THE SHORT ANSWER
The Seattle Seahawks hold No. 1 heading into Week 5 at 1699 ELO after beating the Chargers 30–23. Buffalo (1644) stays second despite a 29–26 home loss to New England, and unbeaten San Francisco (1636) and Minnesota (1619) follow. Full table below.
Every week, Spreadspoke ranks every NFL team by ELO rating, a single number that rises with wins and falls with losses. How far it moves depends on the margin of victory and on how strong the opponent was. The league average is 1500.
NFL ELO power rankings (all 32 teams)
Ratings are before this week's games. Move is the change in rank from last week's rankings.
| Rank | Move | Team | Record | ELO | Week ± | Season | Last Game |
|---|---|---|---|---|---|---|---|
| 1 | – | Seattle Seahawks | 3–1 | 1699 | +5 | +7 | W 30–23 vs LAC |
| 2 | – | Buffalo Bills | 3–1 | 1644 | −21 | +21 | L 26–29 vs NE |
| 3 | – | San Francisco 49ers | 4–0 | 1636 | +18 | ▲ +71 | W 24–14 vs DEN |
| 4 | – | Minnesota Vikings | 4–0 | 1619 | +5 | ▲ +62 | W 15–10 vs MIA |
| 5 | ▲ 1 | Jacksonville Jaguars | 3–1 | 1612 | +17 | ▲ +46 | W 22–17 @ CIN |
| 6 | ▲ 1 | Los Angeles Rams | 2–2 | 1603 | +17 | −19 | W 24–20 @ PHI |
| 7 | ▲ 1 | New England Patriots | 2–2 | 1600 | +21 | −2 | W 29–26 @ BUF |
| 8 | ▼ 3 | Denver Broncos | 2–2 | 1592 | −18 | ▼ −26 | L 14–24 @ SF |
| 9 | ▲ 2 | Kansas City Chiefs | 4–0 | 1569 | +11 | ▲ +77 | W 30–27 @ LVR |
| 10 | – | Chicago Bears | 3–1 | 1569 | +8 | ▲ +40 | W 23–12 vs NYJ |
| 11 | ▲ 1 | Baltimore Ravens | 3–1 | 1557 | +4 | +4 | W 24–18 vs TEN |
| 12 | ▼ 3 | Detroit Lions | 2–2 | 1539 | −24 | ▼ −28 | L 26–32 @ CAR |
| 13 | – | Philadelphia Eagles | 2–2 | 1535 | −17 | ▼ −34 | L 20–24 vs LAR |
| 14 | – | Houston Texans | 0–4 | 1517 | ▼ −25 | ▼ −102 | L 30–34 vs DAL |
| 15 | – | Cincinnati Bengals | 2–2 | 1506 | −18 | ▲ +28 | L 17–22 vs JAX |
| 16 | – | Pittsburgh Steelers | 2–2 | 1504 | −15 | −6 | L 24–27 @ CLE |
| 17 | – | Green Bay Packers | 2–2 | 1487 | +15 | ▼ −46 | W 17–14 @ TB |
| 18 | – | Atlanta Falcons | 2–2 | 1485 | ▲ +35 | +22 | W 45–24 @ NO |
| 19 | ▲ 1 | Dallas Cowboys | 2–2 | 1470 | ▲ +25 | +20 | W 34–30 @ HOU |
| 20 | ▲ 1 | Indianapolis Colts | 2–2 | 1467 | ▲ +30 | +10 | W 30–13 @ WAS |
| 21 | ▲ 4 | Carolina Panthers | 2–2 | 1449 | +24 | ▲ +36 | W 32–26 vs DET |
| 22 | ▲ 2 | Cleveland Browns | 3–1 | 1446 | +15 | ▲ +37 | W 27–24 vs PIT |
| 23 | ▲ 3 | New York Giants | 3–1 | 1443 | +18 | ▲ +34 | W 36–24 vs ARI |
| 24 | ▼ 2 | Los Angeles Chargers | 0–4 | 1431 | −4 | ▼ −97 | L 23–30 @ SEA |
| 25 | ▼ 2 | Las Vegas Raiders | 3–1 | 1422 | −12 | ▲ +93 | L 27–30 vs KC |
| 26 | ▼ 7 | Washington Commanders | 1–3 | 1416 | ▼ −30 | ▼ −41 | L 13–30 vs IND |
| 27 | ▲ 1 | Tampa Bay Buccaneers | 0–4 | 1409 | −14 | ▼ −67 | L 14–17 vs GB |
| 28 | ▼ 1 | New Orleans Saints | 1–3 | 1390 | ▼ −35 | ▼ −32 | L 24–45 vs ATL |
| 29 | ▲ 1 | Miami Dolphins | 0–4 | 1381 | −5 | ▼ −70 | L 10–15 @ MIN |
| 30 | ▼ 1 | Arizona Cardinals | 1–3 | 1381 | −18 | +7 | L 24–36 @ NYG |
| 31 | – | New York Jets | 1–3 | 1344 | −8 | +6 | L 12–23 @ CHI |
| 32 | – | Tennessee Titans | 0–4 | 1278 | −5 | ▼ −51 | L 18–24 @ BAL |
Tied ratings are ordered by unrounded ELO.
