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Data shows Jaxon Smith-Njigba is WR1 in 2023 NFL Draft

WCG’s Lead Draft Analyst heads to the data to see why Jaxon Smith-Njigba enters this season as the WR1 in the 2023 NFL Draft.

Rose Bowl Game presented by Capital One Venture X - Ohio State v Utah Photo by Harry How/Getty Images

Bears fans with their eyes to the 2023 NFL Draft have seemingly all flocked to one wide receiver: Ohio State’s Jaxon Smith-Njigba.

JSN’s stellar 2021 campaign, which saw him outperform first-round picks Chris Olave and Garrett Wilson, has him firmly on the radar for receiver-needy teams heading into the regular season. He closed out last year with 347 yards in the Rose Bowl, an all-time record for the most receiving yards in a single bowl game. With the WR1 spot in the Buckeyes’ offense firmly his, the sky’s the limit for what he can achieve.

Smith-Njigba not only brings a top-notch talent at a big position of need for the Bears, but he also brings some familiarity with Justin Fields to the table. Granted, JSN was a backup receiver who saw limited playing time when Fields was the starting quarterback at Ohio State. But hey, it’s a narrative that will be shoved down your throats by those in Chicago media who don’t actually do draft research, so you better get used to it.

For those not too familiar with his game, here’s my write-up on Smith-Njigba from when I looked at early Round 1 targets for the Bears:

It’s very early in the 2023 draft process, but Smith-Njigba is my current WR1 in this class. He finished this past season with 95 receptions, 1,606 yards and 9 touchdowns while fighting for catches with the likes of Garrett Wilson, Chris Olave and Jeremy Ruckert. He’s an explosive threat with good deep speed and sharp movements coming out of his breaks. His football IQ shows up in spades on tape, as he does a great job of exploiting soft spots in zone coverage and excels his adjusting his footwork to attack leverage points through his stems. Smith-Njigba is a pro-ready weapon who should translate well to the NFL level.

It doesn’t take a data analyst to know that Smith-Njigba was good last year. That production he had wasn’t too shabby.

However, service-level statistics can be deceiving. What do the nerdy stats say?

Using Sports Info Solutions’ DataHub — which I can’t recommend enough for those looking to do NFL or college analysis — I took a look at numerous advanced statistics to see just how effective Smith-Njigba was in 2021. I’m not only comparing him to top prospects in the 2023 draft class, but also some of the top receivers to come out of recent drafts.

Smith-Njigba Among Returning FBS WRs

Category Data (for returning WRs)
Category Data (for returning WRs)
Points Earned/Route 0.131 (2nd)
Points Above Avg 38.93 (1st)
PAA/Route 0.107 (2nd)
EPA 97.37 (1st)
EPA/Target 0.862 (1st)
Positive % 73.5 (1st)
Boom % 46.0 (1st)
Bust % 6.2 (3rd)

In summation: Smith-Njigba is good.

If you’d like a description of what these stats mean, check out the StatHub glossary. It does a much better job of covering each individual category than I could ever do. These rankings generally showcase that not only is Smith-Njigba getting targeted at a high volume, but he’s proven to be extremely efficient with the touches he’s given.

For these next two breakdowns, just know that these rankings are all out of 200 available prospects.

I’ll wholeheartedly admit that comparing Smith-Njigba to other top 2023 wide receivers has its flaws. The biggest one is the absence of a few key names, which you’ll notice as you look down the graph. A handful of notable prospects simply didn’t qualify, whether that be because of an injury or not having enough touches or targets.

It’s also early to deem “this player is better than that player”, simply because there’s another season still to come that can prove those arguments wrong. However, looking at Smith-Njigba compared to other top returning wide receivers shows that he’s generally pretty far ahead of the bunch.

