Week 5 Bayesian Quarterback Rankings
Lamar Jackson quieting marching up the rankings, while Justin Herbert is in freefall
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!)
Normally AQE matches PFF grading a little more closely than unadjusted efficiency. But I don’t know what’s going on with grading on Deshaun Watson. Yes, there were some egregiously bad drops by his receivers last week that hurt his numbers. Even still, he doesn’t look great when we make those adjustments in AQE. I credit him with one of the biggest positive adjustments for poor pass protection and sacks, but he’s still taking way too many and holding the ball too long.
Brock Purdy and Jayden Daniels are right at the top of AQE, with a gap down to other quarterbacks. PFF grading likes Purdy a bit more, and I think his track record should give us more confidence his stellar numbers will continue. Daniels has a big negative adjustment in AQE for low receiver drops and an easy strength of schedule.
PFF grading is the most skeptical of Sam Darnold’s numbers so far. While I share that skepticism, I think the grading formula of treating garbage-time turnover-worthy plays the same as those committed earlier in games probably overstates the negatives of Darnold’s play, especially for last week.
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 all 32 assumed starters, even those who aren’t playing this week with their teams on bye.
“Percentile” is the mean (“best guess”) projection as a percentile of historical franchise quarterback results (min 2K career dropbacks).
Keep reading with a 7-day free trial
Subscribe to Unexpected Points to keep reading this post and get 7 days of free access to the full post archives.