Member-only story

Sports Analytics

Using Clustering Algorithms for Player Recruitment

Which players could help Fulham overcome their major flaws?

Pol Marin

Published in

TDS Archive

9 min readApr 15, 2024

Some days ago, I was fortunate to be able to participate in a football analytics hackathon that was organized by xfb Analytics[1], Transfermarkt[2], and Football Forum Hungary[3].

As we recently received permissions to share our work, I decided to write a blog post about the approach I used.

The goal was to pick a Premier League team, analyze their playing style, highlight two flaws, and prepare two lists of 5 players each that could help the team improve. The premise was that we had to look to fill two different positions (hence the “two lists of 5 players each”).

Then, from those two lists, we had to pick the top target in each and further explain why they were the best fit for their respective positions.

The final result had to be realistic and the sum of both players’ prices had to be below 60M (we were given their Transfermarkt valuations).

Now that you know what it was about, I want to talk about my approach. I’m a data science guy who loves football so I had to perform some sort of technical analysis or modeling with Python.

Well written and explained nicely the use of clustering for player recruitment and extends to other recruitments in HR ,health and academics .Our work on K mean clustering algorithm is well cited and we may extend our approach using this story authored by use. Our work

DOI: 10.5120/16779-6360

Thanks