Smith-Njigba vs. Top 2023 Prospects

Player Completion % Drop % Yards/Route Run Yards/Target Targeted Rating Broken+Missed Tackles/Catch Average Depth of Catch
Player Completion % Drop % Yards/Route Run Yards/Target Targeted Rating Broken+Missed Tackles/Catch Average Depth of Catch
Jaxon Smith-Njigba, Ohio St. 84.1 (1st) 4.8 (T-74th) 4.4 (1st) 14.2 (1st) 141.6 (6th) 0.3 (T-7th) 8.3 (98th)
Cedric Tillman, Tennessee 73.6 (23rd) 4.3 (T-67th) 2.9 (T-32nd) 12.4 (5th) 154.7 (1st) 0.2 (T-41st) 11.6 (26th)
Jordan Addison, Pitt/USC 69.9 (T-47th) 6.7 (T-123rd) 3.0 (T-26th) 11.1 (T-17th) 137.6 (11th) 0.2 (T-41st) 9.3 (T-75th)
Josh Downs, UNC 70.1 (45th) 7.7 (137th) 3.1 (T-20th) 9.3 (T-59th) 109.0 (74th) 0.1 (T-118th) 5.8 (162nd)
Rakim Jarrett, Maryland 68.1 (T-60th) 7.8 (T-138th) 2.4 (T-76th) 9.1 (T-68th) 106.0 (85th) 0.2 (T-41st) 6.2 (155th)
Parker Washington, Penn St. 69.6 (T-49th) 5.6 (91st) 2.2 (T-101st) 8.9 (T-76th) 107.2 (82nd) 0.2 (T-41st) 6.5 (T-144th)
Zay Flowers, Boston Col. 52.9 (183rd) 7.5 (T-134th) 2.4 (T-76th) 8.8 (T-81st) 83.0 (164th) 0.2 (T-41st) 9.6 (T-67th)
A.T. Perry, W. Forest 53.8 (T-177th) 9.2 (T-167th) 2.8 (T-45th) 9.8 (T-41st) 116.2 (41st) 0.2 (T-41st) 13.8 (5th)
Among all FBS pass-catchers in 2021

Among those who didn’t qualify: TCU’s Quentin Johnston, LSU’s Kayshon Boutte, Oklahoma’s Marvin Mims, Maryland’s Dontay Demus Jr.

I have a few key takeaways from this:

  • A.T. Perry is an effective deep threat but not very efficient at this stage
  • Cedric Tillman belongs in more top-50 conversations
  • Zay Flowers was wise to not declare for the 2022 draft

And the biggest one that applies to this article:

  • Jaxon Smith-Njigba is insanely good

Outside of an average drop percentage — which is still better than most of the top 2023 prospects — Smith-Njigba was arguably the most well-rounded receiver from an efficiency perspective last year. The average depth of catch is also an important statistic, as while JSN’s numbers were average in that regard, he was still first in yards per target and was in the top-30 in yards per reception.

That proves that he did a good job of picking up yardage in space and maximizing his touches, and it also shows that he caught most of his targets.

Did playing with a top quarterback like C.J. Stroud help out Smith-Njigba last year, as it did Garrett Wilson and Chris Olave? Most certainly. He faces less in the way of accuracy variance than a player like Rakim Jarrett at Maryland or Zay Flowers without Phil Jurkovec. But JSN ranking this high in so many advanced metrics shows just how efficient it was for Stroud to target him last season.

These numbers are great and all, but how have they translated to the NFL level?

As is the case with essentially all football data, there are outliers in both directions. It’s why football decisions can’t be based entirely off of numbers. However, they can certainly provide valuable insight as to how each player performs, and it is important in today’s game for an NFL team to know how to use data as a tool without relying entirely on it.

That said, when looking at the Pro Bowl wide receivers in SIS’ database (from 2018 to 2021), Smith-Njigba not only holds his own with some of the top receivers in the league, but his resume is arguably the best of the bunch. Here’s how he fared to each Pro Bowl receiver in that time frame, using the NFLers’ final collegiate season as a reference point.

