Linear Metric
Issue Tracking
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.
LinearIssue TrackingFeature Delivery Cycle Time
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.
How to calculate feature delivery cycle time
Why feature delivery cycle time matters for Linear users
Feature cycle time is the most customer-relevant speed metric. It tells you how long it takes from deciding to build something to having it in users' hands. Reducing feature cycle time accelerates the feedback loop and enables faster iteration on product direction.
For Linear teams, feature-level cycle time analysis reveals which pipeline stages contribute most to overall duration. It helps teams identify whether delays are in coding, review, testing, or deployment, enabling targeted process improvements.
Understand and act on feature delivery cycle time with KPI Tree
KPI Tree calculates feature cycle time from Linear workflow transitions in your warehouse, breaking it down by pipeline stage. Place this at the centre of your delivery speed tree with code review and deployment metrics as inputs.
Assign RACI ownership to engineering leads for pipeline optimisation. Set alerts when feature cycle time exceeds your team's target service level.
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
Code Review Bottleneck Analysis
Issue TrackingMetric Definition
Code Review Bottleneck Analysis examines the time Linear issues spend waiting for or undergoing code review. It identifies reviewer capacity constraints, uneven review distribution, and workflow states where issues accumulate, slowing overall delivery throughput.
Issue Resolution Time
Issue TrackingMetric Definition
Resolution Time = Issue Resolved Date − Issue Created Date
Issue Resolution Time measures the total elapsed time from when a Linear issue is created to when it is resolved. It encompasses both waiting time and active work time, providing a full lifecycle view of how long issues take to address.
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.
Workflow State Transition Analysis
Issue TrackingMetric Definition
Workflow State Transition Analysis examines how issues move through Linear workflow states, including forward progress, backwards transitions, and time spent in each state. It identifies the most common transition paths, bottleneck states, and unexpected backflows that indicate process problems.
All Linear metrics
Bug Escape RateCode Review Bottleneck AnalysisComment Collaboration RateCross-Team Dependency ImpactCycle Burndown RateCycle Commitment AccuracyDeveloper Workload BalanceEpic Completion ForecastingIntegration 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 RateUser Story Size ConsistencyWorkflow 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.
