Research How we built our NFL power rating
How we built our NFL power rating
We published a simple model, got called on its blind spot, and rebuilt it on 6,223 games of opponent adjusted margins. The new version won all four test seasons. Here is the whole build, including the number most services would bury.
Detroit is number one in our NFL power rating. The Lions won nine games last year. That sentence is the reason this article exists, because the model that produced it had to be torn down and rebuilt before it could see what the scoreboard was saying.
The challenge
Our first NFL rating was simple on purpose. It predicted season win totals from prior season data, and it carried an uncomfortable finding we published anyway: last year’s wins, regressed toward the mean, beat every richer feature set we tested. Point differential, EPA per play, coaching changes, quarterback turnover. Out of sample, none of it out-predicted the plain baseline, so the plain baseline is what went live, with its error printed next to it.
Then it got challenged from two directions at once. A reader made the obvious objection and the founder made the same one internally: raw wins have a terrible memory. In 2025, Denver won fourteen games on a plus 90 point differential, about plus 5.3 a game. Seattle also won fourteen, on plus 191. A model that reads wins alone thinks those two teams are the same team. Anyone who watched them knows they were not. If the rating cannot tell a dominant fourteen wins from a lucky fourteen wins, it is grading the standings, not the football.
Fair hit. We rebuilt.
The build
The rebuild changed the unit of analysis. Instead of asking “how many games will this team win,” the new model asks a smaller question thousands of times: “by how much should this team beat that team.” Team ratings come from a ridge regression on game margins across 6,223 games from 2002 through 2025, every one of them with a closing spread attached.
Three details matter.
First, the ratings are opponent adjusted by construction. A twenty point win over a good defense moves your number more than a twenty point win over a bad one, because every margin is scored against the rating of the team on the other side.
Second, the model is fit walk-forward. At every week of every season, the ratings are rebuilt using only games played strictly before that week. No prediction ever sees the future. The window covers three seasons with decayed weights: current season games at full weight, last season at 0.2, two seasons back at 0.1. The tuning that picked those weights ran on 2004 through 2021 only, 4,624 games, and never touched the seasons we would later test on.
Third, extra features had to earn their place one layer at a time, judged only on held out data. Rest differential failed. Per team home field advantage failed. Roster age squeaked in with an improvement small enough that we treat it as decoration. The real find was quarterback continuity: a team starting someone other than its expected quarterback costs about 2.8 points, and that layer produced a genuine out of sample improvement. Everything that failed the test was cut, including features we liked.
Did it beat the old model
Held out seasons 2022 through 2025, average absolute error in points when predicting game margins. The old model’s numbers come from its own functional form refit on pre 2022 data, so both models sat the same exam.
| Season | Games | v3 | v1 implied | Closing spread |
|---|---|---|---|---|
| 2022 | 271 | 9.235 | 9.413 | 8.742 |
| 2023 | 272 | 10.490 | 10.682 | 9.901 |
| 2024 | 272 | 9.953 | 10.785 | 9.610 |
| 2025 | 272 | 10.323 | 10.819 | 9.722 |
| Average | 1,087 | 10.000 | 10.425 | 9.494 |
The new model beat the old one in all four seasons, by 0.425 points of error on average. That is the win, and it is real.
Look at the third column, though. The closing spread beat both models in all four seasons. Which brings us to the part of this article most services would never write.
The number most services would hide
Grade the model against the spread. Take its predicted margin, compare it to the closing line, pick the side it implies, and count. Pushes excluded from every denominator.
| Season | Record | Pct |
|---|---|---|
| 2022 | 125-136 | 47.9% |
| 2023 | 118-140 | 45.7% |
| 2024 | 138-130 | 51.5% |
| 2025 | 123-148 | 45.4% |
| Pooled | 504-554 | 47.64% |
47.6 percent against the spread. A model that beats our old model in four straight seasons still loses to the market. That is not a bug in our build. The closing line is the sharpest number in football. It is the compressed opinion of every syndicate, every model, and every dollar that moved it, and any shop telling you their power rating out-picks it week after week is selling you a story.
So we use the model for what it proved it can do, and we refuse to use it for what it cannot. It predicts margins better than our old model, which makes it a better engine for expected wins and for reading season long markets. It is not a spread picking machine, and we do not dress it up as one. That gap between our number and the market’s number, when it beats the model’s own error, is one of the things our three-seat analyst panel weighs before it calls a lean. The model doesn’t fire a pick on its own. The model earns your attention by knowing its limits, and by putting them in a table instead of a footnote.
What it changes on the board
As of the Aug 18 build, before week 1, the old model had Denver sharing the top spot at 10.65 expected wins. The new one dropped Denver to 9.75, seventh, because those fourteen wins came with thinner margins than the record suggested. Detroit took over at number one: 10.76 expected wins, built on nine wins at plus 4.0 points per game. That profile, a strong scoring margin hiding under a mediocre record, is precisely the case a margin model exists to catch, and the market had Detroit’s win total hanging at 10.5.
On that same Aug 18 build, the widest gap on the board belonged to Miami. The model put them at 7.21 expected wins against a 3.5 line, a plus 3.71 gap, a position locked before week 1 that fully saw Miami’s minus 4.5 points per game last season, the new coach, and the new quarterback. It grades at season end against that locked number; the nightly refit does not move it.
The live board has moved since then. As of the Sep 16 refit, which updates nightly on every completed 2026 game, Baltimore leads at 11.28 expected wins, with Detroit fourth at 10.65 and Denver 17th at 8.40. The full table, every rating and every expected win number, lives at edgelabs.bet/nfl/power-ratings. Members get the leans first, plus the tools and the database behind them. Founding access is open at edgelabs.bet/join.