Wednesday, August 31, 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

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!





Tuesday, August 30, 2016

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

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

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 of the league RBs in weeks 16 and 17. Why? 

Lots of fun nuggets buried here!







Monday, August 29, 2016

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

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)





















Saturday, August 27, 2016

Super Risk Report. Lots of Data. Use thoughtfully!


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" make us too thoughtless. 

We do not expect so much so I will not do any deep thinking. 

Orange line shows the average player effort level in their drafts?  One of the reasons I do as many slow drafts. I wish to devote more time to picks as I go into the draft.  

I suggest that you save you draft data and score your picks from round 8 and up. Is this a problem for you? I think your game can be improved if you were to up your skills at the later rounds. 

How can we fight this "drafting fatigue"  ? Save your past drafts and see where you have it at. I on occasion have timed by picks just to see where my time budget is at? Do you have a correlation? 


-----






I took the player's positional based risk numbers and calculated the draft rounds average of that number. I subtracted the average for the original number. That should map the distribution of  riskier players and less risky players within the draft rounds. This can p begin to correct for the tendency for risk in general to go up into the draft. 

Look at the landscape view. It implies that as we get into the draft we believe our drafted players are safer that players in the previous  rounds starting at round . They seem safer because the other players in that draft round seem higher in risk. 

Are we overplaying our hands. How can players in rounds 3 forward be "safer" than round 1 players? This tells me cognitive biases are at work here. 

313pm CST

Questions. If the player is so safe vs other, why is he being drafted later.  I hear the concept "he has a role" We have expectations vs risk level.  Should we rate players at both levels? I try! Do we ignore risk late and "Shoot for the Moon" on players. 

Positional Risk Analysis

I have calculated the risk number within the positions and have those charts coming. The X axis is the player or team and the Y axis is the risk level for that data point. From left to right is the current ranking. 

These data are meant to inspire deeper thinking. Remember the time invest the average plan does not make on their drafting. Be different. 



You can group the first 4 together in a risk grouping. The next four DST look alike. The NE DST seems to be a "sure" thing at 9th while the PHI DST is beyond risky. I have not drafted them in my 35 leagues! I would overlay this risk data with SOS data to tease it out. 


The next figure is the QB risk levels figures. Left to Right current rankings. Green circle denoted "risk tiers" The top group seems to be close in risk levels. I wait on QBs late so this data supports that drafting plan. I have been focusing on Tyrod Taylor as he is a late QB with an acceptable risk vs the top QB group. 

Next is the RB landscape of Risk. This graph shows the deeper level my risk can take us into the data! 
Look at the 2 worlds of RB5 and 6s. Upside Lower Risk and Higher Risk. 

If you can know and expect that we have apples and oranges you can make orange juice or applesauce. Draft Risky RBs or "Safer RBs".

Next will be the RBs names and their risk scores by rank. So I will reveal the late round oranges and apple RBs. Come back. 

7:52pm Back! 



This graph is the top 24th RBs and risk. Doug Martin Stands out here. His Risk is as high as the next group. So does that say C J Anderson should flip the spot with Martin? The first group has a low risk average of 10 while the next group has an average of 30isk. 3X the risk. As you leave the "safety" of the top 12, you enter into the first "risk plateau". If your tolerance for risk is not high then maybe you should draft in the top 12 RBs! 






These figures are fun and can be used to break the late round ties. Good Luck on the RB

Good Morning! Lets finsh this!

How do I use these data?

1) I predict that further into the draft the risk levels become more important

2) I use not as a stand alone but to supplement my thinking. 

That's why I do the slow drafting. Live drafting requires a homework sheet. List of players -targets and color code or use a R to denote higher risk. R/R is what I use for High Risk High Reward Players. 

3) Remember all rankings/info/tweets etc are there to help you not decide for you. 





xx 10 AM Sunday

This new graph is the Risk Landscape for the TE position by high to lower ranking vs Risk. I got this data on Saturday morning. I was sad to hear about Mr. Ben Watson. I had him as a late round TE2 possibility but he had a 73 risk level so a definite risk! 

