Analyze your Peloton workout stats with real-time updates
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Peloton workout stats with real-time updates using Peloton API (template and scripts)

A tutorial on how you can sync and analyze your Peloton workout stats into Coda with custom dashboards using the unofficial Peloton API.

March 2nd, 2021 Update

-Added friends' latest cycling workouts to
-New table
contains latest 10 cycling workouts from people you follow
-
shows how your stats compare with those of your friends

February 28th, 2021 Update

-Added a monthly view of your cycling workouts to the
dashboard (see
)
-Added monthly charts to the
dashboard as well
-Added pie chart of all workouts by instructors to
(see
)
-Some tidying up
: collapsed some charts and views in
-Fixed
to account for a variety of other workouts

Now that gyms are closed, I’m using Peloton more and I love how Peloton tracks
everything
. But I always found it difficult to browse through previous workouts with the user interface on the tablet on the bike. What if I wanted to see summary stats just for 20-minute classes? What are my personal records for a given time period? I know they exist on the app
somewhere
, but I just want one place to visualize and analyze the data that matters to me.

I looked into how to get my Peloton data to do some custom sorting, filtering, and analysis. After a few weeks, I realized I could get
automated
updates to a Coda doc with all my Peloton stats. Read below to see how I was able to get these near real-time stats synced to Coda.

This doc contains the actual template you can use for yourself to track your Peloton stats. Go to
to see the cycling dashboard I’ve always wanted for myself. Click
Copy this doc
to make a copy of this template and use the
or
to start syncing your Peloton data (step-by-step instructions
). Video tutorial below:



See your workout stats on Peloton’s website

You’ve probably used the Peloton website before to
. Similar to the bike tablet, however, you have to click into each workout to see the stats for that individual workout:

0-peloton-website-workouts.jpg

If you click on the “Overview” and “Achievements” tabs, you’ll see some additional information about your workouts that might be useful. I just really wanted to see the stats associated with my bike rides, and those stats are “trapped” in each individual workout. You can’t see your output, calories, etc. over time.

Download a CSV of your workout stats

In the top right of the screenshot above, however, is a “Download Workouts” button. This lets you get a
CSV download
of all your workout data which is pretty sweet! This has most of the info I care about:

1-peloton-download-csv-workouts.jpg

Some of the columns include
Length
,
Type
,
Total Output
, and
Avg. Watts
. This means you can start answering questions like:
What was my max output for 45-minute classes?
What was the max average cadence for each instructor I’ve taken a class from?
How has my average output been trending over time?
For many of you, this CSV should be enough for you to analyze your bike and non-bike workouts. You can throw this data into a spreadsheet (or a Coda doc 🙂), build some charts, and be on your merry way. Any time you want to get your most updated stats, just download the CSV and plug it into your spreadsheet or Coda doc.

For me, I wanted to see
additional stats
and
automate the process
of updating my stats. I don’t want to download the CSV and copy and paste into Coda every time I finished a workout.

Specifically, some questions I wanted to answer were:
Can I get stats like
Difficulty
(based on ratings submitted by Peloton members),
Leaderboard Rank
, and additional info about Peloton instructors?
How can I automate the process of getting my stats into a Coda doc
without
downloading a CSV?

Playing with the unofficial Peloton API

Turns out a few people have dabbled with the unofficial Peloton API.
from Pat Litke is probably the most well-documented project. This library is great if you want to build a custom application on top of the Peloton API, but I wanted to find a way to simply get the data out from my Peloton account and into Coda.

There are quite a variety of endpoints off of
. You can get data about your Peloton account, instructors, and most importantly, your workouts. Here is a sample of data you can get about your account after authenticating with your username and password:

2-peloton-api-about-me.jpg

Some high-level data about your workouts:

3-peloton-api-workouts.jpg

After some more searching, there’s an API explorer on
showing
all
the endpoints you can ping on the Peloton API. One of the more interesting endpoints is
api/workout/{workoutId}/performance_graph
where you can get second-by-second metrics like output, resistance, and more:

4-peloton-second-metrics.jpg

With this data, you can build your own workout chart like the one you see on the bike tablet or on the website:

5-ride-workout-chart.jpg

So I know the data is there, I just need to figure out which metrics I want and the charts and I want to see.

