Sports in AI: changing the game today to transform many industries tomorrow

By Frédéric Genta, Monaco Secretary for Attractiveness & Digital, Visiting professor at ESCP business school, Former Google & Amazon Executive

June 04, 2024

Monegasque Charles Leclerc's stunning victory in the Monaco GP last weekend at the wheel of a Ferrari was like something out of a fairy tale, a dream come true for him, for all Monaco and Formula One fans in general. At the same time, science fiction scenarios are becoming reality: a month ago, it wasn't Charles Leclerc or Max Verstappen, but an AI that finished first in an autonomous car race on the Abu Dhabi F1 circuit.

Between competitions and star-studded benchmark champions, sports offer numerous opportunities to demonstrate how AI can outperform humans because sports is about performance and performance is about measurement.?

Historically, cerebral sports have provided the first spectacular demonstrations of AI power, such as Deep Blue's chess victory over Gary Kasparov in 1997, or AlphaGo's victory over world Go champion Lee Sedol in 2017.

At a time when the world is entering the AI era, when some inevitably question the reality and scale of this revolution, when many are concerned about ethical issues and feel helpless to ensure the responsible use of AI, sport offers us an unparalleled playing field and demonstration area that should serve both to raise awareness and to encourage the translation of AI use to other fields.

Data and sports have a long and common history

The movie Moneyball, starring Brad Pitt, made famous one of the first emblematic cases of using AI in sports. As early as 2002, the Oakland Athletics began using data analytics to build a competitive team on a shoestring budget, improving in-game strategy and optimizing player performance, and were able to compete with some of the best teams in Major League Baseball, where player salaries were more than double their own.

Sports is probably the most widespread application of AI, spanning the entire value chain, with use cases that in some cases look back several years and anticipate the most advanced innovations.

AI touches every aspect of sports, from competition and play to the entertainment business. AI-based tools are already being used in every major sporting discipline, including American football, soccer, basketball, tennis, as well as non-professional sports and recreational activities.

With impressive growth momentum, the sports AI market is expected to reach $4.5 billion by 2024 and exceed $29.7 billion by 2032, growing at a CAGR of more than 30% between 2021 and 2026, according to WorldMetrics.

This advanced use of AI in sports offers 5 concrete cases for learning and potential application to other sectors.

1. AI for Individual and Team Performance

The use of AI in professional sports is now widespread and rapidly spreading into grassroots sports, supported by three key factors:

• A strong data culture in sports, inherent in the quest for performance and comparison, leading to an exponential amount and richness of data.
• The success of connected devices (speed sensors, GPS, connected wristbands, watches, shoes and apparel, heart rate monitors), which themselves provide data and sometimes valuable real-time information during competitions.
• Learning algorithms, which provide real-time analysis to improve game strategies and player performance, and account for nearly 41% of the sports AI market (Allied Market Research).

When it comes to sports performance, the benefits are numerous and have been demonstrated in a very large number of cases in virtually all disciplines.

• For the athlete: tailored training, faster recovery, injury prevention, in-competition support.
• For the coach: advanced analysis of opponents without having to watch hours of video or delve too deeply into the data, development of advanced strategies based on a volume of data that is humanly unmanageable, efficiency of training programs.

Like Formula One technology is often a good indicator of what cars technology will look like in the future, AI in sports should be another proof that the AI era is happening right now and that it will affect many industries, especially everything related to product development and predictions.

?2. AI driving innovation in viewer experience and fan engagement

As an entertainment business, sport has become a massive market: by 2025, for example, more than 90 million Americans will watch live digital sports content every month.

From personalized content such as game analysis to active participation via social networks, chatbots, and virtual or augmented reality solutions, AI is redefining the way fans interact with their favorite sports, teams, and athletes. Some of the most notable areas of innovation include:

• Immersive experiences: AI-powered virtual reality makes fans feel like they're right in the middle of the action. A famous example is the virtual Tour de France on the Zwift platform, which allowed cyclists from around the world to compete on virtual replicas of real stages.
• Player interaction: AI chatbots facilitate interaction with fans' favorite players, answering questions and providing player-related information.
• Personalized content: AI curates game highlights tailored to individual fan preferences to enhance their viewing experience, including access to different camera angles, commentary translations, and real-time player-specific data and insights.
• Fast and smart journalistic content: For example, in conjunction with Wimbledon, IBM Watson AI capabilities enabled the rapid generation of match highlights, curated based on elements such as player reactions and crowd cheers.
• Intelligent Sports Advertising: AI makes advertising more personal, timely and effective, including in the stadium, for example by analyzing audience emotions to deliver more personalized ads at the right time.
• Social media monitoring: AI can measure fan sentiment on social media, enabling teams to address negative buzz in a timely manner.

