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The QBOPS Statistics Glossary

Welcome to back to QBOPS, QWOBA, WROPS, and their well-adjusted friends.

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NFL: Minnesota Vikings at Green Bay Packers Jeff Hanisch-USA TODAY Sports

Despite Aaron Rodgers’ best efforts, it’s time for those who cover the Green Bay Packers to shift their focus to the NFL draft, and that means it’s time for this writer to dive into QBOPS, WROPS, and all of their cousins and friends. I’ve worked on putting together these metrics over the past several years, and despite the fact that QBOPS started off as a laugh, it and its offshoots work pretty well. When I first built QBOPS I used it for professional players (it was originally meant as a humorous way to compare baseball and football players on the same scale, hence the “OPS.”) and to my surprise, it actually tracked DVOA rankings very closely. For that reason, I moved over to the college side where there is no publicly available DVOA or EPA/Play model, and where it can have some actual use.

Here are a few general notes before we jump into definitions:

These statistics are almost all based on baseball statistics. Anything that ends in OPS is scaled to baseball’s “On-Base Plus Slugging” statistic, and is generally presented in conjunction with the OBP (On Base Percentage) and SLG (Slugging Percentage) components that make up OPS. You may occasionally see a baseball-style slash line here, but it represents a few things a bit differently than the traditional Batting Average/Slugging Percentage/OPS line. Just know that we have no direct corollary to Batting Average because Batting Average is frankly kind of weird, and so Bryce Young’s 2022 QBOPS of .381/.666/1.046 is composed of his QBOBP/QBSLG/QBOPS.

Anything that ends in OBA is inspired by Fangraphs’ Weighted On-Base Average (wOBA), but the scale is similar to what are known as the “plus” statistics, such as OPS+. These are scaled in such a way that 100 is average, with every point above or below 100 representing a percentage better or worse than average.

You can view QBOPS, QwOBA, and WROPS data here and here.

Let’s get to it.

Quarterbacks

QBOPS

Literally “Quarterback On-Base Plus Slugging.” QBOPS is scaled to the On Base Plus Slugging (OPS) statistic in baseball, and converts conceptually similar football metrics to that scale. In place of On Base Percentage (OBP) we have Completion Percentage. In place of Slugging (SLG) we have Yards per Completion. It is very simple. I run some formulas, scale them to their baseball counterparts, and there you have it.

(Note: I do not use Yards per Attempt to avoid double-counting completion percentage. Also, Y/A and ANY/A and the like are excellent metrics on their own.)

While the creation of QBOPS was somewhat silly, it actually works quite well in projecting college players, which is why I’ve continued to run and maintain it. The single best metric for projecting quarterbacks is probably Completion Percentage over Expected (CPOE), but there are some problems with CPOE. CPOE is a somewhat complex statistic that uses past information and models to create a baseline for the difficulty of each pass a quarterback attempts, and then credits or debits the quarterback for completing more or fewer passes than the baseline. CPOE works pretty well, but it tends to overrate “game manager” quarterbacks and, most importantly, it is not publicly available for college quarterbacks. (It is publicly available for NFL quarterbacks via several outlets, including RBSDM.com.)

QBOPS isn’t as sophisticated as CPOE, and it will sometimes overrate quarterback prospects who happen to play with outstanding receiver talent, but conceptually it reflects CPOE in rewarding accurate passers who also make big plays down the field.

Since QBOPS was created, I’ve used a cutoff for prospects that works reasonably well, known as the .400/.600 group. A .400 QBOBP is a completion percentage of about 68%, and a .600 QBSLG represents a Y/C of approximately about 12.5. In 2019, the .400/.600 group included Jalen Hurts, Joe Burrow, Tua Tagovailoa, Tyler Huntley, Justin Fields, plus near misses from a sophomore Trevor Lawrence, and Justin Herbert. In 2022, that list includes Hendon Hooker, KJ Jefferson, CJ Stroud, Stetson Bennett, Bo Nix, Caleb Williams, and Grayson McCall. Bryce Young didn’t quite make the cutoff in 2022, but he did in 2021.

