Monte Carlo Delivery Predictor

Run 10,000 simulations using your historical throughput to probability-forecast project completion dates.

Backlog & History

The number of stories/points remaining to be delivered.
The number of items completed in past sprints/periods.

P85 Confidence Delivery

0Sprints

P50 Confidence (Coin Flip)

0 Sprints

P95 Confidence (Safe)

0 Sprints

Documentation

How It Works

The Monte Carlo method uses computational algorithms to simulate a vast array of possible outcomes based on random sampling. In agile forecasting, instead of saying "We average 10 points a sprint, so 100 points will take 10 sprints," it accounts for variation.

Sometimes your team delivers 14 points, sometimes 5. The simulator randomly pulls from your historical data until the backlog reaches 0, logs the number of sprints it took, and repeats this process 10,000 times to build a probability curve.

The Percentiles

  • P50 (50%): Half the time you finish earlier, half later. It is a coin flip. Highly aggressive.
  • P85 (85%): The industry standard for stakeholder commitments. You will succeed 85% of the time based on past variability.
  • P95 (95%): Highly conservative padding. Usually only reserved for strict contractual deadlines.

Last Updated: Current Year

Disclaimer: This tool runs entirely in your browser. Its predictions assume future performance will carry the same distribution profile as your provided historical data.

Frequently Asked Questions
How to Use

About the Monte Carlo Delivery Predictor

What it calculates

This monte carlo delivery predictor supports agile planning, prioritization, capacity forecasting, and team health tracking. Forecast project completion dates using Monte Carlo simulation on historical throughput.

When to use it

Use when planning sprints, estimating delivery dates, or improving team workflows.

Example

Adjust the inputs above to model your specific scenario with the Monte Carlo Delivery Predictor.

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