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Rolling averages and smoothed data in resolution sources

Many official sources publish both raw figures and smoothed versions. The US weekly jobless claims release includes a four-week moving average; UK labour market statistics are based on three-month rolling periods; public health dashboards often show seven-day averages; and climate normals are 30-year averages. Event contracts can settle on either the raw or the smoothed number. This SoftCta guide explains why the difference matters.

How rolling averages work

A rolling average takes the mean of the most recent set of values and moves forward as new data arrive. A four-week moving average of claims averages the latest four weeks; when a new week is added, the oldest drops out. Weighted versions give more importance to recent values. The purpose is to reduce noise: holidays, weather, reporting delays and day-of-week patterns can make raw daily or weekly data jumpy. Our guide on seasonal adjustment covers a related form of adjustment.

Lag and turning points

Smoothing comes at a cost: it lags. If raw claims jump by 40,000 in one week, the four-week average rises by only about 10,000. A contract on whether the average crosses a threshold will react more slowly than one on the raw figure. Conversely, a single unusual week can keep the average elevated for a month after the raw data have returned to normal. That drop-off effect resembles the base effects described in our base effects guide.

Built-in rolling periods

Some headline statistics are rolling averages by design. The UK unemployment rate published by the Office for National Statistics covers a three-month period, so a month's release overlaps two-thirds with the previous one. Climate normals, such as NOAA's 30-year averages, are updated once a decade. A contract saying "above normal" depends on which normal period is in force.

Raw versus smoothed in contracts

Contracts on weekly jobless claims usually settle on the headline seasonally adjusted initial claims figure for the week, not the moving average, but some markets specify the average. Case count markets might use daily counts, weekly totals or seven-day averages. The rules and the named source decide. Our guide on reading resolution source documents explains how to find the exact series.

Revisions to smoothed data

When an earlier raw value is revised, the moving average that includes it changes too. A contract settling on a smoothed figure as first published may therefore differ from the same series a week later.

Smoothing your own reading

Rolling averages are also useful for readers. Looking at a smoothed version of a noisy series makes it easier to avoid overreacting to single data points, as our guide on cherry-picked windows explains. The price chart tool can help when reviewing venue price histories over longer periods.

A reading habit

• Check whether the contract names the raw figure or a smoothed series.
• Note the averaging window and whether it is weighted.
• Look at which old values are about to drop out of the average.
• Account for revisions that can change a past average.

Convert prices with the implied probability tool and verify live prices on the venue.

Primary sources SoftCta readers open

• US Department of Labor weekly claims release
• UK Office for National Statistics labour market bulletins
• NOAA climate normals documentation
• Public health agency surveillance dashboards

Reading the quote step by step

• Read the full title and rules, including the named settlement source and the time zone.
• Convert the Yes and No prices to implied probabilities and note the gap between them.
• Subtract fees and spread to see the effective price you would actually face.
• Write your own probability range and the evidence behind it, then compare.
• Check related and near-duplicate markets on other venues for consistency.

Closing SoftCta checklist

• Re-read the venue definition in your own words before reacting to a price.
• Convert prices with fee and spread awareness; verify live cents on the operator.
• Prefer primary settlement sources over secondary headlines and social summaries.
• Treat ChanceWire hubs, movers, and tools as orientation — not order entry.
• Confirm age, location, and product eligibility; SoftCta pages will not suggest location spoofing.
• If clarity is incomplete, watching is a complete SoftCta outcome — no pick-selling urgency required.

SoftCta habits

• Write your own probability range before checking the crowd price.
• Prefer dated primary sources over social summaries.
• Compare related markets when a single book looks extreme.
• Size curiosity with fees and depth in mind; substantial risk of loss.