NFL · Empirical Bayes

True-Talent Leaderboards

A raw single-season rate is a noisy guess at a player’s real ability, and the noise is worst for the smallest samples — so a naïve leaderboard is topped by whoever got lucky in the fewest tries. This one shrinks every rate toward its position-group prior by how much the sample can be trusted, and shows a 90% credible interval instead of a point. Toggle raw vs. shrunk to watch the flukes fall back to the pack.

Dropback success rate · 2025

Share of dropbacks (incl. sacks & scrambles) with positive EPA.

Beats raw by
15.6%
lower out-of-sample error
RMSE raw → shrunk
5.5% → 4.6%
odd vs. even weeks
Split-half reliability
0.47
how repeatable the raw stat is
Stabilizes at
143
dropbacks to trust the number
shrunkrawthe shrink90% CI
35%40%45%50%55%avg1Drake MayeNE · 54052.4%2Matthew StaffordLA · 61751.6%3Brock PurdySF · 29850.5%4Sam DarnoldSEA · 50250.2%5Mac JonesSF · 30449.9%6Malik WillisGB · 3849.1%7Jordan LoveGB · 46148.5%8Daniel JonesIND · 40948.3%9Mitchell TrubiskyBUF · 3748.2%10Jared GoffDET · 61447.7%11Joe BurrowCIN · 27847.5%12Dak PrescottDAL · 63147.3%13Patrick MahomesKC · 53747.2%14Carson WentzMIN · 18847.1%15Marcus MariotaWAS · 24446.2%16Josh AllenBUF · 50646.2%17Josh JohnsonWAS · 5446.1%18Mason RudolphPIT · 5446.1%19Trevor LawrenceJAX · 60045.9%20Tyler HuntleyBAL · 7445.5%21Tanner McKeePHI · 4645.4%22C.J. StroudHOU · 44645.3%23Lamar JacksonBAL · 33845.3%24Justin HerbertLAC · 56645.1%25Michael Penix Jr.ATL · 29045.0%26Tua TagovailoaMIA · 41544.8%27Jacoby BrissettARI · 53044.7%28Andy DaltonCAR · 4544.6%29Jayden DanielsWAS · 20544.5%30Kirk CousinsATL · 28144.3%31Bo NixDEN · 63844.3%

Each row is a player: the solid dot is the shrunk estimate, the hollow dot the raw rate, joined by the red pull of regression; the grey bar is the 90% credible interval. A hollow shrunk dot means the sample is below the stabilization line — the number is mostly the position prior. Switch to Raw rank and watch the small-sample names climb.

