Advantages of Identifying High and Low-Performing teams

Understanding the differences between high-performing and low-performing teams can offer invaluable insights for organizations, leading to improved team dynamics, enhanced productivity, and increased success. Here are some key advantages of gaining this understanding:

  1. Identifying Best Practices:
  • Analyzing high-performing teams: By studying the characteristics, processes, and tools used by successful teams, organizations can identify best practices and replicate them across other teams. This could include communication strategies, collaboration processes, or agile methodologies.
  • Learning from low-performing teams: Analyzing the challenges and pitfalls faced by struggling teams can help identify potential roadblocks and develop strategies to avoid them.
  1. Improving Team Dynamics:
  • Strengthening communication: Understanding the communication patterns and collaboration styles of both high and low performers can help identify areas for improvement within other teams. This could involve promoting transparency, encouraging active listening, or fostering open communication channels.
  • Building trust and accountability: High-performing teams often exhibit strong trust and accountability among members. Observing these dynamics can reveal how to nurture healthy relationships and individual ownership within other teams.
  1. Boosting Productivity and Performance:
  • Optimizing workflows: By analyzing the workflows and processes used by high performers, organizations can identify bottlenecks and inefficiencies within other teams. This can lead to streamlining processes, reducing rework, and ultimately improving overall productivity.
  • Prioritizing resources: Understanding the factors contributing to low performance allows for focused interventions and targeted resource allocation to address specific needs and challenges.
  1. Making informed decisions:
  • Data-driven allocation: Insights gained from analyzing both high and low performers can inform decisions about resource allocation, skill development, and process improvement across teams.
  • Proactive problem-solving: Identifying potential challenges faced by other teams based on understanding low performance allows for proactive intervention and implementation of preventive measures.
  1. Promoting continuous learning:
  • Benchmarking progress: Comparing team performance with established benchmarks established by high performers can highlight areas for improvement and encourage a culture of continuous learning and growth.
  • Sharing knowledge and best practices: Fostering knowledge sharing between high and low-performing teams allows for cross-pollination of ideas, leading to overall growth and development within the organization.


  • Context matters: Factors like project complexity, team composition, and organizational culture influence team performance. Consider individual contexts before implementing broad changes.
  • Holistic approach: Analyzing both high and low-performing teams, along with qualitative feedback, provides a more nuanced understanding.
  • Focus on improvement: Use this knowledge to create a supportive environment that empowers teams to learn, grow, and achieve their full potential.

By actively seeking to understand the differences between high-performing and low-performing teams, organizations can unlock valuable insights that pave the way for a more thriving workforce.

How do we identify high- and low performing teams?

Identifying high and low-performing teams requires a multifaceted approach, considering both quantitative and qualitative factors. Here are some key steps:

Quantitative Metrics:

  • Track project outcomes: Measure key performance indicators (KPIs) aligned with project goals, such as:
    • Delivered functionalities: Number of features completed compared to planned scope. Here Nesma Function Points are very useful, as this can be measured very fast and accurately.
    • Release frequency: How often the team delivers working software increments.
    • Cycle time: Average time to complete a user story from beginning to end.
    • Defect rate: Number of bugs discovered after deployment.
    • User satisfaction: Feedback and metrics on user experience with delivered functionalities.
  • Compare across teams: While caution is required due to contextual differences, compare these metrics across similar teams to identify potential performance gaps.
  • Monitor trends: Track changes in these metrics over time to identify positive or negative trends in team performance.

Qualitative Assessments:

  • Teamwork and collaboration: Observe team dynamics, communication patterns, and how effectively they work together towards shared goals.
  • Problem-solving and adaptability: Assess how the team tackles challenges, adapts to changes, and learns from past experiences.
  • Morale and engagement: Evaluate team motivation, sense of ownership, and overall satisfaction with their work environment.
  • Leadership and coaching: Analyze the effectiveness of team leadership in providing guidance, coaching, and fostering a growth mindset.
  • Feedback from stakeholders: Gather feedback from internal and external stakeholders on the team’s performance and impact.

 Additional Considerations:

  • Context matters: Always interpret data within the context of each team’s unique project, goals, and challenges. Avoid simple comparisons without considering these nuances.
  • Balanced approach: Utilize both quantitative and qualitative measures to gain a more comprehensive understanding of team performance.
  • Avoid labeling: Instead of focusing on “high” and “low” performers, categorize teams based on specific areas of strength and improvement.
  • Focus on learning and improvement: Use these insights to identify areas for growth and create a supportive environment where all teams can thrive.


The goal is not to punish low-performing teams or create unnecessary competition but to leverage these insights to create a culture of continuous learning, improvement, and collaboration across all teams within the organization. By utilizing a well-rounded approach and focusing on fostering a supportive environment, you can create the conditions for all teams to reach their full potential.

Using Nesma Function Points to measure Team Performance metrics objectively.

Business value is a buzzword and everybody wants to measure this, but this is nearly impossible to do as there are so many aspects that define business value.

However, functional size, when prioritized correctly, is considered to be a good proxy metric for business value. More functional size that is prioritized as ‘Must-Have’ should result in more business value. Nesma Function Points is one of the main standards for functional size measurement. The high-level methods are easy to learn and apply, especially in agile sprints! This allows teams that adopt function point metrics to understand their performance better and to estimate better. The main advantages of (Nesma) function points are:

  • Standardized: Nesma provides a standardized way to measure functional size, allowing for comparison across teams or projects.
  • Early estimation: Initial estimates can inform early planning and effort allocation.
  • High-level communication: Function points communicate project scope and potential value to non-technical stakeholders.
  • Objective: Size is measured independently of the person doing the analysis, and the results are repeatable, verifiable, and defensible.

Nesma FP can be used to measure objective team performance metrics:

  • Project Delivery Rate (PDR): effort hours spent per function point delivered.
  • Cost Efficiency: Cost per function point delivered,
  • Delivery Speed (Velocity): function points delivered per calendar month.
  • Sprint Quality: Defects found during tests and in production per 1000 function points delivered.

The trends in these metrics show team performance over time. Although the comparison between teams is possible, the technical environment and other non-functional requirements have a big impact on for instance the PDR. A PDR of 10 hours per function point means excellent performance for a Cobol team, but not good for a low-code platform. Therefore, comparison against similar environments makes more sense: Compare Cobol team performance with other Cobol teams and compare Low-code team performance with other low-code teams.

This way, it becomes possible to understand which teams are high-performing (+10% or better compared to industry peers) and low-performing (-10% compared to industry peers). The ISBSG repository is an invaluable resource for doing this analysis, as it contains over 11.000 data points of completed projects, releases, and sprints, and many of these were measured in Nesma function points! More information about ISBSG data can be found here: link.

An example of how to visualize this is below. Remember a lower PDR is better, so a lower PDR than industry average results in a percentage higher than 0%.