2026 NFL Week 8 Power Rankings

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.

Ratings are whole numbers; teams with the same rating are listed alphabetically.

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)

# 2026 regular season through Week 7
season = games[games["schedule_season"] == 2026].copy()
season["week"] = season["schedule_week_str"].astype(int)
season = season[season["week"] <= 7]

# Stack home and away sides into one row per team per game
home = season[["week", "home_team_id", "elo_home_pre", "elo_home_post"]]
home.columns = ["week", "team", "elo_pre", "elo_post"]
away = season[["week", "away_team_id", "elo_away_pre", "elo_away_post"]]
away.columns = ["week", "team", "elo_pre", "elo_post"]
teams = pd.concat([home, away], ignore_index=True)

# Each team's latest game (teams on a Week 7 bye keep their Week 6 rating)
latest = teams.sort_values("week").groupby("team").tail(1)

# Rank 1 to 32 on current ELO
latest = latest.sort_values("elo_post", ascending=False).reset_index(drop=True)
latest.index += 1
print(latest[["team", "week", "elo_post"]])

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 7: Carolina Panthers lost to Tampa Bay Buccaneers 13-43
print(elo_update(1486, 1420, 13, 43))   # (1436.3, 1469.7)

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 107 regular-season games from Weeks 1–7 of 2026; Bills, Chargers, Commanders, and Jaguars were on a bye in Week 7 and keep their Week 6 rating. The model uses K = 20, a 65-point home-field advantage (none at neutral sites, such as Pittsburgh vs. New Orleans at Stade de France in Week 7) and a margin-of-victory multiplier. League average is 1500. This page is published weekly while games are played.