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Week 17 · Sun, Jan 3, 1:00 PM

MINatNYJ
High confidencethis band has been right 74.1% of the time
MINNYJ

Model picks

NYJ

Model spread

NYJ -4.8

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

MIN11
NYJ2

EPA allowed

MIN99
NYJ0

Passing efficiency· weight 8.0

Offense

MIN4
NYJ1

EPA allowed

MIN100
NYJ0

Drive-sustaining rate· weight 6.6

Offense

MIN38
NYJ1

EPA allowed

MIN88
NYJ1

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

MIN1
NYJ10

defensive rate

MIN100
NYJ3

Rushing efficiency

Offense

MIN71
NYJ39

EPA allowed

MIN84
NYJ30

Completion % Over Expected

Offense

MIN22
NYJ1

EPA allowed

MIN100
NYJ3

Giveaways per play

Offense

MIN31
NYJ56

defensive rate

MIN69
NYJ0

20+ yard completions per attempt

Offense

MIN13
NYJ0

EPA allowed

MIN99
NYJ3

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 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.

  • Recent formMIN

    MIN +4.5 pts/game trend

    Over the last 10 games (exponentially weighted), MIN has been outscoring opponents by 4.5 points per game.

  • VenueNYJ

    NYJ 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.