Football Data Analyzer

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  Grafana screenshot

Inspiration: Our passion for football and our interest for data gave us the push to complete this project.

What it does: It parses through thousands of row of data in order to analyze football players' statistics and help us find the next profitable player.

How we built it: We used Grafana's Google Sheet integration to vizualize our data and a bit of SQL for data cleaning prior to put them in Google Spreadsheets. In order to find our datasets for league players, we used https://www.transfermarkt.us/ and fbref.com/.

Challenges we ran into: Since we are all brand new to Grafana, there was a bit of a learning curve and we did not really know how to connect our data sources for vizualization.

Accomplishments that we're proud of: Making strategic changes to our projects under pressure. In addition, we had to do a lot of time management. At first, our project had nothing to do with football player analytics. When we realized that out initial project idea would amount to nothing (at about 1am), we rapidly brainstormed and found an alternative solution that would be easier for hackathon newbies.

What we learned: We learned how to use Grafana and manage our stress.

What's next for Football Data Analyser: We would have to continue adding more and more player statistics in order to find possible correlations between player stats and their transfer prices. We also would like to introduce machine learning to create predictions and use web scraping for data gathering more data.

Built With

• google-spreadsheets
• grafana
• sql