The Yesterday’s Weather Scrum Pattern suggests that the number of Estimation Points completed in the last Sprint is a reliable predictor of how many Estimation Points the team will complete in the next Sprint. This pattern emphasizes learning from past performance and managing stakeholder expectations by selecting work that is suitable for the upcoming Sprint’s duration and conditions.
Why is Yesterday’s Weather important?
The Yesterday’s Weather pattern is important because it helps teams avoid setting overly ambitious goals that could lead to shortcuts, disappointments, or failures. It also discourages the imposition of unrealistic “stretch goals” or “BHAGs” (Big Hairy Audacious Goals) from external sources that can undermine team autonomy and the Kaizen approach to continuous improvement.
By utilizing metrics and empiricism, the Yesterday’s Weather pattern enables teams to accurately predict how much work they can commit to in the next Sprint. This is achieved by analyzing historical data, specifically the number of Estimation Points completed in previous Sprints. Empiricism helps the team base their predictions on actual performance, rather than relying on gut feelings or wishful thinking.
Incorporating metrics and empiricism in the Yesterday’s Weather pattern helps teams avoid burnout and maintain a sustainable pace. This is because they will be more likely to set achievable goals, which reduces the risk of overcommitment and the resulting stress. Accurate predictions based on historical performance also allow the team to provide reliable estimates on when work can be completed, leading to better planning and stakeholder satisfaction.
How do you apply Yesterday’s Weather?
To use the Yesterday’s Weather pattern, follow these steps:
a. If the team is just starting and is on their first Sprint, they won’t have an average velocity. In this case, the team should estimate the amount of work they feel they can fully accomplish in a Sprint. It is better to be conservative in their estimation, as they can always add more work later if they complete their initial commitment early.
b. Calculate the team’s velocity by determining the average number of Estimation Points completed in the last three Sprints for subsequent Sprints. Track the completed story points for each of the last three Sprints, then find the average by adding the story points and dividing by the number of Sprints (in this case, 3). The formula for this calculation is:
Velocity = (Story Points in Sprint N-2 + Story Points in Sprint N-1 + Story Points in Sprint N) / 3
c. Manage stakeholder expectations by selecting work that aligns with the team’s historical velocity and is a good fit for the upcoming Sprint’s duration and conditions.
d. Use Running Average Velocity to smooth out variance or Aggregate Velocity when multiple teams work on one product.
e. Monitor the velocity in each Sprint and consider using Updated Velocity if the velocity increases.
f. Continuously improve the process to reduce variance in velocity by minimizing outside interference, ensuring the Product Owner provides Enabling Specifications, and adopting good core practices.
g. If the team completes their work early during a Sprint, they can increase their velocity by bringing additional work from their ready backlog into the Sprint. This approach allows the team to continually adjust and adapt to their capacity, delivering more value to stakeholders while maintaining a sustainable pace.
The Yesterday’s Weather Scrum Pattern is a valuable tool to help teams manage expectations, set realistic goals, and foster continuous improvement. By using past performance as a guide, teams can avoid the pitfalls of overcommitment and better focus on delivering quality work within the constraints of the upcoming Sprint.
Gary P. Latham and Edward A. Locke. “New developments in and directions for goal-setting research.ˮ In European Psychologist 12(4), 2007, pp. 290-300.
Lisa D. Ordóñez, Maurice E. Schweitzer, Adam D. Galinsky, and Max H. Bazerman. “Goals gone wild: The systematic side effects of overprescribing goal setting.ˮ In Academy of Management Perspectives 23(1), 2009, pp. 6-16.
J. Scott Armstrong. “Forecasting by Extrapolation: Conclusions from Twenty-five Years of Research.ˮ In Interfaces 14(6), http://repository.upenn.edu/cgi/viewcontent.cgi?article=1083&context=marketing_papers, 1984, p. 12 (accessed 2 November 2017).