Probabilistic forecasting powered by historical data.
Monte Carlo forecasting uses your team's actual historical performance to simulate thousands of possible futures. Instead of relying on a single estimate, it randomly samples from your past throughput or velocity data — over and over — to model how long remaining work might take.
Each simulation plays out differently, just like real sprints do. Some go fast, some go slow. By running 10,000 of these scenarios, we get a distribution of likely completion dates and can tell you with statistical confidence (e.g., 85% or 95%) when your project will finish.
Why use this over traditional estimation: It removes human bias, accounts for natural variability, and gives you a range of outcomes with confidence levels — not a single date that's almost certainly wrong.