Mort's Report — Sports Data Analytics
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Week 4 · Sun, 1:00 PM EDT · FOX

New York Jets at Chicago Bears

Soldier Field, Chicago, IL

New York Jets
Jets
NYJ
3-14 · #30
57%
CHI to win
CHI -2.0 · Lean
Chicago Bears
Bears
CHI
11-6 · #12

Market line for comparison: CHI -3 · O/U 43.5 (Draft Kings). 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.

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

Chicago Bears

InputRatingWeightContribution
Elo from results
11-6 in 2025, opponent-adjusted
152380%1218.0
Scoring strength
441–415 points (+26.0 differential) → 53.6% expected win rate
15255%76.3
Late-season form
same replay with the closing 6 weeks counted double
150915%226.3
Offense / defense split
25.9 PPG scored, 24.4 allowed → offense 1573, defense 1465
15380%0.0
Blended 2025 rating1520.6
Offseason regression
25% back toward the 1500 league mean
1515.5
Roster / QB adjustment
No offseason adjustment applied.
0.0
CHI 2026 starting rating1515

The calculation

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

Chicago Bears base rating
1553.6
2026 starting Elo, after offseason regression
New York Jets base rating
− 1420.2
2026 starting Elo, after offseason regression
Rating edge (home perspective)
+133.4
CHI is the better-rated side on neutral ground
Home-field advantage
+25.0
Flat 25 points for every host
Rest differential
−5.0
5 Elo per day of extra rest, capped at 25
Analyst override
+0.0
No manual adjustment on this game
Injury adjustment
−104.3
CHI is the more banged-up side — see the injury breakdown below
Adjusted Elo difference
+49.2

Win probability

1 / (1 + 10−(49.2 / 400))

57.0% CHI

Model spread

49.2 ÷ 25, rounded to the nearest half point

CHI -2.0

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

70.7%

CHI by 6.0

Injury term

−104.3 Elo

−4.2 pts of spread, home perspective

After injuries

57.0%

CHI by 2.0 · moved 13.7 pts of probability

New York Jets

−0.3 pts

  • Kiko Mauigoa LBOut−0.10
  • Dylan Parham GQuestionable−0.03
  • Joseph Ossai DEQuestionable−0.03
  • Breece Hall RBQuestionable−0.03

Chicago Bears

−0.5 pts

  • Anthony Johnson Jr. SOut−0.10
  • T.J. Edwards LBOut−0.10
  • Jack Sanborn LBOut−0.10
  • Kyle Monangai RBOut−0.10

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.

1554

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

1420

The two ratings only matter through their difference.

+25 pts

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

−5 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

70.7% CHI

Model value

Model spread

CHI -6.0

Elo diff +153.4 · Confident

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.

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
Chicago Bears
Chicago Bears
#12 of 32 · 11-6 in 2025
2025 final rating
1522.5
Regress 25% toward 1500
−7.1
2026 starting rating
1515.5

2025 Elo trail

Biggest swings

  • L 16–30 at BAL−49.8
  • L 21–52 at DET−46.5
  • W 31–14 vs DAL+44.9

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

CHI hosts at Soldier Field. Every host gets the same flat 25 points — the model does not rate crowds individually.

Rest

−5.0 pts

worth 0.7 points of win probability

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

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.

New York Jets

1518

average opponent rating · 3rd toughest of 32

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

1494

average opponent rating · 25th toughest of 32

vs above-average teams
4-3
vs below-average teams
7-3

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 →