In the fast-paced world of software development, measuring team performance has become something of an obsession. Among the most popular metrics is team velocity—a measure of how much work a team completes in a given sprint or iteration. While velocity can provide useful insights, many teams have fallen into the trap of treating it as the ultimate measure of productivity and progress.

But here’s the uncomfortable truth: your team’s velocity metrics probably aren’t measuring real progress. In fact, an overemphasis on velocity can lead to dysfunctional behaviors, misaligned incentives, and ultimately, poor-quality software.

At AlgoCademy, where we help developers build their algorithmic thinking and coding skills, we’ve seen firsthand how focusing on the wrong metrics can hinder rather than help development. Let’s explore why velocity metrics often fall short and what you can do about it.

What Is Velocity and Why Do Teams Use It?

Before we dive into the problems with velocity, let’s clarify what it is and why it became so popular.

Velocity is typically defined as the amount of work a team completes in a sprint, usually measured in story points or completed user stories. In theory, it serves several purposes:

On the surface, these seem like reasonable goals. After all, who wouldn’t want to measure productivity and make better predictions?

The Fundamental Problems with Velocity as a Progress Metric

1. Velocity Measures Output, Not Outcome

Perhaps the most fundamental issue with velocity is that it measures output (the amount of work completed) rather than outcomes (the value delivered to users or the business).

Consider this scenario: Team A completes 50 story points in a sprint, implementing numerous features. Team B completes only 30 story points but focuses on a few high-impact features that dramatically improve user satisfaction and business metrics. Which team made more progress?

By velocity alone, Team A would appear more productive. But in terms of actual business value and progress toward meaningful goals, Team B might be far ahead.

This disconnect between output and outcome is at the heart of why velocity often fails as a progress metric.

2. Story Points Are Subjective and Inconsistent

Story points, the common currency of velocity measurements, are inherently subjective. They’re meant to represent the relative effort, complexity, and uncertainty of work items. But in practice, point assignments vary widely based on:

This subjectivity means that a 5-point story for one team might be a 3-point story for another. Even within the same team, point values can drift over time as the team evolves.

At AlgoCademy, we’ve observed that teams learning new algorithms or tackling unfamiliar problem domains often see significant fluctuations in their velocity metrics—not because their productivity changed, but because their understanding of the work’s complexity evolved.

3. Velocity Creates Perverse Incentives

When teams or managers focus too heavily on velocity, they inadvertently create incentives for behaviors that may actually harm long-term progress:

One engineering manager we worked with described it perfectly: “We started focusing on velocity, and within three sprints, our velocity went up 40%. But our bug count doubled, and our deployment frequency dropped. We were measuring the wrong thing.”

4. Velocity Ignores Technical Debt

Technical debt—the accumulated cost of cutting corners or implementing suboptimal solutions—is a critical factor in a team’s long-term productivity. Yet velocity metrics typically don’t account for it.

A team can maintain high velocity for weeks or months while accumulating technical debt, only to see their productivity plummet later when that debt comes due. This creates a misleading picture of progress and can lead to poor decision-making.

In algorithm development and complex software systems, technical debt can be particularly insidious. A suboptimal algorithm might perform adequately with small data sets but fail catastrophically at scale—something velocity metrics would never capture.

5. Velocity Doesn’t Reflect Learning and Growth

Software development is a knowledge-intensive activity. Teams constantly learn about their domain, technology, and each other. This learning is crucial for long-term success but often doesn’t translate to immediate velocity gains.

For example, a team might spend time exploring different algorithmic approaches to a problem, resulting in lower velocity for a sprint but setting them up for much more efficient work in the future. Velocity metrics would penalize this investment in learning rather than recognizing its value.

Real-World Examples of Velocity Failures

Let’s look at some real-world scenarios that illustrate how velocity metrics can lead teams astray:

The High-Velocity, Low-Quality Team

A web development team at a financial services company prided itself on consistently high velocity, completing 60-70 story points per two-week sprint. Management celebrated their productivity and held them up as an example to other teams.

Six months later, the application they built was plagued with performance issues, security vulnerabilities, and usability problems. Customer satisfaction plummeted, and eventually, much of the codebase had to be rewritten.

What happened? The team had focused on completing stories at the expense of quality. They skipped proper testing, code reviews, and security considerations. Their velocity metrics looked great, but they weren’t making real progress toward a successful product.

The Refactoring Penalty

A data science team noticed that their model training pipeline was becoming increasingly brittle and hard to maintain. They proposed spending a sprint refactoring the codebase to improve its structure and performance.

Management, concerned about the impact on velocity, pushed back. “We can’t afford to show zero velocity for a whole sprint,” they argued. “Can’t you do this alongside your regular work?”

The team reluctantly agreed to tackle the refactoring incrementally while continuing to add features. The result? The refactoring took three times longer than estimated, several features had to be partially rolled back due to conflicts with the refactoring work, and the team’s morale suffered.

