As we inch closer to the NFL draft, it’s anyone’s guess who will be available for the Pittsburgh Steelers with their 17th pick. One of the most commonly discussed position groups for the team is the offensive line, as the team recently drafted Kenny Pickett in the first round and could use an improvement in the trenches. Indeed, head coach Mike Tomlin didn’t hide his interest in offensive line prospects during the Senior Bowl, and the team could certainly benefit from an upgrade from offensive tackle Dan Moore or offensive guard Kevin Dotson.
To add context to the team’s potential targets with their 17th and 32nd picks, this article will provide data from Pro Football Focus’ premium stats on how they performed in their last season in college. That is, the data below will run blocking efficiency and grades, as well as percentage of run snaps and performance in zone and gap runs.
Needless to say, the offensive line is arguably the hardest position to measure based on raw numbers, as they don’t account for factors such as measurables, strength of opponents, etc. That being said, the numbers below provide insight into the schemes that the prospects in question are accustomed to, and how they performed in contrast to each other.
PFF Run Blocking Data:
| Run Blocking Grade | Percentage of Snaps in Gap Scheme | Gap Scheme PFF Grade | Percentage of Snaps in Zone Scheme | Zone Scheme PFF Grade | |
| Peter Skoronski OT/Northwestern | 81.7 | 52.1% | 69.8 | 39.3% | 88.0 |
| Paris Johnson OT/Ohio State | 80.9 | 26.7% | 71.5 | 65.6% | 80.0 |
| O’Cyrus Torrence OG/Florida | 89.9 | 27.7% | 74.0 | 64.4% | 90.2 |
| Broderick Jones OT/Georgia | 71.7 | 41.3% | 61.4 | 48.4% | 72.9 |
| Anton Harrison OT/Oklahoma | 67.7 | 50.8% | 64.9 | 43.9% | 68.1 |
| John Michael Schmitz IOL/Minnesota | 92.4 | 42.2% | 80.6 | 54.6% | 91.5 |
| Cody Mauch OT/North Dakota State | 90.1 | 65.9% | 90.4 | 24.6% | 71.7 |
For context, according to PFF, of their 468 rush attempts, the Steelers ran 251 zone runs (53.8%), and 116 (24.7%) gap runs in 2022. Moore had a 67.8 blocking grade on zone runs, and a 45.7 grade in gap runs. Meanwhile Dotson had a 60.7 grade in zone runs, and a 53.4 grade in gap runs. While those numbers are deflated due to higher-level competition for the two Steelers’ lineman, they don’t rank well relative to the rest of the NFL. Indeed, no Steelers’ offensive lineman ranked in the top 50 in either zone or gap blocking grade in 2022.
Determining the most likely target for Pittsburgh of this bunch begs the question of whether Dotson or Moore are the odd man out. If it’s Moore, the two likely targets at 17 are Georgia’s Broderick Jones or Oklahoma’s Anton Harrison, and a potential target at 32 is North Dakota’s Cody Mauch. Based on the numbers, none of those three players graded well in zone blocking run plays, nor did they play it at a high rate. Now, it’s entirely possible that Northwestern’s Peter Skoronski or Ohio’s Paris Johnson Jr fall to 17, as crazier things have happened. If they do, the Steelers may just consider replacing Moore.
If Dotson is the odd man out, the best scheme fits for the zone-run heavy Steelers to replace him at a glance are Minnesota’s John Michael Schmitz, and North Dakota’s O’Cyrus Torrence. Out of the two, Torrence is arguably more likely to be selected at 17, as Schmitz makes more sense as a target at 32 or onward. Dotson’s numbers in run blocking are not great, and Torrence makes too much sense as a replacement. Moreover, the team appears to be committed to Moore as their starting left tackle, as he even got the approval of long-time quarterback Ben Roethlisberger, who endorsed Moore as the team’s left tackle of the future.
As mentioned, the numbers do not paint the full picture on these prospects, and are merely to provide scheme-specific context. To get a comprehensive understanding of 2023 NFL draft prospects, be sure to check out our draft profiles. Also be sure to check out a similar study on potential cornerback targets for the Steelers, and stay tuned for a study on the same prospects’ pass-blocking in the near future.
What are your takeaways from this data? Be sure to comment below!
