Time Distribution in Review: Are You Efficient or Just Lucky?

[#1]

What if your team reviews 80% of tasks within 5 days — is that efficiency, or a blind spot hiding behind averages? Let’s find out.

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🎯 Why Review Time Matters (a Lot) 

In every agile team, the Review stage is like airport security: it can be fast and invisible… or a frustrating bottleneck. That’s where Review Time – a time tracking metric from the Time Metrics Tracker | Time Between Statuses – steps in. It shows how long issues stay in review status before getting merged, tested, or shipped.

You might assume that if the average review time is around 4.5 days — life’s good. But is it?

Let’s look at the Histogram Chart to challenge that assumption.

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📊 Let’s Decode the Histogram Chart

The Histogram Chart groups issues by how long they stayed in the selected status – in this case, Review.
Each bar shows how many issues fall into a certain time interval.

Time Interval (days)	Number of Issues
<= 0.06	1
0.06 – 5.32	86
5.32 – 10.59	13
10.59 – 15.85	6
15.85 – 21.12	5
21.12 – 26.38	2
> 26.38	3

The Average Time metric — highlighted as a pink dashed line — is 4.65 days.

Sounds pretty solid… unless you look closer.

🤔 So What’s the Real Story?

Sure, 86 out of 116 issues landed in the 0.06 – 5.32 days range.
That’s 74.14% of your issues being reviewed fairly quickly.
But let’s not ignore the 30 other tasks:

• 13 took more than 5 days

• 11 went beyond 10 days

• 5 of them passed the 15-day mark

• And 3 brave souls stayed in review over 26 days

That’s not “just a few outliers.” That’s a consistent long tail — and a bottleneck waiting to happen.

🎯 Are You Efficient or Just Lucky?

If your Review Time looks great on average, but 25% of your issues are stuck — you're not efficient, you're lucky.

This isn’t about being perfect. It’s about being predictable.

If 1 in 4 issues can spend 10–20+ days in code review, your team has:

• Delays in releases

• Higher risk of merge conflicts

• Reduced team confidence

• Planning uncertainty

Even if it’s “just a few,” those “few” can cost a sprint.

🔎 Tip: Click the Bars, See the Truth

One of the most useful features of this chart?

You can click on any bar — and it opens a list of issues behind that data point.

For example, click on the 15.85 – 21.12 bin → you’ll see 5 specific issues.
Click → drill down into Jira → check:

• Who was the assignee?

• Were there any review comments?

• Was the reviewer unavailable?

• Was it an epic pull request?

From chart to issue in one click = instant investigation mode.

🚀 What You Can Do Next

1. Define Your Review SLA

Example: “Review Time should stay under 5 days.”

2. Track Weekly

Сheck Histogram Chart during retro.

3. Investigate Long Review Tasks

Use click-through to find patterns: same reviewer? Same component? Vacations?

4. Make It Visible

Set up Agile Mertics Gadget and Scatter Plot Gadget to spot outliers quickly 

📌 Summary

• The Average Time metric (4.65 days) looks safe.

• But 26% of your issues are taking over 5 days in review.

• Histogram Chart reveals the distribution behind the average — and shows if the process is under control or quietly breaking.

• Click into bins to see issues and unblock your flow.

• Don’t rely on luck. Use data to build predictability.

🧠 Final Thought

When your histogram chart looks like a skyscraper on the left and a bumpy road on the right — your process isn’t broken. It’s just hiding its weak spots.

Start there. Dig in with Time Metrics Tracker. Make small tweaks. And next quarter, maybe that pink line will mean what you think it means 😉