The NFL Combine and college Pro Days are behind us while the NFL Draft is right around the corner. There has been no shortage of coverage for these prospects, but I wanted to bring a different spin on player comparisons. To do this, I use a technique called clustering, which allows me to bucket these players into several statistical profiles and compare one to another. In the clustering, I included a combination of production, efficiency, athleticism and usage metrics in hopes of capturing who these players are.
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NFL Draft 2024 Prospect Comparisons
This article will cover the methodology with commentary on some of the standout players from the 2024 class. Once again we have a seemingly loaded wide receiver draft class set to shake up the fantasy football landscape. There’s a trio of receivers set to be taken in the top 10 picks of the draft with another four possibly drafted in the first round. This class offers a mix of strong X receivers along with slot role-players. So, no matter what your team needs, they’ll find it in this draft class.
Methodology
Before I get into the analysis, I want to explain the methodology and techniques I used along with delineating what this analysis is and, more importantly, what it is not. Let’s start with the latter.
This analysis is a descriptive way to compare a player’s college stats and athleticism to historical results. This is not a predictive indicator of future NFL and fantasy success or that a player with similar athletic and production stats will have the same career.
In terms of the methodology, I used a principal component analysis (PCA) using data since 2016. If you’re unfamiliar with PCA, it is a way to “squish” several variables (in this case, each of our statistical metrics), into just a couple of variables – our principal components – thus simplifying our dataset and reducing noise. Put another way, PCA helps us find fewer features that will represent our data (or prospects) in a more compressed way.
This also allows me to visualize the results on two axes using the first two principal components, which I wouldn’t be able to do easily with the several metrics we have. This is also where we can see player comparisons – players that appear further away from the center of the chart are more unique in their results and fall into a more distinct category.
For wide receivers, below are the weights for the metrics for each of the two principal components. To calculate a player’s principal component, you can read these as linear equations. So. for principal component one, a player’s score is calculated as (-0.39*Height) + (-0.31*Weight) + (-0.08*Forty-Yard Dash) + (0.39*Receptions/Game) + (0.20*Rec Dominator) + (0.31*YPRR) + (0.422*TPRR) + (-0.23*Yards/Rec) + (-0.35*aDOT) + (0.34*Slot Rate).
I also calculated similarity scores between each prospect’s metrics profile – I only used the metrics used in the PCA above. For this, I calculated the Euclidean distance of each metric between each player to get the Cosine similarity, resulting in our similarity score. Below each player, I’ll give a brief list of the players whose statistical profile is most similar to the prospect, along with the similarity score. These scores are in a range of 0 to 1, with 1 meaning a player’s statistical profile is an exact match.
With that, let’s get into some analysis.
Wide Receiver Prospect Comparisons
Keon Coleman (WR – Florida State)
Most similar players: Collin Johnson (0.900), Isaiah Hodgins (0.895), Auden Tate (0.883)
In college, Keon Coleman was a Sportscenter Top 10 play waiting to happen. The issue, as you may notice in those clips, is Coleman’s lack of ability to create separation. According to PFF, Coleman’s separation against single coverage last year ranked 449th out of 450 qualifying players — not great, Bob. Furthermore, when looking at Coleman’s charted stats he falls short in a lot of areas (photo via Reception Perception):
After transferring from Michigan State to Florida State for his final season, Coleman led the Seminoles in receiving yards while his targets per route run rose slightly from 22.9% to 24.9%. Also, despite running a 4.61-second 40-yard dash at the combine, Coleman reached a top speed of 20.36 miles per hour during the gauntlet drill, the fastest speed by any receiver over the last two seasons. Whether or not I like Coleman’s profile is a complex battle between my head (his production and efficiency are poor) and my heart (the dude can ball), so I think landing spot and development will matter slightly more than some of the other receivers.
Xavier Legette (WR – South Carolina)
Most similar players: Jonathan Mingo (0.907), DK Metcalf (0.835), Chase Claypool (0.831)
In his fifth season at South Carolina, Xavier Legette burst onto the scene with a 1,200-yard receiving season along with seven touchdowns. The problem is that makes up the vast majority of his production as he recorded a combined 42 receptions for 423 yards and five touchdowns in his first four seasons. He wasn’t efficient at all in those seasons either with 0.75 yards per route run and 3.6 YAC per reception. But, with a final season that impressed many, it makes Legette a difficult player to project to the next level.
Legette’s size and athleticism are enticing, though. He ran a 4.39-second 40-yard dash at 222 pounds, giving him the eighth-best speed score (119) in my database of 295 receivers. Unfortunately, Legette turned 23 a couple of years ago, which may take him off of some teams’ boards from the jump.
Roman Wilson (WR – Michigan)
Most similar players: Mecole Hardman (0.852), Jalin Hyatt (0.809), KJ Hamler (0.807)
Like many pieces of the Michigan passing offense the past couple of years, Roman Wilson struggled to see meaningful volume. He only eclipsed 500 receiving yards once in four years (798 yards in his final season). That said, he did have a penchant for scoring with a career 18.9% touchdown rate. Additionally, Wilson had an 81.9 PFF grade for his career, which is solid and what you’d want to see for someone with fewer reps.
For an undersized receiver at 5-foot-11 and 186 pounds, Wilson’s career 52.9% contested catch rate is quite impressive. It also helps that his 4.39-second 40-yard dash was tied for the sixth-best in this class. Wilson played most of his snaps (73.4%) from the slot in his final two seasons at Michigan, so he could offer some PPR value early on if that’s his role.
Ricky Pearsall (WR – Florida)
Most similar players: Freddie Swain (0.834), Devin Duvernay (0.730), Jalin Hyatt (0.710)
Ricky Pearsall’s profile has a lot of similarities to Legette as he’s an older, more athletic prospect. The benefit Pearsall’s profile provides is a bit more consistent production. Pearsall transferred from Arizona State to Florida for the last two of his five seasons in college and had at least 650 receiving yards in each season, but failed to top 1,000 yards. Still, his totals were good enough to lead the team in both years.
Pearsall’s 82.9 PFF grade in his two Florida seasons also ranked in the 95th percentile. He didn’t earn targets at a great clip overall as evidenced by his career 19.1% targets per route run rate. But, like I said, Pearsall is very athletic as evidenced by his 9.91 RAS score, which was good for third-best in the class. He should fit in nicely as a team’s WR3, continuing to develop and polish his route-running.
2024 NFL Mock Drafts
- 2024 NFL Mock Draft: Matthew Jones (1.0 | 2.0 | 3.0 | 4.0 | 5.0)
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- 2024 NFL Mock Draft With Trades: Mike Fanelli (1.0 | 2.0 | 3.0 | 4.0 | 5.0)
- 2024 NFL Mock Draft: Russell Brown (1.0 | 2.0 | 3.0)
- 2024 NFL Mock Draft: PJ Moran (1.0 | 2.0 | 3.0)
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