Fantasy Football 2021: Wide Receiver Age Decline

In both redraft and in dynasty fantasy football leagues, it is extremely important to understand the relationship between wide receiver age and their performance. At what age is it most likely for a wide receiver to have their last season as a top 12, as a top 24 player? If a wide receiver at age X finished as a top 12 or top 24 player last year, what are the odds they repeat? This article is a natural successor to our post last year studying age versus performance in the NFL.

Understanding wide receiver age decline can help dynasty players understand if they should sell their perennial top receiver before they fall off a cliff. In redraft, appreciating the relationship between age and performance can help us predict how likely it is that an aging veteran will have enough left to warrant a high draft pick. Alternatively, we can use age information to put into perspective the outstanding success of young receivers like AJ Brown or Justin Jefferson.

In this article, we study at length the wide receiver age decline question complete with predictions about which players are some of the safest bets this year. In addition to basic statistics relating to wide receiver age, we develop a basic logistic regression model for predicting the most likely wide receivers to be successful in this upcoming season. We begin with the results, the tangible 2021 fantasy predictions, and then take a step backwards to show you how we studied the wide receiver age decline problem.

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Wide Receiver 2021 Rankings via Confidence Level

We plugged each wide receiver’s data into our model to make predictions about performance this year. In particular, we used the following information:

1. Where the player finished last year, and
2. The player’s age.

Using these two parameters, we predict how likely it is that a given wide receiver finishes in the top 12 or in the top 24 this year. The 25 receivers with the largest top 12 probabilities are shown in the table below. If these numbers seem pessimistic to you as the reader, we include some discussion later on about just how variable the top 12 and top 24 wide receivers are year to year. Anything over a 50% chance is really good. We sorted the list by most likely to finish top 12.

Player	2020 Rank	Top 12 Prob	Top 24 Prob
J. Jefferson	6	48%	71%
DK Metcalf	5	44%	66%
AJ Brown	9	42%	65%
C. Claypool	14	41%	65%
T. Hill	2	39%	59%
C. Lamb	21	38%	65%
C. Ridley	4	38%	58%
S. Diggs	3	36%	56%
DJ Moore	18	35%	60%
D. Adams	1	35%	60%
M. Evans	8	33%	54%
A. Robinson	12	31%	52%
J. Smith-Schuster	23	31%	55%
D. Hopkins	10	30%	50%
T. Lockett	11	29%	49%
B. Cooks	16	29%	49%
R. Woods	13	28%	48%
A. Cooper	20	28%	50%
T. McLaurin	24	28%	51%
A. Thielen	7	28%	45%
C. Godwin	31	26%	51%
N. Agholor	22	25%	46%
K. Allen	19	25%	45%
M. Jones	15	23%	41%
M. Gallup	37	23%	48%

A few caveats. First, I used total statistics instead of per-game statistics. So, guys like AJ Brown or Will Fuller who didn’t play full seasons might be under-represented here. (If AJ Brown is under-represented, then I am frightened of his ceiling). Second, while close to being appropriate, this list is not necessarily my ‘2021 wide receiver rankings’. These lists simply rank the likelihood of a player being good where good is measured by chance at a top 12 season. Finally, I don’t take anything into account about team construction. If Aaron Rodgers doesn’t play, that certainly will hurt Davante Adams chances, but the model doesn’t know anything about that.

Wide Receiver Age Basic Statistics

Now that the results are presented, we begin by looking at some basic percentages and probabilities relating to wide receiver age to get us all accustomed to what the data really looks like. For all the following analysis, we looked at every wide receiver season since 1990. First, we looked at the distribution of ‘at what age my last top 12 or top 24 season occurred’. The probabilities in the tables below show the percentage of all ‘last top 12 seasons’ that happened at each age. For example, looking at all the players who had at least one top 12 season in their career, 13.5% of them had their last top 12 season at Age 26.

Age	% of ‘Last Top 12 Seasons’	Age (cont.)	% of ‘Last Top 12 Seasons’
22	1.5%	31	9%
23	5.3%	32	5.3%
24	6.8%	33	1.5%
25	9%	34	3%
26	13.5%	35	3.8%
27	11.3%	36	0%
28	8.3%	37	0%
29	11.3%	38	0%
30	9.8%	39	0%

And, the same data for the distribution of wide receiver’s last top 24 seasons:

Age	% of ‘Last top 24 Seasons’	Age (Cont.)	% of ‘Last top 24 Seasons’
22	2.7%	31	7.6%
23	5%	32	4.5%
24	7.6%	33	2.2%
25	10.3%	34	3.1%
26	10.7%	35	3.6%
27	9.8%	36	1.3%
28	7.6%	37	0.5%
29	11.6%	38	0%
30	11.6%	39	0%

While this is valuable data on its own, what is much more telling is a slight variation on this data. Naively, looking at the top-24 table above, it seems that roughly the same proportion of player’s have their last top 24 season at age 29 and at age 30. This is true, but the more interesting statistic is looking at survivorship. Let me explain survivorship with an example.

The data suggests that 2.7% of wide receivers have their last top 24 season at age 22. These are the one-and-dones. That means roughly 97% of receivers survive to age 23 where survival means “I still have another good season in me”. Then, the 5% of all receivers who have their last top 24 season at age 23 can be re-interpreted as 5.2%=5% / 97% of the remaining or surviving receivers have their last top 24 season at age 23.

