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The Throughput Run chart helps teams understand how much work is delivered over time, how stable the delivery pace is, and how team capacity is distributed across different types of work. Flexible scope selection allows you to analyze throughput for a single team or combine data from multiple boards, projects, or initiatives. You can also visually segment throughput by any Jira field, such as issue type, epic, assignee, component, label, or custom fields, to see what kinds of work consume the team’s capacity.
Together, these capabilities of the Agile Throughput Charts turn throughput data into a practical management signal, enabling teams and delivery leaders to better understand performance, explain changes in output, and support more informed planning decisions.
✅ Team Lead: I use the Throughput Run chart to monitor how consistently the team delivers work over time. By looking at throughput trends and statistical benchmarks, I can quickly see if delivery pace is stable or if something is disrupting the workflow.
✅ Product Manager: When a stakeholder is worried that their feature is being neglected, I can break the chart down by Epic or Initiative to show how the team’s capacity is distributed. For example, the chart may reveal that 20% of the team’s throughput in each sprint is consistently dedicated to that feature, helping stakeholders understand how much capacity is allocated to their request.
✅ Release Train Engineer (RTE): I use the chart to track delivery across multiple teams contributing to the same program or initiative. Aggregating throughput from several boards helps me monitor progress at the ART level and identify trends that might affect planning or commitments.
✅ Engineering Manager: I use the throughput breakdown to understand what types of work consume the team’s capacity - features, bugs, or operational tasks. This helps me balance priorities and ensure that feature development does not get overwhelmed by support or maintenance work.
The Throughput Run chart is not limited to a single Jira board. You can aggregate throughput from multiple Scrum or Kanban boards, as well as from projects, releases, epics, initiatives, or custom JQL queries.
Additionally, the Issue filter allows you to further refine the dataset by including or excluding specific issue types, labels, components, assignees, or any custom fields.
✅ This feature is helpful for:
The Throughput Run chart shows how much work your team completes in each sprint or time interval, helping you understand delivery pace and how consistently work moves to Done. You can visualize throughput as bars or a line chart and track data at the cadence that fits your workflow—daily, weekly, bi-weekly, monthly, or quarterly.
📊 How to read the chart
In the screenshot below, the chart shows how many issues the team completed in each two-week interval (2️⃣) over the last six bi-weekly periods (1️⃣). Looking at the trend, the team’s delivery fluctuates between lower periods (24–26 issues) and stronger periods (46–50 issues). The horizontal reference lines represent statistical benchmarks: the team’s typical (median) delivery is 40 work items per two weeks (3️⃣), with 85% of the periods delivering 47 issues or fewer (4️⃣).
The team can realistically plan roughly 40 completed items per two-week interval. However, the fluctuations indicate that capacity may still be influenced by blocked work or uneven workload distribution.
✅ This feature is helpful for:
Switch to the Stacked bar view to understand how the team’s throughput is distributed across different types of work. Instead of showing only the total number of completed issues per interval, the stacked chart breaks the throughput down by any Jira field, including custom ones. The stacked segments can also be reordered or recolored, allowing you to adjust the visual hierarchy.
In the screenshot below, the throughput of the Feb 15 – Feb 28 interval includes 14 Bugs, 20 Stories, and 12 Tasks. Together, they form the total throughput of 46 completed issues during that period.
✅ This feature is helpful for:
The Throughput Run chart allows you to go beyond aggregated metrics and inspect the underlying data. By clicking a data point, you can open the Breakdown and Issue list, which shows which issues contributed to the throughput during that period.
The Breakdown groups completed work by two selected dimensions, such as board, epic, assignee, or any custom Jira field. Below the breakdown, the Issue list displays the exact completed issues included in the calculation. Each item links directly back to Jira, allowing you to quickly investigate anomalies or follow up on specific tickets:
✅ This feature is helpful for:
The Throughput Run chart can be configured to match how your teams define and measure completed work.
You can specify which workflow transitions count as throughput by selecting Done statuses or from–to columns. This allows teams to align the chart with their delivery process or track delivery up to specific stages, such as Ready for QA or Ready for Release.
You can also choose the estimation field used to measure throughput, including issue count, story points, time-based fields, or any custom numeric field. If some issues are missing estimates, you can define a default estimate value to keep calculations consistent.
✅ This feature is helpful for:
Instead of calculating statistics for the entire dataset, the chart can compute them using a moving window of the last X intervals. This helps reduce short-term noise and highlight how delivery performance evolves over time. In some cases, teams may choose to hide the raw throughput line entirely and visualize only the moving percentiles to focus on the underlying delivery trend rather than individual spikes or dips:
The Throughput Run chart can be easily shared with stakeholders. You can:
Jira does not provide a dedicated Throughput Run chart. Instead, teams typically rely on reports such as the Velocity Chart (Scrum) or the Control Chart, which focus on sprint performance or cycle time rather than throughput trends.
These native reports have several limitations when analyzing throughput:
❌ The Velocity chart works only with Scrum boards and cannot easily combine throughput across multiple boards, projects, or teams.
❌ Throughput is typically shown per sprint, making it difficult to analyze trends across weeks, months, or custom timeframes.
❌ Native Jira charts do not provide statistical reference lines, making it harder to assess delivery stability.
❌ Jira does not allow you to configure key calculation parameters such as the estimation field (story points, issue count, time-based metrics, or custom numeric fields) or define which workflow statuses should count as Done.
❌ Native reports do not allow you to break down throughput by Jira fields such as issue type, epic, assignee, component, or label to understand what the team’s capacity is actually spent on.
Use these examples to create your own Throughput run report use cases on the Jira Dashboard.
Both Jira apps (plugins) featured here offer a 30-day free trial and are completely free for teams of up to 10 users:
The Agile Reports and Gadgets app includes Throughput run chart functionality plus a wide range of additional charts and reports.
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