Iteration spillover

The Iteration spillover report measures how much work in the current iteration has rolled over from previous iterations. Use this metric to identify stuck work items, chronic over-commitment, or issues with work estimation.

Note: Iteration completion is only available when Azure DevOps is connected to DX.

When to use iteration spillover

Iteration spillover answers the question: How much work is rolling over from the past?

This report helps teams:

• Identify stuck work items — Spot work items that have been carried across multiple iterations without being completed.
• Detect chronic over-commitment — High spillover may indicate the team is consistently taking on more than it can deliver.
• Improve estimation accuracy — Recurring spillover on certain types of work can reveal estimation blind spots.

How spillover percentage is calculated

The spillover percentage is calculated as:

Spillover work ÷ Total work

Where:

• Spillover work is the story point/work item count for work items that were in a previous iteration before appearing in the current iteration
• Total work is the story point/work item count for all work items in the iteration

A work item counts as spillover if this is not the first iteration the work item has been attempted in. If a work item appeared in any prior iteration, it is marked as spillover work. Iteration spillover can be calculated based on either story points or work item counts.

In ADO, iterations can be nested, with work items spanning multiple levels of a tree. DX only reports on “leaf node” iterations—iterations that do not have any children or sub-iterations.

How iteration spillover relates to other iteration metrics

Iteration spillover provides insight into why iteration predictability or iteration completion may be suffering:

• High spillover + low completion — Suggests the team is carrying forward work it cannot close out, creating a compounding backlog.
• High spillover + high volatility — Indicates both old and new work are competing for capacity, putting pressure on the iteration.
• Low spillover — Suggests the team is completing work within its original iteration, indicating healthy throughput and realistic planning.