By focusing on maintaining velocity at all costs, the organization actually slowed down real progress.

The Algorithm Optimization Dilemma

At AlgoCademy, we worked with a team building a recommendation engine who faced a classic velocity challenge. They had implemented a simple collaborative filtering algorithm that worked well enough for their initial user base. However, as they scaled, performance degraded significantly.

The team proposed spending time researching and implementing a more sophisticated algorithm. By their estimates, this would take two sprints with little visible output but would ultimately improve recommendation quality and system performance dramatically.

When measured purely by velocity, this investment looked like a productivity collapse. However, the long-term outcome was transformative: the new algorithm scaled efficiently, improved user engagement by 40%, and reduced infrastructure costs by 60%.

This is a perfect example of how velocity metrics can mislead: what looked like a productivity problem was actually a strategic investment in the future.

Better Alternatives to Velocity for Measuring Progress

If velocity isn’t a reliable measure of progress, what should teams use instead? Here are some alternatives that better reflect real progress and value delivery:

1. Outcome-Based Metrics

Instead of measuring how many stories you complete, measure the impact of your work on business and user outcomes:

For example, if you’re building a search feature, don’t just measure that you completed the search stories. Measure whether users can find what they’re looking for more quickly and accurately.

2. Flow Metrics

Flow metrics focus on how work moves through your development process, highlighting bottlenecks and inefficiencies:

These metrics help teams identify where work gets stuck and focus on improving the overall flow of value, rather than just the raw amount of work completed.

3. Quality and Technical Health Metrics

These metrics help ensure that you’re building sustainable solutions, not just rushing features out the door:

For teams working on algorithmic problems like those we focus on at AlgoCademy, additional metrics might include algorithm efficiency, scalability with increasing data size, or robustness across different input patterns.

4. Learning and Growth Metrics

These metrics acknowledge that team learning is a form of progress:

For example, a team might track how many members have become proficient in a new algorithm or data structure that’s relevant to their work.

5. Team Health and Satisfaction

Sustainable progress requires a healthy, engaged team:

Research consistently shows that teams with higher satisfaction and psychological safety are more innovative and deliver better results over the long term.

How to Transition Away from Velocity Obsession

Moving away from an overreliance on velocity metrics can be challenging, especially if your organization has built processes and expectations around them. Here’s a practical approach to making this transition:

1. Start with Education

Begin by educating stakeholders about the limitations of velocity metrics and the benefits of a more holistic approach to measuring progress:

This educational foundation is crucial for getting buy-in for changes to measurement approaches.

2. Introduce Complementary Metrics Gradually

Rather than abandoning velocity metrics overnight, start by supplementing them with some of the alternative metrics discussed above:

This gradual approach allows the team and stakeholders to see the value of these additional perspectives without feeling like you’re taking away familiar measurements.

3. Make the Connection to Business Goals Explicit

For each metric you track, explicitly connect it to business goals and outcomes:

This helps shift the conversation from “how much did we build?” to “what impact did we have?”

4. Reframe Velocity as a Capacity Planning Tool

Velocity does have legitimate uses—primarily as a capacity planning tool. Reframe it this way:

This shift in framing can help reduce the harmful incentives that velocity metrics sometimes create.

5. Create Safe Spaces for Investment

Establish mechanisms that allow teams to invest in technical health, learning, and exploration without penalty:

These approaches acknowledge that different types of work contribute to progress in different ways and shouldn’t all be measured by the same yardstick.

Implementing Better Progress Measurement: A Practical Example

Let’s look at how a team might implement these ideas in practice. Imagine a team building a new coding education platform (similar to AlgoCademy) that helps users learn algorithms and data structures through interactive exercises.

Before: Velocity-Focused Measurement

Initially, the team tracked and reported on:

They found themselves rushing to complete stories by the end of each sprint, often cutting corners on testing and documentation. Features were being delivered, but user engagement was disappointing, and the codebase was becoming increasingly difficult to maintain.

After: Balanced Progress Measurement

The team transitioned to a more balanced approach:

Outcome Metrics:

Flow Metrics:

Quality and Technical Health:

Learning and Innovation:

They still tracked velocity, but primarily used it for capacity planning rather than as a performance metric.

Implementation Process:

  1. The team started by identifying the key outcomes that mattered most to their users and business.
  2. They created a simple dashboard that displayed these metrics alongside their traditional velocity charts.
  3. In sprint planning, they explicitly allocated capacity for different types of work: features, technical improvements, and exploration.
  4. During sprint reviews, they discussed progress in terms of outcomes and impacts, not just completed stories.
  5. They established a quarterly “investment sprint” focused entirely on technical health and exploration.

Results:

Six months after implementing these changes, the team observed:

By focusing on real progress rather than just velocity, they built a more sustainable product and development process.

