Planning Poker vs Other Estimation Techniques: Which Method Works Best for Your Team?

Let's be honest—every development team has been there. You're sitting in a meeting room (or on a Zoom call), staring at a list of user stories, and someone inevitably asks, "So... how long will this take?"

What happens next usually falls into one of several predictable patterns. Either the loudest person in the room throws out a number that everyone else reluctantly agrees with, or you spend the next hour debating whether something should take 3 days or 5 days, with half the team checking their phones and the other half wondering why they didn't just become accountants.

The truth is, there's no perfect estimation method. But some approaches definitely work better than others, and the "best" method often depends on your team's size, experience, and the type of work you're doing. Today, we'll dive deep into the most popular estimation techniques and help you figure out which one will save your sanity (and your sprints).

The Estimation Landscape: More Options Than You Think

Before we jump into comparisons, let's acknowledge something: estimation is hard. Really hard. We're essentially trying to predict the future, which humans are notoriously bad at. The goal isn't to find a method that makes estimation easy—it's to find one that makes it more accurate, more collaborative, and less painful.

Over the years, agile teams have experimented with dozens of estimation approaches. Some have stood the test of time, others have faded into obscurity, and a few have evolved into hybrid approaches that work better than their original forms.

Planning Poker: The Crowd Favorite

Planning poker has become the gold standard for agile estimation, and for good reason. It combines the wisdom of crowds with structured discussion, eliminating many of the cognitive biases that plague other estimation methods.

How Planning Poker Works

The process is elegantly simple. Team members use cards (physical or digital) to vote on story point estimates. Everyone reveals their cards simultaneously, preventing anchoring bias. When estimates differ significantly, the team discusses why, focusing on the highest and lowest estimates.

The Planning Poker Advantage

What makes planning poker special isn't the cards—it's the psychology. By having everyone commit to an estimate before discussion, you get genuine individual assessments rather than groupthink. The person who estimates 13 points might have spotted a technical complexity that everyone else missed. The person who estimates 3 points might know a shortcut that could save days of work.

I've seen teams discover major integration challenges during planning poker discussions that would have blindsided them mid-sprint otherwise. The real value isn't in the final estimate—it's in the conversation that leads to it.

When Planning Poker Struggles

Planning poker isn't perfect. It can be time-consuming, especially with larger teams or complex stories. Some teams get bogged down in debates about whether something is 5 points or 8 points, missing the forest for the trees.

It also requires buy-in from the entire team. If people aren't engaged or don't understand the process, you'll get meaningless estimates and frustrated team members.

The Competition: Other Estimation Methods Explained

T-Shirt Sizing: The Simplicity Champion

T-shirt sizing uses relative sizes (XS, S, M, L, XL, XXL) instead of numeric estimates. It's incredibly intuitive—everyone understands that a Large is bigger than a Medium, even if they can't quantify exactly how much bigger.

This method shines during early roadmap planning when you need rough estimates for dozens of features quickly. A product manager can walk through a backlog and get directional sizing in minutes rather than hours.

But T-shirt sizing falls apart when you need precision for sprint planning. What do you do when your sprint capacity is "2 Larges and 3 Mediums"? The conversion to actual work units becomes arbitrary and often inaccurate.

The #NoEstimates Movement

Some teams have abandoned estimation entirely, arguing that time spent estimating could be better spent actually building features. The #NoEstimates approach focuses on breaking work into small, similar-sized pieces and measuring throughput rather than estimating effort.

This can work surprisingly well for teams with very consistent work patterns and mature continuous delivery practices. If you're cranking out similar user stories week after week, historical throughput might be more predictive than estimates.

However, #NoEstimates requires significant organizational maturity. Stakeholders need to be comfortable with "it'll be done when it's done" answers, and the team needs to be disciplined about keeping work items small and uniform.

Time-Based Estimation: The Traditional Approach

Many teams still estimate in hours or days, especially those transitioning from waterfall methodologies. It feels more concrete than story points—everyone understands what "3 days" means.

Time-based estimation works reasonably well for short-term, well-understood work. If you're fixing bugs or implementing small features in familiar technology, hour-based estimates can be quite accurate.

The problems emerge with complex or novel work. Developers tend to estimate only the "happy path" and forget about edge cases, integration challenges, and the inevitable "oh wait, this is more complicated than I thought" moments.

Affinity Estimation: Speed Dating for Stories

Affinity estimation involves physically (or digitally) grouping stories by relative size without assigning specific numbers. Teams spread stories across a wall or screen, clustering similar-sized items together.

