5 Common Planning Poker Mistakes (And How to Avoid Them)

Planning poker is one of the most effective agile estimation techniques, but even experienced teams fall into traps that kill accuracy. Whether you're new to the practice or looking to improve your approach, avoiding these mistakes transforms frustrating estimation sessions into valuable collaboration opportunities.

After facilitating hundreds of sessions, we've identified five critical errors that consistently wreck estimation accuracy and team dynamics. The good news? Each has a straightforward solution.

Why These Mistakes Matter

Poor estimation doesn't just waste meeting time—it cascades through your entire development cycle:

• Inaccurate sprint commitments damage stakeholder trust
• Over-committed sprints burn out teams
• Critical complexity insights get missed
• False confidence in unrealistic estimates
• Disengaged team members who feel unheard

Effective planning poker creates an environment where every perspective improves estimates. When mistakes creep in, you lose this collaborative advantage and get numbers disconnected from reality.

Mistake 1: Falling for Anchoring Bias

What It Is

Anchoring bias happens when the first estimate shared disproportionately influences everyone else's thinking. A senior developer reveals a "5," and suddenly everyone gravitates toward that number—even if they initially thought differently.

Planning poker uses simultaneous card revelation specifically to prevent this. When teams bypass this safeguard by discussing estimates before voting or allowing sequential reveals, they kill the technique's core advantage.

Why It Happens

Three main sources:

Social dynamics: Junior developers naturally defer to experienced colleagues. When a tech lead speaks first or accidentally reveals their estimate, others unconsciously adjust to align with that authority figure.

Sequential revelation: Some teams show cards one at a time rather than simultaneously, creating a cascading anchor effect.

Pre-vote discussion: Teams discuss story complexity before voting, and specific numbers mentioned become mental anchors that skew estimates.

How to Avoid It

Four tactics eliminate anchoring bias:

Enforce simultaneous revelation: Everyone reveals cards at exactly the same time. Digital tools like planning-poker.app handle this automatically by hiding votes until everyone submits. For physical cards, use a "three, two, one, flip" countdown.

Discuss after voting: Present the story, clarify requirements, then immediately call for votes—zero technical speculation beforehand.

Rotate facilitators: Different facilitators reduce the perception that one person's estimates carry more weight. Don't let your most senior member always run the show.

Call out clustering: When estimates suspiciously cluster around one number after someone speaks, acknowledge it: "We're all gravitating toward 5. Let's revote silently to get our true independent assessment."

Real Example

A SaaS dev team consistently underestimated database migrations. Their database specialist would mention it "should only take a day or two" during story clarification, and suddenly an 8-point story got estimated as a 3.

After implementing "vote first, discuss after," estimates for database work jumped and became far more accurate. The specialist's knowledge still informed final estimates, but team members now freely raised concerns about testing, rollbacks, and edge cases without social pressure silencing them.

Mistake 2: Rushing to Consensus

What It Is

Cards reveal 3, 5, 5, 8, and 13. Many teams immediately accept the majority "5" and pressure outliers to conform. This treats estimation as a race rather than a discovery process.

The entire point of planning poker is surfacing different perspectives. Yet teams routinely skip the most valuable part by settling on "close enough" without understanding why estimates varied.

Why It Happens

Time pressure: Sprint planning already runs long, the backlog seems endless, and everyone wants to finish and start actual work.

Misunderstanding consensus: Teams think consensus means everyone picks the same number. When estimates vary, they quickly average or accept majority vote to escape the discomfort.

Facilitator inexperience: Novice facilitators can't tell when variance represents valuable information versus random noise.

How to Avoid It

Reframe consensus as "shared understanding," not "identical numbers":

Set discussion triggers: "If high and low estimates differ by more than two Fibonacci numbers, we must discuss before revoting." Clear rules eliminate guesswork.

Outliers speak first: Ask the highest and lowest voters to explain their reasoning before anyone else. Extreme perspectives often reveal what the middle missed.

Require minimum discussion: At least 2-3 minutes for any story above a certain complexity, even if estimates align. Surface assumptions and risks.

Embrace variance: Train your team to see estimate spread as valuable information. When half votes 5 and half votes 13, you've discovered the story is poorly understood—that's success, not failure.

Use confidence checks: After reaching consensus, ask "On a scale of 1-5, how confident are you?" Low scores mean continue discussion or break the story down.

Real Example

A mobile team consistently over-committed—taking 40 points but completing only 25-30. The culprit? Accepting first majority vote and moving on. Estimation took 20 minutes.

