Mort's Report — Sports Data Analytics
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Week 3 · Sun, 1:00 PM EDT · CBS

Tennessee Titans at New York Giants

MetLife Stadium, East Rutherford, NJ

Tennessee Titans
Titans
TEN
3-14 · #28
60%
NYG to win
NYG -3.0 · Lean
New York Giants
Giants
NYG
4-13 · #19

Market line for comparison: NYG -6 · O/U 42.5 (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.

Tennessee Titans

InputRatingWeightContribution
Elo from results
3-14 in 2025, opponent-adjusted
142180%1136.5
Scoring strength
284–478 points (-194.0 differential) → 22.6% expected win rate
12865%64.3
Late-season form
same replay with the closing 6 weeks counted double
141315%212.0
Offense / defense split
16.7 PPG scored, 28.1 allowed → offense 1342, defense 1372
12150%0.0
Blended 2025 rating1412.7
Offseason regression
25% back toward the 1500 league mean
1434.5
Roster / QB adjustment
No offseason adjustment applied.
0.0
TEN 2026 starting rating1435

New York Giants

InputRatingWeightContribution
Elo from results
4-13 in 2025, opponent-adjusted
148280%1185.6
Scoring strength
381–439 points (-58.0 differential) → 41.7% expected win rate
14425%72.1
Late-season form
same replay with the closing 6 weeks counted double
151315%227.0
Offense / defense split
22.4 PPG scored, 25.8 allowed → offense 1485, defense 1430
14150%0.0
Blended 2025 rating1484.6
Offseason regression
25% back toward the 1500 league mean
1488.5
Roster / QB adjustment
No offseason adjustment applied.
0.0
NYG 2026 starting rating1488

The calculation

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

New York Giants base rating
1474.0
2026 starting Elo, after offseason regression
Tennessee Titans base rating
− 1395.8
2026 starting Elo, after offseason regression
Rating edge (home perspective)
+78.2
NYG 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
−26.5
NYG is the more banged-up side — see the injury breakdown below
Adjusted Elo difference
+71.7

Win probability

1 / (1 + 10−(71.7 / 400))

60.2% NYG

Model spread

71.7 ÷ 25, rounded to the nearest half point

NYG -3.0

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

63.8%

NYG by 4.0

Injury term

−26.5 Elo

−1.1 pts of spread, home perspective

After injuries

60.2%

NYG by 3.0 · moved 3.6 pts of probability

Tennessee Titans

−0.3 pts

  • Calvin Ridley WROut−0.10
  • Tyjae Spears RBOut−0.10
  • Dorian Mausi LBOut−0.10
  • Tommy Townsend PQuestionable−0.03

New York Giants

−0.6 pts

  • Tremaine Edmunds LBOut−0.10
  • Greg Newsome II CBOut−0.10
  • Jevon Holland SOut−0.10
  • Marcus Mbow OTOut−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.

1474

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

1396

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

63.8% NYG

Model value

Model spread

NYG -4.0

Elo diff +98.2 · Lean

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.

Tennessee Titans
Tennessee Titans
#28 of 32 · 3-14 in 2025
2025 final rating
1420.6
Regress 25% toward 1500
+14.0
2026 starting rating
1434.5

2025 Elo trail

Biggest swings

  • W 26–9 vs KC+47.8
  • L 0–26 at HOU−37.4
  • L 19–33 vs LAR−35.3
New York Giants
New York Giants
#19 of 32 · 4-13 in 2025
2025 final rating
1482.0
Regress 25% toward 1500
+6.5
2026 starting rating
1488.5

2025 Elo trail

Biggest swings

  • W 34–17 vs PHI+52.9
  • W 34–10 at LV+46.7
  • W 34–17 vs DAL+41.6

Matchup factors

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

Home field

+25.0 pts

worth 3.5 points of win probability

NYG hosts at MetLife Stadium. 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.

Tennessee Titans

1523

average opponent rating · toughest of 32

vs above-average teams
0-11
vs below-average teams
3-3
New York Giants

1494

average opponent rating · 24th toughest of 32

vs above-average teams
2-7
vs below-average teams
2-6

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 →