An Advanced Guide to Sprint Forecasting for Agile Teams

Agile PMs are faced with the challenge of predicting and delivering on sprint goals with limited resources, tight timelines, and competing priorities.

Sprint forecasts are a critical part of successful product delivery in agile because they provide visibility into both a team’s capacity and its velocity.

This article will provide an overview of how agile product managers can make more accurate sprint forecasts, allowing you to better manage resources and deliver on commitments. By following these steps, you can ensure that projects are completed on time and within budget.

What Are Sprint Forecasts?

Sprint forecasts are a planning tool used by project managers and team leaders to get a better understanding of their workloads and ensure that project timelines are met. 

They are typically created at the start of each sprint and provide an estimated timeline for the completion of tasks. Sprint forecasts are used to identify potential risks, allocate resources, and set expectations for the upcoming sprint.

A typical sprint forecast should contain three things:

• a list of tasks to be completed in the sprint
• estimated time frames for each task
• dependencies and prerequisites

This allows developers to prioritize tasks based on their level of difficulty, importance, as well as any potential risks associated with them. By creating a sprint forecast, software developers can gain insight into their team’s working capacity and the effort required for a specific project.

The Role of Sprint Forecasting in Sprint Planning

Forecasts are actually a part of the sprint planning process. The sprint forecast is the estimated amount of work that can be completed in a given sprint. It helps the team understand how much work they can handle and plan accordingly.

The sprint forecast helps the team plan their tasks, prioritize their goals, and set realistic expectations for what they can accomplish during the sprint. It also helps them to identify any potential risks or issues that could arise during the course of the sprint.

During the sprint planning meeting, the team should review their forecast and discuss how much work they can accomplish. That will help them determine which user stories to take on first, which ones to do later, and which ones not to do at all. 

Once the team has set a forecast for the sprint, they need to track progress throughout the process to ensure that they stay on track. They should identify any areas where additional resources may be needed or where adjustments need to be made for the best results. 

The forecast also enables teams to plan for future sprints by allowing them to estimate how much work they think they can complete within a certain time frame. This allows them to anticipate any potential delays or roadblocks before they become an issue and adjust accordingly if needed. 

For an agile PM, sprint forecasts are a great way of keeping product owners informed about the team’s progress and approach. It also helps them better map out story points over the course of the project.

Sprint Forecast vs Sprint Backlog: What’s the Difference?

While they may feel similar, a sprint forecast and a sprint backlog are two completely different concepts in agile management. 

The sprint forecast is a plan for the upcoming sprint which outlines the tasks that need to be completed. It also includes an estimate of how long each task will take and any risks or constraints associated with completing it. 

The purpose of the sprint forecast is to provide a roadmap for the team so they can plan their activities and determine what resources they need in order to complete the tasks within the timeline.

The sprint backlog, on the other hand, is a list of all tasks that have been identified for completion during that particular sprint. It includes both those tasks identified in the initial planning phase as well as any new tasks added during the course of the sprint. 

The purpose of this list is to provide visibility into what needs to be done so that team members can prioritize their work accordingly. A backlog provides visibility into all tasks associated with a given project and allows team members to adjust priorities accordingly throughout the course of the iteration.

The main difference between a sprint forecast and a sprint backlog is that while a forecast provides an overview of what needs to be done in order to complete all planned tasks within a given timeline, it does not include any new tasks or changes in priority due to external factors such as customer feedback or resource availability. 

Moreover. a forecast serves only as an obligation-free estimate for guiding the team throughout the sprint. A backlog, on the other hand, is an itemized list of tasks that need to be completed for the sprint to be considered successful.

Steps for Using Historical Data to Create Sprint Forecasts

In the SCRUM framework, historical data refers to the practice of collecting and storing performance data on your development team so that you can later analyze it to get a better understanding of your capacity and speed.

Want to learn how you can use historical data to create more accurate sprint forecasts? Here’s a simple framework:

• Gather: The first step to creating more accurate sprint forecasts is to gather historical data from past sprints. This data should include the total number of tasks completed, the total estimated time for each task, and any additional information that could be helpful in understanding the complexity of each task. It’s also important to ensure that all data collected is accurate and up-to-date.
• Analyze: Once you have collected the necessary data, it’s important to analyze it in order to identify any patterns or trends that may exist within the data set. This analysis should include looking at individual tasks as well as overall sprint performance. It’s also important to look at factors such as team size, experience level, and the number of tasks completed in order to gain a better understanding of how they affect performance.
• Estimate: After analyzing the data, it’s time to estimate the workload for upcoming sprints based on performance metrics. This can be done by taking into account average completion times and estimating how much work can realistically be completed within a given timeframe. It’s also important to consider any changes in team size or experience level that may affect future performance levels when making these estimates.
• Create: Finally, once you have estimated the workload for upcoming sprints, you can create a forecast based on this information by comparing it with past performance metrics and making adjustments accordingly if needed. This will help ensure that your forecasts are as accurate as possible and give you an idea of what resources will be necessary for the successful completion of upcoming sprints.
• Monitor: After creating a forecast, it’s important to monitor actual performance against this forecast throughout each sprint in order to ensure accuracy and identify any potential issues before they become too large or difficult to manage effectively.

Why Use a Time Tracker for Historical Data?

Time trackers provide a reliable way to track the effort spent on various stories by individuals, teams, and departments.

A time-tracking tool can help to identify trends in how long certain tasks take or how often they are completed, as well as any correlations between different types of tasks and the amount of time required to complete them.

Using a time tracker also makes sure that all data is accounted for during analysis. Without a proper tool, it can be difficult to tell if all relevant data has been tracked and recorded properly. Time-tracking tools provide an efficient way to monitor progress and identify areas where improvements could be made over the next sprints.

Time trackers are great for understanding the impact of changes over time on productivity levels.

For example, if a development team implements new software or processes to improve efficiency, tracking the amount of time required for each task before and after the change can give a clear indication of whether or not it was successful in doing so.

This kind of analysis can help organizations identify areas where further improvements could be made in order to increase efficiency even further.

Finally, using a time-tracking tool allows organizations to easily compare their performance against industry standards or other organizations in their field.

This kind of comparison provides valuable insight into how well an organization performs relative to its peers and helps inform decisions about future investments or strategies that could lead to increased success.

At the end of the day, all of this contributes to more accurate sprint forecasting through an improved understanding of historical performance.

How 7pace Can Help Product Teams Create More Accurate Sprint Forecasts

7pace is a time-tracking tool that integrates directly with Azure DevOps and GitHub to provide detailed insights into your development team’s historical performance.

What makes it different from the rest of the time tracking software market? Well, it’s built from the ground up by developers and for developers. 

That means, instead of a highly superficial and tacked-on approach to time-tracking that serves only to enable micromanagers, 7pace provides detailed analyses of your sprints to assist PMs in making better estimations.

Want to learn more about how 7pace can assist your team’s agile workflow? Get started today with a free trial!