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NFL · Week 12 · 2026 Upcoming

Broncos at Steelers

Fri, 27 Nov 2026 · 20:00 UTC
Denver Broncos logo
Denver
Broncos
5-2 · W5
@
Pittsburgh Steelers logo
Pittsburgh
Steelers
5-2 · W5
Denver Broncos to win 54% win probability · projected 22–21 · Lean

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.

DEN 54%
PIT 46%
Projected score
22–21
DEN at PIT
Projected margin
-1.4
home perspective
Projected total
42.7
combined points
Confidence
Lean
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
Acrisure Stadium
Roof
Outdoors
Surface
Grass
Rest · DEN
5 days
Rest · PIT
5 days

Recent Form

Last five completed games this season, most recent first.

Denver Broncos

W @ ARI 30–16
W vs SEA 17–14
W @ LAC 23–17
L @ SF 14–24
W vs LAR 30–26

Pittsburgh Steelers

W @ NO 16–10
W @ TB 26–19
W vs IND 34–22
L @ CLE 24–27
W vs CIN 30–27

Season Scoring

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

DEN · scored
20.6
per game · 7 played
DEN · allowed
20.1
per game
PIT · scored
21.9
per game · 7 played
PIT · allowed
19.7
per game

How The Model Sees It

Our rating has Denver Broncos as the stronger side by 71 rating points, which on its own is worth about 2.8 points on the scoreboard (DEN 1,635, PIT 1,564).

Home field adds 1.4 points to PIT — 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 -1.4, and running that through the historical spread of actual results against projections gives Denver Broncos at 54%.

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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