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Week 12 · Sun, Nov 29, 4:25 PM

WASatARI
Low confidencethis band has been right 57.3% of the time
WASARI

Model picks

ARI

Model spread

ARI -0.7

Market spread

n/a

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

WAS46
ARI49

EPA allowed

WAS3
ARI1

Passing efficiency· weight 8.0

Offense

WAS38
ARI37

EPA allowed

WAS11
ARI2

Drive-sustaining rate· weight 6.6

Offense

WAS64
ARI61

EPA allowed

WAS6
ARI1

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

WAS48
ARI14

defensive rate

WAS79
ARI14

Rushing efficiency

Offense

WAS81
ARI67

EPA allowed

WAS2
ARI12

Completion % Over Expected

Offense

WAS57
ARI57

EPA allowed

WAS51
ARI22

Giveaways per play

Offense

WAS40
ARI58

defensive rate

WAS3
ARI5

20+ yard completions per attempt

Offense

WAS50
ARI53

EPA allowed

WAS60
ARI26

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 formARI

    ARI -13.4 pts/game trend

    Over the last 10 games (exponentially weighted), ARI has been being outscored by opponents by 13.4 points per 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.

  • VenueARI

    ARI 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.