Skip to content

Scrum Metrics

What is Scrum?

Scrum is an Agile framework for managing complex projects. Popular in software development, the Scrum methodology emphasizes adaptability, flexibility, and the ability to manage rapidly-changing or evolving work scenarios. It accommodates a high degree of change and complexity, using an iterative, incremental approach to optimize predictability and control risk.

This process is facilitated by various roles, ceremonies, and artifacts, including the Scrum Master, who ensures that the team is following Scrum principles; the product owner, who represents the customer or stakeholder; and the development team, who carry out the work. 

Ceremonies include the sprint planning, daily Scrum, sprint review, and sprint retrospective. 

Artifacts include the product backlog, sprint backlog, and the potentially shippable increment of the product. Scrum metrics offer a tangible way to track and analyze the team’s progress, productivity, and the quality of the product being developed. They provide critical insight into how the team is functioning and whether or not the product is advancing. 

A burndown chart in Scrum is a commonly-used metric and visual representation of the work left to do versus the time left in the sprint. The X-axis represents time, and the Y-axis represents tasks. It starts with the total number of tasks identified in the sprint backlog at the beginning of the Sprint. As the team completes tasks, the line on the chart “burns down” to zero. This chart helps teams see at a glance if they are on track to complete their work by the end of the Sprint.

Another vital Scrum metric is velocity. Velocity in Scrum is a measure of the amount of work a team can handle during a single Sprint. This is typically calculated in story points, which are units of measure for expressing the overall size of a user story, feature, or other pieces of work. Velocity helps in estimating how quickly the team can work through the backlog.

The Sprint velocity formula is relatively simple: it’s the sum of the estimations of the User Stories that have been successfully completed in a Sprint. For example, if a team completed five User Stories in a Sprint and each had been estimated as two story points, then the velocity for that Sprint would be ten.

Other key Scrum metrics to be aware of are Sprint Burndown, Release Burndown, and Defect Density. The choice of Scrum metrics should reflect the team’s objectives and what the team, Product Owner, and other stakeholders consider most useful when gauging progress.


10 KPIs Every Engineering Leader Should Track

Get Report

Agile Metrics Examples

Understanding and effectively implementing Agile metrics can aid in delivering high-quality products. Here are some agile metrics examples that help measure team performance, quality, predictability, and health in Scrum: 

  • Velocity: So what is velocity in Agile? Put simply, velocity measures the amount of work your team can tackle during a single Sprint and is calculated by combining the estimates of the User Stories, requirements, or backlog items completed during the iteration.
  • Sprint Burndown: This is a visual representation of work left to do during the Sprint. The x-axis represents time, and the y-axis represents the remaining effort. It’s a powerful tool to visualize the work completed and if the team is on track to meet their Sprint goals.
  • Release Burndown: This chart shows the remaining work to be completed before a product release. It helps track progress towards a release and predicts when all work will be completed.
  • Lead Time: Lead time is the total time from the moment a new task is added to the backlog until it’s completed. This includes backlog time, waiting time, and the time during which the work is actively being done. Reducing lead time is often a sign of increased efficiency.
  • Cycle Time: Cycle time is the amount of time it takes for your team to complete an item of work once it’s started. Shorter cycle times generally indicate a more efficient process or team.
  • Throughput: This measures the average number of items (user stories, requirements, etc.) your team delivers in a specific interval (day, week, month, etc.).
  • Work in Progress (WIP): The WIP metric shows how many tasks are currently in progress. Limiting WIP is a common strategy to ensure focus and reduce context-switching.
  • Escaped Defects: Escaped defects refers to the number of defects found by customers after the product release. It’s a measure of product quality and the effectiveness of your team’s testing processes.
  • Defect Removal Efficiency: This measures how effectively your team finds and fixes defects before shipping. A higher percentage implies a better quality assurance process.
  • Customer Satisfaction Score (CSAT): CSAT is among the most significant Agile KPIs. It measures customer satisfaction with your product.
  • Employee Satisfaction: Agile isn’t just about customer satisfaction—it’s also about keeping your team happy. Regular surveys or retrospectives can be used to gauge this metric.

