Monte Carlo Date Forecast

Probabilistic forecasting powered by historical data.

How It Works

This tool answers the question: given your team's historical throughput or velocity, when are you likely to finish your remaining work? Enter your historical data, define your scope, and the simulator will run 10,000 scenarios to show you a realistic range of completion dates at different confidence levels — so you can make commitments backed by data, not gut feel.

Parameters
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The total number of items (tickets, tasks, stories) in your project scope as of today.
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How many of those total items have already been completed. The forecast is based on the remaining work (total minus completed).
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How many weeks each data point in your historical data represents. For example, if you run 2-week sprints, enter 2 here.
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Simulates uncertainty by randomly reducing your throughput each cycle. 0% = no adjustment. 20% means each cycle could lose up to 20% of its throughput — useful for modeling holidays, team changes, or scope creep.
None (0%)
NoneLowMediumHigh
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Adds extra time on top of your forecasted dates to account for unknowns. Applied to the 85% and 95% confidence results. A 10% buffer on a date 100 days away adds 10 days. Use this for external commitments where you want extra padding.
5% 10% 20% 25% 30%
Confidence Levels
Buffer Estimates
Distribution
Confidence Timeline