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Week 8 · Sun, Nov 1, 1:00 PM

ARIatDAL
High confidencethis band has been right 74.1% of the time
ARIDAL

Model picks

DAL

Model spread

DAL -3.8

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

ARI49
DAL73

EPA allowed

ARI1
DAL0

Passing efficiency· weight 8.0

Offense

ARI37
DAL70

EPA allowed

ARI2
DAL1

Drive-sustaining rate· weight 6.6

Offense

ARI61
DAL80

EPA allowed

ARI1
DAL2

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

ARI14
DAL74

defensive rate

ARI14
DAL39

Rushing efficiency

Offense

ARI67
DAL60

EPA allowed

ARI12
DAL2

Completion % Over Expected

Offense

ARI57
DAL33

EPA allowed

ARI22
DAL3

Giveaways per play

Offense

ARI58
DAL66

defensive rate

ARI5
DAL2

20+ yard completions per attempt

Offense

ARI53
DAL60

EPA allowed

ARI26
DAL11

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 formDAL

    DAL -5.6 pts/game trend

    Over the last 10 games (exponentially weighted), DAL has been being outscored by opponents by 5.6 points per 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.

  • VenueDAL

    DAL 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.