How Artificial Intelligence Is Changing Football Analytics And Scouting
Modern football is not just about training, instinct, and physical endurance. It is also about mathematics, big data, and algorithms. And while fans watching matches with a beer in front of the TV or in the halls of the golden mister gambling club may not notice it, a real revolution is taking place behind the scenes. Artificial intelligence (AI) is radically changing approaches to match analysis, player evaluation, and transfer policy planning. Let’s take a closer look at how.
Data is the new oil in soccer
What exactly does artificial intelligence analyze?
Football clubs, especially top-level ones, collect millions of pieces of data every season: number of touches, pass accuracy, distance covered, position on the field, interaction with teammates.
This data is collected from GPS trackers, video analysis, drones, and even smart forms. However, the numbers themselves are worthless without interpretation — and this is where AI comes in.
Machine learning algorithms are trained on huge arrays of historical data and can not only identify patterns but also predict future events: whether a player will score a goal from a certain position, whether he will break down in the next few rounds, or whether he will fit into the coach’s tactical model.
From analysis to decision-making
Coaches and sports directors no longer rely solely on intuition. Decisions about the lineup for a match, a change in tactics, or the purchase of a player are increasingly accompanied by analytical reports prepared by AI. For example, the system can detect that a player has a tendency to lose the ball under pressure in the 65th minute of the game, and the coach will know when to make a substitution.
A new approach to scouting
Talent search is no longer a matter of chance
Clubs used to send scouts to the farthest corners of the planet in search of future stars. Today, thanks to AI, thousands of players are analyzed in real time, even if they play in Vietnam’s second division or at an amateur level. Algorithms compare their performance with ideal profiles for a specific position and the club’s style of play.
Potential assessment algorithms
Machine learning makes it possible to model a player’s development: how their performance will change with age, in a new league, or under a different coach. This is especially important for clubs that specialize in reselling players. The system evaluates not only a player’s “current” value, but also their potential — something that was previously almost exclusively a matter of intuition.
How artificial intelligence works in practice
In modern football, artificial intelligence (AI) is no longer an abstraction or an experiment — it is a real tool used by leading clubs in Europe and around the world. Its work is based on the collection, processing, and analysis of large amounts of data from hundreds of sources. But what does this look like “on the field”?
Data collection: the foundation of everything
AI starts with data. Every match generates thousands of parameters:
• player coordinates every second;
• speed of movement;
• pass accuracy;
• number of tackles won or lost;
• actions in the final third of the field;
• biomechanical indicators — heart rate, pulse, blood oxygen levels.
This data comes from:
• GPS chips in uniforms;
• cameras installed in stadiums;
• drones filming from above;
• wearable sensors and smart bands.
Data processing: the magic of AI
Once the data has been collected, machine learning algorithms come into play. They:
• clean the information from noise;
• identify behavior patterns;
• compare players with each other;
• predict future events (such as the probability of injury or a goal being scored).
Systems such as XPS Network, STATSports, Wyscout, SkillCorner, and Zone7 operate in real time, updating information every second. Algorithms based on artificial neural networks are able to independently determine which data is relevant to a particular match or player.
Visualization: helping humans understand machines
The results of the processing are presented to coaches and analysts in an understandable form:
• heat maps;
• movement diagrams;
• pass matrices;
• injury risk assessments;
• game simulations.
For example, before a match, a coach can see a model that shows that if player A receives the ball in zone X, there is a 70% probability that the pass will go to player B, creating a dangerous situation. This allows for the preparation of a specific tactical maneuver.
Examples of AI use by clubs
Here’s how different clubs are using artificial intelligence in practice:
Brentford FC (England):
The club has abandoned the traditional academy and relies entirely on data analytics. AI searches for “undervalued” players from leagues where other clubs don’t look. This is how Ivan Toney was signed, who went on to become a Premier League star.
FC Midtjylland (Denmark):
They use mathematical models to determine when to take corners, where to press, and which substitutions to make. This has helped them win the Danish championship despite having a smaller budget.
Liverpool FC:
The club has a separate department of PhD analysts. Their work has reduced the number of unsuccessful transfers, optimized player fitness, and helped build match strategies. For example, the purchase of Mohamed Salah was based on in-depth AI analytics.
Barcelona:
The Catalans use AI not only in match analysis but also in load control, avoiding overtraining and loss of form. Algorithms analyze hundreds of variables and provide recommendations for individual training.
Manchester City:
The club has simulation systems that allow it to predict the outcome of a match based on the lineup, tactics, and even weather conditions. These simulations provide the coach with alternative scenarios for how the game might unfold.
Example: a day in the life of an analytics team
Before the match:
• AI analyzes the opponent: playing style, weak areas, tactical patterns.
• Personal reports are created for each player.
• A game scenario is simulated with different starting lineups.
During the match:
• Player performance is analyzed in real time.
• If someone’s performance drops or the risk of injury increases, the system notifies the coaching staff.
• AI suggests substitutions or changes in tactics.
- After the game:
• A report is generated with an analysis of each player’s actions.
• Video clips are prepared automatically.
• A database is created for analyzing future matches and training.
Artificial intelligence in match preparation
Tactical analysis of the opponent
Thanks to AI, coaches can get a highly accurate picture of their opponent. Algorithms study not only positional formations, but also the behavior of individual players in specific situations. For example, if the right winger has a habit of shifting to the center in the 25th minute, AI will flag this information as a pattern. The coach will be able to adjust the wing defender specifically for this moment.
