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.
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 (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+ 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 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 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 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.
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 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.
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 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.
|C.J. Stroud||2022||JR||Ohio State||13||0.391||0.7||1.092||1.014||121||0.654||75.99||128||118|
|Caleb Williams||2022||SO||Southern California||14||0.393||0.668||1.061||1.011||118||-0.036||69.38||118||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|
|Max Duggan||2022||SR||Texas Christian||15||0.376||0.679||1.054||0.959||113||0.234||69.42||115||112|
|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|
|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|
|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|
|Drake Maye||2022||FR||North Carolina||14||0.391||0.619||1.01||0.942||111||0.051||71.82||112||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|
|Michael Penix Jr.||2022||JR||Washington||13||0.385||0.628||1.013||0.941||110||-0.413||68.14||106||104|
|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|
|Darren Grainger||2022||SR||Georgia State||12||0.346||0.688||1.034||0.916||104||-0.438||64.47||100||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|
|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|
|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|
|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|
|John Rhys Plumlee||2022||SR||Central Florida||13||0.372||0.581||0.953||0.837||100||-0.174||68.31||98||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|
|Chris Reynolds||2022||SR||UNC Charlotte||10||0.375||0.619||0.994||0.82||106||-0.717||69.91||99||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|
|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|
|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|
|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|
|Matt McDonald||2022||SR||Bowling Green State||12||0.36||0.563||0.923||0.804||94||-0.483||65.5||89||90|
|Hayden Wolff||2022||SO||Old Dominion||12||0.335||0.609||0.943||0.871||91||-0.18||66.46||89||89|
|N'Kosi Perry||2022||SR||Florida Atlantic||12||0.342||0.618||0.96||0.893||94||-0.857||65.94||85||85|
|Layne Hatcher||2022||JR||Texas State||12||0.367||0.473||0.84||0.727||84||-0.446||64.07||80||81|
|Daniel Richardson||2022||SO||Central Michigan||12||0.329||0.544||0.873||0.796||81||-0.474||61.52||76||81|
|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|