Task Estimation Accuracy & Time Variance Forecasting

Why Human Time Estimation Fails

When you tell yourself, "This bug fix will only take 20 minutes", your brain visualizes the best-case scenario with zero interruptions, bugs, or unexpected dependencies.

Three hours later, you are still debugging.

Flowa's Task Estimation Accuracy Engine (useEstimationAccuracy.ts, EstimationAccuracyView.vue) creates an active feedback loop that turns time forecasting into a calibrated superpower.

The Estimation Calibration Framework

[ Planned Target: 2 Pomodoros (50m) ] ───▶ [ Actual Logged: 4 Pomodoros (100m) ]
                                                       │
                                                       ▼
                             ┌─────────────────────────────────────────────────┐
                             │               Estimation Calibration            │
                             │ • Variance: +100% (Underestimated)              │
                             │ • Root Cause: Unexpected API documentation gap  │
                             │ • Accuracy Score Adjusted: 74% → 71%            │
                             └─────────────────────────────────────────────────┘

1. Estimated vs. Actual Visuals: Every task card and retrospective compares your initial target against real logged focus minutes.
2. Category-Specific Variance Insights: Discover which types of work you tend to underestimate (e.g. backend debugging +60% variance) versus overestimate (e.g. documentation -20% variance).
3. Continuous Calibration Scoring: Watch your overall Calibration Score climb from 50% to 90%+ as your intuitive sense of time sharpens over weeks of deep work.