AI-Powered Scouting: Unearthing the Next Generation of Football Icons

2026-05-28

AI-Powered Scouting: Unearthing the Next Generation of Football Icons

high angel photography of football stadium

Associative photo. Image credit: Unsplash

Artificial intelligence (AI) is bringing revolutionary possibilities to an increasing number of fields. One of them is sports — more specifically, football — where data-driven technology can help identify the most suitable players. In this article, we will explore how AI in scouting is transforming the football market.

How AI Has Changed the Scouting Process in Football

Over the years, as AI capabilities have expanded, football scouting has evolved significantly. Today, the first steps are being taken to enable data-driven technology and make accurate strategic decisions.

Now, AI-powered talent discovery is not only a tool for big clubs like Manchester City or Bayern Munich; even clubs with smaller budgets are starting to adopt these technologies to uncover real talents in lesser-known leagues.

Which AI Capabilities Are Being Used in Football?

In football, AI technology gathers data from various sources. First, when evaluating current players, internal resources are used data related to the pitch, such as physical performance, match outcomes, goals scored, assists, ball recoveries, and more. This information is also useful for coaches, for instance, to optimize player training.

Secondly, when focusing on potential new players, scouting uses external data sources such as media coverage, social media, and similar platforms.

From a broader perspective, team leaders form a holistic player profile based on the following indicators:

Physical endurance
Maximum speed
Decision-making speed
Passing accuracy

Based on these indicators, experts can assess how a player might perform under specific tactical conditions.

How Else Does AI Assist in the Scouting Process?

AI tools are valuable not only because they collect and process large volumes of data but also because machine learning can generate predictive forecasts. These predictions help clubs focus on tactical compatibility, development potential, or resilience under pressure. Data also helps identify the most promising players and provides confidence that the right hiring decisions are being made.

For example, at SL Benfica, the scouting process emphasizes an AI-generated score for technical skill and adaptability, which helps determine whether players will be able to transition to international competitions in the future.

Scouting Systems Depend on Club Size

Different clubs, with varying budgets and resources, apply different scouting strategies. Elite clubs like FC Barcelona or Liverpool can afford highly complex systems. Their data analysis includes psychometric assessments and a global context of scouting. Some systems even use spatial analysis to help identify players based on specific tactical requirements, such as how they perform under extreme conditions.

Other mid-sized clubs, like Brentford FC, focus on undiscovered talents who are difficult to identify using traditional methods. They use metrics such as expected goals (xG) and pressing efficiency.

Smaller, budget-constrained clubs can even use tools like ChatGPT to summarize reports, compare players by position, or assess cultural fit.

There are various types of AI systems. Some, like Hudl, can automatically tag player actions and analyse technical nuances. Others focus on predictions, such as injury risk or career potential. In high-level matches, AI tools are also used to assess players’ mental resilience.

Final Thoughts

In football and the scouting process, AI is becoming an essential tool that helps clubs gain a competitive advantage. Accurate and efficient analysis of players and their potential helps identify the most suitable athletes who can lead clubs to future victories. These tools also automate the talent discovery process and reduce workload while supporting high-quality, data-driven decisions.

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Source: Hugo Vicente.