Quarterbacks who have at least a .400 QBOBP and a .600 QBSLG are much more likely to be successful professional quarterbacks than those who do not, but here, QBOPS has always been a bit awkward due to a quirk it shares with its baseball cousin. You see, OPS and QBOPS underrate completion percentage and overvalue yards per completion, just as OPS overvalues slugging relative to OBP. This means, practically, that whenever I have to explain QBOPS or use QBOPS, there is some art to it. You need to look at the components in addition to the total, and weight completion percentage first, before moving on. OPS (and QBOPS) is quick, dirty, easy to calculate, and freely available, but we can do better! And so we shall.

QwOBA

QwOBA (pronounced phonetically and similarly to a certain fast-casual Mexican chain) makes two baseball-inspired upgrades to QBOPS. The first is weighting QBOBP more heavily compared to QBSLG, putting the emphasis where it should be. QwOBA still incorporates yards per completion to separate the check-down artists from the bombers, but it is first and foremost concerned with completing passes at a high rate. This scaling is inspired by Fangraphs’ wOBA statistic, from which QwOBA derives its name, though it is not a direct translation. wOBA uses the distinct outcomes in baseball (singles, doubles, triples, HRs, walks) as its inputs and we don’t have anything analogous, but we can do something conceptually similar with the inputs we do have. After much tinkering with weights, the result seems to work quite well.

QwOBA is scaled to league average similar to the OPS+ statistic. For every given year, a QwOBA of 100 is average, with each point representing a performance 1 % better than average (or worse than average). This is useful as a simple representation of quality, but also for providing additional context as the offensive environment changes in college football. In 2014, the average FBS quarterback had a QBOPS of .353/.597/.950, but by 2019 that baseline jumped to .365/.612/.977, and the progression is hardly linear. QwOBA tells us, in a given season, how much better (or worse) every quarterback was than the average quarterback that year, and so we can more easily compare across eras.

Finally, QwOBA is helpful in softening my hard cutoff of .400/.600, which is, in the grand scheme of things, a useful but arbitrary heuristic. Patrick Mahomes was never quite as accurate as his peers in college, and he is the exact kind of quarterback that QBOPS (and I) sometimes have a blind spot for, but QwOBA catches what I tend to miss, and sees his junior season as the 13th best of 2016, 15% above average, and well worth taking a look at.

Hendon Hooker, who really did have an outstanding statistical season, led 2022 in QwOBA at 124, followed by CJ Stroud at 121. In the outstanding 2019 class, Joe Burrow and Tua tied with a QwOBA of 139, followed closely by Jalen Hurts’ 138.

QBOPS+

QBOPS+ is QBOPS with an adjustment based on interception percentage.

I don’t use QBOPS+ as much because while Interceptions are bad, they can also be noisy, and I’m not sure how predictive they really are. I personally view QBOPS+ as more of a “what happened” statistic versus a “what will happen” statistic. Football is a small sample size sport. Although over the long haul a quarterback who is prone to throwing interceptions will tend to throw interceptions, over a single season, DBs drop imperceptible passes, wide receivers tip should-be-receptions to defenders, etc. That said, we also shouldn’t ignore interceptions entirely, and QBOPS+ attempts to adjust for them, applying a weighted penalty based on interception percentage. Careful quarterbacks will see almost no change from QBOPS to QBOPS+, but careless ones will.

Having an interception-adjusted version is useful in two ways. First, it provides context as to just how much value a quarterback loses due to throwing picks and how valuable they might be if they stopped. Second, as QBOPS is the baseline statistic for everything else, it allows for us to run interception-adjusted QWOBA numbers. Interception-adjusted metrics will be designated as such on any chart.

SOD

SOD is Strength of Defense, or perhaps more accurately, Strength of Defense faced. SOD relies on the F+ ratings available at Football Outsiders, and specifically the DF+ rankings, which combine Brian Fremeau’s FEI ratings with Bill Connelly’s SP+ ratings in equal parts. The OF+ and DF+ ratings represent an offense or defensive unit’s standard deviation above or below average.

Using a scraper of my own design I’ve compiled a list of every opponent a quarterback has faced in a given season and created an average of the DF+ metrics for those opponents. That number represents the quarterback’s SOD, which is a weighting component in Defense Adjusted QwOBA. You can also view the SOD number in Column K of the QBOPS spreadsheet. In 2022, Payton Thorne of Michigan State faced the toughest defenses overall with a .714 SOD, while Florida International’s Grayson James faced the easiest schedule with a -.928 SOD.