Picking the right Peloton workout metrics

At the very least, I wanted to get the metrics you get from the CSV output. So those columns are a given. The additional metrics and stats I wanted to get include:
Leaderboard rank
- And total leaderboard users too
Difficulty
- That crowdsourced rating from 1-10 you give after a workout
Total workouts
- Total number of workouts others have done for this workout (gives you a sense of the popularity of the workout)
Total ratings
- Should be correlated with
Total workouts
, but just interesting to see which workouts people provide ratings for
Instructor data - basically anything you can find on this
There’s a whole plethora of metrics you can get from the API but these were the ones I wanted to see in addition to the properties from the CSV. You can see every metric I pull in the
page.

Main cycling dashboard

If you go to
, you’ll see the main “dashboard” with the stats I wanted to see.

The 10 last rides is data you can get from the tablet or from the website, but it’s all in one place. Additionally, this list gets re-sorted every time new workouts get synced over from Peloton in
. You can also filter the list by length of workout so that you are doing an apples-to-apples comparison. This is a nice condensed list to see how my workouts have been trending recently:
See details
Workout Date/Time
Total Output (kj)
Distance (mi)
Calories (kcal)
1
See details
12/19/20, 1:46 PM
275
9.1
450
No results from filter
275
Average
9.1
Average
450
Average

Below that 10 latest workouts table, I have a
Personal Records table
that just shows the best workouts I’ve had by
workout length
.
Again, this is all data you can get from the CSV download, but the main difference is that this data gets updated automatically every hour, day, week, or whatever interval works for you.
Workout Length (min)
Workout Name
Instructor
Workout Date
Total Output (kj)
1
5
5 min Cool Down Ride
Jess King
1/4/21
26
2
10
10 min Low Impact Ride
Matt Wilpers
9/2/20
44
3
20
20 min Classic Rock Run
Selena Samuela
9/27/20
2
4
30
30 min HIIT & Hills Ride
Ally Love
12/19/20
275
5
45
45 min Power Zone Ride
Matt Wilpers
11/16/20
498
6
60
60 min Power Zone Endurance Ride
Christine D'Ercole
9/13/20
515
There are no rows in this table

Finally, the charts below these tables show metrics over time so I can get a sense of how my performance have improved or declined over time.

Key views of Peloton workout stats

In sub-pages of the
page, I sliced and diced my data based on different views I wanted to see my workouts. For instance, I’ve always wanted to see one view of my workouts
grouped by workout length
and then
sorted by output (kj)
. Seeing the dates when I reached a really high output encourages me to work harder to get back to that level of performance. You can see this view in
.

Another view I’ve always wanted to see is how my stats look
grouped by instructor
. Maybe certain instructors are more encouraging and therefore lead me to performing better. That’s what the
page allows me to see. Looks like I’ve taken quite a few classes with Matt Welpers 💪.

Finally two other views I thought would be interesting for cycling workouts are:
- See your workouts sorted by your position on the leaderboard. These are stats that you can’t get in the CSV, so it’s neat to see which rides I’ve performed the best in relative to all other Peloton members.
- While there is a calendar on the tablet and website, this view color-codes the events by
workout type
. This means you can see other workout stats like running, strength, meditation, etc.
Two additional pages I set up to quickly sort and filter through Peloton data are:
- Filter across all different dimensions
- See the profiles for all your favorite instructors (this list also gets updated in the sync as new instructors are added)

Syncing data in “real-time” from Peloton API to Coda

What good are these charts and tables if they don’t stay updated? Since we know the API exists, I wanted to find an
automated process
for keeping the workout data in my Coda doc up to date with my workouts on Peloton.

Peloton API caveats

Caveat #1:
As I mentioned earlier, the Peloton API is unofficial and undocumented. This means Peloton (the company) has not “blessed” the use of their API for the public, so the API could change at any time. This would essentially break the scripts I’ve written to sync data over from Peloton to Coda.

