Reference Class Forecasting to Fight the Planning Fallacy. Part 1 2004 to 2015 yearly Top 50 WRs Target Analysis. Baseline Data



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.

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

We must know the average baseline for our picks. 

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

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.

To develop a general reference class database, I have been gathering the data from 2004. 

Here are some of the Data of WR and Targets.  

I took the top 50 WRs for each year based on the seasonal PPR points per game. I subdivided the players into 10 player sectors from 1 to 5. 

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


Table 

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

1B Shows the % of Each Year's Total Targets within each Sector. 

For example the Top 10 1st Sector Players Targets of 1628 was 27.3% of the 2015 Year's Total of 5957 targets. Etc. 

Follow the Data Flow within Years and Between the Years within the 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. 




Table 2 is a Plot of the Tabular Data for your pleasure to "see" the data from Table 1B! 

Note the clear Gaps of Sector Targets. That is your reference class. 

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

WR2s have meshed with WR3s (3rd)!

WR3s are closer to WR2s!

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


NEXT

I wished to measure the difference within the Sector in each year. 

So in 2015 the Sector 1 is assigned a Zero. Sector 2 is assigned as a 5.3. That number is calculated using the data from Table 1B. 

27.3% - 22.0% = 5.3%.  Sector 2 minus the Sector 1 Number etc. 

Note that is the difference in position as shown in Table 2! 

I color coded then the difference numbers from within the years and then Compared between the years in each Sector!


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

Graph 5 is the plot of the data from the tabular data from Table 3 on the Grand total of WR Yearly Targets from the grand average. 

Green circles highlight the pattern on Elevated Targets of WR in the last few years as compared to years 2008 to 11 


Table 6 and Graph 7

I calculated the % of Yearly total to 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 

I plotted the Tabular data from table 6 and color coded it and present in Graph 7.



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? 

I planning to have more goodies in my Drafting Book coming in May I hope. I might have to add a whole chapter to discuss the planning fallacy that are our drafts!!!

Good Luck
===============================================================

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Drafting is now for Sale on Kindle. 


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


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