After implementing a minimum 90-second discussion rule and mandatory deep-dives for variance greater than one Fibonacci number, their sessions grew to 45 minutes. Sprint predictability improved dramatically. Within three sprints, they hit 95% of commitments because estimates finally reflected actual complexity.

Mistake 3: Over-Engineering During Estimation

What It Is

Teams get lost in implementation details, turning 5-minute story discussions into 30-minute technical design sessions. Instead of focusing on relative complexity, they debate architectures, library choices, and optimizations—before actual work begins.

This produces estimates no more accurate than quick gut-checks. Worse, these premature implementation decisions often prove wrong once development starts, making all that discussion time wasted.

Why It Happens

Developer perfectionism: Engineers want to solve problems. When presented with a technical challenge, their instinct is to architect the solution immediately.

Unclear story definitions: Vague acceptance criteria force teams to define every edge case during estimation—essentially doing story refinement in the middle of planning poker. Proper preparation prevents this.

Confusing estimation with commitment: Teams think estimating at "8" commits them to a specific implementation, so they nail down every detail before voting. Reality: estimates should reflect effort based on typical approaches, not detailed specs.

How to Avoid It

Time-box discussions: Five-minute maximum per story. When time expires, vote with current understanding or send the story back to refinement. Prevents rabbit holes while maintaining urgency.

Separate estimation from solution design: Frame planning poker as "How complex is this problem?" not "How exactly will we solve it?" You don't need to choose between Redux and Context API to estimate a state management story—just understand the scope.

Park technical discussions: Capture important technical topics that don't impact the estimate in a "parking lot" for later. Acknowledge their value, then defer until implementation.

Fix upstream refinement: Consistent lengthy discussions signal broken story refinement. Stories should arrive with solid acceptance criteria—you should estimate, not define requirements.

Estimate in ranges: When uncertainty remains, say "between a 5 and an 8" and commit to breaking it down during the sprint. Some ambiguity is fine.

Real Example

An e-commerce team spent 2+ hours on planning poker for 10 stories. Their architect launched into design discussions whenever stories touched checkout—debating payment gateways, retry logic, and state machines before anyone voted.

The fix: Five-minute timer per story plus a separate "design review" session for stories needing deeper analysis. Planning poker dropped to 45 minutes. Estimates didn't suffer—velocity variance actually decreased because they stopped overthinking and started using collective experience.

Mistake 4: Ignoring Outlier Estimates

What It Is

Most vote "5," but one person votes "13." Many teams assume that person misunderstood or just outvote them to move on. Bad move.

Outliers often represent the most valuable information in the session. That "13" voter might be the only one who remembers the authentication service this depends on is being deprecated, or who worked on a similar feature that turned out far more complex than expected.

Why It Happens

Majority rule thinking: Teams treat planning poker as voting where majority wins, rather than discussion where every perspective matters.

Status bias: Junior outlier? "They don't get it." Senior outlier? Teams rubber-stamp without discussion, assuming they know something others don't.

Discussion fatigue: After several stories, energy drops. Accepting majority view without probing becomes the path of least resistance.

Fear of conflict: Some cultures avoid disagreement. Team members won't challenge the majority or ask why estimates differ dramatically.

How to Avoid It

Outliers speak first: When estimates vary significantly, highest and lowest voters explain reasoning before anyone else. This ensures those perspectives get heard before social pressure dismisses them.

Treat outliers as risk signals: Someone voting significantly higher has spotted complexity others missed. That's valuable—explore it.

Create psychological safety: Every estimate is one person's honest assessment based on their unique knowledge. The junior dev who worked on a similar component might spot issues the senior architect would never see.

Validate understanding: Before discussing variance, ask outliers to summarize their understanding. Often they've identified story ambiguity, not misunderstood it.

Track outlier accuracy: Review whether outlier estimates turned out more accurate than majority votes. Evidence that the "13" voter was right when everyone else said "5" builds respect for divergent perspectives.

Real Example

A DevOps team estimated adding health check endpoints to three microservices. Five voted "3"—straightforward work. One backend dev voted "13."

They almost ignored her. The facilitator asked her to explain. She noted one service was owned by another team, requiring coordination, approval, and testing in their environment—easily adding a week.

Team revised to "8" and assigned someone to reach out before sprint start. When the sprint ended, that story was the only one that ran over—not because she was wrong, but because she was exactly right about coordination overhead. They learned to treat outliers as early warnings, not noise.

Mistake 5: Not Tracking Velocity Trends

What It Is

Teams treat each estimation session as isolated—no learning from how previous estimates compared to actual effort. They estimate, complete the sprint, then estimate again with zero analysis of whether recent estimates were accurate.

