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NFL · Week 10 · 2026 Upcoming Division game

Commanders at Giants

Fri, 13 Nov 2026 · 01:15 UTC
Washington Commanders logo
Washington
Commanders
1-4-1 · L4
@
New York Giants logo
New York
Giants
4-2-1 · W4
New York Giants to win 63% win probability · projected 22–26 · Solid

Win Probabilities

From the engine's power ratings, adjusted for home field and rest, then mapped through the margin distribution. Not a blend of the market — this is our own number.

WAS 37%
NYG 63%
Projected score
22–26
WAS at NYG
Projected margin
+4.3
home perspective
Projected total
47.5
combined points
Confidence
Solid
published before kickoff

Matchup Facts

Venue and rest come from the schedule feed and feed directly into the model; surface and weather are context we show but don't yet model.

Venue
MetLife Stadium
Roof
Outdoors
Surface
Fieldturf
Rest · WAS
4 days
Rest · NYG
4 days

Recent Form

Last five completed games this season, most recent first.

Washington Commanders

L @ SF 17–23
T vs NYG 27–27
L vs IND 13–30
W vs SEA 33–31
L @ DAL 20–37

New York Giants

W @ HOU 27–21
L vs NO 27–34
T @ WAS 27–27
W vs ARI 36–24
W vs TEN 12–7

Season Scoring

Points per game either side of the ball, 2026 season to date.

WAS · scored
22.0
per game · 6 played
WAS · allowed
28.7
per game
NYG · scored
23.3
per game · 7 played
NYG · allowed
23.0
per game

How The Model Sees It

Our rating has New York Giants as the stronger side by 72 rating points, which on its own is worth about 2.9 points on the scoreboard (WAS 1,393, NYG 1,465).

Home field adds 1.4 points to NYG — a measured figure from our backtest, not the traditional three, because home advantage in the modern NFL is considerably smaller than it used to be.

That lands on a projected margin of +4.3, and running that through the historical spread of actual results against projections gives New York Giants at 63%.

No betting line has been posted for this game yet — books typically price about three weeks ahead. Until one appears, this page shows the engine's number with nothing to compare it against, which is exactly the situation the model is most useful in.

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