Artificial Intelligence in Sports: What Lessons Can Workers Learn from High-Performance Athletes?
Artificial intelligence (AI) has transformed a number of sectors and elite sport is no exception. In recent years, AI has become an indispensable tool for monitoring and evaluating athletes’ performances, optimizing tactical strategies and improving their safety and health.
However, this development has sparked a growing debate about the processing and use of data collected by AI systems, leading athletes' associations and unions to mobilize to protect their rights against the risks of abuse presented by these technologies.
Some categories of high-level athletes have taken a pioneering position in defining strategies to ensure the application of principles such as privacy, transparency, explainability and non-discrimination, so that algorithmic management systems for monitoring and evaluating athletes' performances are used ethically and their rights are respected in the digital age.
Throughout history, high-performance sport has been a laboratory for cutting-edge technologies that have subsequently been applied in other spaces and environments, including for other purposes. For their part, athletes, in their capacity as workers, have adopted relevant and emblematic positions on current issues. Their ability to influence children and adolescents makes them role models in debates on issues that transcend victories and defeats in the sporting field.
AI in sports performance monitoring and evaluation
The integration of AI in sports has enabled significant advances in performance and in ensuring the health and safety of athletes. Predictive analysis systems generate alerts in case of risks of muscle injuries and wear and tear.
The technologies are used in team and individual sports to analyse large volumes of data collected during training and competitions. This includes biometric data, movement recordings, game tactics and performance indicators, processed to provide real-time feedback and enable tactical adjustments.
One example is the use of high-speed sensors and cameras in football to track players’ positions and movements on the pitch. This data is analyzed by algorithms that can predict game tactics, identify opponents’ weaknesses, and suggest strategies to maximize the chances of victory. Similarly, in sports such as athletics and cycling, AI is used to analyze athletes’ biomechanics, optimize their techniques, and minimize the risk of injury.
In addition, tools such as GPS tracking systems and heart rate monitoring devices have been implemented in endurance sports. These devices collect real-time data that is then processed by AI systems to adjust training intensity and ensure that athletes remain within safe effort parameters, thereby preventing overtraining and reducing the risk of serious injuries.
Football: tactical analysis and injury prevention
In football, the use of artificial intelligence has become a fundamental tool for the technical staff. The English club Manchester City, for example, uses the Slants tool to analyze in real time the position, speed, distance traveled and physical effort of each player.
As a reminder, during the 2014 World Cup, the German national team used a data analysis system to study their opponents' playing tactics and optimize their own tactics. This data-driven approach contributed to the team's success, winning the tournament, highlighting the direct impact of technology on the team's performance.
Today, the Catapult system is widely used by European and South American teams. It collects data on acceleration, speed and heart rate to help coaches tailor training to the needs of each player.
On the privacy front, some players and unions have expressed concern about the handling of this data, arguing that it could be used against them in future contract negotiations.
Tennis, rugby, boxing, baseball and cricket: performance and health
Tennis is among the sports that have adopted AI to improve athletes' performance. IBM's Watson tool, used at tournaments such as Wimbledon, analyzes a wide range of data to provide insights into athletes' performance.
In sports such as rugby and boxing, where the risk of concussion is high, AI has made it possible to develop control systems that detect impacts and automatically assess their severity.
These systems make it possible to quickly decide whether a player should be removed from the game to avoid more serious injuries. Similarly, in baseball, AI is used to monitor pitchers' fatigue, which helps prevent arm injuries that could have lasting consequences on the player's career.
Additionally, AI has been used to create personalized training programs that take into account each athlete's individual fitness level, medical history, and specific goals. Not only is performance improved, but the risk of overtraining and stress-related injuries is also reduced.
In cricket, AI has already been implemented to make in-match decisions and monitor player health. Tools such as Hawk-Eye help to verify umpires’ decisions, while health tracking systems such as sleep and recovery analysis devices give coaches the ability to adjust training and rest schedules to optimise performance and minimise injury risk.
The use of this data has also raised privacy concerns, particularly in leagues such as the Indian Premier League (IPL), where players have expressed concerns about the processing of their biometric data. Players' associations are seeking additional safeguards to prevent this data from being used in detrimental ways, including for salary negotiations and job security.
Athletes' Response: Rights and Privacy in the Digital Age
Access to a large amount of personal information has sparked debates about privacy and data ownership. Unions and athletes’ associations have played a key role in defending athletes’ rights, demanding clear limits on how data is collected, stored and used.
A prominent example of this mobilization is the NBA's National Basketball Players Association (NBPA). In 2017, players successfully negotiated to limit the use of data collected by surveillance devices during salary and contract negotiations. Almost all NBA clubs use a surveillance system set up by the company Kinexon to track athlete performance.
The players argued that information about their health and performance could be used against them in negotiations, potentially impacting their future earnings and opportunities. As a result, it was agreed that certain sensitive data would not be used in contract negotiations, thereby protecting the athletes' rights and privacy.
Moreover, the NBA's collective bargaining agreement expressly states that the data collected can only be used for tactical and athlete health purposes, under the supervision of a bipartisan commission of data and athlete health experts who jointly deliberate on the implementation of technologies and the processing of data obtained through sensors attached to athletes' clothing.
The U.S. Women's Basketball League recently joined the AFL-CIO, which in turn reached a historic agreement with Microsoft to ensure worker participation in the design, programming, testing and monitoring of artificial intelligence tools applied in the workplace.
Similar to the NBA, players in the American Football League (NFL) have also expressed concern about the use of biometric data (e.g., exertion levels and potential injuries) in personnel selection decisions and salary negotiations. Players have demanded strict policies to ensure that such data is only used with the athletes’ consent and that measures be put in place to prevent its misuse.
Similar clauses to those in the NBA players' agreement have been identified in collective bargaining negotiations in other professional categories, demonstrating the power of elite sport to influence the defense of working class interests.
Mobilizing athletes to guarantee their rights
The growing capabilities of AI to monitor all aspects of sports performance have led athletes to mobilize to ensure their rights are respected in this new digital age. Demands for transparency in data use have been a key focal point of these mobilizations. Athletes are demanding access to the data collected about them and are asking for clear information about how it will be used. Some leagues have therefore implemented policies allowing athletes to view their data and object to its use in certain circumstances.
Another key aspect is combating algorithmic discrimination. Athletes have expressed concerns that AI systems could perpetuate existing biases, such as racist or sexist discrimination, if not designed properly.
Athletes and their associations have therefore advocated for the implementation of transparent and fair algorithms that do not discriminate on the basis of personal characteristics irrelevant to sporting performance.
The ability of athletes to organize and collectively bargain to defend privacy, transparency, and non-discrimination in the face of algorithmic management systems demonstrates the importance of collective action in the digital age. This type of mobilization not only strengthens their rights as workers, but also raises awareness of the need to design and apply technologies ethically in all areas of work.
By ensuring that decisions about the use of AI and biometric data are transparent and fair, elite athletes are paving the way for other professions to also consider the impact of these technologies on their working conditions.
This highlights the importance for trade unions and workers' associations from different sectors to adopt proactive positions on the protection of rights in the face of automation and the processing of personal data in the workplace.