Dropback success rate · 2025 · full board

Dropback success rate leaderboard for the 2025 NFL season, shrunk by empirical Bayes with 90% credible intervals, ranked by the shrunk estimate.
#PlayerTeamPosdropbacksRawShrunk90% interval
1Drake MayeNEQB54054.4%52.4%49.3%55.5%
2Matthew StaffordLAQB61753.2%51.6%48.6%54.5%
3Brock PurdySFQB29853.4%50.5%46.6%54.4%
4Sam DarnoldSEAQB50251.8%50.2%47.0%53.4%
5Mac JonesSFQB30452.3%49.9%46.0%53.7%
6Malik Willis◦ provisionalGBQB3865.8%49.1%43.0%55.2%
7Jordan LoveGBQB46149.7%48.5%45.1%51.8%
8Daniel JonesINDQB40949.6%48.3%44.9%51.8%
9Mitchell Trubisky◦ provisionalBUFQB3762.2%48.2%42.1%54.4%
10Jared GoffDETQB61448.4%47.7%44.7%50.6%
11Joe BurrowCINQB27848.9%47.5%43.5%51.5%
12Dak PrescottDALQB63147.9%47.3%44.3%50.2%
13Patrick MahomesKCQB53747.9%47.2%44.0%50.3%
14Carson WentzMINQB18848.9%47.1%42.6%51.6%
15Marcus MariotaWASQB24447.1%46.2%42.1%50.4%
16Josh AllenBUFQB50646.6%46.2%43.0%49.4%
17Josh Johnson◦ provisionalWASQB5450.0%46.1%40.3%52.0%
18Mason Rudolph◦ provisionalPITQB5450.0%46.1%40.3%52.0%
19Trevor LawrenceJAXQB60046.2%45.9%42.9%48.9%
20Tyler Huntley◦ provisionalBALQB7447.3%45.5%40.0%51.1%
21Tanner McKee◦ provisionalPHIQB4647.8%45.4%39.5%51.4%
22C.J. StroudHOUQB44645.5%45.3%41.9%48.7%
23Lamar JacksonBALQB33845.6%45.3%41.6%49.0%
24Justin HerbertLACQB56645.2%45.1%42.0%48.2%
25Michael Penix Jr.ATLQB29045.2%45.0%41.1%48.9%
26Tua TagovailoaMIAQB41544.8%44.8%41.3%48.2%
27Jacoby BrissettARIQB53044.7%44.7%41.6%47.9%
28Andy Dalton◦ provisionalCARQB4544.4%44.6%38.7%50.6%
29Jayden DanielsWASQB20544.4%44.5%40.1%48.9%
30Kirk CousinsATLQB28144.1%44.3%40.4%48.3%
31Spencer RattlerNOQB27244.1%44.3%40.3%48.3%
32Bo NixDENQB63844.2%44.3%41.4%47.2%
33Jameis Winston◦ provisionalNYGQB6943.5%44.3%38.7%49.9%
34Tyler ShoughNOQB35643.8%44.0%40.4%47.7%
35Kyler MurrayARIQB17743.5%44.0%39.5%48.6%
36Cooper Rush◦ provisionalBALQB5341.5%43.8%38.0%49.6%
37Tyrod TaylorNYJQB14742.9%43.7%39.0%48.5%
38Philip Rivers◦ provisionalINDQB9742.3%43.7%38.5%49.0%
39Jalen HurtsPHIQB48643.2%43.5%40.3%46.8%
40Caleb WilliamsCHIQB59443.3%43.5%40.5%46.5%
41Baker MayfieldTBQB58443.1%43.4%40.4%46.5%
42Aaron RodgersPITQB52743.1%43.4%40.3%46.6%
43Bryce YoungCARQB50642.7%43.1%39.9%46.3%
44Quinn Ewers◦ provisionalMIAQB9140.7%43.1%37.8%48.4%
45Geno SmithLVQB50342.1%42.7%39.5%45.9%
46Justin FieldsNYJQB23341.2%42.5%38.3%46.7%
47Jaxson DartNYGQB37541.6%42.4%38.9%46.0%
48Davis MillsHOUQB16939.6%41.9%37.4%46.6%
49Joe FlaccoCINQB43241.0%41.9%38.5%45.3%
50J.J. McCarthyMINQB27340.3%41.8%37.8%45.8%
51Jake Browning◦ provisionalCINQB13438.1%41.4%36.6%46.4%
52Russell Wilson◦ provisionalNYGQB12936.4%40.7%35.9%45.7%
53Riley Leonard◦ provisionalINDQB7031.4%40.3%34.8%45.9%
54Kenny Pickett◦ provisionalLVQB5226.9%39.9%34.2%45.7%
55Brandon Allen◦ provisionalTENQB3116.1%39.6%33.5%45.7%
56Trey Lance◦ provisionalLACQB5927.1%39.5%33.9%45.2%
57Chris Oladokun◦ provisionalKCQB6225.8%38.9%33.4%44.6%
58Cam WardTENQB59236.8%38.3%35.4%41.3%
59Shedeur SandersCLEQB23533.6%37.8%33.7%41.9%
60Max Brosmer◦ provisionalMINQB8525.9%37.6%32.4%43.0%
61Brady CookNYJQB17331.2%37.3%32.9%41.8%
62Dillon GabrielCLEQB20431.9%37.1%32.9%41.4%

How the shrinkage works

Two estimators

Rate stats (completion %, success rate, catch rate) use a beta-binomial model: a Beta(α, β) prior fit by marginal likelihood over each position group, then a Beta posterior per player. Per-play averages (EPA, CPOE, yards) use a normal-normal model with DerSimonian–Laird between-player variance. Both pull each player toward their group by exactly how thin their sample is.

Does it help? & the fine print

The trust panel’s numbers come from a leakage-free odd/even-week holdout: fit on odd weeks, predict even-week raw. Shrinkage lowers out-of-sample error for every stat. Priors are fit per season and per position (WR and TE separately), so a TE’s baseline isn’t a skill. Regular season only, 2016–2025. Rushing and receiving efficiency are heavily scheme-driven — read the wide bands as the honesty they are.

Source: nflverse play-by-play. Counted and computed deterministically — never modeled by a language model. Built by build_nfl_leaderboards.py (byte-reproducible).