Why is this metric better to look at? Suppose we want to know how likely it is that Davante Adams just had his last top 24 season. Because Davante Adams was the best wide receiver in 2020 – his age 27 season – we know that he is one of the 54% of all wide receivers who survive to at least age 27 before having their last top 24 season. Because 9.8% of all wide receivers have their last top 24 season at age 27, we can say that 18%=9.8% / 54% of the receivers who survive to age 27 have their last top 24 season at age 27. Putting this another way: Davante Adams has at least an 82% chance of having another top 24 season before his career is over. Davante Adams survivorship probability is 82%.

This is valuable information for fantasy owners to understand and becomes even more important for more interesting players – like Julio Jones and Marvin Jones, for example. The tables below show the survivorship probabilities for top 12 and top 24 wide receivers as explained above. One last time: the survival probability in the charts below is the probability of someone at that age having another top 12 / top 24 season in them before their career ends.

Age	Survival Prob.	Age (cont.)	Survival Prob.
22	98.5%	31	61.1%
23	94.7%	32	63.1%
24	92.7%	33	83.3%
25	89.6%	34	60%
26	82.5%	35	16.7%
27	82.3%	36	100%*
28	84.3%	37	100%*
29	74.6%	38	100%*
30	70.4%	39	0%

This data matches our intuition very well. As you get older, the probability that you survive to have another top 12 year decreases as you continue to age. 98.5% of 22 year old WR1s will go on to have at least one more top 12 season. However, only 61.1% of the guys at age 31 will have at least one more top 12 season.

Quick interesting note: what is going on at ages 36-38? Jerry Rice.

Below is the survival probability for players in the top 24 of wide receivers

Age	Survival Prob.	Age (cont.)	Survival Prob.
22	97.3%	31	67.3%
23	95%	32	71.4%
24	91.8%	33	80%
25	87.9%	34	65%
26	85.6%	35	38.5%
27	84.6%	36	40%
28	85.9%	37	50%
29	75%	38	100%*
30	66.7%	39	100%*

Again, thank you Jerry Rice for skewing heavily our wide receiver age decline study.

Graphically, the probability of surviving as a top 12 and as a top 24 player is shown below.

Repeat Probabilities

The biggest issue with the above analysis is that – for any individual player – we don’t know if they’ve already had their last top 12 or last top 24 season so far. To use consistent language, we don’t know if they are “still alive”. Thus, the numbers above are more a summary of what has happened in the past rather than helpful for us predicting the future. This distinction, that between describing what we’ve observed and predicting the future, is the whole reason statistics is a very difficult subject. In this section, we will study the wide receiver age decline problem from a slightly different perspective. We will compute the probability at each wide receiver age of a player repeating as top 12 or as top 24.

Again, we went through each wide receiver season since 1990 and looked for repeaters in the top 12 and top 24. The data in the table below shows how frequently players repeated a top 12 or top 24 season broken down by age. For example, the 25% entry in the ‘Repeat Prob’ column next to Age 33 below indicates that if a player was a WR1, or top 24, in their age 32 season, we think there is roughly a 25% chance that they are a WR1 in their age 33 season.

Age	Repeat Prob.	Age (cont.)	Repeat Prob
22	50%	31	44%
23	60%	32	36%
24	45%	33	25%
25	48%	34	71% (5/7)
26	44%	35	44% (4/9)
27	38%	36	0%
28	47%	37	0%
29	56%	38	NA
30	40%	39	NA

I made sure above to explicitly highlight that while the age 34 and 35 seasons have relatively high probabilities, the sample sizes are very small and so the numbers may be a bit misleading. Now, let us look at the chart that shows wide receiver age decline through the lens of top 24 finishes.

Age	Repeat Prob.	Age (cont.)	Repeat Prob
22	60%	31	41%
23	52%	32	49%
24	47%	33	57%
25	54%	34	61% (11/18)
26	57%	35	50% (8/16)
27	54%	36	25% (3/12)
28	57%	37	0%
29	61%	38	NA
30	49%	39	NA

All-in-all, this paints a very bleak picture. My main takeaway here is that it is very difficult to predict how often players will repeat as top 12 and top 24 wide receivers. A rough estimate, combining all the numbers above, is that there is only about a 50% chance that a random top 24 wide receiver repeats in the top 24. However, we can do a bit more.

Predicting Wide Receiver Age Decline

There is one last wrinkle that I want to add before making my predictions about which wide receivers this year are most likely to drop out of the top 24. Let’s think about Julio Jones and, for simplicity of the argument let us forget about his injury season last year. Julio is turning 32 this year. The raw numbers suggest that there is about a 30% chance he finishes outside the top 24 because his age finally catches up to him. However, Julio Jones isn’t just some random top 24 player. He is the furthest thing from that. In fact, the last season he played fully (2019), he was the fifth best wide receiver. Because he in the past has been better than some random top 24 player it should be the case that it is less likely than 30% that Julio drops out of the top 24.

For this reason, I developed a model for predicting likelihood of being a top 12 or top 24 receiver using wide receiver age and their most recent performance. Again, to keep things relatively simple, we use one of the most common models for predicting these probabilities: logistic regression. Logistic regression is a popular tool to predict probabilities given some set of continuous predictive variables.

The results of our study were already presented above, but we can add just a touch more flavor here to give more perspective into understanding wide receiver age decline. In particular, we can offer a rough conversion between aging and position rank.

Our logistic model has two parameters, one for age and one for position rank. The previous year position rank coefficient is -.02025 and the age coefficient is -.11983. The relative size of these numbers means that one year of age is about the same as 6 spots of rank when it comes to probability of being in the top 24 for the next year. That is, if the #1 overall receiver was 28 and the #7 overall receiver was 27, we would estimate that they have roughly the same probability of finishing in the top 24 next year. This really serves to be a quick and easy way to compare between two different players to determine who is a safer pick for your upcoming drafts. One year = 6 spots.