Analysis of the landscape shows you should expect high risk after the first few TE are drafted. Get ready for that. In TE premium leagues I am drafting at least 3 TEs! ie FFPC leagues! 




I have highlight some features of this TE Risk landscape by red ovals. You can "see" the risk clouds "your player" is under. I think Witten and Allen are justified as higher risk in the second cloud area!


WR Currently Ranked by Risk   

Scatter_Gram and Radar Chart (Cool)



vvv

I have now jumped the data shark by using the Radar Chart feature on excel! 





The Next Four Figures Cover the Top WRs downward by Rank in groups of 24 till the end. We see a movement of risk upwards at the 24th WR or so!  The Radar Chart showed it better, I think.  By the 60th or WR its risky city! 



Summary

I would advise you to not look at this data till you have developed a later WR target list and then look. If your targets have a high risk, I suggest you dig deeper and be thoughtful. See my posts of injury matrices or multiple hypothesizes approaches.  

(Wow Dr. Bush that seems like a lot of "extra" work.- Cut the wood and then get the fire not the other way).

Good Luck!  

Friday, August 26, 2016

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

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 heard that in the pundit worlds! The Professor claims another scoop and all the glory its brings (  :) hehe!). 


Thursday, August 25, 2016

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


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 the way Kahneman weaves existing research together. Expert intelligence officers at CIA, an agency with the “human intelligence” mission at its core, have come through experience and practice to understand and exploit the human cognitive processes of which Kahneman writes. These expert officers will have many moments of recognition in reading this book, which gives an empirical underpinning for much of their hard-won wisdom."

Folks in the off season you should read this book multiple times! I bought it on Amazon Kindle and use my Evernote app to pin great quotes.

One part of the story is "The Planning Fallacy" 

Most projects (Fantasy Football Teams) have overly optimistic forecast as to the outcomes.

Pieces of the Puzzle 

1) We must generate baseline stats for the for our positions.  
(reference class)

2) We must know the average baseline for our picks. 

3) We must be successful and draft based on the best case scenarios but know the average possibilities. 

4) We must broad frame the forecasted player's performance within the total realm of all performance possibilities.  

Reference Class Forecasting is the 
Treatment for the Planning Fallacy.


I am going to focus on the WR position

Research Methods

Goal is to develop a general reference class database for my WR drafting, I have been gathering data from the year 2004. 


For Each Year's Data 

A) I took the top 50 WRs for each year based on the seasonal PPR points per game. 

B) I subdivided those 50 WRs into 10 player sectors labeled Sector 1 to 5. 

Sector 1 was the top 10 WRs of that Year. 
Sector 2 is Players 11 to 20th player, etc. 


The initial data was compiled and illustrated in Figures 1A and 1B 

Figure 1A. Top 50 Player of P/G Targets per Year (2004 to 2015)

For example in the year 2015, the top 10 WRs (sector 1) had a seasonal target total of 1628. The average was 162 targets per top 10 WRs in 2015. The 2015 Top 10 WR ranges from 203 to 148.
We expect therefore our TOP WR to have close to 162 Targets

The 12 year average is 1511 targets per the Top 10 WR. 2015 was an above average year for top 10 WR targets. 12 year average was 151 targets per player expected. 

When you are drafting for 2016, you should draft WRs expected to be near 150 targets as a WR 1. 


Figure 1B.  Each Year's WR Sector Targets Expressed as a % of the Year's 50 WR Total Targets  

In Figure 1A for example in the year 2015 the Top 10 1st Sector WR Players Targets were 1628 which was 27.3% of the 2015 Year's Total of 5957 targets. Etc. 

These dat allow you to follow the Data Flow within Years and Between the Years within the WR Sectors. 

Green/Blue is above average and Red/Orange is below average. 

