Generated: 2026-03-02 11:39 EST | Data Source: JIRA | Tickets Analyzed: 1131
Time from when work actively starts (first transition out of To Do / Backlog / Open) to when it is completed (last transition to Done / Closed / Verified). Measures how fast work flows once picked up.
Time from ticket creation to completion. Includes all wait time before work starts. Represents the customer's perspective of how long a request takes.
Number of tickets completed per sprint. Combined with cycle time, throughput reveals whether the team is delivering more items faster or fewer items slower.
Tickets in Done, Closed, Resolved, or Verified are counted as complete.
Median is the middle value — 50% of tickets complete faster. P85 (85th percentile) is the value where 85% of tickets complete faster — it captures the “typical worst case” while excluding outliers. If your P85 cycle time is 14 days, 85 out of 100 tickets finish within 14 days.
project = LOPS AND "products[select list (multiple choices)]" IN (tfMRD, "Latitude (tfMRD)") AND "team[team]" = fee007ac-475e-494f-b3b7-91697cf32c09
Median and P85 cycle time per sprint. The rolling average smooths sprint-to-sprint variation. Lower is better.
Median and P85 lead time per sprint. Lead time includes wait time before work starts, so it is always ≥ cycle time.
How cycle times and lead times are distributed across all completed tickets (all sprints, not affected by the sprint range filter). Vertical lines mark the median (solid green — 50th percentile) and P85 (dashed red — 85th percentile) values. A tight distribution means predictable delivery.
Number of tickets completed per sprint (bars) with total story points delivered (line, right axis). Consistent throughput indicates a stable delivery cadence.
Story points delivered per sprint broken down by Dev and QA. A persistent imbalance may indicate a bottleneck in one discipline.
Distinct tickets in an active status during each sprint (purple area) vs tickets completed that sprint (green bars). High WIP relative to throughput indicates too much parallel work, which increases cycle time (Little’s Law: CT = WIP / Throughput).
Median cycle time for each issue type. Bugs often have shorter cycle times (urgent fixes) while Stories may take longer (more complex).
| Issue Type | Count | CT Median (d) | CT P85 (d) | LT Median (d) | LT P85 (d) |
|---|---|---|---|---|---|
| Defect | 94 | 58.6 | 111.8 | 63.8 | 134.9 |
| Epic | 56 | 21.9 | 176.0 | 272.9 | 329.7 |
| Initiative | 4 | 0.0 | 21.1 | 387.4 | 421.8 |
| Story | 249 | 219.4 | 299.3 | 271.9 | 331.9 |
| Sub-defect | 67 | 16.4 | 108.1 | 19.4 | 114.2 |
| Sub-task | 74 | 14.9 | 48.3 | 15.1 | 52.9 |
| Task | 173 | 6.8 | 24.7 | 13.7 | 45.6 |
| Tweak | 150 | 34.9 | 126.2 | 81.3 | 147.4 |
Completed tickets sorted by cycle time (longest first), filtered to the selected sprint range. Cycle time cells are color-coded: >20 days, >10 days, ≤10 days.
| Key | Summary | Type | Cycle Time (d) | Lead Time (d) | Points |
|---|