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Week 9 · Sun, Nov 8, 1:00 PM

NYJatKC
Low confidencethis band has been right 57.3% of the time
NYJKC

Model picks

KC

Model spread

KC -2.7

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

NYJ2
KC7

EPA allowed

NYJ0
KC82

Passing efficiency· weight 8.0

Offense

NYJ1
KC5

EPA allowed

NYJ0
KC76

Drive-sustaining rate· weight 6.6

Offense

NYJ1
KC8

EPA allowed

NYJ1
KC94

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
KC5

defensive rate

NYJ3
KC58

Rushing efficiency

Offense

NYJ39
KC60

EPA allowed

NYJ30
KC77

Completion % Over Expected

Offense

NYJ1
KC10

EPA allowed

NYJ3
KC73

Giveaways per play

Offense

NYJ56
KC77

defensive rate

NYJ0
KC12

20+ yard completions per attempt

Offense

NYJ0
KC18

EPA allowed

NYJ3
KC65

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 formKC

    KC -3.2 pts/game trend

    Over the last 10 games (exponentially weighted), KC has been being outscored by opponents by 3.2 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.

  • VenueKC

    KC 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.