This is my #2 post on tennis video analyzing, #6 post on using Apple Watch to track tennis performance, and #11 post on sports in general.
The coronavirus pandemic isn’t over yet, but at least recreation tennis is back. The city I live in have allowed for tennis activity since late May, and I have been play a lot of tennis since then.
I also joined a new tennis club, and actively participated in the ladder game. After playing over 10 games using the newly improved SwingVision app (most recent version 7.2), I have found an effective way of generating a 10-15min highlight video of ALL POINTS played in a 1hr ladder match.
The video highlight generated is overlayed with the match score, recorded real time by Apple Watch. This makes it enjoyable to watch for friends and families, because it feel like watching a Pro match highlight between Federer and Nadal.
The finished video is invaluable. It can be used for:
Analyze point by point game performance to look for area of improvements
Share with your partner and other audiences
Store and archive in your personal library for later usage
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.
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.