WR Weekly Performance Differences

By John Bush

Definitions * Wikipedia  

*In probability and statistics, population mean and expected value are used synonymously to refer to one measure of the central tendency either of a probability distribution or of the random variable characterized by that distribution.

*Data will always show variation. One of the key questions is whether the variation is normal for the process or is unexpected, indicating that something special or out of the ordinary is happening.

*In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed.[1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. Dispersion is contrasted with location or central tendency, and together they are the most used properties of distributions.

WR Weekly Performance Differences 2016

Previously, my last two articles dealt with WRs and the cost of missing games



In some ways this article will discuss another related issue Weekly Variations in WR scoring. It must be grasped that ADP is an average based on point projections. Every player has an ADP in the 2017 draft. That ADP can be converted to Point Projects. (Another day for that discussion).

I wanted to take my readers into the variation in WRs. Those are critical for both dynasty and redraft leagues. The data in Figure 1 gives the 2016 WRs weekly averages for performance scoring. The data is not the same, it has variation. On average the early and very late weeks are lower in average production for WRs than the other weeks in between.


Figure 1 Average WR 2016 Weekly Performance Scoring Averages. 

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The next series of figures present the WR data from 2016 including a sorting by Seasonal PF averages, as well as weekly differences in PF scoring. A zero would be no changes while a minus is a drop in PF scoring from the previous week (Red) and a positive number is an increase in PF scoring (Green).

Looking at the entire WR landscape is should be clear that few players are consistent. The rule is that player’s as a whole group are and will be inconsistent by weeks. Our 2017 job is draft those that across the season will be highly productive. Use these data and conclusions to “see” early, mid and late views.


  1. What Players were Good Early, Mid and or Late?
  2. Which Player’s Were Consistent in PF Scoring?
  3. What Players Did Well with Few Opportunities?

Figures 2 to 5 WRs Average Performance Scoring for the 2016 Season and PF scoring Weekly Differences

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Early 2016 Season WR Variations

I was interested to answer the question: Does WR PF scoring variation change from early in the season to late in the season? 

Analysis of the Figures 6 to 8, show a great level of variation between the weeks. The pink dashed line is the weekly average of the differences. The Zero line is the line dividing improvement vs declines between the 2 weeks tested.

The analysis of the differences between week 1 and 2 reveal that in the first 31 WRs only 25% of the WRs improved from week 1 to 2 (Figure 6).  Notice the regression to mean effects within the WRs at the lowest ends who improved at much higher levels.

The improvement between weeks 2 and 3 in the previous week 2 top 31 WRs was only 19% (Figure 7). In Figure 8, this improvement was back to 22%.

The overall WRs that improved in these 3 time points was at 22% or about 7 of the 31 last week’s top WRs improved. The other way to see that data is that 78% of those WRs failed to improve! Not a great number to be sure. In setting weekly lineups and DFS, one should consider these data in WRs. I will do RB, TEs and QBs probably next year.

I also graphed the 3 week weekly differences and that data is presented in Figure 9. We summing the data, the actually 3 week summed data improvement number was 10 out of the 31 WRs or 32% improvement. About 1/3 of last week’s top WRs will improve over a 3 week block!

Figures 6 to 9. Mapped WR Weekly Variation Weeks 1 to 4 2016

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Do these improvement levels increase later in the season? Analysis of the next 4 figure will give us insight into that question. In Figure 10, the improvement number for last week’s top 31 WRs was 3/31 and 4/31 in Figure 11 and finally 6/31 shown in Figure 12. Those numbers are not very different that in the early weeks.

Figure 12, has the 3 late week summed averages of the improvement differences. Analysis of that figure shows us that on the 3 weeks from Week 12 to 15, the improvement was 17/31 or 54% of the top WRs improved over that time. That is a nice gain from 32% (early) to now 54% (late). These findings suggest that a difference may exist in weekly variations in WR weekly scoring. More research is needed but this is the initial finding.

Figures 10 to 13. Mapped WR Weekly Variation Weeks 1 to 4 2016

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Broader view of the weekly scoring differences

I end this investigation with a broader view of the weekly scoring differences. In Figures 14 to 19, I document the 2016 WRs by Rank of 5s. Thus I studied the 1st, 5th, 10th, 15th, 20, 25, 30 , 35, 40 , 40 and 50th Seasonal Best WR and looked at their weekly differences.

Figure 15, highlights the simple comparisons between the 1st and 5th WR, 10, 15, and 20th WRs, 25, 30 and 35th WRs and finally the 40 vs 45th and 50th WR.

Does the rank level change the level of variation? The Figures 16, 17, 18 ,and 19 illustrate the weekly variation in PF scoring in graphical forms. The level of variation seems to increase slightly as one goes into the lower ranked WRs. Remember all the zero scores mean the player do not play in one of the 2 weeks or both.

Figures 14 to 19 2016 WR Segments By Weekly PF Scoring Differences. 

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Final Comparisons WR Weekly Performance Differences

Given these data, I have brought clarity to the idea of variation by weeks. I have also shown a lot of variation exist in WR weekly scores even in the best players.

These last data table and graph highlight a simple +/- count for each segment representative WR as shown in Figures 14 and 15. In the 16 or less games played by each players was the week for them improving or declining. I tabulated those numbers and they are seen in Figure 20. The graph in Figure 21 gives a visual show of the tabular data.


  • The first and fifth had a higher positive average than the other groups. The 3 WRs in the 10th to 20th had a much lower positive weeks of improvement as did the other groups. 
  • I conclude that the top WRs (5th?) will be as rare as the top RBs. This goes against the idea that WRs are solid picks deeper into the draft than RBs.

Figures 20 and 21. Simple Tabular Counts of Positive or Negative Improvements in WR Groups. 

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