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Week 6 · Sun, Oct 18, 1:00 PM

NYJatNE
High confidencethis band has been right 74.1% of the time
NYJNE

Model picks

NE

Model spread

NE -9.1

Market spread

9.5

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

NYJ2
NE43

EPA allowed

NYJ0
NE98

Passing efficiency· weight 8.0

Offense

NYJ1
NE35

EPA allowed

NYJ0
NE98

Drive-sustaining rate· weight 6.6

Offense

NYJ1
NE43

EPA allowed

NYJ1
NE93

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

NYJ10
NE2

defensive rate

NYJ3
NE66

Rushing efficiency

Offense

NYJ39
NE77

EPA allowed

NYJ30
NE64

Completion % Over Expected

Offense

NYJ1
NE76

EPA allowed

NYJ3
NE89

Giveaways per play

Offense

NYJ56
NE54

defensive rate

NYJ0
NE61

20+ yard completions per attempt

Offense

NYJ0
NE97

EPA allowed

NYJ3
NE100

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 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.

  • Recent formNYJ

    NYJ -18.9 pts/game trend

    Over the last 10 games (exponentially weighted), NYJ has been being outscored by opponents by 18.9 points per game.

  • VenueNE

    NE 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.

  • Context

    Divisional matchup

    Familiar opponents; historically tighter margins.

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.