Week 1 · Sun, 4:25 PM EDT · CBS
Green Bay Packers at Minnesota Vikings
U.S. Bank Stadium, Minneapolis, MN
Market line for comparison: MIN -1.5 · O/U 45.5 (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.
Green Bay Packers
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 9-7-1 in 2025, opponent-adjusted | 1461 | 80% | 1168.7 |
Scoring strength 391–360 points (+31.0 differential) → 54.9% expected win rate | 1534 | 5% | 76.7 |
Late-season form same replay with the closing 6 weeks counted double | 1406 | 15% | 210.9 |
Offense / defense split 23.0 PPG scored, 21.2 allowed → offense 1500, defense 1546 | 1546 | 0% | 0.0 |
| Blended 2025 rating | 1456.3 | ||
Offseason regression 25% back toward the 1500 league mean | 1467.2 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| GB 2026 starting rating | 1467 | ||
Minnesota Vikings
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 9-8 in 2025, opponent-adjusted | 1562 | 80% | 1249.4 |
Scoring strength 344–333 points (+11.0 differential) → 51.9% expected win rate | 1513 | 5% | 75.7 |
Late-season form same replay with the closing 6 weeks counted double | 1618 | 15% | 242.8 |
Offense / defense split 20.2 PPG scored, 19.6 allowed → offense 1431, defense 1586 | 1516 | 0% | 0.0 |
| Blended 2025 rating | 1567.8 | ||
Offseason regression 25% back toward the 1500 league mean | 1550.9 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| MIN 2026 starting rating | 1551 | ||
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−(108.7 / 400))
65.1% MIN
Model spread
108.7 ÷ 25, rounded to the nearest half point
MIN -4.5
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
65.1% MIN
Model value
Model spread
MIN -4.5
Elo diff +108.7 · Confident
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
- 1460.9
- Regress 25% toward 1500
- +6.3
- 2026 starting rating
- 1467.2
2025 Elo trail
Biggest swings
- L 24–41 vs BAL−48.3
- W 27–13 vs DET+34.8
- W 35–25 at PIT+32.8
- 2025 final rating
- 1561.8
- Regress 25% toward 1500
- −10.9
- 2026 starting rating
- 1550.9
2025 Elo trail
Biggest swings
- W 48–10 vs CIN+56.5
- L 6–22 vs ATL−48.1
- L 10–37 at LAC−45.4
Matchup factors
Everything beyond the two base ratings that moved this specific number.
Home field
+25.0 pts
worth 3.3 points of win probability
MIN hosts at U.S. Bank Stadium. Every host gets the same flat 25 points — the model does not rate crowds individually.
2025 head to head
- Week 12: GB won 23–6 (MIN on the road)
- Week 18: MIN won 16–3 (MIN 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.
1498
average opponent rating · 19th toughest of 32
- vs above-average teams
- 3-5
- vs below-average teams
- 6-2-1
1501
average opponent rating · 15th toughest of 32
- vs above-average teams
- 1-7
- vs below-average teams
- 8-1
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.