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Azure DevOps Sprint Metrics and Predictability Audit

Table output Has example

Check commitment, completion and scope change over sprints.

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Audit sprint predictability with this data: [SPRINT_DATA with committed, completed, added, removed per sprint]. Team changes in the period: [TEAM_CHANGES].

Lay out the method first: which figure you treated as committed (the count at sprint start), how you treated items added later, and whether you counted points or items. If the data does not distinguish them, say so. Then calculate completion rate, scope change rate, average velocity, its spread as a percentage ((highest minus lowest) divided by average) and carry-over share, with a worked row such as committed 30, completed 24, completion 80 percent. Say whether the numbers are stable enough to plan with.

Warn that small samples mislead and do not compare teams. Identify patterns and give hypotheses with cheap tests, without stating causes as facts. Recommend two planning changes with measures to check them, keep the output to a table and short commentary, and end with one question for the next retrospective based on the pattern.

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Invented data. Sprint 1: committed 24, completed 20 (83.3 percent). Sprint 2: committed 26, completed 18 (69.2 percent). Sprint 3: committed 22, completed 21 (95.5 percent). Average velocity = (20 + 18 + 21) / 3 = 19.7. Spread = (21 - 18) / 19.7 = 15.2 percent.
Observation: sprint 2 had 6 points added mid-sprint. Reading: completion rates range from 69 to 96 percent, so planning on the average of 19.7 points is safer than planning on the 24 to 26 committed. Scope change: sprint 2 added 6 points to a base of 26, which is 23.1 percent, the largest of the three. Caution: three sprints are a small sample, so treat this as a prompt for a retrospective question and not as a conclusion.

Illustrative only, made-up data. Your results will differ and must be reviewed.

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