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.

Rush success rate · 2018

Share of carries with positive EPA. Blocking & scheme are large, un-controlled inputs.

Beats raw by
19.4%
lower out-of-sample error
RMSE raw → shrunk
9.1% → 7.3%
odd vs. even weeks
Split-half reliability
0.20
how repeatable the raw stat is
Stabilizes at
122
carrys to trust the number
shrunkrawthe shrink90% CI
30%35%40%45%50%avg1C.J. AndersonLA · 6746.3%2Marlon MackIND · 19744.3%3Todd GurleyLA · 26044.1%4Gus EdwardsBAL · 13943.8%5Aaron JonesGB · 13343.7%6Phillip LindsayDEN · 19343.3%7Malcolm BrownLA · 4343.3%8Wendell SmallwoodPHI · 8842.6%9Jordan WilkinsIND · 6042.5%10Austin EkelerLAC · 10642.3%11Alvin KamaraNO · 19742.1%12Damien WilliamsKC · 5042.1%13Christian McCaffr…CAR · 21942.0%14Zach ZennerDET · 5542.0%15Marshawn LynchLV · 9041.7%16Cordarrelle Patte…NE · 4241.7%17Kapri BibbsGB · 2141.5%18Doug MartinLV · 17241.3%19Raheem MostertSF · 3441.3%20Jaylen SamuelsPIT · 5641.2%21Mike DavisSEA · 11241.2%22Duke JohnsonCLE · 4241.1%23Derrick HenryTEN · 21541.1%24James ConnerPIT · 21541.1%25Mark IngramNO · 13840.9%26Sony MichelNE · 20940.9%27Matt BreidaSF · 15340.9%28Melvin GordonLAC · 17640.7%29Jay AjayiPHI · 4640.7%30Cameron Artis-Pay…CAR · 1940.7%31Keith FordBUF · 2140.1%32Darren SprolesPHI · 2940.0%33Devontae BookerDEN · 3440.0%34Theo RiddickDET · 4039.7%

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.

