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Week 1 · Wed, Sep 9, 8:20 PM

NEatSEA
High confidencethis band has been right 74.1% of the time
NESEA

Model picks

SEA

Model spread

SEA -5.2

Market spread

3.5

the market forecasts these games better than this model

How the teams compare

heaviest model weight first · 0–100, oriented so higher is always better

Expected Points Added per play· weight 11.8

Offense

NE43
SEA76

EPA allowed

NE98
SEA98

Passing efficiency· weight 8.0

Offense

NE35
SEA75

EPA allowed

NE98
SEA98

Drive-sustaining rate· weight 6.6

Offense

NE43
SEA79

EPA allowed

NE93
SEA82

Position-weighted availability score· weight 6.6

No data yet for this metric. Nothing has been published for these teams at this point in the week. It will fill in once it exists.

Sacks taken per dropback· weight 5.7

Offense

NE2
SEA42

defensive rate

NE66
SEA67

Rushing efficiency

Offense

NE77
SEA64

EPA allowed

NE64
SEA64

Completion % Over Expected

Offense

NE76
SEA73

EPA allowed

NE89
SEA66

Giveaways per play

Offense

NE54
SEA95

defensive rate

NE61
SEA87

20+ yard completions per attempt

Offense

NE97
SEA26

EPA allowed

NE100
SEA47

This chart is not the model. It shows the inputs that read well as a side-by-side. 7 of the model’s heaviest-weighted inputs have no head-to-head form and are not drawn here: point differential, overall matchup, team win rate, pass matchup among them. The full list of all 55 is on the methodology tab.

Around the game

  • Recent formSEA

    SEA +13.9 pts/game trend

    Over the last 10 games (exponentially weighted), SEA has been outscoring opponents by 13.9 points per game.

  • Recent formNE

    NE +6.6 pts/game trend

    Over the last 10 games (exponentially weighted), NE has been outscoring opponents by 6.6 points per game.

  • VenueSEA

    SEA at home

    Home teams have won 54.7% of games in the training window; the model prices this in rather than applying a fixed bonus.

Every figure here comes from Calibrated Logistic Regression (core + market features), validated walk-forward by season (train on all prior seasons, test on the next). For how the model is built, what the metrics mean and how it has actually performed, read the methodology.