Week 1 · Sun, 8:20 PM EDT · NBC
Dallas Cowboys at New York Giants
MetLife Stadium, East Rutherford, NJ
Market line for comparison: DAL -2.5 · 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.
Dallas Cowboys
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 7-9-1 in 2025, opponent-adjusted | 1437 | 80% | 1149.9 |
Scoring strength 471–511 points (-40.0 differential) → 45.2% expected win rate | 1466 | 5% | 73.3 |
Late-season form same replay with the closing 6 weeks counted double | 1386 | 15% | 207.9 |
Offense / defense split 27.7 PPG scored, 30.1 allowed → offense 1617, defense 1324 | 1441 | 0% | 0.0 |
| Blended 2025 rating | 1431.1 | ||
Offseason regression 25% back toward the 1500 league mean | 1448.3 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| DAL 2026 starting rating | 1448 | ||
New York Giants
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 4-13 in 2025, opponent-adjusted | 1482 | 80% | 1185.6 |
Scoring strength 381–439 points (-58.0 differential) → 41.7% expected win rate | 1442 | 5% | 72.1 |
Late-season form same replay with the closing 6 weeks counted double | 1513 | 15% | 227.0 |
Offense / defense split 22.4 PPG scored, 25.8 allowed → offense 1485, defense 1430 | 1415 | 0% | 0.0 |
| Blended 2025 rating | 1484.6 | ||
Offseason regression 25% back toward the 1500 league mean | 1488.5 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| NYG 2026 starting rating | 1488 | ||
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−(65.2 / 400))
59.3% NYG
Model spread
65.2 ÷ 25, rounded to the nearest half point
NYG -2.5
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.
The tuned value is a flat 25 points for every host.
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
59.3% NYG
Model value
Model spread
NYG -2.5
Elo diff +65.2 · Lean
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
- 1437.3
- Regress 25% toward 1500
- +11.0
- 2026 starting rating
- 1448.3
2025 Elo trail
Biggest swings
- W 44–22 vs WSH+46.9
- L 14–31 at CHI−44.9
- L 17–34 at NYG−41.6
- 2025 final rating
- 1482.0
- Regress 25% toward 1500
- +6.5
- 2026 starting rating
- 1488.5
2025 Elo trail
Biggest swings
- W 34–17 vs PHI+52.9
- W 34–10 at LV+46.7
- W 34–17 vs DAL+41.6
Matchup factors
Everything beyond the two base ratings that moved this specific number.
Home field
+25.0 pts
worth 3.5 points of win probability
NYG hosts at MetLife Stadium. Every host gets the same flat 25 points — the model does not rate crowds individually.
2025 head to head
- Week 2: DAL won 40–37 (NYG on the road)
- Week 18: NYG won 34–17 (NYG at home)
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.
1476
average opponent rating · 31st toughest of 32
- vs above-average teams
- 1-5
- vs below-average teams
- 6-4-1
1494
average opponent rating · 24th toughest of 32
- vs above-average teams
- 2-7
- vs below-average teams
- 2-6
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.