Mort's Report — Sports Data Analytics
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Week 2 · Sun, 1:00 PM EDT · FOX

Green Bay Packers at New York Jets

MetLife Stadium, East Rutherford, NJ

Green Bay Packers
Packers
GB
9-7-1 · #21
55%
NYJ to win
NYJ -1.5 · Lean
New York Jets
Jets
NYJ
3-14 · #30

Market line for comparison: GB -4.5 · O/U 44.5 (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.

Green Bay Packers

InputRatingWeightContribution
Elo from results
9-7-1 in 2025, opponent-adjusted
146180%1168.7
Scoring strength
391–360 points (+31.0 differential) → 54.9% expected win rate
15345%76.7
Late-season form
same replay with the closing 6 weeks counted double
140615%210.9
Offense / defense split
23.0 PPG scored, 21.2 allowed → offense 1500, defense 1546
15460%0.0
Blended 2025 rating1456.3
Offseason regression
25% back toward the 1500 league mean
1467.2
Roster / QB adjustment
No offseason adjustment applied.
0.0
GB 2026 starting rating1467

New York Jets

InputRatingWeightContribution
Elo from results
3-14 in 2025, opponent-adjusted
137380%1098.6
Scoring strength
300–503 points (-203.0 differential) → 22.7% expected win rate
12875%64.4
Late-season form
same replay with the closing 6 weeks counted double
132415%198.6
Offense / defense split
17.6 PPG scored, 29.6 allowed → offense 1366, defense 1336
12010%0.0
Blended 2025 rating1361.6
Offseason regression
25% back toward the 1500 league mean
1396.2
Roster / QB adjustment
No offseason adjustment applied.
0.0
NYJ 2026 starting rating1396

The calculation

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

New York Jets base rating
1447.0
2026 starting Elo, after offseason regression
Green Bay Packers base rating
− 1446.3
2026 starting Elo, after offseason regression
Rating edge (home perspective)
+0.7
NYJ is the better-rated side on neutral ground
Home-field advantage
+25.0
Flat 25 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
Injury adjustment
+10.5
GB is the more banged-up side — see the injury breakdown below
Adjusted Elo difference
+36.2

Win probability

1 / (1 + 10−(36.2 / 400))

55.2% NYJ

Model spread

36.2 ÷ 25, rounded to the nearest half point

NYJ -1.5

Confidence band

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

Injury adjustment

Report pulled 2026-10-27 13:20 UTC

Ratings describe a team over a whole season. Who is actually available on Sunday is a separate question, so the injury adjustment is applied last, after home field and rest. Each listed player costs a share of a point of spread based on his position, whether he starts, and how likely he is to play.

Before injuries

53.7%

NYJ by 1.0

Injury term

+10.5 Elo

+0.4 pts of spread, home perspective

After injuries

55.2%

NYJ by 1.5 · moved 1.5 pts of probability

Green Bay Packers

−0.4 pts

  • Zaire Franklin LBOut−0.10
  • Jacob Monk COut−0.10
  • Sean Rhyan GDoubtful−0.08
  • Matthew Golden WRQuestionable−0.03

New York Jets

−0.3 pts

  • Kiko Mauigoa LBOut−0.10
  • Joseph Ossai DEQuestionable−0.03
  • T'Vondre Sweat DTQuestionable−0.03
  • Breece Hall RBQuestionable−0.03

Statuses come straight from the league injury report: out and injured reserve count in full, doubtful three quarters, questionable roughly a third, probable a tenth. Starters are identified from the depth chart; anyone the model cannot place is treated as a backup.

Move the factors yourself

Every term feeds one number: the adjusted Elo difference. Drag any of them and watch the spread and confidence follow.

1447

Drag to see how much a stronger or weaker host changes the call.

1446

The two ratings only matter through their difference.

+25 pts

The tuned value is a flat 25 points for every host.

+0 pts

5 Elo per extra day of rest, capped at 25 either way.

+0 pts

A manual thumb on the scale, in the home team's direction.

Win probability

53.7% NYJ

Model value

Model spread

NYJ -1.0

Elo diff +25.7 · Coin flip

Probability curve

The logistic curve from −600 to +600 Elo, home perspective. The dot is your current setting; the faint vertical line is where the model itself sits. Notice the curve is flattest at the extremes — near a coin flip, every point of Elo moves the number most.

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.

Green Bay Packers
Green Bay Packers
#21 of 32 · 9-7-1 in 2025
2025 final rating
1460.9
Regress 25% toward 1500
+6.3
2026 starting rating
1467.2

2025 Elo trail

Biggest swings

  • L 24–41 vs BAL−48.3
  • W 27–13 vs DET+34.8
  • W 35–25 at PIT+32.8
New York Jets
New York Jets
#30 of 32 · 3-14 in 2025
2025 final rating
1373.3
Regress 25% toward 1500
+22.9
2026 starting rating
1396.2

2025 Elo trail

Biggest swings

  • L 10–30 vs BUF−42.9
  • L 10–34 vs MIA−39.8
  • L 22–37 vs DAL−37.1

Matchup factors

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

Home field

+25.0 pts

worth 3.6 points of win probability

NYJ hosts at MetLife Stadium. Every host gets the same flat 25 points — the model does not rate crowds individually.

Rest

+0.0 pts

Both teams come in on equal rest, 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

These teams did not meet in 2025. Past meetings carry no weight in the model anyway — they are already baked into each team's rating.

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.

Green Bay Packers

1498

average opponent rating · 19th toughest of 32

vs above-average teams
3-5
vs below-average teams
6-2-1
New York Jets

1518

average opponent rating · 3rd toughest of 32

vs above-average teams
1-9
vs below-average teams
2-5

What the model does not know

  • · How an injury actually plays out — the adjustment above prices availability, not how well a replacement performs.
  • · 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 →