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Sports Analytics
Which players could help Fulham overcome their major flaws?
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.
Here’s how I’ll structure this post:
- Introductory Analysis
- Player Clustering
- Picking the Defensive Midfielder
- Picking the Striker
- Conclusions
Take into account that, as said, this was a hackathon. The time to do it was limited and the resources were quite scarce. With proper time and enough data, the results would have been even better.
Introductory Analysis
When it comes to player recruitment, data is probably our best friend. It doesn’t guarantee anything in the future, but it allows us to understand the past and present of a player in a purely objective manner: his playing style, his profile, advantages and disadvantages…
For that reason, I wanted this project to be 90% based on data, and let common sense reign over the remaining 10%.
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