Caveat #2:
As with any API, you may use the API too aggressively and the fine folks at Peloton might rate-limit you. Don’t be the guy or gal who runs these scripts every
minute
thinking you need near real-time updates. In reality, a
daily
sync would probably be sufficient for most of you out there. Long story short, be nice and or else Peloton might block your use of their API 🚫.

Google Apps Script

If you’ve come across my other
, you’ve probably noticed I’m a big fan of
. It’s a low-code, scalable, and most importantly,
free
tool to connect your different tools together. Of course, Google makes it super easy to get data in and out of Google products, but you can set up workflows between non-Google tools as well.

The reason I think Google Apps Script is the right solution to sync your workout data from Peloton to Coda (as of the writing of this post) is because you can set up
. These are basically the rules for how often you want the script to run. As I mentioned in the caveats above, I would highly recommend setting a
daily interval
as the highest frequency to run the Google Apps Script.

Authentication with Peloton

This was probably the most difficult part of the script to figure out. In order to get your Peloton stats, you have to provide your username and password so that Peloton knows that you are, well, you!

In Google Apps Script, there is a
UrlFetchApp
which lets you fetch any URL and convert the response to a JSON object. This part of the authentication code allows you to “login” to Peloton by submitting an
options
parameter which contains your Peloton login details:

var
base_url =
'https://api.onepeloton.com'
var
options = {
'method'
:
'post'
,
'contentType'
:
'application/json'
,
'payload'
:
JSON
.stringify({
'username_or_email'
:
PELOTON_USERNAME
,
'password'
:
PELOTON_PASSWORD
}),
'muteHttpExceptions'
:
true
};
var
login =
UrlFetchApp
.fetch(base_url +
'/auth/login'
, options);

The problem
: We need to make various other calls to the API and those calls
won’t be
authenticated. Peloton won’t just give you workout data for anyone just because you know their Peloton username. This is where the cookie for the login session comes into play:

var
cookie = login.getAllHeaders()[
'Set-Cookie'
];

The
getAllHeaders()
returns all the header information from the HTTP response (which contains the cookie we need). We just store the relevant attribute in the
cookie
variable.

Another problem:
The cookie object returned from the headers looks like this:

[ "__cfduid=d88dc31d26154890903e4ed470ce2d2d31610584581; expires=Sat, 13-Feb-21 00:36:21 GMT; path=/; domain=.onepeloton.com; HttpOnly; SameSite=Lax", "peloton_session_id=57c891b1d8004745b7f19280a25af2cb; Domain=.onepeloton.com; HttpOnly; Max-Age=2592000; Path=/; SameSite=Lax; Secure; Version=1", "__cfruid=14a3728b6c055fbcd9384022acdfdf91f2ec912e-1610584582; path=/; domain=.onepeloton.com; HttpOnly; Secure; SameSite=None" ]

There’s all this expiration and domain attributes we don’t need. We just need the
cfduid
,
peloton_session_id
, and
cfruid
as one long string, so this part of the authentication function creates that string for us:

for
(
var
i =
0
; i < cookie.length; i++) { cookie[i] = cookie[i].split(
';'
)[
0
] }
var
authenticated_options = {
'headers'
: {
'Cookie'
: cookie.join(
';'
)}}

Now we have this variable
authenticated_options
we can use for all our subsequent calls with the API so that Peloton knows we are still “logged in” to our Peloton account.

Getting existing Peloton workouts from Coda

The first time you run the script,
all
your workouts will get synced over to the
table. To prevent unnecessary calls to the API, the script first pulls all the workout IDs from the
table and compares it with the workout IDs returned from the API. We only look at the
new
workouts that need to be synced over to Coda since previous workout data won’t change.

Remember the
api/workout/{workoutId}/performance_graph
endpoint I mentioned earlier? Turns out that returns a
ton
of data, so we want to make sure we ping this endpoint
only if we have to
. This ensures we don’t pull performance graph data from Peloton we don’t need to and gives the Peloton API a break.

