Landover, Maryland, USA; Philadelphia Eagles quarterback Jalen Hurts (1) attempts a pass against the Washington Commanders during the first half at FedExField. Mandatory Credit: Brad Mills-USA TODAY Sports
For these rankings, I utilize a statistical technique called Bayesian Updating to solve the sample size issues. It is a method PFF has used many times in the past, notably when we looked at a number of different draft classes and why the New York Jets needed to draft a quarterback in 2021.
With Bayesian Updating, we can use the dynamics of the historical quarterback market and individual results to project their PFF grades and expected points added (EPA) per play. Even in a single season, there can be drastic differences in sample sizes for quarterbacks. This means that comparing unadjusted rate stats side by side can give too much credit to those who aren’t a big part of their offense and too little credit for those who dominate as their team’s No. 1.
You can find details of how Bayesian Updating is implemented here, including a primer on how we build a posterior belief (or projection) based on historical quarterback results and then update the beliefs (projections) for each quarterback with their actual NFL results on a play-by-play basis.
Before I dive straight into the PFF analytical quarterback rankings, I’ve provided some visualizations to give proper context to how these quarterbacks performed in PFF grade and EPA per play and some important data splits that show the potential for regression moving forward.
Lamar Jackson has the best PFF offensive grade while Tua Tagovailoa posted the best efficiency in terms of EPA per play. Quarterbacks whose efficiency most exceeded our grading were Tagovailoa, Patrick Mahomes and Trevor Lawrence. On the flip side, Davis Mills and Mitchell Trubisky have graded much better than their expected points-based efficiency.
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