Understand contribution in context
Read member contribution with seniority, role, and work type in mind. Follow the distribution of new work, refactoring, helping others, and churn.
Team Performance
Understand the work behind team performance
Connect contribution, work distribution, coding efficiency, and AI code share. Follow the team’s trends alongside quality and delivery symptoms to decide where to investigate next.
Team Performance
Member insights · Work distribution · Team trendsTeam Performance Overview
Engineering performance at a glance
Team Member Insights
- New Work
- Refactor
- Help Others
- Churn
| Member | Impact Ratio | Coding Efficiency | AI Code Share | Work distribution | New Work | Refactor | Help Others | Churn |
|---|---|---|---|---|---|---|---|---|
 Maya PatelSeniorGo to Individual Scorecard | 2.4x | 94% | 36% | 52% | 25% | 17% | 6% | |
 Alex MorganMidGo to Individual Scorecard | 1.6x | 92% | 48% | 38% | 34% | 20% | 8% | |
 Sam TaylorMidGo to Individual Scorecard | 0.8x | 80% | 76% | 47% | 18% | 15% | 20% | |
 Jordan LeeJuniorGo to Individual Scorecard | 0.6x | 72% | 84% | 20% | 30% | 22% | 28% |
Coding Efficiency95.01%↑ 1.76% more than avg
Coding efficiency recovered to 95.01%, above the company average.
Coding EfficiencyExplore the trend
- Active symptoms
- 10
- Critical symptoms
- 2 need attention
- Symptom monitoring
- 10 active
- Most affected area
- Code Review (3)
Review the member work distribution and select a metric to explore its trend.
From signals to decisions
Read contribution, rework, and AI together
Use the signals to focus the team conversation, then investigate the work behind a change.
Investigate repeated work
Review churn and coding efficiency together. Use a change in the trend to locate rework and discuss the underlying changes with the team.
Read AI contribution alongside outcomes
Compare AI code share with efficiency, review flow, and quality. Investigate whether increased AI contribution coincides with more rework or faster delivery.
What the metrics tell you
Know what you are comparing
Each signal answers part of the question. Keep role, work type, and delivery context in the same review.
| Metric | What it shows | How to read it |
|---|---|---|
| Impact ratio | A relative contribution signal shown as a multiple, such as 2.4× or 0.8×. | Read it with the contributor’s role, experience, responsibilities, and work type. |
| Coding efficiency | 100% minus the churn ratio. A churn ratio of 28% corresponds to 72% coding efficiency. | Inspect rework and task context before interpreting a change in efficiency. |
| AI code share | The share of newly added code attributed to AI in the measured scope. | Compare it with churn, review effort, and delivery rather than using it as a standalone productivity measure. |
| Work distribution | The split between New Work, Refactor, Help Others, and Churn. | Use the mix to understand where contribution is going and what the team needs to investigate. |
| Team trends | Metric movement over time, with a short insight alongside the selected trend. | Review the same scope and period, then follow changes into the underlying engineering work. |
| Quality and symptoms | Code quality, technical debt, and active or critical symptoms alongside performance. | Keep delivery and quality in the discussion when contribution patterns change. |
What to look at
Move from a change in the numbers to a useful next step
Start with the team trend, inspect the work, and agree on the improvement with the people responsible for it.
- Team trends and metric insights
- Member work distribution and responsibilities
- AI contribution, churn, and coding efficiency
- Quality findings and active delivery symptoms
- Start with the team trend
- Select a metric and read its movement and insight. Identify what changed before looking for an explanation in individual activity.
- Inspect the work distribution
- Review the contribution mix and AI share with churn and efficiency. Discuss the relevant work, responsibilities, and task complexity with the team.
- Follow the delivery and quality signals
- Investigate active symptoms and quality findings. Agree on a workflow improvement and review the trend again after the change.
From signals to decisions
Follow the signal into delivery, capacity, and risk
Bring the surrounding engineering context into the next team review.
Delivery performance
Follow production deployment frequency, lead time, failures, and recovery alongside the team’s performance trends.
Capacity and priorities
Compare planned effort and project allocation with the contribution patterns you discuss in the team review.
Recurring blockers
Investigate unreviewed pull requests, review bottlenecks, and other symptoms behind delivery friction.
What does Team Performance show?
Team Performance brings member insights, work distribution, impact ratio, coding efficiency, AI code share, metric trends, quality, and active symptoms into a shared engineering review.
How is coding efficiency calculated?
In the work distribution view, coding efficiency is 100% minus the churn ratio. For example, a 28% churn ratio corresponds to 72% efficiency. Interpret that number with the work being performed.
What does work distribution include?
The view separates New Work, Refactor, Help Others, and Churn. Read the distribution with the team’s responsibilities and the types of changes being made.
How should we interpret impact ratio?
Use it as a relative contribution signal alongside role, experience, work type, and team responsibilities. Inspect the context behind a difference before using it in a performance discussion.
Does a higher AI code share mean better performance?
AI share describes contribution origin. Compare it with rework, efficiency, quality, review effort, and delivery outcomes to understand what is changing.
Can we follow trends over time?
Yes. Select a team metric to inspect its trend and the short insight alongside it. Use a consistent scope and review the underlying work when a trend changes.
Which tools can provide engineering context?
Oobeya connects source control, work tracking, CI/CD, documentation, quality, and monitoring systems. Review the integration catalog for your stack and the data available from each source.
See Oobeya in practice
Bring your next team review into focus
Walk through member insights, work distribution, quality, and delivery trends with your engineering workflow in mind.