We estimated the average value of the drafted WRs in the value column. Sector 1 WRs are worth a 100% on average while the next sector WR 2s are worth on 85.7%, Sector 3 WR3 s are worth 76.1% of the Sector 1 WRs etc.  

Green are that years WRs targets above the sector average
Red are below the sector average. 

Grand total color coding - Blue is above and orange is below

So over reference class data can be simplified as to the Value of each WR yet draft on average.  

If keep records compare your picks to see if you are able to draft players that are the higher ones in each sector. 

Do you pick WRs are the lower parts of each sector?  This will define your personal strengths. 



FIGURE 1A and B

Figure 2 is a Plot of the Tabular Data for your pleasure to "see" the data from Table 1B!  The distances between sectors is very distinct. Note the clear Gaps of Sector Targets. That is your reference class. 

Conclusions 

WR1s (1st) have never dipped into the WR2 Sector (2nd)!

WR2s have meshed with WR3s (2 out of 12 years)!

WR3s are closer to WR2s and can overlap.

WRs 4 (4th) and WRs 5 (5th Sector) seem to be much closer to each other!


Analysis of this data:


  • Supports drafting WR1 within the first 10 or so.
  • Supports that you can wait if needed while the drafts are within the WR2 and 3 WR drafting levels
  • Expect WR2 and 3 Targets to be closer than ADPs would predict on average.
  • Expect WR4 and 5 to be on average close together in targets than ADPs would predict on average.


Figure 2


I wished to measure the difference within the WR sectors in each year.  In 2015 the Sector 1 WR group is assigned a zero. Sector 2 is assigned as a 5.3. That number is calculated using the data from Table 1B.  The 5.3 number was from WR1 % of 2015 TGT Total 27.3% - 22.0% ( WR2 % of 2015 TGT Total)   = 5.3%. 

Sector 2 minus the Sector 1 Numbers etc. 
I used color coding then to stain the difference numbers from within the years and then compared each years sector difference between the years in each sector!


Figure 3 Tabular Data of the Sector Difference.
Figure 4 is the Plotted Tabular Data from Table 3 



Figure 5 is the plot using the data from Table 3. This data shows the deviation for 2015 to 2004 yearly sector differences as compared to the 12 year grand difference average of 8.3. We can generate a look at each years information in Figure 5. In that Figure 5 the green circles highlight the pattern on Elevated Targets of WR in the last few years as compared to years 2008 to 11.

We now have a clarity to say the WRs have been an elevated targeting environment since 2011! However, pundits are being saying that has never happened. Note the years of 2005 to 07. If you looked at 2011 data and backward you would have been on the radio saying the WRs targets are going down!


Figures 6 and 7. 

I calculated the % of Yearly total vs the the grand Sector average and scaled that number. Green is above average and red is below the average. 

So in Sector 1 of 2015 the % from Table 1B was 27.3. The Grand Sector 1 average of percents from 2004 to 15 was 25.9.  27.2-25.9 = 1.4 above the average. 

All Sectors were done. Note that WR 1 Sector has had 3 above target years in 2015, 2012 and 2009. Sector 2 WRs have been above average in 2013 and 2010. The bottom 3 segments have been quiet. 

I conclude the WR1 and 2 had seen an increase in targets relative to the 12 year average but not as dramatic as has been discussed. 


So use this data to think above the reference classes in WR Targets. 
Thus you must decide if your WR1 or 2 
(Sector 1 and 2 ) is going to be better than the others in that mix. 

We have an average and value. This data is here to ground your optimistic planning fallacy

"Oh my WR2 and WR3 are going to get WR 1 Targets" Hum is that right?  That statement is not accurate on the average! 

Good Luck

===============================================================

My Textbook for Winning Fantasy Football 

Drafting is now for Sale on Kindle. 


https://www.amazon.com/author/john_bush


$$$$ Read and Plan Now 


Crush your Drafts Later!$$$

Wednesday, August 24, 2016

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

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! 





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