---
title: "Cycle Time Scatterplot"
canonical: "https://support.55degrees.se/space/SP/4312957014/Cycle%20Time%20Scatterplot"
format: markdown
---
[Jira Cloud & Data Center] [Azure devops] [standalone]
### Cycle Time
> ℹ️ **Cycle Time is the total number of days it takes to complete a single piece of work**.
> ℹ️
> ℹ️ In ActionableAgile® Analytics we measure it from the day you start working on it until the day it is finished. and + 1 day so no time is missed.
Cycle Time is measured in calendar days, thus **includes weekends, bank holidays,** etc.
### Uses
- Find the Cycle Time of an individual piece of work.
- See if your Cycle Time trendslonger or shorter;
- Set realistic expectations using percentiles and probabilistic forecasting.
- Learn about your work by exploring the clustering patterns of dots and the empty space on the chart.
### The Chart
Each data point represents a work item that was **completed**. The placement of the data point tells you two things:
1. The **completion date** of the item on the horizontal axis,
2. The **cycle time** for the work item on the vertical axis.
#### **Cycle Time Calculation**
```
Cycle Time = (End Date - Start Date) + 1 day.
```
We add the 1 so that nothing has a 0-day cycle time. The minimum Cycle Time is 1 as days are our smallest time unit.
The Workflow Stage Chart Control on the right, determines your start and end dates as follows:
- **Start Date **is the day the item entered the first checked workflow state.
- **End Date** (aka *completion* date) is the day the item entered the last checked workflow state.
> ℹ️ Cycle Time is typically tracked in Calendar Days. This means **it includes weekends, holidays, and any time the work is just sitting around waiting or being blocked**. It is a measure of **total elapsed time**, not just the active hours someone spent typing or building.
### Analyzing the Chart
> ℹ️ #### From Guesswork to *Predictability*
> ℹ️
> ℹ️ When stakeholders ask "**when will it be done?**", traditional estimation often fails to provide a reliable answer because it relies on subjective guesswork. **The Cycle Time Scatterplot solves this problem by using your actual historical delivery data to generate highly accurate, probabilistic forecasts**. By simply visualizing exactly how long past work has taken, you can replace false certainty with reliable, data-driven expectations.
#### Probabilistic Forecasting
A **probabilistic forecast** acknowledges uncertainty by providing a **range of possible outcomes** and assigning a **probability **(or likelihood) to each one.
Instead of guaranteeing a single, exact result, like a specific delivery date, it always relies on two key components:
1. A probability
2. A range
For example, rather than saying a project will be done on June 3rd, a probabilistic forecast would state that there is a **85% chance (the probability)** of finishing a specific amount of work **on or before June 3rd (the range)**.
In short, it translates your data into a realistic, confidence-based estimate that allows you to better understand and manage risk. If you have an 85% chance of finishing 20 items or more by June 3rd, it also means that you have a 15% chance of finishing fewer items.
**Forecasting an *****individual***** work item, using the Cycle Time Scatterplot**
To know how long it is likely to take for an *individual* piece of work to be completed, you want to look at how long it has taken you to complete work in the past. This is exactly what the Cycle Time Scatterplot is showing you, and the percentiles allow you to quickly see what percentage of items finished in a certain range of time.
This can be used as a **probabilistic forecast for single work items**, as the percentage you choose becomes your **probability** and the **range** is all the possible cycle times (number of days it took to complete an item).
For examples, if your 85th percentile line sits at 16 days, it means that historically, 85% of your team's work items were completed in 16 days or less.
You can turn that into the following **probabilistic forecast: There's an 85% chance that you'll finish a work item in 16 days or less.**
> ℹ️ **Want to forecast *****multiple***** work items? **👉** Check out the *****Monte Carlo***** simulations**
> ℹ️
> ℹ️ While the Cycle time scatterplot is great for **single** item forecasting, we need a something more to forecast **multiple** items - a simulation model - and for that you can use the Monte Carlo Simulations.
> ℹ️
> ℹ️ Read more about them here: [Monte Carlo: How Many?](https://support.55degrees.se/space/SP/4302503954) , [Monte Carlo: When?](https://support.55degrees.se/space/SP/4303257603)
### The Controls
The **Cycle Time Scatterplot** has a couple of controls that are unique to it, so we’ll be covering them first.
> ℹ️ Did you know that Jira users can save their carefully crafted data configuration as a **View**? [Here’s how!](https://support.55degrees.se/space/SP/3924886850/Views)
> Macro (refined-tabs)
>
> > Macro (refined-tab)
>
> 
>
> **Percentile Summary **[new]
>
> The **Percentile Summary **shows your delivery speed at different confidence levels. Each percentile tells you *"****X%**** of ****items**** finish within ****Y**** ****days****."*
>
> The **50th** percentile is your typical speed, while the **85th** percentile is a common choice for SLEs and forecasting. If you have custom percentiles configured, those appear too!