This method is fast—you can size 50+ stories in an hour. It's also collaborative, with team members moving stories around and discussing placement. Once stories are grouped, you can assign story points to each cluster.

Affinity estimation works well for backlog refinement sessions where you need to size many stories quickly. It's less effective for detailed sprint planning where you need more precise estimates.

Bucket System: The Compromise Solution

The bucket system combines elements of affinity estimation with planning poker. Stories are placed into numbered buckets (0, 1, 2, 3, 5, 8, 13, etc.) based on relative size. Team members can move stories between buckets, and discussion focuses on items that people disagree about.

This approach is faster than traditional planning poker while maintaining some of the collaborative benefits. It works well for teams that find planning poker too slow but want more structure than pure affinity estimation.

Head-to-Head Comparisons: The Numbers Game

Let's get specific about how these methods compare across the dimensions that matter most to development teams.

Accuracy: How Close Do You Get?

Planning poker typically produces the most accurate estimates for sprint-level work. The combination of individual assessment and group discussion catches more edge cases and assumptions than other methods.

Time-based estimation can be accurate for very short-term, familiar work but degrades rapidly with complexity or novelty. T-shirt sizing is inherently imprecise but can be surprisingly good for high-level roadmap planning.

Affinity estimation accuracy depends heavily on the team's experience and the facilitator's skill. Done well, it can approach planning poker accuracy. Done poorly, it's barely better than random guessing.

Speed: Time is Money

T-shirt sizing and affinity estimation are the clear winners for speed. You can size dozens of stories in the time it takes to planning poker estimate five.

Planning poker is slower but not unreasonably so. A well-run session can estimate 8-12 stories per hour, depending on complexity and team size.

Time-based estimation can be fast for simple work but often requires multiple rounds of discussion for complex stories, making it slower than you'd expect.

Team Engagement: Keeping Everyone Involved

Planning poker excels at team engagement. The simultaneous reveal creates natural discussion points, and the card-based format keeps people focused and participating.

Affinity estimation can be highly engaging, especially when done physically with team members moving around and actively placing stories. Digital versions lose some of this energy.

T-shirt sizing and time-based estimation often devolve into one or two people doing most of the talking while others tune out.

Learning and Knowledge Sharing

One of planning poker's underappreciated benefits is how much knowledge sharing happens during estimation discussions. When estimates diverge, the conversation often reveals different implementation approaches, potential challenges, and domain knowledge that not everyone possessed.

Affinity estimation can also generate good discussions, especially when people disagree about story placement. T-shirt sizing and time-based estimation provide fewer opportunities for this kind of knowledge transfer.

The Hybrid Approaches: Best of Both Worlds?

Smart teams often combine multiple estimation methods to get the benefits of each. Here are some popular hybrid approaches:

Affinity + Planning Poker

Start with affinity estimation to quickly group stories by relative size, then use planning poker for stories where there's disagreement or uncertainty. This gives you speed for obvious stories and precision for complex ones.

T-Shirt to Story Points

Use T-shirt sizing for initial roadmap planning, then convert to story points for sprint planning. This works well when you need to estimate large numbers of features quickly but want numeric precision for detailed planning.

Async Planning Poker

Team members submit planning poker estimates asynchronously using online planning poker tools, then meet to discuss only stories with significant variance. This reduces meeting time while maintaining the collaborative benefits for complex stories.

Choosing the Right Method for Your Team

The best estimation method depends on several factors specific to your team and organization.

Team Size and Experience

Small, experienced teams can often get away with lighter-weight methods like T-shirt sizing or even #NoEstimates. They have the context and communication to handle ambiguity.

Larger teams or those with varying experience levels benefit from more structured approaches like planning poker that force discussion and knowledge sharing.

Work Complexity and Variability

If you're doing similar work repeatedly (like maintaining a mature product), throughput-based approaches might work better than estimation-heavy methods.

Teams working on novel features, integrating with new systems, or using unfamiliar technologies need the deeper discussion that planning poker provides.

Organizational Culture

Some organizations demand detailed estimates and precise delivery commitments. Others are comfortable with ranges and approximations.

Planning poker tends to satisfy stakeholders who want "scientific" estimation while still being agile-friendly. T-shirt sizing might work for progressive organizations but struggle in more traditional environments.

Time Constraints

If you're constantly under pressure to estimate quickly, affinity estimation or T-shirt sizing might be necessary compromises. But remember—spending 30 minutes on good estimation can save hours of rework later.

Making Your Chosen Method Work

Regardless of which estimation method you choose, success depends on implementation details that teams often overlook.

