Athleet.AI Performance Intelligence Series
Casting the Net Wider
AI as a force multiplier for talent discovery in grassroots and academy football
Founder and Chief AI Officer, Athleet.AI
Abstract
Football's talent problem is not finding the best player in Saturday's match. It is that the system for recognising potential is narrow, biased in ways it can name, and almost entirely without memory. Players are seen once, judged against children who are a year older or a year further into puberty, and then either signed or forgotten, with no record surviving either decision. This paper argues that the honest opportunity is capacity and memory rather than prediction. A system can help people observe more players, record what they saw in a usable form, hold the context that makes a comparison fair, and prompt somebody to look again. It argues equally firmly that no model should rank a child, score their potential, or be trained on a club's own past selections, because such a model learns the club's history rather than the game.
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BibTeX
@techreport{forrest2025castingthene,
author = {Forrest, Paul},
title = {Casting the Net Wider: AI as a force multiplier for talent discovery in grassroots and academy football},
institution = {Athleet.AI},
series = {Athleet.AI Performance Intelligence Series},
year = {2025},
month = {10},
pages = {34},
url = {https://papers.ecaveo.com/casting-the-net-wider-ai-talent-identification-football/}
}