WRGPC

WRGPC is Wide Receiver Grade per Completion. While the defense a quarterback faces definitely has an impact on his play, the quality of his receivers matters at least as much, and likely more. It’s no secret that CJ Stroud was able to rely on better receivers this year than Bryce Young, but how much should that matter? We can use WRGPC to take a crack at finding out.

Here we’re relying on Pro Football Focus’s full season receiving grades for wide receivers and tight ends. While PFF college grades are hardly perfect, they are still among the best attempts to separate receiver production from quarterback production, and over the course of an entire season, they should be reliable enough for our purposes.

WRGPC is compiled by taking every completion a quarterback had over the course of a season, tracking the recipient, and multiplying the number of receptions by that receiver’s PFF grade, adding the totals for each individual team, and dividing by total receptions to create an average. The league average WRGPC is almost exactly 66, and remains remarkably consistent from year to year since 2018.

It will likely surprise no one that CJ Stroud benefitted from the best receiving corps this season with a 75.99 WRGPC. The worst? Colorado’s JT Shrout, with a WRGPC of 57.44. In 2018, the ridiculous Alabama team of Jerry Jeudy, Henry Ruggs, Jaylen Waddle, DeVonta Smith, and Irv Smith scored an 82.4.

DQwOBA

DQwOBA is Defense-Adjusted QwOBA. Using SOD, we reward quarterbacks who succeeded against tougher defensive slates, and penalize those that picked on weak stuff. While Hendon Hooker led 2022 in regular QWOBA, CJ Stroud faced a much tougher defensive slate, which pushes them into a dead heat for first with 128 DQwOBA.

DQwOBA is especially useful in normalizing performances from some of the small school quarterbacks who blow away lesser competition. Ohio’s Kurtis Rourke posted a very good 121 QwOBA, however he faced some of the worst defenses in football, which knocks his number down to a more reasonable 117. The same can be said of James Madison’s Todd Centeio, who played against the 6th easiest schedule in football with an SOD of -.783. He was adjusted from a 120 QwOBA all the way down to 112.

QWOBA+

Finally we come to the fully adjusted QWOBA+, which takes Defense-adjusted QwOBA and weights receiver quality as well. What CJ Stroud gained in tough defenses, he gives back (and then some) in receiver quality, where Marvin Harrison Jr. and Emeka Egbuka were among the league’s true elites. We start with Stroud’s 121 raw QwOBA. He gains 7 points to a 128 DQwOBA based on his schedule, and loses 10 when adjusting for his elite weapons to finish at 118, which is still the 3rd best number of the this past season.

In the 2019 class, Jalen Hurts actually slightly edged out Joe Burrow 138 to 135, with Tua on their heels at 132. Tua enjoyed the second highest WRGPC that season and still put up an elite score, which I think demonstrates how useful this can be. Contrast Tua, who remained elite despite adjustments, to Tanner Morgan, the Minnesota quarterback who, in 2019, enjoyed the best WRGPC that season at 81.52. Minnesota’s top two targets were Tyler Johnson, who was an outstanding college receiver, along with Rashod Bateman, the future first-round pick of the Ravens. Morgan’s QwOBA+ is 16 points lower as a result (from 121 to 105), reflecting the good, rather than great quarterback that he actually is.

The top 10 Quarterbacks per QwOBA+ in 2022 were Hendon Hooker, KJ Jefferson, CJ Stroud, Bo Nix, Stetson Bennett, Caleb Williams, Dillon Gabriel, Grayson McCall, Clay Miller, and Jordan Travis. Bryce Young was essentially tied with Travis in the ten spot. Anthony Richardson was exactly average with a 100 score. Will Levis was 13th overall with a 112.

