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NFL · Week 14 · 2026 Upcoming

Steelers at Jaguars

Tue, 15 Dec 2026 · 01:15 UTC
Pittsburgh Steelers logo
Pittsburgh
Steelers
5-2 · W5
@
Jacksonville Jaguars logo
Jacksonville
Jaguars
4-2 · L2
Jacksonville Jaguars to win 61% win probability · projected 21–25 · 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.

PIT 39%
JAX 61%
Projected score
21–25
PIT at JAX
Projected margin
+3.8
home perspective
Projected total
45.4
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
EverBank Stadium
Roof
Outdoors
Surface
Grass
Rest · PIT
8 days
Rest · JAX
8 days

Recent Form

Last five completed games this season, most recent first.

Pittsburgh Steelers

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

Jacksonville Jaguars

L vs HOU 31–34
W vs PHI 19–17
W @ CIN 22–17
W vs NE 35–6
L @ DEN 13–20

Season Scoring

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

PIT · scored
21.9
per game · 7 played
PIT · allowed
19.7
per game
JAX · scored
25.7
per game · 6 played
JAX · allowed
17.3
per game

How The Model Sees It

Our rating has Jacksonville Jaguars as the stronger side by 59 rating points, which on its own is worth about 2.4 points on the scoreboard (PIT 1,564, JAX 1,623).

Home field adds 1.4 points to JAX — 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 +3.8, and running that through the historical spread of actual results against projections gives Jacksonville Jaguars at 61%.

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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