AI Football Scouting Sends Brazilian Teen Leo Veiga to Spezia Youth Academy
Brazilian teen Leo Veiga used an AI football scouting app to secure a place in Italy’s Spezia academy, highlighting how AI football scouting is changing talent discovery.
From Santa Catarina to Spezia
Eighteen-year-old Leo Veiga was playing for a modest club in Santa Catarina when a tech-driven opportunity altered his path to the professional game. He uploaded footage to an app that analyses skills with artificial intelligence, and the resulting score surfaced him to European attention. The exposure led to a short training invitation in Italy and ultimately a youth contract with Spezia, a club competing in Italy’s second division. Veiga’s journey illustrates a new entry point into professional football for players outside traditional scouting networks.
Footbao and the app that scored him
The Swiss company behind the app, Footbao, operates a platform where players can submit phone-recorded clips showcasing dribbling, passing, ball control and finishing. The app applies automated analysis to those clips and assigns performance scores, which are then highlighted to scouts and clubs seeking prospects. In Veiga’s case, a high score elevated him onto a shortlist that resulted in a training stint with Italian side Lecce and subsequent interest from other clubs. Start-ups marketing such tools say their products are designed to “democratise” access to talent pathways otherwise dominated by geography and connections.
How AI football scouting evaluates talent
AI football scouting generally combines computer vision, pose estimation and performance metrics to convert video into quantifiable outputs that can be ranked. Algorithms detect body movement, ball trajectory and repetition of skill sequences to produce standardized measures of technique and explosiveness. These systems can also generate highlight reels and comparative scores so scouts can quickly filter large numbers of applicants. While the technical details vary across companies, the core idea is to turn unstructured smartphone footage into actionable data for talent evaluators.
Scouts balance algorithmic leads with live trials
Clubs and scouts increasingly use algorithmic leads as a preliminary filter but continue to rely on live observation to make final decisions. In Veiga’s breakthrough, a scout who watched him during a trial chose to take a chance based on what the footage suggested and what he observed in person. Human scouts say AI tools speed up the discovery process by narrowing candidate lists, allowing scouts to prioritise who to invite to in-person sessions. This hybrid approach—algorithmic shortlist followed by traditional scouting—has become a common pathway for youth recruitment.
Bias, verification and privacy concerns
Analysts and practitioners warn that AI football scouting can reproduce and amplify biases if training data are skewed toward particular regions, body types or playing styles. Video quality, camera angle and pitch conditions also affect an algorithm’s ability to assess true ability, creating risks of false negatives and positives. There are additional concerns about the handling of minors’ data, consent, and how performance metrics are monetised by intermediaries. Clubs and regulators face pressure to establish verification steps such as live trials and transparent data practices to ensure fair evaluation.
Youth development and market consequences
Wider adoption of AI football scouting could broaden exposure for overlooked players while also intensifying competition for academy places worldwide. For many young athletes in remote or under-resourced regions, app-driven visibility offers a new route to professional environments that were previously inaccessible. At the same time, the pipeline could favour players who can produce polished video content or who have access to reliable internet, shifting some selection power toward those with better resources. The commercialisation of scouting data also introduces new intermediaries into youth recruitment, reshaping how clubs source and value prospects.
Standards, regulation and club responsibilities
Industry experts argue for standard protocols on data protection, consent and algorithmic transparency to govern the use of AI in scouting young players. Clubs that adopt these tools are being urged to maintain robust human oversight, provide clear pathways for verification and ensure contractual protections for players invited to trials. Some federations and youth organisations are beginning to discuss guidance and best practices, although comprehensive regulation remains limited in many jurisdictions. For AI-driven scouting to deliver on its promise, stakeholders say both ethical standards and technical audits will be essential.
Veiga’s case demonstrates how a single digital submission can convert local promise into professional opportunity when technology, exposure and human judgement align. His move to Spezia’s youth ranks underlines both the potential and the caveats of algorithm-led talent identification, as clubs, regulators and families adapt to a landscape where an app can open a door previously accessible only through scouts on the ground.
