Dynasty Rookie Draft Comparisons: J.J. McCarthy, Bo Nix, Michael Penix Jr (2024)

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.

This article will cover the methodology with commentary on some of the standout players from the 2024 class. The 2024 quarterback draft class is impressive and could turn around several NFL franchises. It is headlined by a quartet of players expected to go in the first round (maybe all even within the first four picks) with another two that could potentially wind up there as well. It’s been believed Caleb Williams and Drake Maye would be the number one and number two picks in the draft for over a year now, but Jayden Daniels and J.J. McCarthy’s stock has risen tremendously to the point where some teams have considered them in the top five.

Here are all of my draft comparisons for the 2024 NFL Draft class of quarterbacks. Below we dive into a few of the notable names.

2024 NFL Draft Guide

Dynasty Rookie Draft Comparisons

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 quarterbacks, 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 (0.46*Height) +(0.36*Weight) +(0.36*Pass Att/Game) + (-0.27*Completion %) + (-0.48*Yards/Att)+ (-0.41*Scramble %) + (-0.12*Pressure-to-Sack %) + (-0.22*aDOT).

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.

Quarterback Prospect Comparisons

J.J. McCarthy (QB – Michigan)

Most similar players: Mitchell Trubisky (0.903), Mac Jones (0.793), Tua Tagovailoa (0.667)

Perhaps the most divisive prospect in this class, J.J. McCarthy is fresh off of a national championship season at Michigan. However, one of the biggest knocks on McCarthy’s profile was his lack of usage. For reference, McCarthy played in three more games than Maye over their final two seasons but attempted nearly 300 fewer passes in those games. Still, he had a solid 10.4-yard average depth of target (aDOT) in his final two seasons, which makes his jump in on-target rate more impressive. Perhaps more than any of these prospects, who drafts McCarthy and the scheme he’s paired up with will dictate his performance to start his career.

Michael Penix Jr. (QB – Washington)

Most similar players: Sam Darnold (0.752), Jordan Love (0.735), Davis Webb (0.685)

Michael Penix Jr. begins the tier of two players who find themselves on the fringe of potentially being drafted in the first round. Some of Penix’s advanced metrics from last year are very good as he boasted a 6.1% completion percentage over expected and a 10.8% pressure-to-sack ratio. He also got the ball out very quickly with a 2.78-second time to throw. But, digging deeper, the accuracy he displays may not be what is needed to compete at the NFL level. Among 219 quarterbacks with at least 200 attempts last year, Penix’s 71.5% adjusted completion percentage ranked 71st (per PFF), the lowest of any player in this draft. His proclivity for throwing deep (11.2-yard aDOT) certainly impacts that, but some of the top quarterback evaluators have also noted his lack of accuracy when manually charting his throws. Finally, we need to keep in mind he was also throwing to three players — Rome Odunze, Ja’Lynn Polk and Jalen McMillan — who are expected to be taken in the first two days of the draft and who boosted some of his raw accuracy stats.

Bo Nix (QB – Oregon)

Most similar players: Gardner Minshew (0.872), Brock Purdy (0.786), Deshaun Watson (0.777)

Like a fine wine, Bo Nix got better with age as his PFF passing grade rose in every season throughout his five-year stint in college. Nix had a league-leading 83.7% adjusted completion percentage last year, per PFF, while his 77.4% completion percentage last year broke the FBS single-season record. In the same way Penix’s aDOT impacted his accuracy rates, so too did it for Nix, just in the opposite way. He had only a 7.1-yard aDOT last year with nearly 30% of his passes being thrown behind the line of scrimmage (66.2% came within 10 yards of the line of scrimmage), a signal he benefitted from a great supporting cast. Nix could work well in an offense fit with great after-the-catch playmakers. Most of the teams with those in hand already have quarterback figured out, though. Nix would be fun as a sit-and-develop prospect but he’ll already be 24 years old by the time draft weekend rolls around.