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Week 3 · Sun, Sep 27, 1:00 PM

SEAatWAS
Low confidencethis band has been right 57.3% of the time
SEAWAS

Model picks

SEA

Model spread

SEA -4.1

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

SEA76
WAS46

EPA allowed

SEA98
WAS3

Passing efficiency· weight 8.0

Offense

SEA75
WAS38

EPA allowed

SEA98
WAS11

Drive-sustaining rate· weight 6.6

Offense

SEA79
WAS64

EPA allowed

SEA82
WAS6

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

SEA42
WAS48

defensive rate

SEA67
WAS79

Rushing efficiency

Offense

SEA64
WAS81

EPA allowed

SEA64
WAS2

Completion % Over Expected

Offense

SEA73
WAS57

EPA allowed

SEA66
WAS51

Giveaways per play

Offense

SEA95
WAS40

defensive rate

SEA87
WAS3

20+ yard completions per attempt

Offense

SEA26
WAS50

EPA allowed

SEA47
WAS60

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 formWAS

    WAS -5.7 pts/game trend

    Over the last 10 games (exponentially weighted), WAS has been being outscored by opponents by 5.7 points per 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.

  • VenueWAS

    WAS 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.