Monte Carlo Estimation v2.0

Probabilistic project forecasting using historical sprint data

Enter numbers separated by spaces or commas
Total stories in your backlog
💡 Note: You can use whatever units you like, e.g. stories → story points or tasks, sprints → weeks or days

Results

Full Range
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90% Confidence
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Median
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Cumulative Probability Distribution

💡 How to read this chart: Find a sprint count on the x-axis, look up to the curve, then across to the y-axis to see the probability of completing by that sprint.

About This Tool

Monte Carlo simulation uses random sampling from historical data to predict future outcomes. This tool runs 10,000 simulations of your project, each time randomly selecting sprint velocities from your past performance to estimate completion time.

Created by: Dan Prager with contributions from Tim Newbold and modernised with Claude Sonnet 4.5

November 2024 update: Rebuilt with vanilla JavaScript, Chart.js, and responsive design

GitHub: github.com/danprager/Monte-Carlo-Estimation

Creative Commons License Licensed under Creative Commons Attribution-ShareAlike 4.0 International License