Does this sound familiar? It's sprint planning time, and your team gathers for the ritual of story point estimation. The first story seems straightforward - but wait, one developer spots a potential complexity that others missed. A debate ensues. Is it a 3 or a 5? Maybe even an 8? Twenty minutes later, you've finally reached consensus, only to move on to the next story and repeat the process.
As the meeting stretches on, you notice the familiar patterns. Some team members consistently vote higher than others. The backend developer sees complexities the frontend team doesn't, and vice versa. Stories that seemed like a "quick 2" last sprint somehow took the whole week to complete. And let's not forget that one story everyone agreed was a "5" but ended up being the simplest task of the sprint.
Then there's the pressure of velocity tracking. Your team averaged 30 points last sprint, so naturally, stakeholders expect at least 30 points this sprint. But wait - two team members have planned vacation days, there's a company all-hands meeting, and you're onboarding a new developer. How do you account for all of that in your story points?
This is where Monte Carlo simulation changes the game. Instead of relying on subjective estimates and gut feelings, what if you could let your team's actual performance data do the talking? Rather than debating whether a story is a 3 or a 5, imagine focusing on what really matters - understanding and discussing the work itself.
Monte Carlo simulation looks at how your team actually performs - not how they think they might perform in an ideal scenario. It takes into account all those real-world factors that story points can't capture. Those days when unexpected production issues ate up half the sprint? That's part of your team's reality. The week when everyone was super focused and completed stories faster than usual? That's part of your pattern too. Even those "abnormal" sprints during holiday seasons or team changes provide valuable data about your team's natural rhythm.
Think of it like predicting the weather. Meteorologists don't just guess - they use historical data and run thousands of simulations to generate accurate forecasts. They know that while you can't predict exactly what will happen, you can understand the patterns and probabilities. That's exactly what Monte Carlo simulation does for your sprint planning.
This tool runs thousands of simulations based on your historical data, accounting for:
Even "abnormal" sprints (holidays, sick days, etc.) are valuable data points as they represent the natural rhythm of team performance over time.
This tool was inspired by the work of Stephen Angood, Scrum Master, Agile Coach, and author of "Monte Carlo Simulation for Scrum".
Developed by Lucas Bonsel, Scrum Master
Hey there! 👋 Just want to let you know that this tool runs completely in your browser - like a calculator on your computer. I can't see any of your team's data, and nothing gets saved anywhere on any server. All the fancy calculations happen right there on your machine, and as soon as you close the tab or refresh the page, poof! - it's gone. So feel free to experiment with your team's data; it stays between you and your browser!
No worries at all if you're not using Jira - this tool works great with any issue tracking system that can export to CSV! The instructions above should get you going, but if you're having trouble getting your data in the right format, feel free to reach out to me on LinkedIn. I'm happy to help you set things up with your system!
To get the right data:
Note: Replace PROJ with your project's short name (e.g., "TEAM" or "API"), not the full project name.
Select these fields:
Export as CSV using your current column configuration.
No data loaded yet