Goldman changed their oil forecast four times in six days last week. Four times. I want to know whether that's new information or noise management. So I'm running an experiment.
Eight weeks. Every Sunday I publish a point estimate for Brent crude seven days out, confidence intervals at 68% and 95%, and the scenario probabilities that generated them. The model assigns weights to five scenarios (Escalation, Stalemate, De-escalation, Demand Destruction, Black Swan) and the forecast is the probability-weighted outcome. Full methodology here. All of it public from day one. If you want to see how the numbers move in real time, the live dashboard is here.
I should be honest about why this is terrifying. I'm not an oil analyst. I have no proprietary data. What I do have is a willingness to be wrong in public and document exactly how I got there. If I score worse than a naive baseline (literally just using last week's price, no model at all) I'll say so. No smoothing, no framing the errors away. The whole point is to find out what I actually believe versus what I'm performing.
Week 1 opens with Escalation at 45%. Not because I want it to be, but because that's where the evidence points and anchoring to hope is a terrible way to start a forecasting experiment.
Week 1 Forecast
Published 15 March 2026 at 13:03 UTC. Target date 20 March.
- Point Estimate: $120.16
- 68% Confidence Interval: $85.11 – $138.08
- 90% Confidence Interval: $71.21 – $145.15
- Current Price (Brent): $98.91
Scenario Probabilities:
- Escalation: 45%
- Stalemate: 25%
- De-escalation: 15%
- Demand Destruction: 10%
- Black Swan: 5%
The data lives on GitHub. If you think my scenario weightings are wrong, run your own. That's the point.