Wide Receivers

WROPS

Wide Receiver On-base Plus Slugging uses the same concept as QBOPS and applies it to receivers. Instead of Completion Percentage we use Catch Percentage, and Yards per Completions remains the Slugging statistic. The scale is very similar, with possession receivers generally landing in the .400/.450/.850 range, while deep threats look more like sluggers, with something around a .350/.500 profile. The best WROPS this year belongs to Tennessee’s Jalin Hyatt and his brilliant .442/.624/1.066 slash line, followed closely by Ohio’s Jacoby Jones, Georgia State’s Jamari Thrash, and Missouri’s Dominic Lovett. Quentin Johnson from TCU is 10th.

WROBA

Like QwOBA, WROBA scales a receiver’s production, using the WROPS inputs, to the same 100 point scale, where 100 is average and every point up or down represents a percentage point. Hyatt also led the league in WROBA at 154. Northwestern’s Donny Navarro scored an almost unbelievable 33.

WRAPS

WRAPS isn’t an acronym, it’s a combination of WROPS and Kent Lee Platte’s Relative Athletic Score (RAS). WRAPS exists on a 20 point scale (though a truly outstanding WROPS can occasionally push someone slightly over), and tells us, in one number, which prospects have the best combination of college production and athleticism. WRAPS is still ongoing as Pro Day scores come in and RAS is updated.

So far in 2022, Jalin Hyatt, Matt Landers, and Marvin Mims lead the way with WRAPS over 19.

2021’s WRAPS leaders were Christian Watson, Kevin Austin, and George Pickens.


For your reference, here is the table showing these various statistics for the 2022 FBS quarterback class.

