EP-07
SP Consistency by Assignee — Estimation Bias
Estimation Precision
Default severity: medium
EstimationQualityBusiness ValueEffort
What it detects
Different assignees consistently estimate the same type of work with significantly different story point values — indicates personal estimation bias that distorts team-level metrics when members rotate between items.
Detection formula
FOR each_issue_type T: FOR each_assignee A: median_sp(A, T) = MEDIAN(story_points WHERE assignee=A AND type=T AND similar_complexity) sp_variance_across_assignees = STDEV(\[median_sp(A,T) FOR each A\]) / MEAN(\[median_sp(A,T) FOR each A\]) IF sp_variance_across_assignees > config.ep.assignee_bias_cv_threshold // default: 0.4 THEN FLAG estimation_bias for type T Report: per-assignee median SP for comparable work itemsExamples in practice
- A team shows different assignees consistently estimate the same type of work with significantly different story point values while assignee bias cv threshold is set to 0.4.
- Example signal: Different assignees consistently estimate the same type of work with significantly different story point values — indicates personal estimation bias that distorts team-level metrics when members rotate between items.
Suggested response
Recalibrate estimation precision—check size classes, drift, and BV vs effort trade-offs.
Coaching playbook
Symptom
Different assignees consistently estimate the same type of work with significantly different story point values — indicates personal estimation bias that distorts team-level metrics when members rotate between items.
Why it matters
When "SP Consistency by Assignee — Estimation Bias" 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
Recalibrate estimation precision—check size classes, drift, and BV vs effort trade-offs.
Facilitation questions
- What system change would stop "SP Consistency by Assignee — Estimation Bias" 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.