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Week 1 · Thu, 8:35 PM EDT · Netflix

San Francisco 49ers at Los Angeles Rams

Neutral site · Melbourne Cricket Ground, Melbourne, VIC

San Francisco 49ers logo
49ers
SF
12-5 · #8
52%
LAR to win
LAR -0.5 · Coin flip
Los Angeles Rams logo
Rams
LAR
12-5 · #6

Market line for comparison: LAR -3.5 · O/U 48.5 (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.

San Francisco 49ers

InputRatingWeightContribution
Elo from results
12-5 in 2025, opponent-adjusted
163180%1304.9
Scoring strength
437–371 points (+66.0 differential) → 59.6% expected win rate
15675%78.4
Late-season form
same replay with the closing 6 weeks counted double
166115%249.1
Offense / defense split
25.7 PPG scored, 21.8 allowed → offense 1567, defense 1530
15970%0.0
Blended 2025 rating1632.4
Offseason regression
25% back toward the 1500 league mean
1599.3
Roster / QB adjustment
No offseason adjustment applied.
0.0
SF 2026 starting rating1599

Los Angeles Rams

InputRatingWeightContribution
Elo from results
12-5 in 2025, opponent-adjusted
165280%1321.4
Scoring strength
518–346 points (+172.0 differential) → 72.2% expected win rate
16665%83.3
Late-season form
same replay with the closing 6 weeks counted double
162515%243.7
Offense / defense split
30.5 PPG scored, 20.4 allowed → offense 1686, defense 1567
17530%0.0
Blended 2025 rating1648.4
Offseason regression
25% back toward the 1500 league mean
1611.3
Roster / QB adjustment
No offseason adjustment applied.
0.0
LAR 2026 starting rating1611

The calculation

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

Los Angeles Rams base rating
1611.3
2026 starting Elo, after offseason regression
San Francisco 49ers base rating
− 1599.3
2026 starting Elo, after offseason regression
Rating edge (home perspective)
+12.0
LAR is the better-rated side on neutral ground
Home-field advantage
+0.0
Neutral site (Melbourne) — no home-field points applied
Rest differential
+0.0
Both teams open the season equally rested
Analyst override
+0.0
No manual adjustment on this game
Adjusted Elo difference
+12.1

Win probability

1 / (1 + 10−(12.1 / 400))

51.7% LAR

Model spread

12.1 ÷ 25, rounded to the nearest half point

LAR -0.5

Confidence band

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

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.

San Francisco 49ers logo
San Francisco 49ers
#8 of 32 · 12-5 in 2025
2025 final rating
1631.1
Regress 25% toward 1500
−31.8
2026 starting rating
1599.3

2025 Elo trail

Biggest swings

  • W 4122 at ARI+49.2
  • L 1526 at HOU41.5
  • W 2010 vs ATL+38.3
Los Angeles Rams logo
Los Angeles Rams
#6 of 32 · 12-5 in 2025
2025 final rating
1651.8
Regress 25% toward 1500
−40.5
2026 starting rating
1611.3

2025 Elo trail

Biggest swings

  • W 357 at JAX+50.0
  • L 2831 at CAR39.8
  • W 3319 at TEN+39.5

Matchup factors

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

Home field

Neutral

Played at Melbourne Cricket Ground, Melbourne. Neither side gets the 35-point host bonus.

Rest

+0.0 pts

Week 1: both teams come off the same offseason, 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

  • Week 5: SF won 26–23 (LAR at home)
  • Week 10: LAR won 42–26 (LAR on the road)

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.

San Francisco 49ers

1515

average opponent rating · 7th toughest of 32

vs above-average teams
4-4
vs below-average teams
8-1
Los Angeles Rams

1521

average opponent rating · 5th toughest of 32

vs above-average teams
6-4
vs below-average teams
6-1

What the model does not know

  • · Injuries, suspensions and depth-chart changes.
  • · 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 →