>
> Use these numbers for *quick* forecasts → for example, "we can deliver 85% of similar items within 12 days."
>
> **SLE Insights **[new]
>
> The **SLE Insight **visualises if you’re becoming more or *less* predictable.
>
> - Your **fastest **&** slowest work item types** at your set SLE percentile (or 85% if no SLE is set).
> - The **Cycle Time Spread** shows if a single SLE is realistic; a large gap suggests type-specific SLEs.
> - See the** Cycle Time Spread** in the past 30 days compared to all days before.
> - **Up From Earlier** means Cycle Times are rising — investigate why.
> - **Down From Earlier** means delivery is faster than it used to be.
> - **Similar to Earlier** means Cycle Times are steady. Needs 60+ days with 10+ items per window to show the trend.
>
> **Blocked Time Impact **[new]
>
> The** Blocked Time Impact** shows how blocked items delay your delivery.
>
> - The **count of items with blocked time** tells you if blockers are just rare issues or a regular problem.
> - **Total blocked time for all items** is how many days your Cycle Time grew because of blocked items. It shows the impact of blockers and dependencies.
> - The **most blocked work item type** shows which kind of work gets blocked the most.
> - **Impact to 85 percentile **shows your Cycle Time without blockers, giving you a realistic goal to shoot for.
>
> > Macro (refined-tab)
>
> > ℹ️ Soon, you’ll no longer find Summary Statistics in the Chart Controls. We’re giving it a more visible home where it’s easier to use alongside your charts → **Insights**.
>
> This provides you concise, relevant information about your cycle time data.
>
> 
>
> This box shows you:
>
> - How many work items are represented in your chart
> - The date range represented in your chart
> - The cycle time for each percentile line
> - How many items fall between percentile lines
> - The cumulative number of items for each percentile line (including all percentiles below it)
>
> If you zoom into a subset of your data using the Data Control, the information in this box will change.
>
> > Macro (refined-tab)
>
> The dots on the chart can be colored based on any available item filter. The options to choose from should be identical to the options available in your item filter. The list is determined by the data that you imported.
>
> You can select a filter type to use for dot colors, select which options you want to show colors for from that filter, and you can even adjust the colors you use for each chosen option.
>
> - **Select Attribute:** Select which attribute you wish to assign colors to.
> - **Auto-Assign All**: Simplify adding colors to your attributes by auto-assigning them.
> - **Reset All:** Reset all of the dot colors assigned to your attributes.
> - **Individual Attribute Colors**: Pick a color for the dot of each individual attribute.
> - **Individual Attribute Assign:** Assign the color of the dot of each individual attribute.
> - **Individual Attribute Reset:** Reset the dot color of individual attributes.
> - **Default Dot Color:** Choose the default color of the dots.
> - **Default Color Palette:** Choose a color palette that will be the default for your dots.
>
> 
>
> > ℹ️ **Using Story Points? **
> > ℹ️
> > ℹ️ **Use the dot colors to compare the story point value (estimation) vs. the cycle time ( what really happened).**
> > ℹ️
> > ℹ️ This is perfect during a retrospective to look back at the results and see if they match. If they don’t match, you can investigate why.
> > ℹ️
> > ℹ️ To do this, make sure to add ‘Story Points’ as an Additional Field in the data loading process. When your data is loaded, go to the Dot Colors and select ‘Story Points’ as the Attribute. Then choose or auto-assign the colors you wish to display work items with Story Points as.
> > ℹ️ Here is an example.
> > ℹ️
> > ℹ️ 
>
>
> > Macro (refined-tab)
>
> 
>
> If your cycle time data includes information about blocked time, you can highlight which items were blocked on their way to completion. This makes the blocked items more visible. If you click on a dot you can get information about how long that particular item was blocked.
>
> You can also deselect the option to include blocked time to get a picture of what your cycle time would look like if none of your items were blocked. (see the above section on individual dots)
>
> In the video below we’ll show you how to Block work in Jira, so that ActionableAgile Analytics can accurately track and analyze your blocked items.
>
> > Macro (refined-tab)
>
> When you choose this option, the chart examines all of the plotted work items and draws percentile lines that correspond with specific dates.
>
> These lines can be interpreted as "*X% of the time, our work items finish in X days or less*." So, if there is an 85% line at 15 days then you can interpret that line as meaning "*85% of the time our work finishes in 15 days or less*."
>
> The chart determines where to draw the lines by counting up from the bottom and finding the point at which the appropriate percentage of items are shown.
>
> > ℹ️ This is a way to understand whether the expectations are reasonable aligned with what really happens at work. For example, if 85% of the work takes 40 days or less, maybe 2-week cadences are not the best suited for your team.
>
> 
>
> > ℹ️ **Pro tip: Use the percentile lines to make a probabilistic forecast for single work items. **Read more further down on this page: [Probabilistic-Forecasting](#Probabilistic-Forecasting)
>
> **Highlight Percentile **[NEW]** **
>
> You can highlight a specific Percentile by ticking the circle next to that percentile, under ‘**SLE**’.
