WR 2017 Consistency vs 2018 ADP

By John Bush

WR 2017 Consistency vs 2018 ADP

As we travel into the last part of the preseason, I wanted to remind my readers of last year positional performances and consistency as compared to current ADP.

Please see at the end of this article my- Short Data Analysis philosophy.

Links to the previous articles in this series

QB 2017 Consistency vs 2018 ADP

RB 2017 Consistency vs 2018 ADP

TE 2017 Consistency vs 2018 ADP

Questions to consider:

  • Which Players had excellent 2017 metrics yet are not highly ranked by 2018 ADP?
  • Which Players had weaker 2017 metrics yet are more highly ranked by 2018 ADP?

First Tier WRs by %TT

The WR 2017 Consistency vs 2018 ADP metrics I wish to cover in this article related to the WR position. (See table below)

  • Weeks a Player was in the Top Ten of that Position
  • Games Played (More Games Increases Data Points and Certainty)
  • % of Played Games the Player was in the Weekly Top Ten in Position
  • PPR Fantasy Points Per Game

I sorted the players by %Top Ten (%TT) Games They Delivered in 2017.


  • The top players in 2017 were Brown, Hopkins, and Beckham who were all above 50% of %TT games.

  • Note the drop into the 20% level starting at 13th WR.

  • The slope is steep in WRs!

  • WR 2017 Consistency vs 2018 ADP Slide1

This visual plot highlights these top players from the above table by weeks TT, %TT Games and Actual Games played. The ratings seem steady as most of the top WRs played 14 games or more in 2017. Note OBJ only played 4 games and more uncertainty would exist with him for 2018.

WR 2017 Consistency vs 2018 ADP Slide2

Second Tier WRs by %TT

  • At this point, the PPR/G metric might be more important.

  • Those WRs above 13 FP/G were Thomas, Diggs, Jones, and Woods.

  • Woods could provide some value as his ADP is much different than the other 3 WRs.

WR 2017 Consistency vs 2018 ADP Slide3

This visual plot highlights these 2nd Tier top players from the above table by weeks TT, %TT Games, and Actual Games played.

WR 2017 Consistency vs 2018 ADP Slide4

Third and Lower Tier WRs by %TT

Many “Break Glass in case of emergency” players only here. Streaming and bye week WRs in here. I suggest FP/G metric is the “boss” in these tables (see red star players)

WR 2017 Consistency vs 2018 ADP Slide6

WR 2017 Consistency vs 2018 ADP Slide7

WR 2017 Consistency vs 2018 ADP Slide8

WR 2017 Consistency vs 2018 ADP Slide9

WR 2017 Consistency vs 2018 ADP Slide10

WR 2017 Consistency vs 2018 ADP Slide11

WR 2017 Consistency vs 2018 ADP Slide12

2017 WR Metrics vs 2018 ADP

Top Tier WRs from 2017 Data

I add to the metrics table the list of 2018 ADP vs the 2017 data. I note by the DIFF metric the players that are the extremes between the 2017 data and 2018 ADP.

  • ADP is the current WR PPR based ADP.
  • Rank %TT 2017. % of Top Ten Weeks the Player had in 2017.
  • DIFF is a measure of the rankings from 2017 vs ADP rankings.
    • Which Players were worse than predicted by 2018 ADP (Negative Diff)?
    • Which Players did better in 2017 vs 2018 ADP (Positive Diff)?

Players with positive Diff can be overlays in 2018. WRs who might outperform their 2018 ADP. Players with negative Diffs could be underlays in 2018 and underperform. The point of these metrics is to point you to these extremes. It is your job to do more research and focus.

  • Negative Diff WRs – Jones, M Thomas, Green, Hilton, Diggs, Cooper, and D Thomas.

  • Positive Diff WRs – Fitz, Landry, and Cooks.

WR 2017 Consistency vs 2018 ADP Slide14

A visual plot of the main WRs DIFF numbers with 2018 ADPs and 2017 %TT metrics.

A landscape view of the data looking at WRs that did Worse vs Better (%TT vs ADPs). The Yellow bars that are positive are the potential overlays in the WR s vs the negative DIFFs are the underlays in WRs! This data visualize the player discussion shown above in call-outs of players.

WR 2017 Consistency vs 2018 ADP Slide15

Next WRs at Second Tier by 2017 Data.

  • Gordon at -48, Robinson is at -108, Davis at -61, Goodwin at -31 and Garcon at -33. These are the high negative Diffs below -30! Be Cautious higher underlay potential.

  • Positive Diffs are Crabtree, Fuller, Kupp, and Anderson. Key WRs for overlay candidates!

WR 2017 Consistency vs 2018 ADP Slide16

WR 2017 Consistency vs 2018 ADP Slide17

Further WR Tiers.

