VL-11
Commitment Accuracy — Committed vs Completed Ratio
Velocity
Default severity: medium
PrioritizationEstimationOutputEffort
What it detects
Measures how accurately each team commits to what they actually complete, per dimension. The commitment accuracy rate is the primary measure of sprint planning quality — it reveals both over-planning (low completion rate) and under-planning (unexpectedly high completion, typically from unplanned work). Tracked as a trend across all N baseline sprints.
Detection formula
FOR each_sprint S AND each_dimension D: accuracy(D, S) = completed(D, S) / committed(D, S) // 1.0 = perfect; >1.0 = delivered more than committed (unplanned work); \<1.0 = under-delivered avg_accuracy(D) = AVG(accuracy(D), baseline_sprints) IF avg_accuracy(D) \< config.vl.min_commitment_accuracy // default: 0.75 THEN FLAG chronic_under_delivery (over-planning) IF avg_accuracy(D) > config.vl.max_commitment_accuracy // default: 1.20 THEN FLAG chronic_over_delivery (under-planning + unplanned work) IF linear_regression(\[accuracy(D,S)\]).slope \< 0 THEN FLAG accuracy_declining Report: accuracy per sprint per dimension as time series; target zone highlightedExamples in practice
- Per sprint at start
- A team shows measures how accurately each team commits to what they actually complete, per dimension while min commitment accuracy is set to 0.75.
- Example signal: Measures how accurately each team commits to what they actually complete, per dimension.
Suggested response
Plan to historical capacity—avoid repeated over or under-commitment and unstable velocity.
Coaching playbook
Symptom
Measures how accurately each team commits to what they actually complete, per dimension. The commitment accuracy rate is the primary measure of sprint planning quality — it reveals both over-planning (low completion rate) and under-planning (unexpectedly high completion, typically from unplanned work). Tracked as a trend across all N baseline sprints.
Why it matters
When "Commitment Accuracy — Committed vs Completed Ratio" keeps appearing, the team is signalling a repeatable process gap. Left unexamined, the pattern hides where work really stalls and makes improvement metrics harder to trust.
What you can achieve
Plan to historical capacity—avoid repeated over or under-commitment and unstable velocity.
Facilitation questions
- What system change would stop "Commitment Accuracy — Committed vs Completed Ratio" from firing again?
- What do the cited issues have in common — same root cause or same workaround?
- Who owns the two-week experiment and how will we verify on the next import?
Run this rule against your own tracker data with Flow Analyzer.