estimation-stinger

Sprint estimates miss deadlines due to planning fallacy, estimation drift, and stakeholder commitment traps. Diagnose these estimation dysfunctions and apply probabilistic forecasting with Monte Carlo simulations to predict realistic delivery confidence percentiles.

Transitioning to NoEstimates involves evaluating throughput-based forecasting methods instead of relative sizing. This approach replaces traditional story point calibration with evidence-based probabilistic prediction to generate realistic delivery timelines.

Calculate P50, P85, and P95 delivery confidence by running Monte Carlo simulations on historical throughput data. This probabilistic forecasting method generates delivery percentiles that communicate schedule uncertainty clearly to stakeholders.

Calibrate story points using Planning Poker and Fibonacci sequences to establish relative sizing discipline. This prevents estimation misuse as commitments by focusing on comparative effort rather than absolute time guarantees.

Use Monte Carlo simulations for probabilistic delivery forecasting across multiple sprints, and T-shirt sizing for quick relative sizing of individual backlog items. Choose based on whether you need schedule prediction or task granularity assessment.

Yes, estimation-stinger diagnoses planning fallacy, estimation drift, and granularity problems within your development process. It then recommends the appropriate evidence-based forecasting technique to improve delivery prediction accuracy.