Forecasting 2022 NFL Rookie WR Success: 3-Year Model

May 16, 2022
Forecasting 2022 NFL Rookie WR Success: 3-Year Model

The 2022 NFL Draft is now in the books, giving us dozens of new offensive players to consider for our fantasy teams. In this article, I will discuss some of the value picks available at wide receiver in rookie drafts.

Our friends over at Dynasty League Football (DLF) have already compiled some post-draft rookie ADP, and Drake London is usually the top receiver off the board. While I would personally take Treylon Burks ahead of him, London scores incredibly well in both of my models and has a clear opportunity to see a large number of targets in Atlanta, so I would not argue with anyone who drafts London there.

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In fact, to be honest, my models don't have a great track record of predicting the best receivers to take early in the draft. Instead, their excellent track record is primarily in finding undervalued players later in the draft. Let me show you what I mean.

Evaluating the Models

I've been publishing the 3-year success model for six years now. Each year, I identify 3-5 receivers that appear undervalued in rookie drafts at the time. Looking back over those six years, here are the names that stand out to me as the best picks made so far, grouped into where they were going in drafts.

Best Picks by the Model
ADP Player Year
Late 1st Elijah Moore 2021
Tyler Boyd 2016
Will Fuller 2016
2nd Amon-Ra St. Brown 2021
Michael Gallup 2018
Cooper Kupp 2017
3rd Diontae Johnson 2019
Kenny Golladay 2017

As you can see, nearly all of the best picks have been of players available in the late-first, second, or third rounds of rookie drafts. I find the successful picks in the second and third rounds especially impressive given the low overall chances for these players to be successful fantasy assets. While Cooper Kupp stands out as probably the best pick of the bunch, simply finding a player in this range that becomes a reliable fantasy starter is an achievement.

Forecasting Wide Receiver Success

I estimate the odds of each player putting up a top-36 season within the first three years of their career by using a combination of two models. The first is a standard statistical model (built using logistic regression) and the second is a more sophisticated machine learning model. Note, however, that I put more weight on the standard statistical model, which uses only college production and draft capital. That is the model responsible for most of the good picks above.

It is important to note that we cannot reasonably expect to correctly predict the success odds of all receivers using just two variables. As we discussed above, however, that is not the goal of the model: the goal is to identify undervalued players. Using the model in this way will continue to work as long as fantasy drafters continue to put insufficient weight on college production and draft capital, as I expect they will do.

With that in mind, here are the players that stand out to me as most undervalued (the full table of rankings appears at the bottom of the page).

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