Agile Forecasting 101: Why Monte Carlo Beats Velocity

"When will it be done?"

It’s the question every Product Manager, Engineering Lead, and Scrum Master dreads. Not because we don't know the work, but because traditional Agile estimation methods—specifically Story Points and Velocity—are fundamentally flawed when it comes to answering that specific question with confidence.

The Trap of Velocity-Based Planning

Velocity is a useful metric for internal team capacity, but it fails as a forecasting tool for one simple reason: it assumes a localized, deterministic future based on a subjective, volatile past.

• Story points are subjective: A "5" to one developer is an "8" to another.
• Velocity fluctuates: Holidays, sick days, and production fires skew the average.
• Averages lie: If your average velocity is 20, planning for 20 points means you have a 50% chance of failing.

Enter Monte Carlo Simulations

This is where Forecasting with Monte Carlo Simulations changes the game. Instead of asking "How many story points is this?", we look at "How many items do we typically finish?" (Throughput).

A Monte Carlo simulation uses your historical throughput data to simulate the remaining work thousands of times. It doesn't give you one date; it gives you a range of dates with associated probabilities.

Why Throughput Beats Story Points

Research shows that for batches of work, simply counting the number of items is just as accurate as summing story points, but costs zero time to estimate.

By using an Agile forecasting tool like ForecastFlow, you can stop spending hours in refinement meetings arguing over poker planning cards and start using the data you already have in Jira, Azure DevOps, or Linear.

Start Forecasting Today

You don't need to change your team's process to get better forecasts. You just need to change how you measure.

ForecastFlow connects directly to your board and runs thousands of simulations in seconds, giving you the answers you need to manage stakeholder expectations with confidence.