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NFL · Week 17 · 2026 Upcoming

Texans at Packers

Tue, 5 Jan 2027 · 01:15 UTC
Houston Texans logo
Houston
Texans
2-5 · L5
@
Green Bay Packers logo
Green Bay
Packers
4-3 · L3
Green Bay Packers to win 56% win probability · projected 21–23 · 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.

HOU 44%
GB 56%
Projected score
21–23
HOU at GB
Projected margin
+1.9
home perspective
Projected total
44.4
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
Lambeau Field
Roof
Outdoors
Surface
Grass
Rest · HOU
11 days
Rest · GB
10 days

Recent Form

Last five completed games this season, most recent first.

Houston Texans

L vs NYG 21–27
W @ JAX 34–31
W @ TEN 26–3
L vs DAL 30–34
L @ IND 17–19

Green Bay Packers

L @ DET 17–20
W vs DAL 23–13
W vs CHI 31–7
W @ TB 17–14
L vs ATL 14–35

Season Scoring

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

HOU · scored
23.6
per game · 7 played
HOU · allowed
24.3
per game
GB · scored
20.6
per game · 7 played
GB · allowed
20.7
per game

How The Model Sees It

Our rating has Green Bay Packers as the stronger side by 16 rating points, which on its own is worth about 0.6 points on the scoreboard (HOU 1,504, GB 1,520).

Home field adds 1.4 points to GB — 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. Rest shifts it a further -0.1 points (HOU 11 days, GB 10).

That lands on a projected margin of +1.9, and running that through the historical spread of actual results against projections gives Green Bay Packers at 56%.

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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