>
> This will highlight that Percentile in green, making it clear to see on the chart.
>
> To unselect your choice, simply select [no sle] (below the option to add a custom Percentile).
>
> 
>
> 
>
> #### Custom Percentile [NEW]
>
> Percentile lines show how predictable you are and the type of probabilistic forecast you can make. Using one of these lines to manage your work and improve predictability means you are applying it as a Service Level Expectation (SLE) - so having a custom percentile is great for applying this feature to anyone’s context.
>
> Simply enter your desired percentile (1-99) in the designated box and select [add].
>
> You can Highlight a Custom Percentiles by selecting it and unselect your choice, simply select [no sle]
>
> To remove your Customer Percentiles, select the bin 🗑️ symbol.
>
> 
>
> > ℹ️ The maximum Custom Percentiles you can add is two.
>
> > Macro (refined-tab)
>
> When these configurations are active, the chart looks at where your cursor is and tells you:
>
> - the number of items finished on that date
> - the percentage of items finished by that cycle time
>
> 
>
>
> > Macro (refined-tab)
>
> To see a trend line for your cycle time, click the option "**Moving Trend**” below the chart.
>
> 
>
> In the Cycle Time Trend Control, you can choose between showing the trend of the **average, 50%, 70%, 85% and 95% percentiles. **
>
> You can also choose your cycle time intervals in days. The interval affects the time period used for calculating cycle time.
>
> > Macro (refined-tab)
>
> By default, the chart shows a dot for each unique calendar day/cycle time combination. However, if you'd like it to calculate by week or month, choose from these alternative options.
>
> Here's an example, if you choose one of the Week options, the horizontal axis now updates to reflect weeks instead of days and each dot now represents all items that finished within the same week with the same cycle time.
>
> 
>
> > Macro (refined-tab)
>
> To show the Data Control, click the symbol in the top right-hand corner of the chart, and it will appear below the chart.
>
> With this control, you can further **narrow down the data included in the chart.** When doing so, the percentiles and summary statistics will be **re-calculated**.
>
> All you have to do is click and drag over the desired time period and/or cycle time range, or use the date selector in the top-right corner of the Data Control.
>
> > ℹ️ **Want to zoom in on the chart? **
> > ℹ️
> > ℹ️ You can zoom in on the cycle time chart itself, without triggering a re-calculation of percentiles and summary statistics.
> > ℹ️
> > ℹ️ If you use the Data Control to zoom in, it will re-calculate those automatically.
>
> 
### Additional Chart Controls
> Macro (refined-tabs)
>
> > Macro (refined-tab)
>
> This All Charts Control allows you to choose static dates or rolling period of time, so you can see data from a specific date range. The Data Control will only show and use data from the time period chosen here.
>
> 
>
> - To get **Static Dates**, select directly on the Visible Period’s calendar or manually enter the dates you want in the designated boxes below. These dates will remain the same, hence the *static*.
> - For a **Rolling Period**, pick one of the pre-made options to the right of the Visible Period’s calendar. This period will update to the current day, hence the *rolling*.
>
> > Macro (refined-tab)
>
> In the Workflow Stage Control, you can define what stages are regarded as [NOT Started] , [in progress] and [finished] , respectively.
>
> All the stages marked in blue will count as [in progress], and the cycle time will be counted from the day a work item entered the first stage in blue, until the day it left the last stage in blue.
>
> All the stages marked in green will count as [Finished work], and those work items will therefore be displayed on your Cycle Time Scatterplot.
>
> To change what stages are included in your cycle time, simply check or uncheck the tick boxes. And remember - all the stages in blue are included in your cycle time. Here’s an example.
>
> | | |
> | --- | --- |
> | 
1. No changes were made.
Cycle time counts from when an item enters ‘In progress’, until it enters the ‘Done’ stage. | 
2. ‘Done’ has been unchecked.
Cycle time counts from when an item enters ‘In progress’, until it enters 'Pending Release’. |
>
> > ℹ️ **If you deselect workflow stage(s) in the middle of your workflow**
> > ℹ️
> > ℹ️ The workflow stage(s) will still count as [in progress], thus the overall cycle time doesn't change. Because it still entered and exited the workflow stages that count as [in progress] on the same dates. In order to handle this unexpected loss of data, the chart adds the time for the unchecked stage(s) to one of the checked stages.
> > ℹ️
> > ℹ️ In the example below, the ‘Review’ stage has been unchecked, thus the time spent in that stage rolls up to the previous one - the ‘In progress’ stage.
> > ℹ️
> > ℹ️ 
>
> > Macro (refined-tab)
>
> You can filter down the data shown on this chart by choosing one or more available filters.
>
> If you want to clear your filters so that all dots show up again, you click the 'Clear All' button.
>
> 