6-pace-yourself.gif

You can get workout data from the
api/workout/{workoutId}/performance_graph
endpoint
and
the
api/workout/{workoutId}
endpoint. You would think that the most detailed stats would be in the “performance graph,” but it turns out there is some data in
api/workout/{workoutId}
like leaderboard stats. So I created a
workoutData
object that contains “summary” and “performance" metrics.

Once we have all the relevant columns, we just format the data in a way that the
needs and push that data to the
table.

Python script

The
works pretty much the same way as the
, without the added benefit of being able to schedule the script to run at some interval in an easy way. Authentication, however, was much easier to accomplish with the Python script.

By importing the requests
, you get a user-friendly way to manage the logged-in session on Peloton. For instance, with these four lines of code, we can authenticate with Peloton so that for future calls to the API, we just need to use
s.get()
with various parameters:

s = requests.Session()
base_url = 'https://api.onepeloton.com'
payload = {'username_or_email': peloton_username, 'password': peloton_pw}
s.post(base_url + '/auth/login', json=payload)

There’s no messing around with 🍪, it just works.

Deploying a serverless function on Google Cloud

You could run the script on your local machine or on some public cloud platform. The tradeoff for having a more difficult method for
automating
how often the script runs is the
flexibility
with where/how you run the script.

Serverless functions were all the rage the last few years so I thought I’d try it out and see if I could spin up an example on Google Cloud Platform.
The goal:
get a URL that we can ping to trigger the Python script to run.

Setting up Google Cloud

Just setting up Google Cloud to get one Cloud Function to run was a bit of a pain. In addition to setting up a billing account (requires credit card) and tying a project to a billing account, you need to enable the
and
APIs (two services we need) to even start using Cloud Functions:

7-google-cloud-apis.jpg

A helpful blog post I came across from a developer advocate at Google Cloud is this one. It’s a little harder to use since the blog post assumes you are using the
gcloud
command line tool. Nonetheless, Dustin creates a pattern for running the function that I wouldn’t have otherwise figured out (via Cloud Scheduler).


Setting up the Cloud Function

Once you start creating a Cloud Function, the key settings are to set the
trigger type
to “HTTP” and “Allow unauthenticated invocations.” I didn’t want to mess with IAM policies and all that stuff, so this should be fine for our needs:

8-cloud-functions-settings.jpg

Once you’re in the source code editor, you just need to set the runtime to “Python 3.7” and rename the
Entry point
to something like
runPelotonSync
. Then you can copy over the
to the code editor.

In order for this script to work with Cloud Functions, however, you have to wrap the script inside a function like this. Almost all the code goes into the
pelotonData(request)
function:

9-cloud-functions-editor.jpg

After that, hit
Deploy
and wait for Google Cloud to deploy your function. If everything works out correctly, you’ll get a unique “Trigger URL” on the “Trigger” tab of your Cloud Function:

10-cloud-function-trigger-url.jpg

Get your Cloud Function to run every day

Unlike Google Apps Script where you can simply tell Google how often you want your script to run on a dropdown menu, we have tell Google Cloud how often the python script should run via cron rules. We can set up the interval for calling the URL we got from our Cloud Function by using Google Cloud Scheduler.

The key field to fill out when you set up a new Cloud Scheduler job is the
URL
. This is the
Trigger URL
you got from your Cloud Function:

11-cloud-scheduler-setup.jpg

The
Frequency
is where you need indicate the cron rules for how often you want Cloud Scheduler to ping the URL. In the above screenshot, this job would run every minute. A helpful site to figure out what characters you should put for the cron rule is
. Once this is all set up, you can get detailed logging on your job and see manually run the job from the main Cloud Scheduler interface:

12-cloud-scheduler-jobs.jpg

Final notes

I’m still playing around with this template for visualizing my Peloton stats and with the data that gets returned from the Peloton API. If you have any suggestions or see mistakes with the scripts, feel free to submit a PR here:


Hope these stats encourage you to hit new PRs whether you’re cycling, running, or doing a strength exercise. If you’d like to try another template for tracking your running, I created this
to do just that.

Train hard, train smart, and have fun! - Matt Welpers




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