to throw away the safety of pundits telling you whats whats. See the data that I use! Enjoy!
Example David Johnson in weeks 13 to 15 was above average of all the RBs in the league those weeks. Note his production dropped to the 30% level …

2015 Target Data. Weekly Numbers of Players by Position and Performance. Color Coded Superior Data Treats!

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2015 Target Data. Weekly Numbers of Players by Position and Performance. Color Coded Superior Data Treats! Focus on the Tiers I have setup in these tables. 
(Green/Blue are high targets. Red are low targets)




















Super Risk Report. Lots of Data. Use thoughtfully!

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Starting the first " research as you" go Blog Post! 
First Graph is a Risk Stat that Uses Average of  3 Player's Positional Risk Numbers. 

IE
Player -1 WR -15
Player 1 RB - 10
Player 2 WR -5

3 players 3 risk numbers. The average is (15+10+5)/3  30/3 = 10. I assign that to the middle player. He is in the middle of a risk 10 region. I continue this calculation forward. 

Below is the Scatter-gram of that "neighbor risk data"  Risk goes up and plateaus then drops into the 20 draft round. It also gets wide in the data. Our Statistical confidence limits are very broad. More uncertainty stats to widen out at round 7/8. So the game changes and the ice gets very thin on average at that point! 
This next figure is where I placed our expectations using a "pink" line. I have not data to plot this. Its a guess. Note the intersects of Our expected opinions or player value/risk and the actual neighborhood risk data cross at round 9 or 10.

Does hitting the "singularity&q…

Analysis of 2015 Top 50 Preseason RBs vs their End of Season Finish. How many made the Regular Season Top 100 RBs? Part 2

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My Area at the Fantasy Greek .Com

I have the 2015 Preseason Data and had a series of questions. 
Question 1: How many of the top 50 2015 Preseason WRs made an impact by the end of the season?


Figure 1 to 4 present the Top 50 preseason RBs. I used the stat of rushing yards per game (YDS/G). An area graph follows to each table. Green Stars highlight the preseason RBs that had an impact in the regular season (Top 100 RB Regular Season)
Figure 1
Figure 2

Figure 3


Figure 4
Figure 5 and 6

This figure show a summary of the RB rankings in Preseason (1 to 50) vs the Regular Season (1 to 100). 

Color Coded Regular Season Rankings
Green- Top 25% 
Yellow - Middle Group
Red-Bottom 25% 

Figure 5


Figure 6




Figure 7
Where are we now? RBs vs WRs 
2015 Pre vs Reg Seasons

Green Stars = winners of the Pre vs Reg transition. 

Analysis of the data reveals that the Preseason RBs have a higher chance of making a regular season impact vs WRs. 

** Double the chances**

I would pay sharper attention to the top RBs vs WRs!

I have not he…

WRs Targeting - 2004 to 2015 Deep Sector Analysis! Reference Class Forecasting is the Treatment for the Planning Fallacy.

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Intelligence in Public Literature Thinking, Fast and Slow Daniel Kahneman, (New York: Farrar, Straus and Giroux, 2011), 418 pp Reviewed by Frank J. Babetski Studies in Intelligence Vol. 56, No. 2 (Extracts, June 2012)
Quoting

" Few books are “must reads” for intelligence officers. Fewer still are “must reads” that mention Intelligence Community functions or the CIA only once, and then only in passing. Daniel Kahneman has written one of these rare books. Thinking, Fast and Slow represents an elegant summation of a lifetime of research in which Kahneman, Princeton University Professor Emeritus of Psychology and Public Affairs, and his late collaborator, Amos Tversky, changed the way psychologists think about thinking. Kahneman, who won the 2002 Nobel Prize in Economics for his work with Tversky on prospect theory, also highlights the best work of other researchers throughout the book.

Thinking, Fast and Slow introduces no revolutionary new material, but it is a masterpiece because of th…

Analysis of 2015 Top 50 Preseason WRs vs their End of Season Finish. How many made the Regular Season Top 100? Part 1

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I have the 2015 Preseason Data and had a series of questions. 
Question 1: How many of the top 50 2015 Preseason WRs made an impact by the end of the season?
Figure 1 and 2 Present the Top 50 WRs. I used the stat Yards per Catch. 
I took the Stat and convert the Raw data into Rankings from 100 High to 0 Low. Green highlights to Top WRs in preseason and also those that were in the top 100 WRs at seasons end. 
The Diff is a subtraction of the Preseason and Regular rankings.




In Summary 11 of the top 50 preseason WRs had a regular season impact! 1 out of 5. So 4 of 5 were not relevant! 

The Area Graphs of the Tabular Data give you a landscape view of the data!