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Week 10 · Sun, Nov 15, 1:00 PM

KCatATL
Medium confidencethis band has been right 63.4% of the time
KCATL

Model picks

ATL

Model spread

ATL -3.1

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

KC7
ATL46

EPA allowed

KC82
ATL49

Passing efficiency· weight 8.0

Offense

KC5
ATL48

EPA allowed

KC76
ATL65

Drive-sustaining rate· weight 6.6

Offense

KC8
ATL43

EPA allowed

KC94
ATL61

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

KC5
ATL75

defensive rate

KC58
ATL94

Rushing efficiency

Offense

KC60
ATL42

EPA allowed

KC77
ATL23

Completion % Over Expected

Offense

KC10
ATL19

EPA allowed

KC73
ATL81

Giveaways per play

Offense

KC77
ATL76

defensive rate

KC12
ATL58

20+ yard completions per attempt

Offense

KC18
ATL36

EPA allowed

KC65
ATL25

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 formATL

    ATL -1.1 pts/game trend

    Over the last 10 games (exponentially weighted), ATL has been being outscored by opponents by 1.1 points per 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.

  • VenueATL

    ATL 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.