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Your first Monte Carlo sprint forecast

Time: 5 minutes. You'll need: a workspace that has completed a handful of tasks in the last 90 days (the sampler needs a throughput history), and an active sprint (cycle) or module (epic) with open tasks.

By the end you'll be able to read a P50 / P85 / P95 completion band off a sprint header and see the band shift live as you drag scope in or out.

1. Open a sprint or a module

From a project's left rail, open Sprints (or Modules) and click into a sprint that has open work in it. The sprint's header carries a small forecast pill on the right, like ≈ Oct 14. That's the P85 date (the "safe commit" percentile). If the pill reads Forecast pending, the workspace has zero completed tasks in the last 90 days: ship a few and come back.

2. Hover the pill

Hover the pill. A tooltip opens with the full band:

Monte Carlo forecast
P50 (most likely)     Oct 3
P85 (safe commit)     Oct 14
P95 (worst plausible) Oct 22

18 open · 2.1/day avg
Based on last 90 days of throughput · 10,000 sims

How to read the three lines:

  • P50 is the date half of the simulations finished by. Your "most likely" date. Do not commit here.
  • P85 is what most teams cite as a commit-able date. Land dates here. This is the number shown on the pill by default.
  • P95 is the widest plausible finish. Useful for scenario planning and answering "worst case?" honestly.

The tooltip also shows the count of open tasks in scope, the average daily throughput the sampler used, and the size of the history window. Under 14 days of history the forecast is soft, and the tooltip carries a note saying so.

3. Change scope, watch the band move

Open the sprint's task list. Move one open task out of the sprint (drag it out, or click into the task and unassign the sprint). Come back to the sprint header and hover the pill again: the three percentile dates slide left. Do the opposite (drag a task into the sprint) and they slide right.

The number is defensible because it comes from the same days the team actually shipped. When someone asks "why that date?", you can show them the throughput history the sampler drew from.

4. Try the same on a module

Open a module (epic) with multiple open tasks. The pill lives on the module header too, with the same tooltip. Modules are the right scope for "when does the whole epic finish", sprints for "will this sprint hit its scope".

5. Read the forecast for a dashboard

If you want to pull the same numbers into a dashboard or a report, the API is:

GET /workspaces/{slug}/projects/{projectID}/modules/{moduleID}/forecast/
GET /workspaces/{slug}/projects/{projectID}/sprints/{cycleID}/forecast/

Both return the P50 / P85 / P95 dates plus history stats. The sampler runs inline in about 2 ms, so a dashboard can poll every minute without concern.

What next

  • Monte Carlo forecasts: the sampling algorithm, why zero-completion days matter, and the v2 roadmap (estimate weighting, per-team history).
  • Analytics and insights: the historical throughput chart that feeds the forecast.