This prevents calibration. Planning poker refines your shared understanding of what a "5" or "13" means through experience. Without tracking outcomes against estimates, calibration never happens. Learn more about which metrics matter most.

Why It Happens

Lack of tooling: Comparing estimated versus actual story points manually via spreadsheets rarely happens consistently.

Delivery focus: Teams are measured on features shipped, not estimation accuracy. Little incentive to analyze whether estimates were correct.

Misunderstanding velocity: Many think velocity is just for measuring capacity, not recognizing it's a feedback mechanism for improving estimates.

Retrospective gaps: Retros focus on team dynamics and blockers, skipping quantitative estimation accuracy analysis.

Tool limitations: Physical cards or basic tools that don't persist historical data make tracking hard. Platforms like planning-poker.app automatically capture and visualize estimation history.

How to Avoid It

Use automatic tracking: Platforms that automatically record estimates alongside completion data. Manual tracking dies after a few sprints—automation ensures consistency.

Review velocity every retro: Make "estimation accuracy" a standing agenda item. Examine stories that took more or less effort than estimated. Spot patterns—do certain story types consistently run over?

Track commitment accuracy: What percentage of committed story points did you complete? Graph this over 6-10 sprints to spot trends. These metrics drive improvement.

Identify systematic biases: Do backend stories run over while frontend comes in under? Do stories with third-party APIs always take longer? These patterns reveal blind spots.

Recalibrate explicitly: When you notice consistent over- or under-estimation for story types, call it out during planning poker: "The last three auth stories we estimated at 5 actually took 8+ points. Let's adjust."

Share data transparently: Make velocity trends visible through dashboards. When everyone sees patterns, collective accuracy improves—the feedback loop closes.

Separate estimation error from scope creep: Not all variance reflects poor estimation. Sometimes requirements change mid-sprint. Track separately to measure true accuracy.

Real Example

A product team kept committing to 35 points but only completing 20-25. Morale tanked—they felt perpetually behind.

After tracking completion rates for six sprints, they discovered something surprising: estimates weren't wrong. The problem was scope creep. Stories estimated at 5 were indeed 5 points for original requirements, but mid-sprint clarifications regularly added 3-5 more points that never got re-estimated.

They implemented a new process: stories gaining significant requirements mid-sprint get re-estimated, with the delta tracked separately from original estimation accuracy.

Within three sprints, completion rate hit 90%+ because they could distinguish "we estimated wrong" from "the story grew." This data-driven approach only worked because they systematically tracked velocity trends.

Better Planning Poker Starts Today

These five mistakes—anchoring bias, rushing to consensus, over-engineering, ignoring outliers, and not tracking velocity—share a theme: they sacrifice collaboration and learning for speed or comfort.

The good news? Fixing them doesn't require revolutionary changes. Small, intentional adjustments to facilitation, discussions, and analysis dramatically improve estimate accuracy and team engagement.

Quick Action Steps

Start with these concrete actions:

1. Next session: Enforce simultaneous reveals and discuss every spread greater than two Fibonacci numbers
2. This week's retro: Add "estimation accuracy" as an agenda item—review stories that ran significantly over or under
3. Before next sprint: Set up tracking (spreadsheet or planning poker tool) for estimated vs. actual effort
4. Team agreement: Establish a protocol for discussion length and when to send stories back to refinement

Tools That Help

Physical cards and spreadsheets work, but modern platforms make avoiding these mistakes easier. Planning Poker handles simultaneous voting automatically, tracks historical estimates for velocity analysis, and structures flow to prevent rushing.

Built-in features like anonymous voting stop anchoring bias, discussion prompts encourage outlier exploration, and automatic velocity tracking means you'll actually analyze accuracy over time.

Transform Your Estimation Process

Every team falls into these traps occasionally. High-performing teams recognize them quickly and have systems to course-correct.

By understanding these five mistakes and implementing these solutions, you transform estimation from a frustrating guessing game into valuable collaboration that improves sprint planning and team alignment.

Remember: planning poker's goal isn't perfect estimates—it's shared understanding, risk identification, and continuous improvement. Focus on avoiding these mistakes and fostering genuine discussion. The estimates take care of themselves.

For deeper dives, explore our guides on handling disagreements, fixing consistently wrong estimates, and speeding up slow sessions. Or learn more about planning poker best practices from the Agile Alliance.

Ready to run better sessions? Try Planning Poker and experience how the right tools and practices transform your agile estimation process.