Handling Estimation Inconsistencies Across Story Sizes

The Problem of Inconsistent Estimates

Inconsistent story point estimates refer to a lack of alignment and variability in the story points assigned to user stories of similar scope and complexity. This inconsistency stems from differences in the way team members interpret and apply story points, as well as changes in team velocity over time.

Inconsistent estimates create several problems in agile software development:

Planning and tracking becomes less reliable when the point values are inconsistent
It becomes difficult to gauge progress and the amount of work remaining
Forecasting release dates and project completion is less accurate
It hinders data-driven decision making about capacity, work intake, and process improvements

While some variability is expected, too much inconsistency means the points lose their ability to provide project visibility and support effective agile practices. Taming estimation inconsistencies should be a priority for teams that rely on story points for planning and tracking.

Table of Contents

Common Causes of Inconsistent Estimates

Lack of Shared Understanding of Story Points

The most common cause of estimate inconsistency is team members having different ideas of how story points should be assigned. Without an aligned understanding of what a story point represents and the framework used to assign points, estimates end up all over the place.

For example, a 3-point story may represent 1 day of work to one person and 2 days to another. Similarly, one person may consider performance needs and another may not when sizing stories.

These gaps in the team’s shared mental model lead to misalignment in estimates. Team members need regular calibration on how story points should be used in their context to size stories consistently.

Changing Team Velocity

Even with an initially shared understanding, a team’s velocity tends to change over time. Early on, the team is learning and ramping up, causing throughput to increase sprint over sprint. On the other hand, events like losing a team member, increased technical debt, or shifts in priority can decrement velocity.

If the points assigned to stories do not shift along with changes in throughput, story sizes get out of sync with how much work the team can actually complete. This misalignment manifests as greater estimate inconsistencies.

Poorly Defined User Stories

Lack of clarity in the user stories themselves also opens the door for inconsistent estimates between team members. If details are unclear, scope is ill-defined, or acceptance criteria is incomplete, each person uses their own assumptions when assigning points.

Furthermore, complex stories that are not decomposed into smaller, testable chunks leave room for individual judgement calls on size. Better articulation and splitting of stories during grooming and planning limits guessing and makes sizing more uniform.

Strategies to Improve Estimate Consistency

While no team will ever estimate perfectly consistently, there are several strategies teams can employ to significantly improve alignment:

Establish Clear Definition of Story Points

Every team needs to agree on what a story point means to them. A common approach is using the Fibonacci sequence (1, 2, 3, 5, 8…) where a story point represents an ideal day (or concentrated hours) of development work for the team.

The definition should also clarify that points capture just the development effort, not every activity like testing, documentation etc. This shared meaning forms the foundation for consistent estimates.

Regularly Calibrate Understanding of Story Sizes

Even with an established definition, there needs to be periodic synchronization of what point values mean in practice. Teams should routinely discuss sizing examples during grooming and planning sessions to realign on standards.

Exposing outliers and allowing friendly debates on estimates helps calibrate the group over time. Team leads can further reinforce consistency by highlighting patterns of variation.

Refine Grooming and Decomposition of Complex Stories

Missed details and inadequately broken down stories often undermine estimate alignment. Taking the time to deeply groom user stories with the team clarifies details upfront.

Furthermore, concerted effort must be spent on splitting broad, complex stories into standalone chunks that can be consistently sized. Disciplined grooming and decomposition removes a major source of sizing variability.

Review Velocity Trends and Fluctuations

The team must track its actual throughput and delivery of story points over time. Sudden changes or trends in velocity can de-calibrate estimates. Reviewing cumulative flow data and making adjustments to point assignments brings estimates back in line.

This may mean up-leveling story points after a velocity increase or downsizing after a throughput drop. Estimates stay fresh when velocity is monitored.

When and How to Re-Baseline Velocity

Despite best efforts, sometimes team velocity and estimate practices drift far enough that the points become fundamentally unreliable. Major changes like different product priorities, staffing loss/gain, or interruptions can also necessitate a reboot.

Re-baselining should happen when there is sustained, fundamental mismatch between assignments and delivery. Teams can pick a recent iteration or a fresh start to re-baseline.

The process involves resetting the velocity tracking to 0 and re-estimating the remaining backlog from scratch. Past velocity is archived but not carried forward. By fully repointing, the team gets back to a reliable baseline.

Example Re-Baselining Policy

Teams should define a policy specifying when and how velocity re-baselining occurs. Below is an example:

Timing: Re-baselining may occur when both variation and inaccuracy in estimates exceed 30% over a rolling 3-sprint period
Approach: The product owner will select a sprint to serve as the start point and remove all points history before then
Activities: The team will re-estimate all remaining stories in the product backlog using the standard story pointing process
Alignment: During re-baselining, the team will use calibration techniques to align on estimate standards
Documentation: Rationale, timing, and findings from re-pointing exercise will be captured in sprint retrospective notes

Having a defined policy eliminates ambiguity on when and how velocity gets reset.

Risks of Frequent Re-Baselining

Although necessary in some scenarios, re-baselining team velocity too frequently has a number of downsides:

It disrupts historical data on throughput
Forecasting becomes more complex without reliable trends
Frequent rework on estimates de-motivates the team
It can enable avoidance of underlying issues driving inconsistency
Stakeholders lose visibility when metrics frequently restart

For these reasons, re-baselining should be a last resort rather than routine exercise. It works best when fixes to estimating practices are also implemented.

Key Takeaways and Best Practices

Inconsistent story point estimates reduce the efficacy of core agile practices for planning, tracking, forecasting and more. While no team will assign identical points across all stories, teams should actively minimize unnecessary variation.

Managing estimate consistency relies on:

Owning a shared definition of story points
Committing to regular calibration of estimates
Enforcing disciplined detailing and decomposition of complex stories
Tracking changes in velocity and adjusting points accordingly
Re-baselining as a last resort after making process improvements

Institute these practices proactively to enable reliable metrics for evidence-based agile software development.