Charts & Trends

Coaching charts for the current PI plus sprint- and iteration-scoped views of how the team is delivering today. Click a small card to swap it into the large view. Reach for the chart that fits the conversation you're having — you don't need to look at every one every time.

On an ART, the charts aggregate across every team on the train — see Scaling with ARTs.

Delivery & flow

• Epic Progress — per-epic Done / Total story-point bars across the PI, with an overall donut and a By Type breakdown. The Epic Growth toggle reveals what was added or removed mid-PI.
• Velocity — story points completed per sprint (Scrum) or iteration (Kanban). The dashed line is the rolling average across delivered iterations only, so unstarted ones don’t drag it down.
• Issue Type Completion — a stacked bar per sprint/iteration across the PI, each bar split by issue type (story, bug, task, spike, support, …) showing how much of each type was completed. Segment colors match your Organization → Issue Types config. The bottom-right By Points / By Count toggle switches between story points and work-item counts. Good for spotting a sprint that was all bugs, or where sub-tasks/support quietly dominated delivery.
• Throughput — items finished per sprint/iteration. Watch for sudden drops (blockers, scope shifts) and sustained climbs (real improvement).
• Predictability (Scrum) — completed-vs-committed ratio per sprint. Healthy bands sit between 80–120%; wide swings suggest over-committing.

Cycle time & WIP

• Cycle Time Scattergram — each dot is a completed ticket, plotted by finish date and days taken, and coloured by story-point size (see the legend across the top — one colour per point value, plus Unsized). Dots above the trendline are the outliers worth a retro; watch for large-point items landing high.
• Cycle Time (Trend) — average cycle time per sprint/iteration. A slowly rising line often means WIP creep — pair it with the WIP chart.
• Cycle Time Breakdown — splits average cycle time into the stages work moves through (Dev, Code Review, QA, waiting), so you can see where the days go rather than just the total. Toggle between the current sprint/iteration and the whole PI.
• Work In Progress — average concurrent active items. Keep it near the team’s WIP target; too high correlates with longer cycle time and more context-switching.
• Flow Efficiency (Kanban) — share of cycle time spent actively progressing vs waiting in queues, per iteration. Higher means smoother flow with less idle hand-off time. (Kanban teams see this in place of Predictability, which needs a sprint commitment.)
• Developer Timeline (PI-scoped) — a per-person timeline of the tickets a developer worked across the PI; pick the developer from the dropdown. Useful for spotting overload or fragmented focus.

Burndown, burnup & CFD

• Sprint Burndown / Burnup (Scrum) — remaining SP vs an ideal line, and cumulative done vs total scope. A jumping scope line is mid-sprint scope creep.
• Sprint / Iteration CFD — Cumulative Flow Diagram: stacked To Do / In Progress / Done bands. A widening In Progress band means work is piling up — ease off starting new things.
• Kanban CFD (Kanban) — the same cumulative-flow view tuned for a flow window, so a Kanban team gets a CFD without sprint boundaries.
• Aging WIP (Kanban) — tier counts (green / orange / red) for in-flight tickets vs the team SLE, plus a per-ticket bar sorted oldest-first. Anything past the SLE marker is a coaching conversation.

PI-level views

PI Burndown, PI Burnup, and PI CFD extend the same ideas across the whole increment, with iteration boundaries marked at end-of-iteration — useful for retrospectives spanning the PI.

Release cadence

• Release Run and Days Between Releases — track how often you actually ship: release events across the PI and the gaps between them. Works for both Scrum and Kanban.

PI Forecast (Monte Carlo)

The Forecast card leads with a scope-decision row, then Monte Carlo percentiles, then a calendar timeline.

The simulator uses average velocity over the last 5 non-IP sprints, the standard error of the mean as its variance (so one off sprint doesn’t drive the forecast to doomsday), and a sprint-count time budget. With fewer than three completed sprints it returns “insufficient history” — Monte Carlo needs at least three data points to estimate variance honestly.

Hover any point or bar for the exact value.