FF Players Biased Decisions Part 2

Slide1

By John Bush

FF Players Biased Decisions Part 2

FF Players Biased in Decisions because System 1 thinking bias is strongest when the FF information is at the smallest amount. That system (you) jumps to conclusions! See part 1 of this series)

(FF Players Biased Decisions Part 1 http://www.fakepigskin.com/?p=36738 )

You must start by formulating questions within FF information that is in the smallest amount such as:

  • Rookies and their team roles.
  • Free Agents and their team roles.
  • New Coaches and Team Systems.
  • Players coming back from injury.

Thus look at your slate of data and determine where the amount of information is the weakest. Those are system 1 minefields as Dr. Kahneman would describe or false-alarm opportunities as Dr. Fold would say.

  • Teams with Significant Coaching,
  • Free Agent Additions, and
  • Rookies Draft.

What Teams are naturally going to have less information for our decisions?

In this article, I focus on finding and analyzing Teams with new Coaches, Free Agents, and Rookies. In the end, I combine them all into a landscape view of each team.


Teams with New Coaches and thus Weaker Levels of Information

https://en.wikipedia.org/wiki/2018_NFL_season

The figure below has the coaching changes for 2018. The discussion in part 1 points to these teams being less predictable. They are our potential bias minefields in fast and frugal thinking.  These team will undergo changes from last year. Player usages and schemes will change. Identifying these teams as higher risk for your player rankings. 

Teams with a coaching change in 2018

Slide17

 Coaches Ranked and Uncertainty Levels. 

I analyzed the available information on all 32 coaches and determined a ranking system metric from high 100 to 0 low. Additionally, I present the uncertainty level (UL) I perceived concerning that coach and team system (Low to High Uncertainty).

Note I combine the coaching rankings in context of UL.  The higher UL would point out those teams with bias potential.

Some teams of concern via coaching perspective

Slide18

These are the top NFL Coaches as ranked going into 2018. I note that SEA has a good coach but a lot of concerns based on Team’s changes and past years coaching performance. 
Slide19

This figure above highlights coaches at a mid-tier level with teams that have questions going into 2018.  The coaches with high levels of concern in this group are GB, WAS, JAX, DAL, and CHI.  
FF Players Biased Decisions Slide20

 I also have issues with the Coaches from NYJ, NYG, and CIN who are in the lower tier of the league coaches. The coaches listed are high uncertainty implies new players or coaches,  loss of players and/or 2017 team coaching results. 

Takehome- Consider all these HIGH UL teams to have high bias potential from your 2018 drafting. Given the discussion in Part 1, I suggest these teams have high levels of missing information vs other lower/ mid-UL teams.  That fact leads to higher chances of less rigorous research being done by you. You can miss important aspects on your pre-draft homework.


Rookie Analysis (Important Source of Uncertainty within Teams)

These next figures contain rookie information included their current ADP based rankings. The UL is high for any rookie. The level of ranking points out the increased UL in the higher ranked rookies. These high ranked rookies thus carry high levels of UL in the team.

Rookies by Position

FF Players Biased Decisions Slide1

FF Players Biased Decisions Slide2

FF Players Biased Decisions Slide3


Rookies by Team and Rankings

The figures below present Team by Team of all important rookies on that Team by position and ranking. Teams with higher levels of highly ranked rookies would tend to have higher levels of uncertainty.

The overall point of this article to reveal areas of higher decisional biases and these high impact rookie containing teams fit that characteristic. 

FF Players Biased Decisions Slide5

BAL has 5 Rookies on that team in 2018. 


FF Players Biased Decisions Slide6


FF Players Biased Decisions Slide7

DAL and DEN have 4 rookies in those teams. 


FF Players Biased Decisions Slide8


FF Players Biased Decisions Slide9


FF Players Biased Decisions Slide10


FF Players Biased Decisions Slide11


Team’s Average Rookie Rankings: Impact Levels

Besides the actual rookie numbers to point to UL Teams (BAL, DAL, and DEN).  I next wanted to judge the level of rookie’s ranking to spot team’s with levels of high ranked rookies. The level of rookie impact for 2018 can be predicted by high vs low levels of rookie rankings.

FF Players Biased Decisions Slide13

The colorized rankings highlight SEA, DET, CLE, NYG, ARI, CHI, BUF, and MIA as teams with highest ULs. 


Landscape view of Team Rookie Impacts via Scaled to Rookie Ranking Averages. 

The table below is sorted by the scaled average of Rookie Rankings. The top two UL teams from rookies are SEA and DET in 2018.

FF Players Biased Decisions Slide14

The waterfall graph completely illuminates the entire NFL 32 Teams from the UL levels of low to high.

FF Players Biased Decisions Slide15

The top rookie based UL teams are SEA, DET, CLE, NYG, ARI, and CHI. The lowest UL teams, on the other hand, are TEN, CIN, HOU, DAL, GB and NYJ.  Using the idea of bias the high UL teams need extra research of their rookies given the potential 2018 impacts.