QWOBA+ 2022

Player Year Class School G QBOBP QBSLG QBOPS QBOPS+ QWOBA SOD WRGPC dQWOBA QWOBA+
Player Year Class School G QBOBP QBSLG QBOPS QBOPS+ QWOBA SOD WRGPC dQWOBA QWOBA+
Hendon Hooker 2022 SR Tennessee 11 0.411 0.671 1.081 1.051 124 0.418 68.38 128 126
K.J. Jefferson 2022 JR Arkansas 11 0.401 0.636 1.037 0.954 117 0.161 64.66 119 120
C.J. Stroud 2022 JR Ohio State 13 0.391 0.7 1.092 1.014 121 0.654 75.99 128 118
Bo Nix 2022 SR Oregon 13 0.424 0.599 1.023 0.937 120 -0.236 66.27 118 117
Stetson Bennett 2022 SR Georgia 15 0.402 0.652 1.054 0.977 119 0.309 71.59 122 117
Caleb Williams 2022 SO Southern California 14 0.393 0.668 1.061 1.011 118 -0.036 69.38 118 114
Dillon Gabriel 2022 JR Oklahoma 12 0.37 0.675 1.045 0.963 111 0.094 63.88 112 114
Grayson McCall 2022 JR Coastal Carolina 11 0.411 0.639 1.05 1.017 120 -0.437 67.64 116 114
Clay Millen 2022 FR Colorado State 10 0.426 0.554 0.98 0.852 115 -0.136 66.43 114 113
Jordan Travis 2022 JR Florida State 13 0.378 0.697 1.074 1.004 116 0.172 70.53 118 113
Bryce Young 2022 JR Alabama 12 0.381 0.666 1.046 0.98 113 0.077 66.61 114 113
Max Duggan 2022 SR Texas Christian 15 0.376 0.679 1.054 0.959 113 0.234 69.42 115 112
Will Levis 2022 SR Kentucky 11 0.386 0.637 1.023 0.846 112 0.197 68.08 114 112
Casey Thompson 2022 JR Nebraska 10 0.372 0.682 1.054 0.872 113 0.148 68.81 114 112
J.J. McCarthy 2022 SO Michigan 14 0.381 0.641 1.022 0.944 110 0.386 68.45 114 111
Jayden Daniels 2022 SR Louisiana State 14 0.405 0.537 0.941 0.903 105 0.389 63.71 109 111
Jake Haener 2022 SR Fresno State 10 0.425 0.563 0.988 0.945 116 -0.321 68.34 113 110
Jalon Daniels 2022 JR Kansas 9 0.39 0.649 1.039 0.952 115 0.134 72.22 116 110
Kurtis Rourke 2022 JR Ohio 11 0.408 0.654 1.062 1.005 121 -0.397 73 117 110
Garrett Shrader 2022 JR Syracuse 12 0.381 0.629 1.01 0.885 109 0.26 67.71 112 110
Jaxson Dart 2022 SO Mississippi 13 0.368 0.645 1.013 0.861 107 0.399 68.01 111 109
Sean Clifford 2022 SR Penn State 13 0.38 0.612 0.992 0.892 106 0.366 66.83 110 109
Jalen Mayden 2022 JR San Diego State 13 0.351 0.705 1.057 0.846 108 -0.407 62.02 104 108
Taulia Tagovailoa 2022 SR Maryland 12 0.395 0.563 0.958 0.856 105 0.29 66.05 108 108
Michael Pratt 2022 JR Tulane 13 0.375 0.686 1.061 0.987 114 -0.478 68.21 109 107
Sam Hartman 2022 JR Wake Forest 12 0.372 0.672 1.044 0.904 111 0.352 74.39 115 106
Spencer Rattler 2022 JR South Carolina 13 0.391 0.563 0.954 0.802 104 0.229 66.35 106 106
Blake Shapen 2022 SO Baylor 13 0.373 0.587 0.96 0.824 101 0.121 62.39 102 106
Drake Maye 2022 FR North Carolina 14 0.391 0.619 1.01 0.942 111 0.051 71.82 112 106
Tommy Devito 2022 SR Illinois 13 0.411 0.505 0.916 0.862 103 0.369 67.17 107 106
Parker McNeil 2022 SR Louisiana Tech 8 0.339 0.76 1.099 0.912 111 -0.423 67.62 107 105
Todd Centeio 2022 SR James Madison 10 0.376 0.73 1.106 1.018 120 -0.783 73.32 112 105
Chandler Rogers 2022 SO Louisiana-Monroe 12 0.398 0.545 0.943 0.834 104 -0.071 64.81 103 104
Michael Penix Jr. 2022 JR Washington 13 0.385 0.628 1.013 0.941 110 -0.413 68.14 106 104
Dorian Thompson-Robinson 2022 SR UCLA 13 0.411 0.584 0.994 0.864 113 -0.523 70.25 108 104
Davis Brin 2022 SR Tulsa 9 0.349 0.708 1.057 0.897 108 -0.429 67.03 104 103