Establishing Baselines

Whatever method you use, establish clear baselines or reference points. In planning poker, this might be a well-known 3-point story that everyone can relate to. In T-shirt sizing, it might be a "Medium" story that represents typical complexity.

Without baselines, estimates become meaningless. A 5-point story means nothing if different team members have different ideas of what 5 points represents.

Regular Calibration

Estimation accuracy improves over time, but only if you're paying attention to it. Regularly compare your estimates to actual effort and discuss patterns as a team.

Are you consistently optimistic? Pessimistic? Do certain types of stories always take longer than expected? Use this information to calibrate future estimates.

Handling Disagreement

All estimation methods will produce disagreement sometimes. The key is having a process for resolving it that doesn't bog down the session.

Set timeboxes for discussion. If you can't reach consensus quickly, either split the difference, research the story further, or break it down into smaller pieces.

Tool Selection

For remote teams, your estimation tool can make or break the process. Free online planning poker platforms have made distributed estimation much easier, but the quality varies significantly.

Look for tools that are fast, reliable, and don't require complicated setup. The last thing you want is to spend half your estimation session troubleshooting technology issues.

The Data: What Actually Works?

While every team is different, some general patterns emerge from teams that have experimented with multiple estimation methods:

Planning poker consistently produces the most accurate estimates for sprint-level work, especially for complex or novel features. Teams typically see 15-25% improvement in estimation accuracy compared to time-based methods.

T-shirt sizing works well for roadmap planning and initial feature prioritization but requires conversion to more precise methods for sprint planning.

Affinity estimation can be nearly as accurate as planning poker for experienced teams but requires more skill to facilitate effectively.

Time-based estimation accuracy varies wildly depending on work complexity and team experience. It can be quite good for maintenance work but poor for new feature development.

Common Pitfalls and How to Avoid Them

The Precision Trap

Don't confuse precision with accuracy. Spending 20 minutes debating whether something is 5 points or 8 points isn't adding value—it's adding overhead.

Focus on getting estimates in the right ballpark rather than achieving false precision. The difference between 5 and 8 points rarely matters for sprint planning purposes.

The Consensus Trap

Not every estimate needs unanimous agreement. If most of the team is clustered around 5-8 points, you're probably done. Don't hold up the process trying to get everyone to agree on exactly 6 points.

The Velocity Pressure

Pressure to estimate stories smaller to fit more into sprints defeats the purpose of estimation. Estimates should reflect the team's genuine assessment, not wishful thinking.

If there's pressure to fit more work into sprints, address scope or capacity, not estimates.

The Tool Trap

Don't let your estimation method become more important than the work itself. If you're spending more time discussing estimation methodology than estimating stories, you've lost the plot.

Choose a method that works for your team and stick with it long enough to see results. Constantly switching methods prevents you from building the consistency that makes estimation valuable.

The Future of Estimation

Estimation methods continue evolving. AI-assisted estimation is emerging, where machine learning models suggest estimates based on historical data and story characteristics. These tools aren't replacing human judgment but providing additional data points for discussion.

Continuous integration and deployment practices are also changing how teams think about estimation. When you can deploy changes in minutes rather than weeks, the stakes for estimation accuracy change significantly.

But the fundamental challenge remains: how do you make informed decisions about what to build and when? Whether you use planning poker, T-shirt sizing, or some hybrid approach, the goal is the same—better conversations about the work ahead.

Your Next Steps

Ready to improve your team's estimation? Here's a practical approach:

1. Assess your current method honestly. Is it producing accurate estimates? Are team members engaged? Are you spending too much time on estimation relative to the value you're getting?

2. Try a different approach for 3-4 sprints. Don't judge based on one session—estimation methods need time to show their true value.

3. Measure and compare. Track estimation accuracy, time spent estimating, and team satisfaction with different methods.

4. Iterate and improve. Most successful teams end up with hybrid approaches that combine elements from multiple methods.

If you're starting from scratch or want to try planning poker, online planning poker tools make it easy to get started without any setup overhead. You can be running your first session in minutes.

For deeper background on planning poker fundamentals, check out our getting started guide or our complete planning poker guide for more advanced techniques.

Remember, the best estimation method is the one your team actually uses consistently. Don't let perfect be the enemy of good—start with something simple, measure the results, and improve over time.

Your estimates will never be perfect, but they can definitely be better than they are today. And better estimates lead to better sprint planning, more realistic commitments, and happier stakeholders. That's worth the effort, no matter which method you choose.