Week 1 · Thu, Sep 10, 8:35 PM
Model leans
LAR
this band is 49.8% accurate, so treat it as no call
Model spread
LAR -3.4
Market spread
3.5
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
EPA allowed
Passing efficiency· weight 8.0
Offense
EPA allowed
Drive-sustaining rate· weight 6.6
Offense
EPA allowed
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
defensive rate
Rushing efficiency
Offense
EPA allowed
Completion % Over Expected
Offense
EPA allowed
Giveaways per play
Offense
defensive rate
20+ yard completions per attempt
Offense
EPA allowed
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
- SchemeLAR
LAR faces a 4-3 front
SF's roster profiles as a 4-3 base defense.
Base front is inferred from roster composition (agrees with snap data at Spearman 0.50). Most teams play nickel by default, so this is a weak descriptor: measured defensive efficiency carries far more weight in the model.
- 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.
- Recent formSF
SF -2.4 pts/game trend
Over the last 10 games (exponentially weighted), SF has been being outscored by opponents by 2.4 points per game.
- Context
Divisional matchup
Familiar opponents; historically tighter margins.
- Context
Neutral site
No home-field advantage applied.
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.