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Football Player Position Recommender System

A recommender that analyses 750+ players' attributes by position and surfaces players who would excel in an alternative role.

750+ · Player profiles analysed

Scoutium Recommender System Development
Recommender Systems Sports Analytics Machine Learning Python Data Science Scouting
Football Player Recommender System

The challenge

Players are usually scouted for the position they already play, which means hidden value goes unnoticed: a centre-back whose attribute profile would actually make him a strong defensive midfielder rarely gets seen in that light. Spotting those alternative-role fits by hand, across a large squad and every position, is impractical.

Scoutium wanted to surface those opportunities systematically - to know not just how good a player is, but where else he might excel.

The approach

I built a recommender system that analyses 750+ players' attributes broken down by position and recommends players for alternative roles. The engine compares a player's attribute profile against the profiles that define success in other positions and flags strong cross-position matches.

In practice, that means surfacing insights like a centre-back whose profile suggests he would perform well as a defensive midfielder - giving scouts a data-driven way to discover positional value they might otherwise miss.

The outcome

Results that moved the needle.

750+

Player profiles analysed

Attributes broken down by position for cross-role matching

Alt-position

Recommendations

Surfaces players who would excel in a different role

Hidden value

Made visible

e.g. a centre-back profiled as a strong defensive midfielder

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