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The WHOOP Monthly Performance Assessment and How to Interpret the Data

June 30, 2020

We’ll break down the May Performance Assessment for WHOOP Founder and CEO Will Ahmed in order to help you get the most out of your own MPA.

By Marjolein Pawlus

At WHOOP, our mission is to help you unlock your best performance with 24/7 personalized data. We’re also constantly striving to better enable our members to understand and interpret that data. We recently released a new feature called the Monthly Performance Assessment–a perk available to members who have been on WHOOP for at least 28 days. These assessments are delivered each month and display insightful charts and analysis from the previous month’s data.



This page allows you to compare the current month to the previous month, as well as look at how sleep deprivation and restorative sleep changed over the course of the month.


In the graph comparing your average sleep stats between the current month and the previous month, the left two bars show your average sleep need in green and average sleep duration in blue, with the corresponding averages printed below. The right two bars show your average REM sleep duration in the lighter blue and average slow wave sleep (SWS, or deep sleep) duration in the darker blue with corresponding averages printed below as well. REM and SWS together are considered “restorative sleep.”

A comparison of you sleep performance and restorative sleep from the past two months.


You can see that Will improved his sleep performance by 2% between April and May by closing the gap between his sleep need and the sleep he got. Will also brought up his average nightly restorative sleep by 12 minutes.

There are two graphs at the bottom that show the same stats as the summary graph above, but broken down by each day of the month.

A day-by-day breakdown of your sleep vs sleep need, and restorative sleep.


The top graph shows sleep need in green and sleep duration in blue. For the most part, Will’s sleep need was constant throughout the month, indicating that his daily strain was fairly consistent and he didn’t let his sleep debt rack up. You can see a spike in sleep need on Sunday May 24th, since the night before Will only got about 4.5 hours of sleep. The goal should be to have these two lines as close to each other as possible, indicating that you achieved close to 100% of your sleep need.

The bottom graph shows REM (light blue) and SWS (dark blue). You can see that despite getting similar total amounts of sleep on May 1 and May 24, Will got about an extra 1.5 hours of restorative sleep.

Make use of the app’s Sleep Coach feature to maximize your sleep performance and optimize your sleep consistency.



This section of the Monthly Performance Assessment gives members a new way of breaking down cardiovascular load by strain and time in heart rate zones across different sports and activities. Will recorded running, weightlifting, and golf workouts as well as a number of unspecified activities in May. Using the legend, we see the distribution of activities in terms of day of week, heart rate zones, and intensities.

An analysis of your training in the WHOOP montly performance assessment.


The Strain By Day of Week Graph shows how much of your day strain comes from the different activities you log. The width of every bar and the strain next to each corresponds to your average strain on that day of the week. The colors in the bar indicate the amount of strain attributable to each sport/activity, with white space indicating strain accrued outside of your logged activities.

A chart depicting your WHOOP strain broken down by day of the week.


The figure above illustrates that Will’s primary contributions to strain come from running, which is supplemented by weightlifting and golf on the weekend. Based on the size of the Tuesday bar, we can determine that Will runs consistently on Tuesdays and at a decent strain. His Friday running bar is smaller, which could indicate he ran every Friday at lower strains or that he might have skipped his Friday run once or twice during the month.

To the right of the Strain By Day of Week Graph we provide summary statistics about workouts logged and show a comparison to the previous month. Will logged a total of 24 workouts in May, compared to 19 in April. Of those, 17 were running, 5 weightlifting, 1 activity, and 1 golf. He worked out for a total of 24 hours and 1 minute during May, while his average workout strain was 11.7, with running activities yielding the highest average strain and weightlifting the lowest. He also extended his average workout time to 1 hour, an 11-minute increase from April.

A summary of your activities with average strain and duration.

Each heart rate zone is believed to impart a different training stimulus; the Heart Rate Training Graph shows your total time in each zone over the course of the month, color coded by the contribution of each activity type. Will has a fairly even spread across his heart rate zones, with running workouts making up the bulk of time in his 70%+ zones. Will rarely exceeds 70% of his max heart rate when playing golf or weightlifting.

