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Week 16 · Sun, Dec 27, 1:00 PM

TBatATL
Low confidencethis band has been right 57.3% of the time
TBATL

Model picks

ATL

Model spread

ATL -2.3

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

TB52
ATL46

EPA allowed

TB20
ATL49

Passing efficiency· weight 8.0

Offense

TB45
ATL48

EPA allowed

TB16
ATL65

Drive-sustaining rate· weight 6.6

Offense

TB48
ATL43

EPA allowed

TB27
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

TB37
ATL75

defensive rate

TB23
ATL94

Rushing efficiency

Offense

TB77
ATL42

EPA allowed

TB80
ATL23

Completion % Over Expected

Offense

TB43
ATL19

EPA allowed

TB10
ATL81

Giveaways per play

Offense

TB64
ATL76

defensive rate

TB56
ATL58

20+ yard completions per attempt

Offense

TB68
ATL36

EPA allowed

TB41
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 formTB

    TB -2.1 pts/game trend

    Over the last 10 games (exponentially weighted), TB has been being outscored by opponents by 2.1 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.

  • 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.