49ers vs. Rams: Three Models Pick Los Angeles. None Says 94%

My pick is the Rams. My biggest question is the 93.6% attached to that pick.

All three active models favor Los Angeles over San Francisco. None of them gives the Rams a probability that high. The final number comes from what happens after their forecasts are combined, and tonight is a much sharper test of that confidence adjustment than Seattle’s opener was.

The 49ers and Rams meet at the Melbourne Cricket Ground on Thursday, September 10, at 7:35 p.m. Central, live on Netflix. It will already be Friday morning in Melbourne. The Rams’ official first look confirms the venue and matchup; Netflix’s viewing guide lists 8:35 p.m. Eastern / 5:35 p.m. Pacific.

These are the frozen, corrected Week 1 probabilities from the same audit record used for the Seattle preview. Yesterday’s result has not changed them.

Same pick, a 25-point confidence gap

That is three models and two stages of the ensemble, not five independent votes.

Logistic is the most confident individual model here. Elo and Bayesian both favor the Rams, but leave San Francisco at 37.3% and 33.1%, respectively. The gap between Logistic and Elo is 25.1 percentage points.

For Seattle, Elo was the loudest voice. For this game, it is Logistic. The common winner can hide a very different mix of opinions underneath it.

San Francisco’s strongest counterargument inside this forecast is that the 6.4% headline is much smaller than any individual model’s estimate of a 49ers win. That does not make an upset the pick. It does make the confidence worth examining before kickoff.

How 74.5% becomes 93.6%

The saved active weights are approximately 21.1% Elo, 40.6% Logistic, and 38.3% Bayesian. Logistic has both the largest weight and the highest Rams estimate, pulling the blend above the other two models. Together, they produce a raw Rams probability of 74.49%.

Temperature scaling at T = 0.4 raises that to 93.58%, an increase of 19.09 percentage points. The final probability even exceeds Logistic’s 87.8%.

That is mathematically consistent with the transformation. A weighted average stays within the component forecasts; the sharpening step can move it outside that range. It adds confidence without adding another model or a new observation about the teams.

XGBoost and Random Forest are unavailable in this saved row. Neither contributes an extra vote or a placeholder 50–50 forecast.

The calibration limitations described in the Week 1 preview still apply: the retained temperature setting came from a different model combination and included fitted-sample inputs. This three-model lineup still needs evidence from games it has not already seen.

Melbourne is more than a home-team label

The audit file identifies San Francisco as the away team and Los Angeles as the home team. Tonight’s venue is in Australia. Those labels tell me how to read the saved probabilities; they do not establish that the forecast has correctly accounted for this particular setting.

The comparison CSV does not show a Melbourne-specific travel or venue adjustment. I cannot turn it into a claim that the models understand acclimation, the crowd, or the effect of playing at the MCG. I am keeping the saved forecast intact and naming that uncertainty.

Both teams enter their season opener at 0–0. There is no completed 2026 regular-season performance from either team to describe as a current trend. The rivalry and the international stage give us context; they do not explain away the probability gap.

What Seattle taught me to say before kickoff

Seattle won 13–10 after every active model picked Seattle. In the follow-up analysis, I corrected the way I had connected win probability to how close the game might look.

I want that correction in this preview before the result arrives.

93.6% is a probability of a Rams win. It is not a predicted margin, a promise of a comfortable game, or a final-score forecast. A one-point Rams win and a four-touchdown Rams win produce the same outcome for this winner-only task. A close game would not automatically vindicate Elo; a blowout would not validate the final ensemble’s confidence.

Seattle’s win rewarded the sharper forecast on one observation. It did not establish that the temperature setting is right. Nor would a San Francisco win, by itself, establish that every strong favorite is overestimated. Calibration needs a record of probabilities and outcomes across games.

Put the grading rules on the page now

Here is what the two ensemble stages would score under either decisive outcome. These are hypothetical scores, not results. Calculations use the full-precision saved probabilities.

Lower is better. Binary Brier score is (p - y)^2, with p the Rams probability and y equal to 1 for a Rams win or 0 for a 49ers win. Log loss is the negative natural logarithm of the probability assigned to the winner. A tie would require an explicit grading rule; this table covers wins by either team.

The sharper forecast earns more credit if Los Angeles wins and takes a much larger penalty if San Francisco wins. That tradeoff is visible now. There is no need to invent a different standard afterward.

The pick and the record

Rams to win: 93.6% final ensemble, 74.5% raw blend.

I am recording both. I am not adding a spread, total, or score prediction to a file that does not supply one, and these probabilities are not a claim of value against current betting prices.

After the game, the job is to grade all three saved model forecasts and both ensemble stages against the verified outcome. Keep the pregame article intact. Explain the result in a follow-up. Let the season’s evidence decide how much confidence this system has earned.

The full Week 1 board has the other matchups. Tonight, the Rams are the pick. The extra nineteen points of confidence are the part I want the record to keep honest.

Source note: Forecasts and effective weights were read September 10 from the original post-audit Week 1 artifacts, using the full-precision after-audit values for game 2026_01_SF_LA. Game logistics were checked against the official Rams preview and Netflix guide linked above. No model was retrained, forecast refreshed, or prediction ledger updated for this article. The scenario table is an editorial calculation for two possible outcomes, not a recorded game result.