During the winter of 2018-2019, I was able to take my tennis tracking journey into a new level. By now most of my tennis hitting partners are calling me a “data nerd”, clicking my watch like crazy during the game. But when I show them the stats after the game, they all (seem to be) impressed.
I did the following two new things in particular:
Used the “Point by Point + ” score tracking in the Swing App to track all the points I have played. In total, I tracked 18 matches over the last 4 month, all of them were single matches and played in 1 hour.
Exported the captured data into spreadsheets. By analyzing the data set, I was able to identify some of the limitations, as well as some opportunities to further enhance the analytics experience.
This blog post is aiming to provide a step-by-step guide to perform advanced analytics on swimming data, captured by Apple watch. Microsoft PowerBI and Python on Jupyter Notebook are the primary tools to prepare, analyze and visualize the data.
You will learn how to export the workout data efficiently to your PC, make necessary data transformation, and understand what metrics and dimensions are available. Then I will walk you thru how to analyze the data to answer typical questions related to why certain behaviors happened. You will then see my preliminary attempt to use advanced analytics tools to predict future swimming performance.
Most importantly, you will find quite a few reference articles related to this topic, hopefully fulfilling your intellectual curiosity.
It is also the #3 articles of a series, the previous articles can be found here:
Sleep tracking isn’t new. But what’s unique about using Apple Watch?
Sleep is the single most important activity for a normal human being, measured by time spent. Having a good night of sleep usually means a jump start of the day. On the flip side, bad sleep (or no sleep at all) will lead to serious implication – both physically and mentally.
In this blog post, I am happy to share my story of using the Apple Watch + 3rd Party App to better measure the sleep. Hopefully, it will inspire you to pick up a few things here and there and take action to make your own sleep better.
Playing tennis has been a major part of my adult life: It is fun, competitive and a truly global sport. More importantly, it has shaped my character and my social network. Over the past 18 months, I have been using my Apple Watch to track, and subsequently, to analyze my tennis performance along with swimming
In this post, we will cover the background, the pros and cons of different apps, and how the additional metrics playing a role in my mindset shift.
Background – how it started
Prior to receiving my Apple Watch in Sep 2016, there are only two ways to see my tennis performance in retrospect: win/lose — a quantitative measure, and my feeling after the game — a qualitative measure.
To be honest, I am happy with it for over 10 years. The smartwatch was intended to track swim, which will be so boring without the number. Pressing buttons before and after a tennis game doesn’t require much effort. It also brings at least one significant benefit: bragging about my “closed rings” to friends and family.
New data points shape my view
The availability of the additional metrics, empowered by technology (Watch + Apps), allows me to have a much more quantitative pulse of the workout.
The 1st thing I always look at is the “distance ran” during the workout because it is a measure reflecting how active on my court coverage.
The 2nd metric is the “average heart rate”. It is a metric to measure the intensity. An app I used called “HeartWatch” did a great job bringing additional context to interpret the breakdown of each heart rate range.
Over time, It has gradually changed how I look at my tennis game. I know a lot more how I did, and subsequently cares a lot less about winning (No more ugly winning) . Even if lose the game, or not play a match at all, I know it is a good workout, as long as running over 2km , and has an average bpm of 130+.
In short, my own focus has shifted toward fitness, instead of fulfilling my internal “competitive fire”.
Bonus – Subconsciously, I felt I am getting more “value” out of each workout as well. Just by looking at the number just generated, I must be feeling pretty proud. (Bingo! another mental trick)
Apps to track tennis
Not many Apps were available at the beginning, and my only choice was the native “workout” app offered by Apple.
The app starts fast – native app offered by Apple always has the advantage. But it is almost the only benefit. Tennis is not even included in the default drop-down list, and can be only available after selecting “others”.
I have been using the above two 3rd party apps:
One is called “Tennis Keeper“, built by a Canadian tennis athlete.
The other one is called “Swing” from the famous silicon valley
Both of these two offered a lot more functionality than the native app: Running distance – the one matters the most. Also, both of them claimed to track shot type and speed. Unfortunately, the result wasn’t the as accurate as I would hope.
Swing is the app of my choice so far, mostly due to it can show “running distance” in the activity dashboard above. The loading speed of the app is near my tolerance level – in my Series 2 apple watch, it requires over 10 seconds. That is not fun at all. Maybe Apple is the one who should be blamed, considering its record of slowing down existing product to boost sales…
HeartWatch deserved special attention – It is a sophisticated health data analyzing app. My current usage is only touching its surface. What I like it the most, is it breaks down the workout by heart rate, and provide additional context for each heart rate range.
Fat burn: 112-130
Build Fitness: 131-148
High Intensity: 149 – 167
For example, I don’t like playing doubles because it is not intense enough.
The data validates my “lack of intensity” theory, but also kindly reminding me – it also helps burning fat. Of course, I want to burn fat – how can I burn more fat and faster, I guess I gotta play more doubles…
Compared to swimming, I feel I am just touching the tip of an iceberg using data to understand my tennis workout. I am hoping the app can make tracking a bit easier – for example making the app load within 3 seconds. Also, if switching the watch to the dominating hand can make the app count 90% of the shot properly, I am willing to try it. Right now it is not even close.
Back in February in my first post of using Apple Watch to track swim, I wrote about the metrics available and usage in the Apple Watch, a handful of data quality challenges, and my plan of using data captured to improve performance. After logging another 22 workouts since Mar 2017, I would like to give an update, and also share a few more new interesting lessons learned.