Vanity metrics dress up bad sprints. The four that actually move decisions are velocity (capacity sense), cycle time (flow), throughput (output), and escape rate (quality). Calculate them, then read what to do when they shift.
Three failure modes: optimising for velocity (Goodhart's law), single-point summaries hide tail risk (mean cycle time vs p85), and tracking commit-vs-complete with no second metric to sanity-check it. The toolkit has the calculator for each metric and the post that explains when each one signals what.
Four agile metrics worth tracking (velocity, cycle time, throughput, escape rate), four that lead you wrong (story-point precision, individual velocity, sprint commitment %, lines-of-code), and Goodhart's law applied.
Paste last 6 sprints, get rolling average, trend, and a planning-range (low/likely/high). Calibrates capacity without anchoring on the most-recent sprint.
Paste raw cycle-time values, get p50/p85/p95, histogram, and tail-shape diagnosis. The metric that actually predicts when stories will finish — not velocity.
Velocity dropped 30% in two sprints? The calm playbook — when a dip is noise vs signal, the seven real causes (in probability order), the one-meeting fix, and what NOT to do under pressure.
Why benchmarking against other teams is meaningless, what your own velocity range should look like, and the five contextual factors.
ReadThe four shapes (on-track, hockey-stick, late-slip, under-delivery) and the structural cause behind each. Read the chart, not the line.
ReadWhy the mean lies (long tails), what p85 means, the math, and the stakeholder message that earns trust (always share p50 *and* p85).
ReadThe actual difference, when relative beats absolute, when teams should switch to hours, and the four anti-patterns that conflate them.
ReadSprintFlint tracks velocity, cycle time, throughput, and escape rate automatically — no spreadsheet rebuild. Plus burndown, sprint goal hit-rate, and the four anti-patterns dashboard tells you when one's drifting.