Focus on the extreme for overlays and underlays in WRs.

  • Overlays +DIFFs: Aghlor, Shepard, Stills, Sanu, Matthews, and Ginn

  • Underlays -DIFFs: M Williams, Parker, Brown, K Benjamin, and Westbrooke


  • Overlays +DIFFs: T Williams, Grant, Terr Williams, Wallace, and Kearse

  • Underlays -DIFFs: Allison, Switzer, Erickson, T Taylor, and Austin,


  • Overlays +DIFFs: C Coleman, A Wilson, T Benjamin, Trent Taylor, Jaron Brown, J Maclin, JJ Nelson, A Humphries, B LaFells and J Grant.

  • Underlays -DIFFs: W Snead, P Dorsett, K White and L Treadwell


  • Overlays +DIFFs: T Boyd, K Wright, D Thompson, R Higgins, B Fowler, S Roberts, J Hardy, M Hollins, and B Miller

  • Underlays -DIFFs: C Williams, A Roberts, T Davis, S Gibson, and B Tate.


Consistency vs Max and Min Weekly Efforts.

The tables below show the week 1 to 17 in 2017 and the FP scored by that player. I highlighted in yellow that top ten scores for that week. Note the string of TT vs non-TT.

  • Brown only had 5 non-TT games vs Hopkins at 7. The ADP between the two can be due to the lower consistency.

  • Skipping OBJ (4 games)

  • Landry had 9 non-TT, Thielen at 10, Allen at 10, Fitz at 10, Evans, at 10 etc.

The top WRs from 2017 still had 10 games median of non-TT. That is 62% out of the top ten weekly WR group. The idea of “safe” WRs may not exist. Note Brown was so special at only 31% non-TT games.

Given the inconsistency in WRs, this metric may not be as powerful as FP/G!

Slide27Slide28Slide29Slide30Slide31WR 2017 Consistency vs 2018 ADP Slide32Slide33Slide34Slide35WR 2017 Consistency vs 2018 ADP Slide36

Max vs Min Metrics vs MAX/DIFF.

I calculated the 2017 Best and Worst Weeks for FP/G of each WR (MAX vs MIN) as well as FP Differences between those player extremes. I then normalized the MAX data by division using the DIFF number. That allows comparisons across all players

The Max/Diff is a way to measure the floor a player had in 2017. Note that Landry is at 1.49 a high number for his high floor value. OBJ at 1.36 and Hopkin at 1.37 also had high floors. Brown was very good in 2017 as we saw only 5 non-TT weeks but at 1.12 his floor was below many of the others.

Allen, Thielen, Tate, JuJu, and Kupp also had nice higher floors in 2017 performance.

The closer to 1 in this metric implies extremes in consistency and lowest floor values.

This metric is a good tiebreaker.


The plot of the MAX/DIFF metrics highlighting potential 2018 RB FP Floors. See discussion above.


M Thomas at 1.17 is the only WR of note in this group.



Nice figure from Bryant at 1.39 but given his FA a solid floor for him is unpredictable.

WRs of note: Funchess at 1.12, Sanders, K Benjamin, Amendola, and Wallace have standout metrics in this group.



Weak group



Marshall at 1.23 is solid and Ellington has a 1.12 metric.



Westbrooke and Jackson are of note here in this group.



Mathews and Gordon are standouts in here.






M Clark at 1.29 and Garcon at 1.21 are nice.



Short Data Analysis philosophy.

We just finished the main positions looking at multiple aspects of 2017 vs 2018 ADP. All my work should lead to help in drafting players not having readers blindly following the numbers. Please let me focus you onto possible player bargains or metrics to break ties between closely ranked players.

Thinking Points

Here is a great quote: ” Data is only as valuable as the insights you can draw from it, and with all the information that’s floating about it’s easy to find yourself being led astray.” from https://www.sisense.com/blog/5-steps-to-data-driven-business-decisions/.

Also, see my article on More Information Increasing Accuracy?

Below is from that article- (FYI my textbook has more on this aspect as well)

Analysis of data suggests that more information does not increase our understanding unless the information increases our coherence of the data set.

Any Fantasy sports data you use must have meaningful relationships. Those relationships have to have some logical foundation.

KEY *** The increasing amounts of opinion-based information do not increase our coherence by looking at all of the available sources. That said many of these opinions from pundits are based on their view of the data.

In science, the worst sin can be summarized as “An Idea in Search of Data”. In Fantasy Sports we must gather the data following a question first and only then have thoughts based on that journey!

Thus, if we cross the level of data that we can “handle” then it is suggested you prune your data and eliminate the less coherent parts! This process will require effort and practice and it will use your slower System 2 thinking. Your fast and frugal System 1 will not like pruning. However, record keeping can help establish your baseline of coherence and is the beginning of reference class data from future activities.


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