Will Rogers 2022 JR Mississippi State 13 0.401 0.469 0.87 0.805 96 0.417 63.53 100 103
Jayden De Laura 2022 JR Arizona 12 0.369 0.664 1.033 0.883 109 -0.204 70.4 107 103
Holton Ahlers 2022 SR East Carolina 13 0.396 0.577 0.973 0.92 108 -0.319 68.36 105 102
Frank Harris 2022 SR Texas-San Antonio 14 0.411 0.607 1.018 0.922 116 -0.633 73.33 110 102
Chase Brice 2022 SR Appalachian State 12 0.371 0.654 1.024 0.938 108 -0.448 67.24 104 102
Aidan O'Connell 2022 SR Purdue 12 0.378 0.534 0.913 0.782 96 0.681 67.21 103 102
Clayton Tune 2022 SR Houston 13 0.397 0.598 0.995 0.894 110 -0.401 70.58 106 101
Dylan Hopkins 2022 SR Alabama-Birmingham 11 0.373 0.689 1.063 0.97 114 -0.857 70.02 105 101
Darren Grainger 2022 SR Georgia State 12 0.346 0.688 1.034 0.916 104 -0.438 64.47 100 101
Seth Henigan 2022 SO Memphis 13 0.378 0.612 0.989 0.9 106 -0.494 66.02 101 101
Jaren Hall 2022 JR Brigham Young 12 0.389 0.627 1.016 0.936 111 -0.448 71.7 107 101
Payton Thorne 2022 SR Michigan State 12 0.369 0.542 0.911 0.769 94 0.714 67.34 101 100
Anthony Richardson 2022 SO Florida 12 0.317 0.71 1.027 0.889 97 0.348 66.81 100 100
Gunnar Watson 2022 JR Troy 13 0.363 0.67 1.033 0.854 108 -0.375 70.68 104 100
Graham Mertz 2022 JR Wisconsin 12 0.338 0.638 0.976 0.801 95 0.422 66.2 99 99
Donovan Smith 2022 SO Texas Tech 12 0.39 0.505 0.895 0.714 96 0.168 64.75 98 99
Drew Pyne 2022 SO Notre Dame 11 0.381 0.604 0.985 0.867 106 -0.063 72.8 105 99
Brady Cook 2022 JR Missouri 13 0.382 0.542 0.924 0.832 98 0.05 66.71 99 98
Tanner Mordecai 2022 SR Southern Methodist 12 0.384 0.6 0.983 0.87 106 -0.308 71.28 103 98
Tyler Van Dyke 2022 JR Miami (FL) 9 0.373 0.562 0.935 0.836 98 -0.138 65.02 97 98
Malik Cunningham 2022 SR Louisville 10 0.368 0.565 0.933 0.818 96 0.189 66.64 98 97
Trenton Bourguet 2022 JR Arizona State 7 0.421 0.504 0.925 0.802 107 -0.561 70.33 101 97
Austin Aune 2022 JR North Texas 14 0.333 0.749 1.082 0.899 108 -0.71 69.93 101 97
Taylor Powell 2022 JR Eastern Michigan 9 0.382 0.594 0.976 0.828 105 -0.751 66.68 97 97
Doug Brumfield 2022 JR Nevada-Las Vegas 10 0.381 0.567 0.948 0.85 101 -0.688 63.68 94 96
Riley Leonard 2022 SO Duke 13 0.376 0.582 0.958 0.881 101 -0.32 67.41 98 96
John Rhys Plumlee 2022 SR Central Florida 13 0.372 0.581 0.953 0.837 100 -0.174 68.31 98 96
Cameron Rising 2022 JR Utah 13 0.382 0.597 0.979 0.875 105 -0.405 71.04 101 96
Collin Schlee 2022 JR Kent State 11 0.348 0.658 1.006 0.912 101 -0.315 68 98 96
Taylen Green 2022 FR Boise State 13 0.362 0.603 0.964 0.854 99 -0.281 66.39 96 96
Austin Reed 2022 SR Western Kentucky 14 0.381 0.598 0.978 0.887 105 -0.684 68.83 98 95
Tanner McKee 2022 JR Stanford 12 0.366 0.547 0.913 0.819 93 -0.122 62.67 92 95
Chris Reynolds 2022 SR UNC Charlotte 10 0.375 0.619 0.994 0.82 106 -0.717 69.91 99 95
Ben Bryant 2022 SR Cincinnati 11 0.361 0.628 0.99 0.889 102 -0.635 66.89 96 95
James Blackman 2022 SR Arkansas State 11 0.379 0.543 0.922 0.879 98 -0.152 67.77 96 95
Carter Bradley 2022 JR South Alabama 13 0.381 0.59 0.972 0.831 104 -0.655 69.02 97 94
Quinn Ewers 2022 FR Texas 10 0.343 0.62 0.963 0.862 95 0.354 70.92 99 94
Kedon Slovis 2022 SR Pittsburgh 11 0.345 0.638 0.983 0.84 98 -0.115 69.25 97 94
Cameron Ward 2022 SO Washington State 13 0.38 0.495 0.875 0.784 92 -0.194 62.65 90 93