AI also allows you to predict the most likely scenarios for the game: at what stage the team will start pressing high, where the most chances are created, and which players are responsible for key actions. This data is displayed on dashboards that coaches and analysts work with before each match.
Individual recommendations
Each player receives personalized analytics: what actions to avoid, who to pay attention to, how to position themselves better. Some clubs use mobile apps that use AI to generate short video clips with commentary — a kind of “smart video.” This allows players to prepare for upcoming matches as effectively as possible on an individual basis.
Personalization of the training process
Physical condition monitoring
Modern clubs have complete control over the physical condition of their players. Data from trackers and sensors is read in real time: heart rate, recovery speed, muscle load. AI analyzes the dynamics of changes and determines when a player needs rest or, conversely, increased load.
The algorithms also predict the likelihood of injury. For example, if the load on the thigh muscles exceeds the norm in combination with sleep deprivation, the system can issue a warning that the player is in the “risk zone.”
Building an individual plan
Training is no longer universal. Each footballer has their own plan, developed with the help of AI, which takes into account their biomechanical characteristics, psycho-emotional state, and tactical role. This results in maximum efficiency: the player develops in a targeted manner, without overloading the body or doing useless exercises.
Predicting transfer value
Player economic model
One of the most promising areas of AI application is predicting the economic feasibility of transfers. Clubs no longer rely solely on market intuition. AI models the impact of a new player on the team, the likelihood of his future growth in value, and his ability to adapt to new conditions.
All this allows you to develop a model of a player’s “investment attractiveness.” For example, a hypothetical midfielder from France’s Ligue 2 may have greater potential for growth in value than a reserve player from a top Serie A club. And this is exactly what artificial intelligence notices — where scouts are limited by subjectivity.
Risk minimization
Before a transfer, AI can simulate several scenarios for a player’s career development: adaptation to a new league, the impact of climate, language, playing style, and the likelihood of getting playing time. This approach significantly reduces the risk of unsuccessful purchases that can cost millions.
How the role of scouts and analysts is changing
From intuition to interpretation
The traditional role of a scout is changing with the development of technology. Previously, they were the eyes of the club — traveling the world, looking for talent, and forming personal impressions. Today, the key tool of a scout is interpreting data that has already been collected by artificial intelligence. Scouts no longer just search, but verify models built on analytical algorithms.
The role of the football analyst is also undergoing a similar transformation. Whereas previously they were providers of statistics, they are now becoming part of the coaching staff, making decisions during matches or transfer campaigns. Modern analysts must understand football, coding, and mathematics.
This transformation is discussed in detail by The Athletic in an article entitled “The rise of football data analysts” which notes that analysts are now “part of the decision-making process at every level, from the first team to the academy.”
New professions in football clubs
AI has brought completely new specializations to football:
• Data Scientist — responsible for modeling, classifying, and predicting results based on big data.
• AI Performance Coach — adapts AI recommendations to the physiological and tactical parameters of the player.
• Tactical Data Analyst — researches micro-tactics and positional interactions.
More and more clubs are creating entire analytics departments that not only support coaches but also actively influence the philosophy of the game. As FIFA notes in its technical report, modern scouts must understand how algorithms work, assess risks, and make decisions in collaboration with AI systems.
Challenges and ethical considerations
Overreliance on algorithms
On the one hand, AI helps minimize errors. On the other hand, there is a danger that coaches and managers will begin to rely solely on algorithms, ignoring the human factor. Football is, after all, about emotions, unpredictability, and psychology. If intuition is completely replaced by data, there is a risk of losing the very essence of the game.
Privacy issues
A system that tracks every movement of a player, even off the field, can be a source of conflict. How ethical is it to monitor a player’s condition 24/7? Isn’t this an invasion of privacy? Some associations have already begun to develop ethical codes for the use of data in sports.
Impact on fans and the media
Analytics as part of the show
Football analytics, which used to be the secret sauce of clubs, is increasingly becoming public. The media uses xG graphs, analytical models of expected actions, and AI predictions. For fans, this is a new level of engagement. They not only watch, but also analyze, argue about statistics, and compare models.
On platforms such as Opta or Understat, you can play around with the data yourself, build hypotheses, and create your own rankings. Fans become amateur analysts, which immerses them even deeper into the game.
Impact on the football business
AI is also shaping new ways for clubs to interact with their audience.
For example, match analytics are used to create customized highlights that are automatically selected based on the viewer’s interests. If you are only interested in the actions of one player, that is what you will see. If you want to see all the moments when the team used a high defensive line, you can do that too.
This approach creates new value for the content produced by clubs and the media.
The future of analytics in football
Integration with virtual reality
AI is increasingly being integrated with VR technologies, allowing players to replay matches in a virtual environment.
For example, a midfielder can “relive” a situation from a past match, make a different decision, and receive analytical feedback. This is not just watching a video, but actively participating in the simulation of game situations — an effective tool for learning and improving spatial thinking.
Self-learning systems
The future lies in self-learning algorithms that will adapt to changes in the game. They will be able to formulate new performance metrics, create unique player profiles, and find non-standard solutions. This means that the role of humans will shift even more toward control and editing rather than decision-making.
Conclusion
Football has long gone beyond intuition and luck. In the game of winners, the one who works best with data wins. Artificial intelligence, while it doesn’t score goals, is the unsung MVP of modern football. Whether you’re the Golden Mister or a Liverpool analyst, everyone understands that you can’t win without technology today. And while the human factor will remain important, it is the synergy between human and artificial intelligence that will determine the course of the game in the 21st century.
Image: Igor Omilaev on Unsplash
