I received a special gift from my lovely wife during last year’s Christmas.
It is an add-on lens to put on my iPhone to capture more area. It is particularly useful when recording the tennis match from the baseline, because my iPhone doesn’t have a wide angle lens built-in so it doesn’t capture enough area.
To be honest, originally I was just planning to try out the new AI feature offered by SwingVision app. After using it for over 6 weeks and multiple rounds of trial and errors, post game video has become an essential piece of my tennis life. The app itself is still in its infancy stage with all kinds of limitations, however I can see a lot of potential in this area.
If you are interested in tennis, and subscribed to the Pro version of the Swing App, you will be able to export all the data to a clean Excel format. That is really cool, but what can you do about it?
In this blog, I will share my experience of playing and analyzing the raw data for over 30 hrs over the past few months. Hopefully by reading this article, you will have slightly more incentive to make use of the data, after your hard fought game and logging via Apple Watch.
We will cover the following three topics with hands-on examples:
Basic data cleaning and data modeling for the required analysis using Excel build-in feature
How to breakdown the first and second serve performance with speed and distribution
How to breakdown the short, medium and long rally on game points
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.
It has been 9 months since I first shared experience to track tennis performance with Apple Watch. Backing up by popular demand(Surprised so many visitors found this blog from search engine all over the world), I’d like to take it further with a more in-depth review, of my own experience tracking and analyzing my tennis workout with the Swing app.
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.
I have recently wrapped up 5 years (4 years + 9 months to be exact) of analytics career in a Fortune 100 company. In this precious in-between moments, I’d like to take the time to reflect on 3 lessons learned as an analytics professional in the digital marketing industry.
You are a proficient Microsoft Excel user – vlookup is an appetizer, pivot-table is the main course, and VBA/Macro is your dessert. And your company just gave you an upgraded Microsoft 365 subscription, and a brand new version of Excel is just being served. You are wondering – “What’s new?”
As an analyst living and breathing with Excel, PowerPivot and PowerQuery are the two killer features, period. I’d like to share my story on how you can save time and make your spreadsheet work more efficient.