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Week 7 · Sun, Oct 25, 4:25 PM

LARatLV
Low confidencethis band has been right 57.3% of the time
LARLV

Model picks

LV

Model spread

LV -1.7

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

LAR96
LV0

EPA allowed

LAR30
LV67

Passing efficiency· weight 8.0

Offense

LAR90
LV2

EPA allowed

LAR33
LV43

Drive-sustaining rate· weight 6.6

Offense

LAR95
LV1

EPA allowed

LAR28
LV71

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

LAR88
LV2

defensive rate

LAR44
LV85

Rushing efficiency

Offense

LAR90
LV1

EPA allowed

LAR30
LV63

Completion % Over Expected

Offense

LAR35
LV18

EPA allowed

LAR48
LV34

Giveaways per play

Offense

LAR92
LV43

defensive rate

LAR45
LV9

20+ yard completions per attempt

Offense

LAR96
LV5

EPA allowed

LAR45
LV88

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 formLV

    LV -11.4 pts/game trend

    Over the last 10 games (exponentially weighted), LV has been being outscored by opponents by 11.4 points per game.

  • Recent formLAR

    LAR +5.2 pts/game trend

    Over the last 10 games (exponentially weighted), LAR has been outscoring opponents by 5.2 points per game.

  • VenueLV

    LV 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.