Affinity Estimating
Affinity Estimating is a relative estimation technique that involves sorting and grouping a large number of user stories by their perceived size, comparing them against one another rather than assigning numerical values to each one individually. It's especially useful for estimating large backlogs at the start of a project.
It shouldn't be confused with Affinity Mapping: although both techniques use visual grouping, Affinity Estimating applies only to size estimation, whereas Affinity Mapping is used to organize and categorize ideas of any kind.
When to use it
At the start of a project, when a large backlog needs to be estimated in a short time. When the team struggles to reach consensus with other techniques such as planning poker. When you want to identify dependencies and patterns among stories before prioritizing them.
How to apply it
Before the session, gather all the user stories or tasks to be estimated.
- Silent Sort: copies of the stories are handed out to the participants. Each person sorts them silently from smallest to largest relative size, without discussing with the others. The silence is deliberate: it prevents the opinions of the team's more influential members from biasing the sort from the outset.
- Discussion and adjustment: once all the stories are sorted, the team discusses them. Any member can move a story, but must explain their reasoning. The goal is to reach consensus on the relative order.
- Grouping: the team groups the stories into blocks of similar size.
- Assigning values: an estimation value is assigned to each group. The scale can be Fibonacci, T-shirt sizing, or any system the team normally uses.
Affinity Estimating with AI
When the backlog includes stories with a generative AI component, affinity estimating gets more complicated: two seemingly similar stories can turn out to be very different in size depending on data availability, the quality of the base model, or the acceptance criteria for the output. It's a good idea to create a separate grouping for AI stories until the team has its own velocity benchmarks for this kind of work.
Some collaborative digital whiteboard tools (Miro, FigJam) now include AI features that can suggest initial groupings. These can be useful as a starting point, but the manual Silent Sort has a value that goes beyond estimation: the process of sorting in silence activates individual thinking before the group discussion, something AI can't replicate.
Common mistake
Breaking the silence during the Silent Sort. If, during the individual sort, someone says out loud why they're placing a story in a particular spot, it contaminates everyone else's judgment. The value of this phase is precisely that each person forms their own opinion before being exposed to the group's.
See also
Want to take your agility further? You can look for upcoming course and exam dates, or go at your own pace by becoming a Club Agile member. Membership includes exclusive resources, e-learning classrooms, and access to Skill Arena: a space to practice and measure your agile skills whenever it suits you.