ML-powered Performance Analytics Tennis App

Advanced match analysis to boost athlete’s performance


Refactoring wearable MVP app for fault-free data processing

Amplitude monitoring tool integration

Vitals data transfer from wearables to a mobile app

Companion mobile app for reporting

Customer location
  • Germany Germany
Project Duration
  • 3 months

Turn the MVP into a feature-rich ML-powered system

Our client is a startup run by a famous German tennis coach and concurrently a data scientist. To encourage professional tennis players to improve their performance and achieve superior results, they floated an idea to launch a sophisticated ML-powered performance coaching app, gathering stats about the athlete’s KPIs and providing actionable insights on the player’s skills and abilities.

After the MVP was developed, the customer started looking for a reliable partner to turn it into a comprehensive system. The main criterion imposed by the client was machine learning development proficiency as he was manually running a self-developed ML algorithm at that moment. The potential contractor was also to be well-versed in mobility solutions and backend development, as well as UI/UX design, and Symfa appeared to be a complete match.

Seamless data transfer for enhanced stats accuracy

There was no clearly defined list of features at the initial development stage, and the scope was iteratively clarified and refined during project execution.

Initially the application implied direct metadata transfer from watchOS to a data file, which resulted in a huge number of duplications. To fix the issue, we refactored the code and ensured clean and filtered data transfer to the phone.

Two standalone apps for data collection and visualization

The system is presented by a Swift-based watchOS app and a mobile companion app. During the match the watchOS app utilizes ML algorithms to pull in and record data on match stats and the player’s physiological indicators, while the mobile app provides the match analytics and statistics overview.
2standalone apps
10+physiological indicators collection

Work Done


Requirements definition


User stories writing 


Code review


Code refactoring


UI/UX design


Functional testing


Integration with Amplitude monitoring tool

WatchOS App Capabilities


Integration with iOS app 


Metadata processing 


Heart rate widget

iOS App Capabilities


Localization to English and German 


Sign up/in page


Profile/home page 


Match start page


Overview stats and match analytics with filters & swiping functionality


Social media stats sharing button


  • Node.js
  • Swift
  • WebView

Workout strategy definition through data analysis

The team reviewed and refactored the existing MVP app, eliminated data collection issues, and extended it with a feature-rich reporting companion app.

The application visualizes gathered data, helps tennis coaches identify the athlete's weaker sides and fine-tune the workout strategy efficiently.

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