The term "data" refers to raw facts, figures, or information that is collected, observed, or measured from various sources. Data represents the qualitative or quantitative details of an object, event, or phenomenon, which can be organized, processed, and analyzed to draw conclusions, make decisions, or create insights. In a technological and scientific context, data is often stored digitally and manipulated using statistical, analytical, or computational techniques to transform it into meaningful knowledge.

In football, data is instrumental in enhancing both individual and team performance, collected from various sources both on and off the field. It encompasses metrics from players' actions during games—like goals, assists, or more complex metrics like Expected Goals (xG), and defensive recoveries—as well as data gathered through training sessions, physical assessments, and even wearable technology. This comprehensive data pool provides coaches, analysts, and players with valuable insights to inform training, optimize tactics, and support strategic decisions, all aimed at driving competitive success.

Studies in Journal of Sports Sciences explain that data can reveal intricate aspects of player performance, such as decision-making under pressure, consistency in passing accuracy, and even resilience in high-stakes moments.

There are three primary categories of data in football scouting:
Physical Data: Metrics on stamina, sprint frequency, and distance covered.

Technical Data: Measures like passing accuracy, shooting success, and pressing actions.

Tactical Data: Insights on off-ball movement, positioning, and reaction times, critical for assessing tactical fit.

However, managing vast amounts of data can be overwhelming and challenging to interpret effectively. This is where machine learning comes into play—by identifying patterns and making sense of complex data sets, machine learning helps streamline analysis, uncover hidden insights, and even make predictive recommendations. By leveraging these advanced tools, teams and analysts can transform overwhelming data into practical, actionable strategies, maximizing the potential of the information available.

Elite clubs use machine learning to process data and form predictive insights, as evidenced by studies from Carnegie Mellon’s AI Lab, which for example detail the use of algorithms to forecast players' success and injury risks.

It's not about the data, its about the insight we can get from data. Converting raw data into actionable insight is at the core of effective scouting. Clubs increasingly focus on synthesizing data into clear, actionable insights—whether to predict a player’s tactical compatibility, growth potential, or resilience in critical moments.

At SL Benfica, a club known for its high-profile player development, scouts prioritize metrics that align with both technical excellence and adaptability, which are essential for players destined for international transfers. This approach has been widely covered in studies from the Portuguese Journal of Sports Science, highlighting the club’s method of blending data with qualitative assessments to maximize transfer value.

Integrating multiple data types like physical endurance, top speed, decision-making speed, and passing accuracy provides scouts with a holistic view of a player’s potential. Combining these metrics with video analysis offers a rich perspective on how a player might perform under specific tactical conditions.

At the end of the day, data serves as a powerful filter to identify potential transfer targets, helping clubs make informed decisions. It not only highlights promising players but also minimizes risks throughout the entire recruitment process by providing objective insights that complement traditional scouting. This strategic use of data supports clubs in making precise, data-driven investments aligned with team needs and goals.

Elite Clubs: Sophisticated, Multi-Layered Scouting

Top-tier clubs like Manchester City, Liverpool, and FC Barcelona operate complex scouting systems. These clubs combine extensive data analysis with psychometric assessments, machine learning, and global scouting networks. For instance, Liverpool FC’s use of spatial analysis to measure player adaptability within their pressing system allows them to recruit players suited to specific tactical demands.

Scouting isn’t just about identifying talented players; it’s fundamentally about finding players who align with your playing style, team culture, and long-term vision. The ideal recruit not only brings skill but also enhances the team’s identity and cohesiveness, ensuring a seamless fit both on and off the field.

Mid-Tier Clubs with Data-Centric Approaches: Brentford and Midtjylland

Clubs like Brentford FC and FC Midtjylland have established themselves as industry leaders in data-driven recruitment. Brentford uses Expected Goals (xG) and pressing efficiency as core metrics, focusing on undervalued players who are often overlooked by traditional scouting methods. Studies in European Sports Management Quarterly have shown how this data-centric approach allows mid-tier clubs to identify high-potential recruits without overspending on star players.

Midtjylland, on the other hand, has pioneered the use of set-piece data to optimize scoring opportunities from corners and free kicks. Their approach has been shown to deliver consistent results in tight matches by maximizing high-probability scoring scenarios, as discussed in Football Data Review.

Lower-Budget Clubs and the One-Person Scouting Approach

In lower-budget leagues, such as Norway’s second tier, clubs often rely on network-based scouting, either due to limited resources or a lower prioritization of scouting infrastructure. Player evaluations are frequently managed by the sports director, a single scout, or an assistant coach, who rely heavily on personal contacts and regional networks to identify potential talent.

This approach, however, is not always effective; it can lead to missed opportunities, inefficiencies, and a heavy reliance on a limited network of contacts. While these networks remain invaluable, especially in leagues without the budgets for large investments, the smart application of AI can drastically optimize the scouting process. AI-driven analysis expands the pool of potential recruits, reduces dependency on specific contacts, and enables clubs to uncover talent that fits their needs more precisely—all while staying within budget constraints.