A UK-based sports-tech enthusiast approached us with the vision of building a smart sports web app tailored specifically for football clubs, team managers, and player agents. Their aim was to use data-driven insights to improve team selection and player performance evaluation. It is basically an athlete performance tracking software which allows them to track each and every performance of the players and their area of expertise. After a few years they have planned to open the app for the general public.
ReactJS
.Net Core
Python
Apache Airflow
D3.js
The goal was to create a web app that helps clubs and managers analyze players, understand team chemistry, and make better decisions using match data.
It was difficult for the client to find a place where they can trac and analyze real-world game data.
It was difficult to make sense of the performance data through simple charts and illustrations. The user required dashboards with clear designs that allowed clubs and managers to work freely.
Since info came from WyScout for statistics and Capology for finances, everything had to be integrated and managed properly, given the size of the data sets.
They requested that the solution should display the statistics as well as well as the suggestions of the players and clubs. It was not simple for them to create accurate recommendations using data.
We built a smart football analytics platform that helps clubs and managers understand player performance and team dynamics through data. The solution combines reliable data sources, intelligent insights, and a scalable backend to support accurate analysis and long-term growth.
We designed an AI football analytics platform called Machine Football, using advanced algorithms to produce helpful information out of data.
We seamlessly integrated WyScout API which helped us provide data on players’ statistics, while Capology API was used to access salary and market value information.
User can make list of players quickly by reviewing their performance, finishing, creativity, passing and defense skills. You can also highlight certain pitch regions and get details on every player, including their stats, history, value and much more.
For making the backend perform well and be scalable, .NET Core was used and Python along with Apache Airflow were selected to automate and handle data processing.
They now have access to data that allows them to decide wisely.
The web app highlights a player’s skills, displays who they have a good connection with and reveals their current worth.
The client loved the work we did for them. The tool is effortless to operate, functions well and meets all their requirements.
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