The average amount of time you spend in each heart rate zone during workouts.

The last graph on this page is the Training Intensity Graph, in which we plot strain against duration for each activity in the past month. This graph shows you how long it took to earn various strain scores across your different sports. You can see that Will’s running activities range from 20 minutes to 2 hours, but stay within the 8-15 strain range–his more intense runs took less time to complete. Will’s weightlifting activities are all within the 5-9 strain range, but it took him between 45 minutes and 2:15 to achieve these strains. You can use this chart in conjunction with the Strain Coach’s optimal strain recommendation to decide the sport and duration that will get you to your goal Strain for the day.

A chart depicting training intensity (strain vs duration) of your various activities.



Next up are the strain, recovery, and sleep performance heatmaps. The goal of this page is to give you an overview of how you are trending throughout the year. Averages by weekday are displayed to the left of the heatmap, and averages by month are displayed below. The darker the color of the strain boxes, the higher the strain for that day. You can see that Will’s average strain increased during the COVID-19 pandemic while physical distancing mandates in Massachusetts kept him home and forced him to swap his normal gym workouts for outdoor activities.

Heatmaps showing your daily strain, recovery and sleep performance.


You can also see the progression from light to dark in his sleep performance heatmap, showing that he started closing the gap between sleep need and the sleep that he got. His May sleep performance was 5% better than February and also 2% better than May of last year. Will has a mix of yellow and green recoveries with only a handful of red recovery days in there. Because of the way our recovery algorithm normalizes to each individual member, most people have an average monthly recovery somewhere in the yellow.



The WHOOP Journal analysis pages are crucial in understanding which behaviors impact your recovery. The behaviors listed include data from up to the past year. If you have not answered a question in the current month, it won’t show up on your assessment. Behaviors are sorted by their impact on your recovery, from positive to negative. In order for the behavior to be included in the sort, you must have answered both “Yes” and “No” at least five times each. If you don’t meet this minimum, those behaviors will be included below your sorted behaviors.

whoop journal behavior recovery analysis

Learn More: MPA Now Features Enhanced Recovery Analysis



The final page of the Monthly Performance Assessment puts all the data into context by showing how you balanced strain and recovery (top), and how your cardiovascular fitness (bottom) responded to that training over the past 6 months. The top graph is adapted from the first page of your Weekly Performance Assessment, but shown now as a time-series instead of a scatter-plot. The gray line shows the difference between your optimal and actual strain, with higher values indicating periods of overreaching and lower values indicating restorative periods. Sustained periods of overreaching or restoring are highlighted in red and blue, respectively.

A chart depicting how your body responds to training stimuli.


In the bottom section of the page, you can see how your body responded to your training behavior. If you were overreaching and then your HRV and RHR improved, that’s a sign that the overreaching was functional, meaning you were more fit because of it. If, however, your HRV and RHR got worse following the overreaching period, that can indicate non-functional overreaching.

Similarly, periods of rest can result in a physiological restoration that enables harder training and better performance – think tapering before a big competition – or can lead to loss of fitness and detraining. Looking at the top and bottom graphs together can help to understand if your training had the intended impact.

Will’s data shows that a long period of restorative training in January and February negatively impacted his HRV and RHR. Once work from home started in March due to COVID-19, you can see that he started getting closer to optimal training, followed by a short period of overreaching. The green lines on his HRV and RHR adaptation graphs show how his cardiovascular fitness came back to the levels he had in January.

We encourage you to make the most of your Monthly Performance Assessment by aiming to improve your stats month over month. If you feel like you have a good sense of how your current tracked behaviors affect you, try experimenting with new ones!


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Marjolein Pawlus

Before starting as a Data Scientist at WHOOP, Marjolein studied Data Science at Northeastern University. She is a competitive road and mountain biker that has competed at the national level. Marjolein combines her love of data and sport to empower athletes on WHOOP through new data driven features and research.