How to analyze velocity trends over multiple sprints?
We've just completed our 12th sprint and I'm looking at our velocity chart. The numbers are all over the place:
Sprint 1: 32
Sprint 2: 45
Sprint 3: 38
Sprint 4: 52
Sprint 5: 41
Sprint 6: 58
Sprint 7: 49
Sprint 8: 44
Sprint 9: 51
Sprint 10: 47
Sprint 11: 53
Sprint 12: 50
How do I make sense of this data? What should I look for in trends? Should I be worried about the variation? Management wants to know our "average velocity" for planning, but giving them a single number feels wrong when there's this much variance.
Great question! Your velocity data actually tells a positive story once you know how to read it. Let me break down the analysis:
1. Calculate Rolling Average (Most Important)
Don't look at individual sprints - use a 3-sprint rolling average:
- Sprints 1-3: (32+45+38)/3 = 38.3
- Sprints 4-6: (52+41+58)/3 = 50.3
- Sprints 7-9: (49+44+51)/3 = 48.0
- Sprints 10-12: (47+53+50)/3 = 50.0
This smooths out the noise and shows your real trend: you started at ~38 and stabilized around ~50.
2. Analyze Trend Direction
Your velocity improved from early sprints (32-45) to later sprints (47-53). This is normal as teams learn the codebase and work together better.
3. Measure Stability (Coefficient of Variation)
Look at recent 6 sprints only (7-12): 49, 44, 51, 47, 53, 50
- Average: 49
- Standard deviation: ~3.2
- Coefficient of variation: 3.2/49 = 6.5%
Under 10% variation is excellent. Your team is very stable now.
4. Identify Outliers and Root Causes
Look at sprint 6 (58 - your highest) and sprint 1 (32 - your lowest):
- Sprint 1: Likely low because team was forming, learning codebase
- Sprint 6: Any special circumstances? (Extra team member? Simpler stories? Tech debt payoff?)
Understanding outliers helps predict future variance.
5. What to Report to Management
Give them a range, not an average:
"Based on the last 6 sprints, our velocity is 47-53 points with high confidence. For planning, use 47 points (conservative) or 50 points (realistic)."
6. Visualization Tips
Create a chart with:
- Bar chart: Actual velocity per sprint
- Line overlay: 3-sprint rolling average
- Shaded band: ±1 standard deviation from average
This makes trends visible at a glance.
Your Data's Story:
You have a healthy, mature team. You improved from 38 to 50 average velocity (31% improvement) and are now delivering consistently. That's exactly what good velocity trends look like.
Also look for seasonal patterns. We noticed our velocity dips 10-15% during:
- December (holidays)
- Quarter-end months (everyone in planning meetings)
- Summer months (vacations)
Once we accounted for this, our forecasting got way more accurate.
This is incredibly helpful! I didn't realize we should be looking at rolling averages instead of individual sprints. The 47-53 range makes way more sense to communicate than trying to give a single number.
I'll create the visualization you suggested for our next stakeholder review. Thanks!
One warning: if your velocity is climbing steadily sprint after sprint, your team might be unconsciously inflating estimates to "hit velocity targets."
Healthy velocity stabilizes after 3-4 sprints. Continuous growth often means estimation drift, not actual productivity improvement.
Tools like Jira have built-in velocity charts, but they're often not very insightful. I recommend exporting to Google Sheets and creating:
- Velocity bar chart with trend line
- Commitment vs. Completion (did you finish what you planned?)
- Scope change mid-sprint tracking
These three together tell the full story of team predictability.