Week 1 · Thu, 8:35 PM EDT · Netflix
San Francisco 49ers at Los Angeles Rams
Neutral site · Melbourne Cricket Ground, Melbourne, VIC
Market line for comparison: LAR -3.5 · O/U 48.5 (DraftKings). The model never sees this.
Rating inputs
Each starting rating is a weighted blend of five real football inputs, not just wins and losses. Every term below is measured from the 2025 season.
San Francisco 49ers
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 12-5 in 2025, opponent-adjusted | 1565 | 80% | 1251.9 |
Scoring strength 437–371 points (+66.0 differential) → 59.6% expected win rate | 1567 | 5% | 78.4 |
Late-season form same replay with the closing 6 weeks counted double | 1585 | 15% | 237.8 |
Offense / defense split 25.7 PPG scored, 21.8 allowed → offense 1567, defense 1530 | 1597 | 0% | 0.0 |
| Blended 2025 rating | 1568.0 | ||
Offseason regression 25% back toward the 1500 league mean | 1551.0 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| SF 2026 starting rating | 1551 | ||
Los Angeles Rams
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 12-5 in 2025, opponent-adjusted | 1568 | 80% | 1254.8 |
Scoring strength 518–346 points (+172.0 differential) → 72.2% expected win rate | 1666 | 5% | 83.3 |
Late-season form same replay with the closing 6 weeks counted double | 1568 | 15% | 235.2 |
Offense / defense split 30.5 PPG scored, 20.4 allowed → offense 1686, defense 1567 | 1753 | 0% | 0.0 |
| Blended 2025 rating | 1573.3 | ||
Offseason regression 25% back toward the 1500 league mean | 1555.0 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| LAR 2026 starting rating | 1555 | ||
The calculation
Every term the model applies, in order, from both teams' starting ratings to the number on the card.
Win probability
1 / (1 + 10−(4.0 / 400))
50.6% LAR
Model spread
4.0 ÷ 25, rounded to the nearest half point
LAR 0.0
Confidence band
Move the factors yourself
Every term feeds one number: the adjusted Elo difference. Drag any of them and watch the spread and confidence follow.
Drag to see how much a stronger or weaker host changes the call.
The two ratings only matter through their difference.
Neutral site, so the model applies 0. The tuned value elsewhere is 25.
5 Elo per extra day of rest, capped at 25 either way.
A manual thumb on the scale, in the home team's direction.
Win probability
50.6% LAR
Model value
Model spread
LAR 0.0
Elo diff +4.0 · Coin flip
Probability curve
The logistic curve from −600 to +600 Elo, home perspective. The dot is your current setting; the faint vertical line is where the model itself sits. Notice the curve is flattest at the extremes — near a coin flip, every point of Elo moves the number most.
Where the ratings came from
Each rating is the end state of a full 2025 season of Elo updates, regressed toward the league mean for the new season.
- 2025 final rating
- 1564.8
- Regress 25% toward 1500
- −13.8
- 2026 starting rating
- 1551.0
2025 Elo trail
Biggest swings
- W 41–22 at ARI+43.9
- L 26–42 vs LAR−35.5
- L 15–26 at HOU−34.7
- 2025 final rating
- 1568.5
- Regress 25% toward 1500
- −13.5
- 2026 starting rating
- 1555.0
2025 Elo trail
Biggest swings
- W 35–7 at JAX+43.8
- W 42–26 at SF+35.5
- W 33–19 at TEN+35.3
Matchup factors
Everything beyond the two base ratings that moved this specific number.
Home field
Neutral
Played at Melbourne Cricket Ground, Melbourne. Neither side gets the 25-point host bonus.
2025 head to head
- Week 5: SF won 26–23 (LAR at home)
- Week 10: LAR won 42–26 (LAR on the road)
Strength of schedule
Who each team actually played in 2025, measured by their opponents' current ratings. This is context for reading the ratings — it is not an input to the prediction above, because beating good teams already raises a team's Elo more.
1509
average opponent rating · 7th toughest of 32
- vs above-average teams
- 5-4
- vs below-average teams
- 7-1
1510
average opponent rating · 6th toughest of 32
- vs above-average teams
- 6-4
- vs below-average teams
- 6-1
What the model does not know
- · Injuries, suspensions and depth-chart changes.
- · Coaching and roster turnover between seasons.
- · Weather, travel distance and short-week fatigue beyond raw rest days.
- · Anything a betting market has priced in since the season ended.