Trend View

Sprint Trends — Team University

One-screen view of predictability, throughput, misses, and quality signals

University trend points are generated from persisted sprint issue facts and centralized formulas. The dashboard is designed to show miss patterns and execution quality, not just output volume. This view also includes 3 simulated weekly checkpoints so you can preview a one-month chart layout before the historical sprint archive is fully backfilled.

Overview

University now has 1 source-backed checkpoint(s) in the persisted trend store.

These cards bias toward leadership questions: can we trust the promise set, what diluted it, and what actually came out of the sprint system.

Committed completion
53.8%
Improved by 15.3 pts vs prior checkpoint
Planning quality
38.5%
Improved by 9.7 pts vs prior checkpoint
Added load
18.8%
Improved by 4.4 pts vs prior checkpoint
Workflow mismatch
2
Improved by 0 vs prior checkpoint
Bug share
70.0%
Improved by 25.5 pts vs prior checkpoint
Product stories
1
Improved by 0 vs prior checkpoint
Preview mode: 3 earlier checkpoints are simulated so we can inspect a one-month trend layout before backfill is complete.

Demo mode is on for this view: 3 prior weekly checkpoints are simulated for presentation only and are not written to the canonical trend dataset.

Trend explorer

Choose a consecutive week window to inspect how the delivery system changed over time for this scope.

Select a consecutive window to reslice the same persisted trend facts.
Delivery predictability
Committed completion, finish predictability, and planning quality should move together if the sprint system is healthy.
Committed completionFinish predictabilityPlanning quality
0%25%50%75%100%W15W16W17W18
Percentage scale, higher is better.
Scope pressure
Carryover, added load, and bug share show how much execution was displaced by noise or late scope motion.
Committed carryoverAdded loadBug share
0%25%50%75%100%W15W16W17W18
Percentage scale, lower is better.
Delivered mix
Stories, tasks, and bugs should stay legible enough that leaders can see where capacity actually landed.
StoriesTasksBugs
0123411101310W15W16W17W18
Stacked counts by checkpoint.
Miss pattern
Partial completion, dependency delay, and not-started misses help distinguish breakdown, coordination, and focus failure.
PartialDependencyNot started
012343342W15W16W17W18
Stacked miss counts by checkpoint.
Execution truth
Workflow mismatches, not-started committed items, and delivered product stories show whether the sprint closed cleanly and usefully.
Workflow mismatchNot-started committedProduct stories
01234W15W16W17W18
Grouped counts by checkpoint.

Predictability

MetricW15W16W17W18
Committed completion49.8%47.8%38.5%53.8%
Finish predictability50.2%46.9%39.9%54.5%
Planning quality35.3%32.1%28.8%38.5%
Committed carryover48.6%54.6%60.9%46.2%
Added load17.9%20.9%23.2%18.8%
Bug share74.7%82.4%95.5%70.0%

Delivery and misses

MetricW15W16W17W18
Stories1.801.51
Tasks2.42.41.92
Bugs7.17.69.77
Partial1.82.11.81
Dependency0.30.51.41
Not started0.800.90
Workflow mismatch2.72.12.12
Not-started committed0.5000
Product stories1.700.51