Week 6 Bayesian Quarterback Rankings

Kyler Murray and Jayden Daniels now projecting better than some "elite" veterans quarterbacks

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!)

The top-tier of AQE is Jayden Daniels, Brock Purdy and Joe Burrow. Interestingly, only Daniels’ team has exceeded W-L expectations so far this year. Quarterback performance is the most important thing, but not the only thing driving team performance.

Outliers in AQE versus what you’d project based on grades are Sam Darnold on the high end, and Deshaun Watson on the low. I think there’s some truth to the fact that Darnold hasn’t been as good as his numbers, and Watson not as bad. Although, I’d be much more comfortable using their AQE rankings as an indicator of how well they’ve played this year versus their grades, which have them as basically equal.

Jacoby Brissett is in the bottom-8 for grading and bottom-2 for AQE, so you can see why the Patriots are moving on to the rookie. Gardner Minshew hadn’t been that bad by AQE or grading metric, though not good either. His performance sits in the same realm as some of the highest paid quarterbacks, including Dak Prescott, Jared Goff, Jordan Love, and Matthew Stafford.

PROJECTED ADJUSTED EFFICIENCY

These results are the ranking for the go-forward projections of adjusted quarterback efficiency starting this week. 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.

Included are 31 assumed starters (including those on byes), with rookie Spencer Rattler excluded. He’s the only quarterback without an NFL dropback, and he’d be positioned in the bottom-5 solely based on his draft-position prior.

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