These cases of personalization and participation helping to build a more robust and engaged fan community can be directly applied to B2C industries.

3. AI for good or how to use AI as a solution against some scourges

?AI is being used to combat the great evils of sport:

• In the fight against doping, algorithms are regularly refined to model performance trends and detect anomalies. On a more preventative note, the Monaco Anti-Doping Committee has just launched an advanced application designed to inform athletes and coaches about the products they are taking.
• With the scourge of crime and corruption in sport, such as match-fixing and fraudulent betting, AI makes it possible to analyze sports betting worldwide and identify unusual patterns.
• To protect athletes from cyberbullying, intelligent devices to filter malicious comments have been developed and will be provided by the IOC for the duration of the Olympic and Paralympic Games.
• To eliminate hooliganism and reduce insecurity in stadiums, AI is providing highly responsive warning systems, thanks in particular to facial recognition.

While some of these cases can be transferred to other sectors, it seems even more relevant for a given sector to systematize the use of AI to combat its main characteristic ills (exposure of workers to dangerous factors, the fight against plagiarism or fraud, corruption, acute psychosocial risks, etc.).

4.?Making better and fairer decisions to keep the game more interesting

Faced with the difficulty of accepting AI in decision-making, sport provides a concrete case for advancing mentalities.

In recent years, several systems have emerged to assist referees, such as the Hawk-Eye in tennis or Video Assisted Refereeing (VAR) in football.

Not only does VAR interrupt the flow of the game, but it also causes considerable debate and disagreement, with fans, players and commentators often claiming that VAR is wrong! And with good reason: these systems use classic slow-motion technology and still require a human eye to make the final call.

The paradox is that in order for these systems to stop being controversial, the decision-making process needs to be fully computerized.

To make decisions faster and more accurate, new technologies have been introduced that use high-resolution sensors to determine the exact position and speed of a shot or player. In tennis, for example, an electronic "in/out" umpire now indicates to his human counterpart when a ball has gone out of bounds. Goal-line technology has also become an important part of football matches and a key element of video refereeing. And the scoring system in gymnastics, which has been much maligned in the past with judges regularly accused of bias, was improved at the recent World Championships by a deep learning-based system developed by Fujitsu that uses multiple laser sensors and computer vision technology to track athletes' movements.

AI is playing an increasingly important role in sports officiating, driving a new standard of accuracy and fairness that benefits players, teams and fans alike. At the same time, it provides a perfect demonstration of the relevance of applying AI to decision-making in other industries.

5.?Concrete ethical challenges for AI

Questions of ethics and responsible use are regularly raised against the use of AI, with the persistent myth of loss of control, to the point where these questions end up being more philosophical than tangible.

When it comes to dealing with very concrete ethical issues, the intensive use of AI in sports is an instructive case.

While sport is fundamentally based on integrity and fairness, the advanced capabilities of AI can change the nature of competition, even amounting to a form of "technological doping".? Sport, which has been deeply transformed by AI, is already being challenged to regulate its use by addressing fundamental issues such as:

• Technological dependence and inequalities: In a context of increasing reliance on AI in sports, how can we limit the impact of inequalities between teams with significant technological resources and those without? Even more, how can we maintain the fairness objectives between countries when technology will increase countries inequalities towards top athletes performances ?
• Ethics of predictive analytics: Is it ethical to use artificial intelligence in professional sports to predict and counter opponents' strategies in real time, essentially turning tactical preparation into a form of high-tech cheating?
• Technological Doping and Competitive Fairness: How can technological doping be defined and regulated to maintain competitive fairness between athletes who use advanced technologies and those who do not?
• Transparency of decisions and accountability: How can we ensure transparency of decisions by AI systems in sport and accountability in case of failures or mistakes?
• Social acceptance of AI in sport: How can we overcome cultural and social resistance to the use of AI in sport, especially when critical decisions depend on these technologies?

Over the past decade, leaders in all sectors have often looked to great athletes to inspire changes in leadership, management practices and motivational dynamics within their companies. We should now dare to do the same with AI, drawing inspiration from its early use in sports to accelerate its adoption in other fields, especially through better understanding and acceptance.