Placebo test of the effect method
The effect method reports a standardised score for any window you give it. This page asks how often that score looks large when nothing was recorded at all. We ran the published engine on random windows that no approved record covers, then counted how many it would have flagged. This is a measured change in public data during the event window, compared with a baseline before it. It shows timing, not cause. We describe an event as causing a change only when an official source says so.
What was done
For each sampled port or chokepoint and each daily series, we drew random start dates from 2022-01-01 (1 January 2022) to the data date, 2026-10-25 (25 October 2026), with window lengths of 3, 7, 14, 28 days. A window was kept only if neither it nor the 120 days before it overlapped any approved event on that place, plus a 14 day margin after each event. Each kept window went through the same code that produces published figures, method wfm-effect-1.2, with the same baseline rules, holiday exclusions and noise floors. The random draw is seeded (20261028), so the run can be repeated. Dates before 2022-01-01 (1 January 2022) are left out because pandemic shutdowns were real, unrecorded disruptions.
A window counts as flagged by the same rule the anomaly detector uses: a standardised score of 3.0 or more in either direction, a change of 25 percent or more, and a baseline of at least 3.
How to read it
Every window here is one where no event was recorded, so a flag is either a false alarm or a real disruption nobody has written up yet, such as an unreported closure, a schedule change, or a data gap. The share flagged is therefore an upper limit on the method's false-alarm rate, not a measurement of it. A large score on a published event is a reason to read the sources, and is not proof of cause.
By window length
| Window | Tested | Flagged | Share flagged | Score of 2 or more | Score of 3 or more | Score of 4 or more |
|---|---|---|---|---|---|---|
| 3 days | 1,651 | 6 | 0.4% | 2.2% | 1.0% | 0.7% |
| 7 days | 1,658 | 8 | 0.5% | 1.7% | 1.3% | 0.9% |
| 14 days | 1,660 | 10 | 0.6% | 2.9% | 2.2% | 1.7% |
| 28 days | 1,644 | 6 | 0.4% | 2.5% | 1.6% | 1.3% |
By series
| Series | Tested | Flagged | Share flagged | Score of 3 or more |
|---|---|---|---|---|
| Transit trade volume | 546 | 2 | 0.4% | 0.4% |
| Transits, container ships | 560 | 0 | 0.0% | 0.0% |
| Transits, dry bulk carriers | 560 | 1 | 0.2% | 0.4% |
| Transits, tankers | 560 | 0 | 0.0% | 0.0% |
| Transits, all vessel types | 560 | 0 | 0.0% | 0.5% |
| Port calls | 800 | 0 | 0.0% | 0.0% |
| Container ship port calls | 800 | 0 | 0.0% | 0.0% |
| Dry bulk port calls | 800 | 0 | 0.0% | 0.0% |
| Tanker port calls | 800 | 0 | 0.0% | 0.0% |
| Estimated export volume | 268 | 13 | 4.9% | 15% |
| Estimated import volume | 359 | 14 | 3.9% | 15% |
Source: IMF PortWatch (portwatch.imf.org), International Monetary Fund, using UN Global Platform AIS data. Statistics derived by World Freight Monitor. Terms. Retrieved 2026-10-29 (29 October 2026).
Computed 2026-10-29 (29 October 2026) by pipeline/effect/placebo.py. Effect method. Methodology.