Artificial Vision in Sport: Beyond VAR and Hawk-Eye

Technology has profoundly transformed sport over the last decade, but few innovations have had such a decisive and silent impact as artificial vision. Thanks to the combination of intelligent cameras, advanced algorithms and artificial intelligence, it is now possible to analyse the game in real time, reduce human error and generate tactical data without the need for physical sensors on athletes.

In this context of technological transformation, the Master’s Degree in Digital Transformation and Artificial Intelligence in Sport trains professionals capable of understanding and applying solutions such as computer vision in real sporting environments, integrating data analysis, automation and strategic decision-making in clubs, leagues and sports organisations.

What is Artificial Vision?

Artificial vision also known as computer vision is a branch of artificial intelligence that enables machines to interpret, analyse and understand images and video automatically. Using deep learning algorithms, computer vision systems identify patterns, objects, movements and behaviours within a visual environment.

In sport, this technology allows the game to be “read” in a way similar to a human analyst, but with far greater precision, speed and processing capacity.

From VAR and Hawk-Eye to Advanced Artificial Vision

For years, VAR and Hawk-Eye represented the technological standard in sports officiating. However, artificial vision goes far beyond these traditional tools.

While VAR relies heavily on human interpretation, computer vision enables:

• Automated refereeing decisions
  
  

• Real-time analysis of complex actions
  
  

• Elimination of subjectivity in specific situations
  
  

This represents a qualitative leap towards more accurate and consistent officiating.

How Computer Vision Works in Sport

Computer vision systems combine several key technologies:

Image capture through intelligent cameras

Multiple high-resolution cameras are strategically positioned around the stadium or sports venue. These cameras record every action from different angles.

Processing through artificial intelligence

AI algorithms analyse video frame by frame, detecting players, the ball, field lines and specific movements.

Generation of tactical data and metrics

From visual analysis, the system generates data such as:

• Real-time positioning
  
  

• Distance covered
  
  

• Speed and acceleration
  
  

• Space occupation
  
  

• Tactical patterns
  
  

All without the need for wearables or physical sensors.

Artificial Vision Applied to Sports Officiating

One of the most visible uses of artificial vision is its application in refereeing.

Elimination of human error

Computer vision makes it possible to detect:

• Semi-automated offsides
  
  

• Ball in or out of play
  
  

• Infractions invisible to the naked eye
  
  

This reduces critical errors and increases the credibility of competitions.

Real-time decision-making

Unlike traditional systems, artificial vision provides immediate decisions, minimising interruptions and improving the flow of the game.

Tactical Analysis Without Physical Sensors

One of the greatest advances of artificial vision is the ability to analyse performance without devices attached to players.

Advantages over wearables

• No interference with athletic performance
  
  

• Usable in official competitions
  
  

• Lower operational costs
  
  

• Enables retrospective analysis of recorded matches
  
  

This democratises access to advanced analysis, even in youth and grassroots sport.

Artificial Vision and Sports Performance

Beyond officiating, artificial vision is a key tool for improving performance.

Objective evaluation of the game

Coaches and analysts can study:

• Collective tactical behaviour
  
  

• Individual decision-making under pressure
  
  

• Efficiency in space occupation
  
  

Objective data allows errors to be corrected with greater accuracy.

Injury prevention

Video-based biomechanical analysis enables the detection of movement patterns that increase injury risk, facilitating preventive interventions.

Applications in Scouting and Talent Identification

Artificial vision is also revolutionising talent identification.

Data-driven scouting

Algorithms analyse thousands of hours of video to identify:

• Players with specific profiles
  
  

• Decision-making patterns
  
  

• Performance evolution over time
  
  

This reduces bias and expands the reach of traditional scouting.

Ethical Challenges and Limitations of Artificial Vision

Despite its advantages, computer vision raises important challenges.

Privacy and image usage

The widespread use of cameras generates debates around:

• Athlete consent
  
  

• Commercial use of images
  
  

• Protection of biometric data
  
  

Technological dependency

Excessive reliance on technology may reduce human analytical capacity if not integrated in a balanced way.

The Future of Artificial Vision in Sport

The evolution of artificial vision points towards increasingly autonomous systems capable of:

• Interpreting complex tactical contexts
  
  

• Integrating with predictive AI
  
  

• Offering automated recommendations
  
  

In the coming years, this technology will become a central pillar of sports management, officiating and performance analysis.

Artificial vision has moved from being an experimental technology to becoming a strategic tool in modern sport. Its ability to eliminate human error, generate tactical data without sensors and analyse the game in real time is redefining how sport is played, coached and managed.

Understanding what computer vision is and how to apply it correctly is essential for any professional aiming to lead digital transformation in the sports industry. The future of sport is no longer only played on the pitch it is also analysed pixel by pixel.