Week 14 Bayesian Quarterback Rankings

Jordan Love is now a top-5 quarterback by the projections

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!)

AQE provides a better correlation to PFF grading than unadjusted numbers, but one quarterback sticks out as looking significantly better by AQE: Jordan Love. I can’t know exactly why that’s the case without the play-by-play PFF data, but I have some ideas.

• Harsher PFF grading on “turnover-worthy plays” than looking at the actual value losses in EPA. Sure, Love is prone to making particularly bad-looking interceptions, but he’s not that much worse at throwing them for higher EPA losses.

• AQE gives Love and other sack-avoiding quarterbacks more credit than PFF grading that places more responsibility on blocking.

• AQE has weather and strength-of-schedule adjustments that favor Love.

• A higher proportion of receiver drops that Love has suffered came on potentially huge play, thereby getting big positive adjustments in AQE.

When you get to the top-10 Bayesian rankings below, you’ll see how this boost in Love’s AQE has affected his projection, especially versus those of Justin Herbert and Joe Burrow, who are underperforming in AQE versus their PFF grading.

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 AQE, 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).