Welcome to QwOBA+

With the Green Bay Packers more or less eliminated from playoff contention, and their star quarterback more interested in “the Aya” than hitting open guys in the middle of the field, it’s time to start looking at the upcoming 2023 NFL Draft class and, in particular, the quarterbacks.

2023 is allegedly a great class for quarterbacks, but the position is among the most difficult to scout in all of sports. How can you ensure you don’t wind up with a dud?

QBOPS

Many years ago, mostly on a whim, I developed QBOPS. QBOPS is based on 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.

(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.)

I run some formulas, scale them to their baseball counterparts, and there you have it. (More on QBOPS here, and please note, there have been some very minor tweaks since last year.)

While the creation of QBOPS was somewhat silly, it actually works quite well (as does its sister stat, WROPS) 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. 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. 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.

Among the many weird coincidences between OPS and QBOPS is the fact that it underrates completion percentage and overvalues 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+ (henceforth just QwOBA, and 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, 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 to the inputs we do have. After much tinkering with weights, the result seems to work quite well.

Second, and the reason there is a + on the end of QwOBA+, is that the statistic 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 sees his junior season as the 13^th best of 2016, 15% above average, and well worth taking a look at.

QwOBA is not perfect of course. It does not (yet) incorporate interceptions, and there will always be developmental players like Josh Allen who evade statistical scouting altogether. There will also be “great college quarterbacks” who excel due to the scheme and talent around them, Dwayne Haskins being the quintessential example. All stats require additional context, scouting input, critical pushback, and more than anything, humbleness. That said, QwOBA can be very useful for identifying under the radar prospects, as well as hyped prospects who may struggle. The same prospects that QBOPS picked out (Tyler Huntley, Bailey Zappe, Skylar Thompson) are all still there, and they pop immediately. Jordan Love’s “good” season still doesn’t look that good. Baker Mayfield is still an amazing college quarterback.

So far this season, big names like CJ Stroud, Will Levis, and freshman Drake Maye rank highly, but there are fun under the radar guys like my personal favorite, Grayson McCall, plus TCU’s Max Duggan and Ohio’s Kurtis Rourke. I’m sure we’ll also have plenty of discussions on Tennessee’s Hendon Hooker as well.

And you can have a look at every year since 2010 right here, sorted by QwOBA, which is column Q. QBOPS and its components are still included along with QBOPS+ (which DOES adjust for interceptions), to provide context. The last two columns contain the league average QBOBP and QBSLG. This season will be updated regularly.

Without Rashan Gary the Packers are likely to be bad, and likely to be drafting in the range where a new franchise quarterback can be had. We may as well get familiar with the candidates now.