Who has moved most since Week 1
ELO points gained or lost since the start of the season.
Sample code: build the rankings yourself
The rankings come from spreadspoke_enhanced.csv, which carries pre-game and post-game ELO for both teams in every game.
import pandas as pd
# Spreadspoke enhanced game file: one row per game, 1966 to present
games = pd.read_csv("spreadspoke_enhanced.csv", low_memory=False)
# Week 4 of the 2026 regular season
wk4 = games[
(games["schedule_season"] == 2026)
& (games["schedule_week_str"].astype(str) == "4")
]
# Stack home and away sides into one row per team
home = wk4[["home_team_id", "elo_home_pre", "elo_home_post"]]
home.columns = ["team", "elo_pre", "elo_post"]
away = wk4[["away_team_id", "elo_away_pre", "elo_away_post"]]
away.columns = ["team", "elo_pre", "elo_post"]
teams = pd.concat([home, away], ignore_index=True)
# Rank 1 to 32 on post-Week 4 ELO
teams["wk4_change"] = teams["elo_post"] - teams["elo_pre"]
teams = teams.sort_values("elo_post", ascending=False).reset_index(drop=True)
teams.index += 1
print(teams.round(0))
The ELO update behind each post-game number uses K = 20, a 65-point home-field edge (none at neutral sites) and a margin-of-victory multiplier. Here it is as a function you can run on any game:
import math
def elo_update(home_elo, away_elo, home_pts, away_pts, neutral=False, k=20, hfa=65):
diff = home_elo - away_elo + (0 if neutral else hfa)
p_home = 1 / (1 + 10 ** (-diff / 400)) # home win probability
result = 1 if home_pts > away_pts else 0 if home_pts < away_pts else 0.5
winner_diff = diff if result == 1 else -diff
mov_mult = math.log(abs(home_pts - away_pts) + 1) * 2.2 / (winner_diff * 0.001 + 2.2)
shift = k * mov_mult * (result - p_home)
return home_elo + shift, away_elo - shift
# Week 4: Atlanta won 45-24 at New Orleans
print(elo_update(1425, 1450, 24, 45)) # (1389.9, 1485.1)
GET THE DATA
The file behind these rankings is included in the Spreadspoke ANALYST package. It includes historical data for every NFL game since 1966—including scores, closing spreads, weather, and pre- and post-game ELO ratings.
See ANALYST pricing →Frequently Asked Questions
What is a good ELO rating?
ELO centers around 1500 for an average team. Strong playoff-caliber teams sit above 1600; rebuilding teams fall below 1400. The gap between two teams maps to an expected point spread.
Methodology
All figures are computed from the Spreadspoke Analyst data package: spreadspoke_enhanced.csv (results with pre- and post-game ELO: elo_home_pre, elo_home_post, etc.) and nfl_elo_handicapping_master.csv (ELO, closing spreads and model edge per game). Coverage is all 64 regular-season games from Weeks 1–4 of 2026. The model uses K = 20, a 65-point home-field advantage (none at neutral sites, such as Washington vs. Indianapolis in Week 4) and a margin-of-victory multiplier. League average is 1500. This page is published weekly while games are played.
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