Planning accuracy

Most engineering teams discover their planning accuracy problem the same way: a sprint ends, 40% of the committed work didn't ship, and nobody has a clear answer for why. Planning accuracy measures how well your squad's commitments match actual delivery, sprint over sprint. When it's low, stakeholders lose confidence, roadmaps slip, and your team spends more time explaining delays than building. This page covers the definition, how to measure it, benchmarks, how to improve it, and how DevStats surfaces it.

• Planning accuracy measures the percentage of committed sprint work that a squad actually completes within the sprint. It matters because low scores signal estimation problems, scope creep, or hidden dependencies that quietly erode your team's ability to deliver on schedule.
• The formula is: Planning Accuracy = (Stories Completed ÷ Stories Committed) × 100. No universal published benchmark exists across all team types, but squads consistently hitting 80% or above are generally considered high-performing, while scores below 60% warrant investigation into estimation practices or unplanned work patterns.
• The most common mistake teams make is treating planning accuracy as a performance grade for individual players. It is a process signal. A squad that consistently commits too aggressively, carries excessive unplanned interruptions, or inherits vague tickets will score low regardless of individual effort or skill.
• DevStats tracks planning accuracy automatically by connecting to your issue tracker, with sprint-by-sprint breakdowns and benchmarks against 1,000+ engineering teams. Start a free trial at app.devstats.com/register to see your squad's numbers in under two minutes.

Planning accuracy definition

Planning accuracy is the percentage of work a squad commits to in a sprint that it actually completes by the end of that sprint. It is calculated as: Planning Accuracy = (Stories Completed ÷ Stories Committed) × 100. A score of 100% means the squad delivered exactly what it planned. Scores below that signal a gap between estimation and execution.

The metric draws on data from your issue tracker, specifically the tickets committed at sprint start versus those marked done at sprint close. It connects directly to a business outcome: teams with high planning accuracy give product, sales, and leadership reliable delivery windows, which makes roadmap commitments credible. You can see how DevStats surfaces this in the planning accuracy feature.

Why planning accuracy matters for engineering teams

When squads don't track planning accuracy, the consequences tend to be invisible until they compound. Sprints regularly end with a pile of carry-over tickets, but no one knows whether the root cause is over-commitment, unplanned interruptions, scope changes mid-sprint, or something else entirely. Without the data, every retrospective becomes a conversation based on memory and instinct rather than evidence.

For engineering leaders, planning accuracy connects directly to on-time delivery, stakeholder trust, and team health. Chronic under-delivery creates pressure cycles: leadership pushes for more commitments, the squad over-commits to compensate, and accuracy drops further. You can get ahead of this pattern by reviewing your sprint data alongside planning accuracy to see whether carry-over work is a consistent drag. Within the SPACE framework, planning accuracy sits at the intersection of Performance and Efficiency, reflecting how well the delivery process converts intent into output.

Measurement is the starting point, not the fix. Once you have the data, you decide what it means and what to change.

How to measure planning accuracy

To calculate planning accuracy, divide the number of stories (or points) completed in a sprint by the number committed at sprint start, then multiply by 100. Your issue tracker is the primary data source: Jira, Linear, GitHub Issues, or any tool that captures sprint scope at kickoff and completion status at close. The key requirement is a clear definition of "committed" (items in the sprint at start) and "completed" (items meeting your squad's definition of done before sprint end).

No single published benchmark exists for planning accuracy the way DORA benchmarks exist for deployment frequency. Benchmarks vary meaningfully by team size, ticket granularity, and sprint length. The table below reflects qualitative ranges observed across engineering teams. You can compare your squad's numbers against peers using DevStats benchmarks.

Note: these ranges are qualitative and should be interpreted alongside team size, sprint length, and ticket sizing norms rather than treated as absolute thresholds.

Planning accuracy in practice: a real example

A VP of Engineering at a 45-person SaaS company noticed that her squads were consistently reporting sprint completion rates around 55%, but the retrospectives never surfaced a clear cause. She pulled three months of sprint data and found that two squads were carrying over the same types of tickets repeatedly: backend integration work with unclear acceptance criteria. The data didn't tell her what to do, but it told her exactly where to look.

She introduced a pre-sprint ticket review session, specifically for any ticket estimated above three points, to ensure acceptance criteria were written before the sprint started. She also began tracking issue cycle time alongside planning accuracy to see whether individual ticket complexity was a contributing factor. Over the following six weeks, planning accuracy for both squads moved from 55% to 76%. The change came from her decision to address the process, not from the metric itself.

How to improve planning accuracy

1. Audit your carry-over tickets by type. Before the next sprint, categorize every ticket that didn't complete in the last three sprints. If carry-over is concentrated in a specific ticket type, squad, or dependency pattern, that's your intervention point, not a general estimation problem.
2. Set a ticket-size ceiling. Stories estimated above a certain threshold (commonly five points or more) should be split before entering a sprint. Large tickets are the single biggest driver of inaccurate completion predictions. Watch your throughput data after implementing this: smaller, more consistent ticket sizes tend to produce steadier output.
3. Reserve capacity for unplanned work. If your squad regularly absorbs interruptions (bug escalations, support requests, ad-hoc asks), build that buffer into sprint commitment explicitly. Committing 80% of theoretical capacity and delivering 100% of that commitment beats committing 100% and delivering 70%.
4. Review scope changes mid-sprint. Track how often tickets are added after sprint start. Scope creep is a planning accuracy killer that looks like an execution problem. Use your allocation data to see whether unplanned work is consuming a disproportionate share of squad capacity.
5. Run accuracy trend reviews quarterly, not just per-sprint. A single sprint's score is noisy. Trends across eight to twelve sprints reveal whether your planning process is improving or whether you're masking a structural issue with selective retrospectives.

Planning accuracy vs. sprint velocity

Planning accuracy and sprint velocity are related but measure different things. Velocity measures the volume of work completed per sprint, while planning accuracy measures how close the completed volume was to the committed volume.

A squad can have high velocity and low planning accuracy (consistently delivering a lot, but not what was planned) or low velocity and high planning accuracy (delivering less, but reliably). Use both together for a complete picture of your squad's delivery process.