These High UL are where the chances of missing information are the greatest. 


Team Landscape based UL analysis with Player Rankings, Free Agents, Rookies, and Team Coaches. 

These landscapes of all the teams highlight the visible sources of Team UL! The list of players and their positions highlighting who are Free Agents, Rookies, and Coaching Issues. I have now included the levels of Team Free Agents along with the Rookies and Coaches.


Slide27

ARI has 5 new players with a new coach


Slide28


Slide29

BAL has 7 new players but a solid coach for direction


Slide30


Slide31


Slide32

CHI has 3 new players in their WR corps along with a new high UL coach. 


Slide33


Slide34

CLE has 2 new RBs with a low ranked coach.


Slide35

DAL has 5 new players with a high UL coach. The WRs are especially high in UL. 


Slide36

DEN has 5 new players with a low ranked coach.


Slide37


Slide38

GB has 4 new players 3 of which are WRs. 


Slide39


Slide40

IND has 6 new players especially 2/4 RBs and 3/6 WRs with a new low ranked coach. 


Slide41


Slide42


Slide43


Slide44


Slide45

MIA has 6 new players including 2/3 RBs and 2/5 WRs with a lower ranked coach. 


Slide46


Slide47

NE has 5 new players but the number of ranked players is very high. 


Slide48


Slide49

NYG has 4 new players and a new low ranked UL coach. The RB position sees 2/3 of the main players. 


Slide50

NYJ has 6 new players with a high UL and low ranked coach. 


Slide51


Slide52


Slide53


Slide54

SEA has 2 new WR in 5 players with a High UL. 


Slide55


Slide56


Slide57


Slide58

WAS has 2 of 4 new WRs with a High UL coach. 


Lack Of Information By Team and Position as Measured by Player and Coach UL %.


I present an overview of team’s rookies and free agents. The teams with highest levels of new players are determined and presented as player UL levels. The data from coach UL levels previous seen is also included.  The table below is sorted by the level of Player UL on the Team.

Table of Team’s Free Agents, Rookies, Players Ranked, Player UL Levels and Coach UL. 

Slide60

Slide61


Scaled Team Player UL Levels

Slide62


The landscape view of the Scaled Player UL by the Team. 

          How we arrived at this point was a declaration that biases exist in FF players

(FF Players Biased Decisions Part 1 http://www.fakepigskin.com/?p=36738 )

Many pundits tell you the best players, sleepers, bust etc. However, the best information for FF players is where does the uncertainty exist in Fantasy Football. This led to my thinking about the concept of Uncertainty Avoidance in FF.

Uncertainty Avoidance Definition*

 source document – https://en.wikipedia.org/wiki/Uncertainty_avoidance

In cross-cultural psychologyuncertainty avoidance is a society’s tolerance for uncertainty and ambiguity. Uncertainty avoidance is one of five key qualities or dimensions measured by the researchers who developed the Hofstede model of cultural dimensions to quantify cultural differences across international lines and better understand why some ideas and business practices work better in some countries than in others.

The uncertainty avoidance dimension relates to the degree to which individuals of a specific society are comfortable with uncertainty and the unknown. Countries displaying strong uncertainty avoidance index (UAI) believe and behave in a strict manner. Individuals belonging to those countries also avoid unconventional ways of thinking and behaving. Weak UAI societies display more ease in regards to uncertainty.[2]

People in cultures with high uncertainty avoidance try to minimize the occurrence of unknown and unusual circumstances and to proceed with careful changes step by step by planning and by implementing rules, laws and regulations. In contrast, low uncertainty avoidance cultures accept and feel comfortable in unstructured situations or changeable environments and try to have as few rules as possible. People in these cultures tend to be more pragmatic and more tolerant of change.

Uncertainty Avoidance in Fantasy Football

I suggest the “experts” are of a different population than the casual players.  Experts are from the Low Uncertainty Side of FF players and have over time learned to accept and feel comfortable in unstructured and uncertainty laced FF.

Causal players will be on the opposite side of the player spectrum by following perceived rules, experts, ADPs to minimize uncertainty. IE Early QB drafting, balanced drafting RB WR RB WR etc pattern, heavily focused on last year’s results (safe to draft the top player from last year)

FF has many risks and uncertainty embedded and this article is the first one to propose this application of Uncertainty Avoidance to FF.

This last figure is my attempt at spotlighting where the higher UL exists in the league by Teams. This is not the only way but a beginning for my future investigations. Can ADPs be understood better in the light of UL Avoidance?

Waterfall graph of Team UL for 2018!

The UL Problem Teams are the extremes to the Left. MIA, BAL, IND, ARI, CLE, NYG and NYJ etc.

The fewer UL problem teams in terms of UL are LAC, LAR, KC, CIN, OAK, TEN etc.

 I suggest your pre-draft research focus on those teams of higher UL because they promise to be where many FF player will make their mistakes! Advantage you! Good Luck

Slide63

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>