As we head into the NFL draft, anyone can guess who will be available to the Pittsburgh Steelers with their 17th pick. One of the most discussed position groups for the team is the offensive line, as the team recently selected Kenny Pickett in the first round and could get better in the trenches. In fact, head coach Mike Tomlin made no secret of his interest in offensive line prospects during the Senior Bowl, and the team could certainly benefit from an upgrade to 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 final season in college. Meaning, the data below will run blocking efficiency and grades, as well as run snapshot percentage and performance in zone and gap runs.
It goes without saying that offensive line is arguably the hardest position to measure based on raw numbers, since they don’t take into account factors like measurables, strength of opponents, etc. That being said, the numbers below provide insight into the schemes the prospects in question are used to and how they performed compared to others.
PFF Execution Block Data:
|Execute degree of blocking||Percentage of snapshots in the Gap scheme||Gap Scheme PFF Grade||Percentage of snapshots in the zone scheme||Area Scheme PFF Degree|
|Peter Skoronski OT/Northwest||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/Minnesota IOL||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 his 468 run attempts, the Steelers made 251 zone runs (53.8%) and 116 (24.7%) gap runs in 2022. Moore had a block rating of 67, 8 in zone races and a 45.7 zone race rating. gap racing. Meanwhile, Dotson had a 60.7 rating in zone races and a 53.4 rating in gap races. While those numbers are deflated by the higher level competition for lineman from the two Steelers, they don’t rank well relative to the rest of the NFL. In fact, no Steelers offensive lineman ranked in the top 50 in any zone or degree of gap-blocking in 2022.
Determining the most likely target for Pittsburgh from this group raises the question of whether Dotson or Moore is the outsider. If it’s Moore, the two possible targets at 17 are Georgia’s Broderick Jones or Oklahoma’s Anton Harrison, and a potential 32-year-old target is North Dakota’s Cody Mauch. By the numbers, none of those three players scored well on zone-blocking run plays, nor did they play it at a high pace. Now, it’s very possible that Northwestern’s Peter Skoronski or Ohio’s Paris Johnson Jr. will drop to 17, since crazier things have happened. If they do, the Steelers could consider replacing Moore.
If Dotson is the odd man out, the best scheme for the heavyweight Steelers running around the area to replace him at a glance is Minnesota’s John Michael Schmitz and North Dakota’s O’Cyrus Torrence. Of the two, it’s arguable that Torrence is more likely to be drafted at 17, as Schmitz makes more sense as a target at 32-plus. Dotson’s run-blocking numbers aren’t great, and Torrence makes too much sense as a replacement. Additionally, the team appears to be committed to Moore as its starting left tackle, having even earned the approval of veteran 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 of these perspectives and are merely to provide specific context for the outline. For a complete understanding of the 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 keep an eye out for a study on pass blocking of the same prospects in the near future.
What are your conclusions from these data? Be sure to comment below!