These Agile KPI examples demonstrate the breadth and depth of the components that make for a successful Scrum project. By prioritizing Agile KPI metrics, teams can get one step closer to achieving their ultimate objectives. 

Agile Metrics For Leadership

Agile metrics for leadership help leaders make informed decisions about ongoing projects. But not all Agile and Scrum metrics are relevant at the leadership level—granular metrics, such as Velocity or Sprint burndown charts, are immensely valuable for Scrum Masters and team members, but may not provide the strategic insights required by leadership. 

Instead, leaders should focus on metrics around improving customer satisfaction and increasing market share. Here’s a closer look at what these metrics entail: 

  • Customer Satisfaction: Metrics like NPS and CSAT give leaders a better understanding of how well the product is received. They reflect whether or not products meet or exceed customer expectations, and correlate highly with market success. 
  • Team Productivity: Rather than focusing on individual velocity or story points, which are team-specific and can vary significantly, leadership may be more interested in broader productivity measures. These could include lead time and cycle time, which offer greater visibility into the efficiency of the development process as a whole.
  • Product Quality: Metrics that demonstrate the quality of the product are crucial. Escaped defects rate, for example, can highlight the percentage of defects found by customers after the product’s release. Low rates indicate high product quality and effective testing processes.
  • Predictability: Being able to predict delivery timelines is essential for business planning. Metrics like release burndown and feature completion rate can help leaders understand if the team is on track to deliver the product or feature on time.

By learning how to improve predictability in Agile, leaders can take full advantage of Scrum. Most importantly, they can improve processes over time to achieve an even greater level of success.

Maximize Engineering Impact

To maximize engineering impact, teams should focus on a combination of productivity, quality, and innovation. They can do so by adopting best practices and Agile frameworks like Scrum. The following best practices are key to boosting engineering performance: 

  • Iterative Development: Scrum promotes iterative development. By breaking down work into manageable chunks, teams can focus on delivering high-quality software more frequently. This not only reduces the risk of defects, but ensures that the product is continuously improving, which can drastically improve customer satisfaction.
  • Cross-Functional Teams: A key tenet of Scrum is having cross-functional teams, meaning that every team member possesses a wide range of skills. This approach allows for faster problem-solving and more innovative solutions. 
  • Collaboration and Communication: Open, frequent communication is key to success in Scrum. Daily stand-ups and sprint reviews provide forums for discussing progress, challenges, and feedback, fostering a culture of shared understanding and teamwork. This enhances productivity and leads to more effective decision-making.
  • Embracing Automation: Automation of routine tasks, such as testing and deployment, can greatly increase productivity and quality. Automated testing reduces the likelihood of defects slipping into production, while automated deployments ensure faster delivery times.
  • Continuous Learning and Improvement: Retrospectives provide an opportunity to reflect on the team’s processes and identify areas for improvement. By continually refining their practices, teams can improve their performance, resulting in maximized engineering impact. 

While these strategies can boost Scrum metrics, software engineering leaders and teams need the right tools to accurately measure impact and use obtained insights to guide decision-making. To effectively translate engineering impact into tangible results, it’s important to have a clear understanding of how engineering activities contribute to broader organizational goals. 

This often involves linking engineering metrics to business outcomes. For instance, leaders might measure how improvements in cycle time speed up time-to-market, or how reducing technical debt impacts the number of new features delivered. To facilitate these measurements, a team might choose to utilize a software platform that provides deeper visibility into engineering activities. 

One such platform, called an Engineering Management Platform, can help consolidate and visualize engineering metrics, making it easier for leaders to understand the ROI of engineering activities. Jellyfish’s software for engineering leaders allows users to measure the effectiveness of engineering work with respect to their wider objectives. 

With more and more businesses seeking to link their engineering efforts with their business goals, they’re looking for solutions to help measure impact in the most accurate way possible. Jellyfish not only allows users to track engineering efforts, but optimize operations and see how various engineering activities are affecting those operations.