Linear Metric
Issue Tracking
User Story Size Consistency analyses the distribution and variance of issue size estimates in Linear. It measures whether teams consistently break work into similarly sized pieces and identifies patterns of oversized or undersized stories that affect planning accuracy.
LinearIssue TrackingUser Story Size Consistency
User Story Size Consistency analyses the distribution and variance of issue size estimates in Linear. It measures whether teams consistently break work into similarly sized pieces and identifies patterns of oversized or undersized stories that affect planning accuracy.
Why user story size consistency matters for Linear users
Consistently sized stories are a hallmark of mature agile teams. When story sizes vary wildly, velocity becomes unreliable as a forecasting tool, and cycle commitment accuracy suffers. Small, consistent stories also enable more frequent delivery and faster feedback.
For Linear teams, size consistency analysis reveals whether the team's decomposition practices are effective. It helps identify patterns where certain work types are routinely oversized, enabling targeted coaching on story splitting and scope management.
Understand and act on user story size consistency with KPI Tree
KPI Tree analyses estimate distributions from your Linear warehouse data. Place this in your estimation quality tree alongside commitment accuracy and velocity metrics.
Assign RACI ownership to product managers for story decomposition standards. Set alerts when estimate variance increases, indicating degrading decomposition discipline.
Get started with your Linear data
MCPPull metrics from Linear directly through the Model Context Protocol.
Connect your existing warehouse where Linear data already lands.
Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.
Related Linear metrics
Cycle Commitment Accuracy
Issue TrackingMetric Definition
Commitment Accuracy = (Completed Committed Issues / Total Committed Issues) × 100
Cycle Commitment Accuracy measures the percentage of issues committed at the start of a Linear cycle that are completed by cycle end. It excludes work added mid-cycle to provide a clean measure of planning accuracy.
Team Velocity Analysis
Issue TrackingMetric Definition
Velocity = Total Estimate Points Completed per Cycle
Team Velocity Analysis measures and analyses the amount of work completed per cycle by Linear teams. It tracks velocity trends, variability, and the factors that influence throughput to provide a reliable basis for capacity planning and delivery forecasting.
Feature Delivery Cycle Time
Issue TrackingMetric Definition
Feature Delivery Cycle Time = Delivery Date − Development Start Date
Feature Delivery Cycle Time measures the total elapsed time from when work begins on a feature in Linear to when it is delivered. It captures the full pipeline duration including development, review, testing, and deployment stages.
Epic Completion Forecasting
Issue TrackingMetric Definition
Epic Completion Forecasting uses historical team velocity data and remaining scope to predict when Linear projects and epics will be completed. It applies probabilistic models to provide a range of likely completion dates rather than a single point estimate.
All Linear metrics
Bug Escape RateCode Review Bottleneck AnalysisComment Collaboration RateCross-Team Dependency ImpactCycle Burndown RateCycle Commitment AccuracyDeveloper Workload BalanceEpic Completion ForecastingFeature Delivery Cycle TimeIntegration Impact AnalysisIssue Aging AnalysisIssue Priority Distribution AnalysisIssue Reopening RateIssue Resolution TimeLabel Work Classification AnalysisMilestone Delivery PredictabilityProject Health ScoreRoadmap Progress TrackingSeasonal Development PatternsTeam Capacity UtilisationTeam Velocity AnalysisTechnical Debt Accumulation RateWorkflow State Transition AnalysisEmpower your team to understand and act on Linear data
Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.