JSN vs. Pro Bowl WRs

Player Points Earned/Route Points Above Avg PAA/Route EPA EPA/Target Positive % Boom % Bust %
Player Points Earned/Route Points Above Avg PAA/Route EPA EPA/Target Positive % Boom % Bust %
2021 Jaxon Smith-Njigba 0.131 (2nd) 38.93 (1st) 0.107 (2nd) 97.37 (1st) 0.862 (1st) 73.5 (1st) 46.0 (1st) 6.2 (5th)
2019 Ja'Marr Chase 0.127 (3rd) 47.91 (1st) 0.099 (4th) 86.20 (2nd) 0.695 (2nd) 62.1 (T-17th) 42.7 (3rd) 10.5 (T-47th)
2019 Justin Jefferson 0.085 (T-34th) 31.87 (4th) 0.058 (34th) 91.45 (1st) 0.682 (3rd) 70.1 (1st) 38.8 (11th) 6.7 (9th)
2019 CeeDee Lamb 0.126 (5th) 28.93 (11th) 0.096 (T-5th) 60.36 (9th) 0.649 (5th) 55.9 (T-51st) 45.2 (2nd) 14.0 (T-109th)
2018 Deebo Samuel 0.090 (T-31st) 23.71 (18th) 0.065 (T-32nd) 24.31 (76th) 0.243 (T-83rd) 45.0 (T-150th) 22.0 (166th) 14.0 (T-100th)
2018 A.J. Brown 0.096 (22nd) 29.59 (5th) 0.071 (23rd) 42.68 (22nd) 0.356 (39th) 55.8 (40th) 31.7 (50th) 10.8 (T-43rd)
2018 Diontae Johnson 0.043 (T-110th) 5.49 (119th) 0.017 (T-114th) 20.47 (90th) 0.220 (90th) 44.1 (160th) 25.8 (113rd) 12.9 (T-85th)
Rankings for WRs in each of their respective seasons - 2018 through 2021

D.K. Metcalf and Hunter Renfrow — both in 2018 — are the only players to not have a large enough sample size to be included on the list. It’s worth noting that early 2021 draft picks DeVonta Smith, Kyle Pitts and Elijah Moore all performed incredibly well in 2020 from an analytics perspective. While it’s very early in their careers and they didn’t make the Pro Bowl cut (a tough job for a rookie), the returns on all of them have been very positive so far.

Some more takeaways from these:

  • Diontae Johnson is a major outlier and deserves a lot of credit for how he’s developed for the Steelers
  • Deebo Samuel was raw coming out of South Carolina, but the physical traits were obvious: he led college football with broken tackles and missed tackles per catch at 0.5
  • That 2019 LSU offense was mind-blowingly good

Is this a perfect analysis that will settle all debates between now and next April? Not quite.

Like Diontae Johnson is an outlier for succeeding despite the data not being too kind to him, there are several receivers who haven’t lived up to their stellar analytical profiles. In 2020, Dax Milne — a seventh-round pick by the Commanders who’s currently a backup — was one of the most efficient receivers in the nation in essentially every metric. Though it’s early in his NFL career, Seahawks second-round pick and fellow analytic darling D’Wayne Eskridge didn’t exactly light it up as a rookie.

This practice also lacks a bit in substantial draft prospect data. LSU’s Kayshon Boutte is widely seen as a WR1 candidate and did not have enough targets to qualify for this data. Other notable receivers were listed previously in the article, with many being seen as likely Day 2 picks at the moment. It also doesn’t factor in that anything can happen in the upcoming 2022 season, and there will surely be receivers whose production differs from year to year.

But this practice does back up what the eye test shows: JSN was fantastic last year. If he can put together another season anywhere near as strong as 2021, he could be a top-10 lock when it’s all said and done. If the Bears pick as highly as the national consensus expects them to, then Smith-Njigba could be available for them, and if selected, he might just be the answer to all of their problems.