Week 1 · Mon, 8:15 PM EDT · ESPN/ABC
Denver Broncos at Kansas City Chiefs
Arrowhead Stadium, Kansas City, MO
Market line for comparison: KC -3 · O/U 42.5 (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.
Denver Broncos
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 14-3 in 2025, opponent-adjusted | 1592 | 80% | 1273.4 |
Scoring strength 401–311 points (+90.0 differential) → 64.6% expected win rate | 1605 | 5% | 80.2 |
Late-season form same replay with the closing 6 weeks counted double | 1633 | 15% | 244.9 |
Offense / defense split 23.6 PPG scored, 18.3 allowed → offense 1514, defense 1618 | 1632 | 0% | 0.0 |
| Blended 2025 rating | 1598.5 | ||
Offseason regression 25% back toward the 1500 league mean | 1573.9 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| DEN 2026 starting rating | 1574 | ||
Kansas City Chiefs
| Input | Rating | Weight | Contribution |
|---|---|---|---|
Elo from results 6-11 in 2025, opponent-adjusted | 1416 | 80% | 1132.7 |
Scoring strength 362–328 points (+34.0 differential) → 55.8% expected win rate | 1541 | 5% | 77.0 |
Late-season form same replay with the closing 6 weeks counted double | 1331 | 15% | 199.6 |
Offense / defense split 21.3 PPG scored, 19.3 allowed → offense 1457, defense 1593 | 1550 | 0% | 0.0 |
| Blended 2025 rating | 1409.3 | ||
Offseason regression 25% back toward the 1500 league mean | 1432.0 | ||
Roster / QB adjustment No offseason adjustment applied. | 0.0 | ||
| KC 2026 starting rating | 1432 | ||
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−(-116.9 / 400))
66.2% DEN
Model spread
-116.9 ÷ 25, rounded to the nearest half point
DEN -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
66.2% DEN
Model value
Model spread
DEN -4.5
Elo diff −116.9 · 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
- 1591.7
- Regress 25% toward 1500
- −17.8
- 2026 starting rating
- 1573.9
2025 Elo trail
Biggest swings
- L 20–34 vs JAX−42.5
- W 28–3 vs CIN+38.2
- W 44–24 vs DAL+35.0
- 2025 final rating
- 1415.8
- Regress 25% toward 1500
- +16.2
- 2026 starting rating
- 1432.0
2025 Elo trail
Biggest swings
- L 9–26 at TEN−47.8
- W 30–17 vs DET+42.5
- W 22–9 at NYG+39.4
Matchup factors
Everything beyond the two base ratings that moved this specific number.
Home field
+25.0 pts
worth 3.1 points of win probability
KC hosts at Arrowhead Stadium. Every host gets the same flat 25 points — the model does not rate crowds individually.
2025 head to head
- Week 11: DEN won 22–19 (KC on the road)
- Week 17: DEN won 20–13 (KC 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.
1471
average opponent rating · 32nd toughest of 32
- vs above-average teams
- 3-2
- vs below-average teams
- 11-1
1500
average opponent rating · 16th toughest of 32
- vs above-average teams
- 1-8
- vs below-average teams
- 5-3
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.