---
title: "Iteration predictability"
canonical_url: "https://docs.getdx.com/reports/iteration-predictability/"
md_url: "https://docs.getdx.com/reports/iteration-predictability.md"
last_updated: "2026-06-08"
---

# Iteration predictability
The **Iteration predictability** report measures how accurately a team delivers on its initial iteration commitment. Use this metric to evaluate planning accuracy and establish a basis for forecasting future iterations.

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

![Iteration predictability report in DX](https://docs.getdx.com/assets/images/reports/iteration-predictability.png)

## When to use iteration predictability

Iteration predictability answers the question: _Did we deliver what we promised at the start?_

This report helps teams:

- **Measure planning accuracy** — High predictability indicates the team is skilled at estimating capacity and scoping work appropriately.
- **Identify estimation gaps** — Low predictability may signal issues with story sizing, scope creep, or capacity planning.
- **Build stakeholder confidence** — Consistent predictability builds trust in iteration commitments.

## How predictability percentage is calculated

The predictability percentage is calculated as:

**Completed work ÷ Planned work**

Where:

- **Completed work** includes all completed work items in the iteration, whether planned or unplanned
- **Planned work** is the total story point/work item count for work items that were in the iteration when the iteration started

This metric focuses on whether the team delivered on its original commitment, regardless of additional work that may have been added during the iteration. Iteration predictability 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.

## Understanding planned vs. unplanned work

| Term                 | Definition                                                                                               |
| -------------------- | -------------------------------------------------------------------------------------------------------- |
| **Planned points**   | Story points for work items that existed in the iteration at the iteration start time                    |
| **Unplanned points** | Story points for work items added after the iteration started                                            |
| **Completed points** | Story points for all work items marked complete during the iteration, regardless of when they were added |

## How unplanned work is handled

Iteration predictability compares the total delivered value against the initial commitment.

While unplanned work is excluded from the target (Planned Points), it is counted towards the result (Completed Points) if finished. This allows teams to measure if they delivered the volume of work promised, even if the specific tasks changed during the iteration.
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