Spencer Sanders 2022 SR Oklahoma State 10 0.34 0.611 0.95 0.828 93 0.117 67.37 94 93
Demarcus Irons Jr. 2022 JR Akron 10 0.394 0.505 0.899 0.806 98 -0.62 65.12 92 93
Chevan Cordeiro 2022 JR San Jose State 12 0.358 0.615 0.973 0.903 99 -0.584 66.6 93 93
Robby Ashford 2022 SO Auburn 12 0.29 0.643 0.933 0.793 80 0.658 60.05 87 93
Phil Jurkovec 2022 SR Boston College 8 0.351 0.57 0.921 0.759 91 0.004 64.7 91 92
Chase Cunningham 2022 SR Middle Tennessee State 12 0.394 0.5 0.894 0.786 97 -0.563 65.15 91 92
Grant Wells 2022 JR Virginia Tech 11 0.348 0.543 0.891 0.755 87 -0.224 58.55 85 92
D.J. Uiagalelei 2022 JR Clemson 13 0.365 0.539 0.905 0.81 92 -0.16 64.29 90 92
TJ Mcmahon 2022 JR Rice 10 0.355 0.644 0.999 0.736 102 -0.84 67.62 94 92
Hunter Dekkers 2022 SO Iowa State 12 0.39 0.494 0.884 0.731 95 0.127 70.6 96 92
Kyle Vantrease 2022 SR Georgia Southern 13 0.362 0.562 0.925 0.792 94 -0.238 66.2 92 91
JT Daniels 2022 JR West Virginia 10 0.361 0.516 0.877 0.74 88 0.168 65.12 90 91
Cooper Legas 2022 JR Utah State 10 0.36 0.544 0.905 0.678 91 -0.25 63.98 89 91
Brennan Armstrong 2022 SR Virginia 10 0.323 0.585 0.908 0.731 84 0.056 60.47 85 90
Ryan Hilinski 2022 JR Northwestern 8 0.329 0.559 0.889 0.753 83 0.395 63.06 87 90
Matt McDonald 2022 SR Bowling Green State 12 0.36 0.563 0.923 0.804 94 -0.483 65.5 89 90
DeQuan Finn 2022 SO Toledo 12 0.352 0.589 0.941 0.751 94 -0.56 65.28 88 89
Hayden Wolff 2022 SO Old Dominion 12 0.335 0.609 0.943 0.871 91 -0.18 66.46 89 89
Jack Plummer 2022 SR California 12 0.369 0.538 0.907 0.807 93 -0.425 66.72 89 88
N'Kosi Perry 2022 SR Florida Atlantic 12 0.342 0.618 0.96 0.893 94 -0.857 65.94 85 85
JT Shrout 2022 SO Colorado 9 0.261 0.664 0.926 0.729 73 0.358 57.44 77 85
Spencer Petras 2022 SR Iowa 12 0.33 0.538 0.868 0.779 80 0.507 66.38 85 85
E.J. Warner 2022 FR Temple 11 0.357 0.554 0.911 0.775 91 -0.549 67.9 86 84
Ben Wooldridge 2022 JR Louisiana 10 0.334 0.59 0.924 0.821 88 -0.546 66 83 83
Johnathan Bennett 2022 JR Liberty 12 0.345 0.574 0.919 0.718 90 -0.573 67.94 84 82
Cam Fancher 2022 SO Marshall 13 0.327 0.583 0.91 0.783 85 -0.605 63.04 79 82
Layne Hatcher 2022 JR Texas State 12 0.367 0.473 0.84 0.727 84 -0.446 64.07 80 81
Cole Snyder 2022 SO Buffalo 13 0.347 0.548 0.895 0.808 87 -0.855 63.71 78 81
Daniel Richardson 2022 SO Central Michigan 12 0.329 0.544 0.873 0.796 81 -0.474 61.52 76 81
Connor Bazelak 2022 JR Indiana 10 0.326 0.482 0.808 0.69 72 0.412 62.02 76 80
Brayden Schager 2022 SO Hawaii 12 0.326 0.521 0.847 0.722 77 -0.49 59.1 72 79
Nate Cox 2022 SR Nevada 12 0.312 0.54 0.852 0.752 74 -0.08 60.25 73 79
Andrew Peasley 2022 JR Wyoming 12 0.309 0.536 0.845 0.681 73 -0.409 58.09 69 77
Zion Turner 2022 FR Connecticut 13 0.341 0.464 0.805 0.611 75 -0.162 63.33 73 76
Gavin Hardison 2022 JR Texas-El Paso 10 0.307 0.611 0.918 0.791 82 -0.758 67.06 74 73
Aveon Smith 2022 JR Miami (OH) 10 0.292 0.589 0.881 0.767 74 -0.448 63.23 70 72
Jack Salopek 2022 FR Western Michigan 7 0.29 0.605 0.895 0.636 75 -0.581 63.56 69 72
John Paddock 2022 SR Ball State 12 0.352 0.466 0.817 0.672 78 -0.735 65.38 71 71
Grayson James 2022 SO Florida International 11 0.346 0.459 0.806 0.652 76 -0.928 63.51 67 69