Coding Example: Measuring Algorithm Performance Beyond Simple Metrics

To illustrate how focusing solely on simple metrics can be misleading, let’s consider an example from algorithm development—a core focus at AlgoCademy.

Imagine a team is implementing a search algorithm for a large dataset. They might be tempted to measure progress by counting completed story points or features. But a more meaningful approach would be to measure the algorithm’s actual performance characteristics.

Here’s how we might evaluate two different search implementations beyond simple completion metrics:

def linear_search(arr, target):
    """
    Search for target in arr using linear search.
    Returns the index if found, -1 otherwise.
    """
    for i in range(len(arr)):
        if arr[i] == target:
            return i
    return -1

def binary_search(arr, target):
    """
    Search for target in arr using binary search.
    Requires arr to be sorted.
    Returns the index if found, -1 otherwise.
    """
    left, right = 0, len(arr) - 1
    
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
            
    return -1

# Performance testing function
def test_algorithm_performance(search_func, arr_sizes, trials=100):
    import time
    import random
    
    results = {}
    
    for size in arr_sizes:
        # Create a sorted array of the specified size
        arr = sorted(random.sample(range(size * 10), size))
        
        # Measure search time for both found and not found cases
        start_time = time.time()
        for _ in range(trials):
            # Test with targets that exist in the array
            target_idx = random.randint(0, size - 1)
            search_func(arr, arr[target_idx])
            
            # Test with targets that don't exist
            search_func(arr, -1)
            
        elapsed = time.time() - start_time
        results[size] = elapsed / (trials * 2)
    
    return results

Instead of just marking the search algorithm implementation as “done” and counting the story points, we could evaluate:

  1. Time complexity across different input sizes: How does performance scale as the dataset grows?
  2. Memory usage: How much additional memory does the algorithm require?
  3. Edge case handling: How does the algorithm perform with empty arrays, single elements, or duplicate values?
  4. Real-world performance: Actual execution time with production-like data.

This more comprehensive evaluation might reveal that while both implementations “work” and would count equally toward velocity metrics, they have dramatically different performance characteristics:

With large datasets, binary search might be thousands of times faster—a difference that wouldn’t be captured by simple completion metrics.

Common Objections and How to Address Them

When proposing a shift away from velocity-centric metrics, you’re likely to encounter some resistance. Here are common objections and how to address them:

“How will we know if the team is productive?”

Response: “Productivity isn’t about how much work we do—it’s about the impact we create. By measuring outcomes like user engagement, business metrics, and quality indicators, we’ll have a much clearer picture of whether our work is actually creating value. These metrics are more directly tied to what the business cares about than velocity ever could be.”

“We need velocity for planning and forecasting.”

Response: “We’re not suggesting abandoning velocity entirely—just using it for its proper purpose. Velocity can still be a useful capacity planning tool, but we shouldn’t use it as the primary measure of team performance or progress. We’ll continue tracking velocity for forecasting while adding complementary metrics that give us a more complete picture of our real progress.”

“Management wants simple metrics they can track.”

Response: “Simple but misleading metrics lead to poor decisions. We can create simple visualizations of our key outcome metrics that are easy to understand while still capturing the complexity of our work. In fact, business stakeholders often find outcome metrics more intuitive because they directly relate to business goals, unlike abstract story points.”

“Other teams are measured by velocity. Won’t we look bad in comparison?”

Response: “Comparing teams by velocity is problematic because teams estimate differently and work on different types of problems. By focusing on outcomes, we can have more meaningful conversations about each team’s contribution to business goals. This actually makes cross-team comparisons more useful, not less.”

“Won’t this just create more overhead and meetings?”

Response: “We can start small with just a few key metrics that matter most to our context. Many of these measurements can be automated or integrated into our existing processes. The insights we gain will save time by helping us focus on high-impact work and avoid waste, more than offsetting any additional tracking effort.”

Conclusion: Measuring What Matters

Velocity has its place in the software development toolkit, but it’s a deeply flawed measure of real progress. When teams and organizations treat velocity as the primary metric for success, they create incentives that can actually hinder true progress and value delivery.

At AlgoCademy, where we help developers master algorithms and coding skills, we’ve learned that measuring the right things is just as important in team performance as it is in algorithm performance. Just as a poorly chosen algorithm can seem fast with small inputs but fail catastrophically at scale, a team focused solely on velocity might appear productive in the short term while building up problems for the future.

Real progress in software development comes from delivering outcomes that matter—working software that solves real problems, creates value for users and the business, and can be sustainably maintained and evolved over time.

By shifting from an overreliance on velocity to a more balanced set of metrics that include outcomes, flow, quality, learning, and team health, you can create a more accurate picture of your team’s true progress. This not only leads to better decision-making but also creates healthier incentives that align the team’s day-to-day work with long-term success.

The next time someone asks about your team’s velocity, perhaps the best response is: “Let me show you what we’ve accomplished and the impact we’ve had.” That’s a conversation about real progress.