Rush success rate · 2018 · full board

Rush success rate leaderboard for the 2018 NFL season, shrunk by empirical Bayes with 90% credible intervals, ranked by the shrunk estimate.
#PlayerTeamPoscarrysRawShrunk90% interval
1C.J. Anderson◦ provisionalLARB6759.7%46.3%40.3%52.2%
2Marlon MackINDRB19747.7%44.3%39.8%48.9%
3Todd GurleyLARB26046.5%44.1%39.9%48.3%
4Gus EdwardsBALRB13948.2%43.8%38.8%48.9%
5Aaron JonesGBRB13348.1%43.7%38.6%48.8%
6Phillip LindsayDENRB19346.1%43.3%38.7%47.9%
7Malcolm Brown◦ provisionalLARB4355.8%43.3%37.0%49.7%
8Wendell Smallwood◦ provisionalPHIRB8847.7%42.6%37.0%48.2%
9Jordan Wilkins◦ provisionalINDRB6050.0%42.5%36.5%48.6%
10Austin Ekeler◦ provisionalLACRB10646.2%42.3%36.9%47.7%
11Alvin KamaraNORB19744.2%42.1%37.6%46.7%
12Damien Williams◦ provisionalKCRB5050.0%42.1%36.0%48.3%
13Christian McCaffreyCARRB21943.8%42.0%37.7%46.5%
14Zach Zenner◦ provisionalDETRB5549.1%42.0%36.0%48.2%
15Marshawn Lynch◦ provisionalLVRB9045.6%41.7%36.2%47.3%
16Cordarrelle Patterson◦ provisionalNERB4250.0%41.7%35.4%48.1%
17Kapri Bibbs◦ provisionalGBRB2157.1%41.5%34.8%48.4%
18Doug MartinLVRB17243.0%41.3%36.6%46.0%
19Raheem Mostert◦ provisionalSFRB3450.0%41.3%34.9%47.8%
20Jaylen Samuels◦ provisionalPITRB5646.4%41.2%35.2%47.3%
21Mike Davis◦ provisionalSEARB11243.8%41.2%35.9%46.5%
22Duke Johnson◦ provisionalCLERB4247.6%41.1%34.8%47.5%
23Derrick HenryTENRB21542.3%41.1%36.7%45.5%
24James ConnerPITRB21542.3%41.1%36.7%45.5%
25Mark IngramNORB13842.8%40.9%35.9%46.0%
26Sony MichelNERB20942.1%40.9%36.5%45.4%
27Matt BreidaSFRB15342.5%40.9%36.0%45.8%
28Melvin GordonLACRB17642.0%40.7%36.1%45.5%
29Jay Ajayi◦ provisionalPHIRB4645.6%40.7%34.5%47.0%
30Cameron Artis-Payne◦ provisionalCARRB1952.6%40.7%34.0%47.6%
31Jamaal Williams◦ provisionalGBRB12142.1%40.5%35.4%45.7%
32Ezekiel ElliottDALRB30441.1%40.5%36.6%44.4%
33Kareem HuntKCRB18141.4%40.4%35.8%45.1%
34Joe MixonCINRB23740.9%40.2%36.0%44.5%
35Keith Ford◦ provisionalBUFRB2147.6%40.1%33.5%46.9%
36Darren Sproles◦ provisionalPHIRB2944.8%40.0%33.5%46.6%
37Devontae Booker◦ provisionalDENRB3444.1%40.0%33.6%46.5%
38Nick ChubbCLERB19240.6%39.9%35.4%44.5%
39Theo Riddick◦ provisionalDETRB4042.5%39.7%33.5%46.1%
40Frank GoreMIARB15640.4%39.7%34.9%44.6%
41Chris CarsonSEARB24740.1%39.7%35.5%43.9%
42Josh Adams◦ provisionalPHIRB12139.7%39.3%34.2%44.5%
43Alex Collins◦ provisionalBALRB11439.5%39.1%34.0%44.4%
44Tarik Cohen◦ provisionalCHIRB9939.4%39.1%33.8%44.5%
45Jordan HowardCHIRB25039.2%39.1%35.0%43.3%
46Spencer Ware◦ provisionalKCRB5139.2%39.0%32.9%45.1%
47Kenneth Dixon◦ provisionalBALRB6038.3%38.7%32.8%44.7%
48Justin Jackson◦ provisionalLACRB5038.0%38.6%32.6%44.8%
49John Kelly◦ provisionalLARB2737.0%38.5%32.0%45.1%
50Adrian PetersonWASRB25138.3%38.4%34.3%42.6%
51DeAndre Washington◦ provisionalLVRB3036.7%38.4%32.0%45.0%
52Brian Hill◦ provisionalATLRB2035.0%38.3%31.7%45.1%
53T.J. Yeldon◦ provisionalJAXRB10437.5%38.2%33.0%43.6%
54Marcus Murphy◦ provisionalBUFRB5236.5%38.1%32.2%44.3%
55James White◦ provisionalNERB9437.2%38.1%32.8%43.6%
56Kerryon Johnson◦ provisionalDETRB11837.3%38.1%33.0%43.3%
57Leonard FournetteJAXRB13336.8%37.8%32.9%42.8%
58Wayne Gallman◦ provisionalNYGRB5135.3%37.8%31.8%43.9%
59Javorius Allen◦ provisionalBALRB4134.2%37.6%31.5%44.0%
60Rod Smith◦ provisionalDALRB4434.1%37.6%31.5%43.8%
61Chase Edmonds◦ provisionalARIRB6035.0%37.6%31.8%43.5%
62Tevin ColemanATLRB16736.5%37.5%32.9%42.2%
63Lamar MillerHOURB21036.7%37.5%33.1%41.9%
64Jeff Wilson◦ provisionalSFRB6634.8%37.4%31.7%43.3%
65Nyheim Hines◦ provisionalINDRB8535.3%37.4%31.9%43.0%
66Dwayne Washington◦ provisionalNORB2729.6%37.2%30.8%43.8%
67Kenjon Barner◦ provisionalCARRB1926.3%37.1%30.6%43.9%
68Trenton Cannon◦ provisionalNYJRB3831.6%37.1%30.9%43.5%
69Royce FreemanDENRB13035.4%37.0%32.1%42.1%
70Jacquizz Rodgers◦ provisionalTBRB3330.3%37.0%30.7%43.5%
71Kalen Ballage◦ provisionalMIARB3630.6%36.9%30.7%43.4%
72Ronald Jones◦ provisionalTBRB2326.1%36.8%30.3%43.5%
73Alfred Morris◦ provisionalSFRB11134.2%36.6%31.5%41.9%
74Dalvin CookMINRB13334.6%36.6%31.7%41.6%
75Rashaad Penny◦ provisionalSEARB8532.9%36.4%31.0%42.0%
76Corey Clement◦ provisionalPHIRB6931.9%36.3%30.7%42.1%
77Giovani Bernard◦ provisionalCINRB5630.4%36.2%30.3%42.2%
78Kenyan Drake◦ provisionalMIARB12033.3%36.1%31.1%41.2%
79Chris Ivory◦ provisionalBUFRB11533.0%36.0%31.0%41.2%
80Ito Smith◦ provisionalATLRB9032.2%36.0%30.7%41.5%
81Chris Thompson◦ provisionalWASRB4327.9%36.0%29.9%42.2%
82Elijah McGuire◦ provisionalNYJRB9231.5%35.7%30.4%41.1%
83Latavius MurrayMINRB14032.9%35.6%30.8%40.6%
84Jalen Richard◦ provisionalLVRB5628.6%35.6%29.8%41.6%
85Saquon BarkleyNYGRB26333.8%35.4%31.5%39.5%
86Stevan Ridley◦ provisionalPITRB2920.7%35.3%29.1%41.8%
87Bilal Powell◦ provisionalNYJRB8030.0%35.3%29.9%40.9%
88Peyton BarberTBRB23433.3%35.2%31.1%39.4%
89Carlos HydeJAXRB17232.6%35.2%30.6%39.8%
90Alfred BlueHOURB15032.0%35.1%30.4%39.9%
91Mike Gillislee◦ provisionalNORB166.3%35.0%28.5%41.8%
92Rex Burkhead◦ provisionalNERB5726.3%34.8%29.1%40.8%
93Dion LewisTENRB15529.7%33.7%29.1%38.4%
94Isaiah CrowellNYJRB14328.7%33.3%28.6%38.2%
95David JohnsonARIRB25929.7%32.6%28.7%36.6%
96LeSean McCoyBUFRB16128.0%32.6%28.1%37.3%
97LeGarrette BlountDETRB15526.5%31.9%27.4%36.6%

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).