Week 3 Bayesian Quarterback Rankings

Kyler Murray making a run at the top-10, while some rookies struggles to move out of the rankings basement

Sam Darnold is on the rise, but anchored by poor historical performances

The big, fundamental change to the rankings this season is the integration of Adjusted Quarterback Efficiency (AQE) numbers. This produces rankings that align more closely to what the typical football observer or data-based analysts would assign based on a combination of observation and statistics.

For the Week 1 projection, I weaved the AQE figures for 2023 and 2022 into the mix. In Week 2, I discovered the addition of prior years’ charting from FTNData, enabling us to go back to 2019 and calculate AQE. Because we’re shifting the historical data for several years in the new projections, the projection movement from Week 1 wouldn’t be primarily based on last week’s quarterback performances, but mostly on revisions to 2019-2021 efficiencies.

You can find all the previous weekly editions of the Bayesian Quarterback Rankings here, and the backlog for Adjusted Quarterback Efficiency is here.

COMPARING GRADES AND EFFICIENCY

PFF grades aren’t part of the analysis, but I find it helpful to make not of how they align with EPA per play, as many contextual elements of quarterback play (drops, interception-worthy throws, easier throws that become big gains, etc) are part of the grading methodology, but aren’t accounted for in EPA. At the same time, I think EPA does a vastly superior job of weighing what is and isn’t important in points-based results.

The plot below is a bit different than previous iterations of this post, substituting AQE for unadjusted EPA per play, and you might notice that the data has less dispersion (i.e. something like a higher r²) than using straight EPA. Even so, AQE doesn’t perfectly align with PFF grading, and you can decide which measure is more representative of fundamental quarterback play. (hint: it’s AQE!)

We have a clear one-two emerging after two weeks of the season by grading and AQE: Kyler Murray and Derek Carr. Murray showed flashes of MVP-level play back in the first half of the 2020, and Carr hasn’t been seen as a potentially elite quarterback since the 2016 season (six MVP votes!!). I think it’s more likely that Murray can maintain his level of strong play than Carr going forward, and (spoiler alert) the Bayesian Model agrees, as the former doesn’t have as much historical sample weighing down current results.

We’ve seen strong stretches from San Darnold flame out in the past, but that doesn’t mean we ignore the evidence from Weeks 1-2. AQE and PFF grading agree that he’s been solid.

Bryce Young basically had to go, with the worst PFF grade, by far, and AQE that showed his results-based efficiency had been even worse over the first two weeks. Malik Willis and Skylar Thompson are on opposite ends of the spectrum as two quarterbacks with the least historical evidence to judge. In limited action prior to 2024, both were bad, so I’m not buying Willis being a high-efficiency option going forward, no matter how good Matt LaFleur is at putting together a friendly gameplan.

WEEK 3 PROJECTED ADJUSTED EFFICIENCY

These results are the ranking for the go-forward projections of adjusted quarterback efficiency starting in Week 3. I also included the AQE rankings for each quarterback over the last five seasons (minimum 250 dropbacks) so you can see the evidence going into the projections. All of these ranks are now based on AQB, including the 2024 numbers.

Older data is decayed over time, so the 2023 and 2024 AQE data matters more than those from pre-2020. That said, older data can’t be fully discounted, or else you miss bounce-back performers of great quarterbacks returning to form, like Aaron Rodgers in 2020 and 2021.

“Percentile” is the mean (“best guess”) projection as a percentile of historical franchise quarterback results (min 2K career dropbacks).