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Week 1 · Sun, 8:20 PM EDT · NBC

Dallas Cowboys at New York Giants

MetLife Stadium, East Rutherford, NJ

Dallas Cowboys logo
Cowboys
DAL
7-9-1 · #27
57%
NYG to win
NYG -2.0 · Lean
New York Giants logo
Giants
NYG
4-13 · #23

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

InputRatingWeightContribution
Elo from results
7-9-1 in 2025, opponent-adjusted
139880%1118.4
Scoring strength
471–511 points (-40.0 differential) → 45.2% expected win rate
14665%73.3
Late-season form
same replay with the closing 6 weeks counted double
131715%197.6
Offense / defense split
27.7 PPG scored, 30.1 allowed → offense 1617, defense 1324
14410%0.0
Blended 2025 rating1389.3
Offseason regression
25% back toward the 1500 league mean
1417.0
Roster / QB adjustment
No offseason adjustment applied.
0.0
DAL 2026 starting rating1417

New York Giants

InputRatingWeightContribution
Elo from results
4-13 in 2025, opponent-adjusted
141080%1127.6
Scoring strength
381–439 points (-58.0 differential) → 41.7% expected win rate
14425%72.1
Late-season form
same replay with the closing 6 weeks counted double
145415%218.2
Offense / defense split
22.4 PPG scored, 25.8 allowed → offense 1485, defense 1430
14150%0.0
Blended 2025 rating1417.9
Offseason regression
25% back toward the 1500 league mean
1438.4
Roster / QB adjustment
No offseason adjustment applied.
0.0
NYG 2026 starting rating1438

The calculation

Every term the model applies, in order, from both teams' starting ratings to the number on the card.

New York Giants base rating
1438.4
2026 starting Elo, after offseason regression
Dallas Cowboys base rating
− 1417.0
2026 starting Elo, after offseason regression
Rating edge (home perspective)
+21.4
NYG is the better-rated side on neutral ground
Home-field advantage
+30.0
Flat 35 points for every host
Rest differential
+0.0
Both teams open the season equally rested
Analyst override
+0.0
No manual adjustment on this game
Adjusted Elo difference
+51.4

Win probability

1 / (1 + 10−(51.4 / 400))

57.4% NYG

Model spread

51.4 ÷ 25, rounded to the nearest half point

NYG -2.0

Confidence band

Coin flip · under 55%Lean · 55–65%Confident · 65–75%Strong · 75% and up

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.

Dallas Cowboys logo
Dallas Cowboys
#27 of 32 · 7-9-1 in 2025
2025 final rating
1398.0
Regress 25% toward 1500
+19.0
2026 starting rating
1417.0

2025 Elo trail

Biggest swings

  • W 4422 vs WSH+59.0
  • L 1734 at NYG57.9
  • L 1431 at CHI52.8
New York Giants logo
New York Giants
#23 of 32 · 4-13 in 2025
2025 final rating
1409.5
Regress 25% toward 1500
+28.9
2026 starting rating
1438.4

2025 Elo trail

Biggest swings

  • W 3417 vs PHI+69.8
  • W 3417 vs DAL+57.9
  • W 3410 at LV+53.1

Matchup factors

Everything beyond the two base ratings that moved this specific number.

Home field

+30.0 pts

worth 4.3 points of win probability

NYG hosts at MetLife Stadium. Every host gets the same flat 35 points — the model does not rate crowds individually.

Rest

+0.0 pts

Week 1: both teams come off the same offseason, so there is no rest edge to give.

Analyst override

+0.0 pts

No manual adjustment. The number here is pure model output.

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.

Dallas Cowboys

1461

average opponent rating · 30th toughest of 32

vs above-average teams
1-6
vs below-average teams
6-3-1
New York Giants

1493

average opponent rating · 21st 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.
Read the full methodology →