How to use data to build a Moneyball soccer team

What’s the best team you could make with €100m? Andy Clarke and Victoriano Izquierdo gave it a go using data from the FIFA 2020/2021 dataset, a set of performance attributes, and a machine learning prediction model to help them find the best players within their budget (that’s the Moneyball part).

This is the realm of statisticians and scouting teams, where models are applied to the minutiae of match events and clustering is deployed in attempts to find the next emerging superstar. Rather than telling us about performance, data science techniques are harnessed to affect performance. This can be in terms of adjusting tactics, finding weaknesses or crucially, and often at a hefty price, bringing in new players that closely match a club’s requirements.

Plugging holes in football teams with new players is a risky business. Transfers involve a multitude of variables that are difficult to control and with price tags and agent fees soaring to dizzying heights, it’s hardly any wonder that new signings feel enormous pressure to perform. One thing that clubs can control is ensuring that their transfer targets are genuine prospects. Scouting teams analyze the characteristics of thousands of players before matching this against the value of players to find opportunities and ultimately deciding to make a move on one.

Leave a Reply