Smart technologies are increasingly permeating almost all spheres of our lives. The adoption of Artificial Intelligence (AI) and various Machine Learning and Deep Learning technologies in the sport industry is just one of many examples.
AI is changing the ways we practice and experience sport and the business related to it in both the real and virtual worlds. It is affecting all stages of the sports life-cycle and all the actors involved. AI-based tools are also being increasingly used in non-professional leisure sport activities.
The professional sports industry is a highly profitable one, and it’s no surprise that it attracts huge investments and stands at the forefront when it comes to adopting technological innovations that can boost its results. The global artificial intelligence sports market was worth $1.8 billion in 2021 and is forecasted to reach $19.9 billion by 2030, with a total Compound Annual Growth Rate (CAGR) of 30.4%. Sport managers today know that in order to stay in the game, they must keep up with the pace of technological developments and adopt novel AI tools to boost performance and attractiveness of their teams and sporting events.
AI technologies and applications in sports
From basic systems to the new generation of chatbots, the term Artificial Intelligence covers a variety of “smart” technologies which can serve to collect all sorts of data from different sources, analyse it to identify patterns and trends and make predictions, as well as fully-autonomous tasks like interact with fans or deploy targeted advertising messaging. AI can also be used to create virtual reality environments for training or fan engagement. These technologies include various sensors, wearables, computer vision-powered cameras, machine learning (ML) and deep learning (DL) systems.
There are numerous aspects of sport where AI is being applied, including pre-game, game, and sport business segments. Here are a few key ones.
Player performance and value
Tools like smart wearables, various sensors and positioning systems, connected clothing, and motion tracking cameras can all serve to monitor and analyse both professional and amateur athletes’ health conditions and physical and mental stress levels. The collected data can then be used for various purposes, including to:
- prevent injuries and help in recovery
- understand the strengths and weaknesses of individual players and teams
- generate personalized training programs and diet plans to boost performance
- help in team selection, game strategy and player rotation plans.
Prompted with data about players’ past performance and current physical state, predictive models can be used to assess their future potential and market value, and help managers and sport clubs make decisions about player signings and trades.
This is a field where AI has been already present for many years, notably in systems like the Video Assistant Referee (VAR), the Hawk-eye line-calling system used in tennis, and the Goal-line technology that confirms whether the entire ball has crossed the line to validate or cancel a goal. These technologies are constantly being improved to enable more efficient and precise decision-making in sport officiating.
Sport events management and fan experience
Successful sport event management is in part about attracting the most spectators to the game and providing them with the best possible experience. AI is today used to facilitate ticketing, predict fan attendance to help in event organization and resource management, improve in-stadium security with face recognition and movement monitoring, improve spectators’ experience generating real-time analytics and virtual replays, and help with post-event cleaning and maintenance.
Broadcasting and media
Off-field, broadcasting companies are leveraging AI-based platforms to enhance the audiences’ experience in many ways:
- By efficiently processing huge amounts of data into brief news reports, enabling real-time statistics, more engaging storytelling, and wider event coverage;
- Grabbing the audiences’ attention and providing personalised advertising, by pinpointing game highlights with the help of computer-vision and machine learning algorithms used to track and link players’ actions to fans’ emotional responses;
- By giving viewers more choice, such as allowing them to track their favourite players during a game broadcast or to choose different camera angles.
Ethical AI usage in sport
Though it cannot be denied that deployment of AI technologies in sports is producing many benefits, caveats remain around their ethical use. Most importantly, there is concern around the processing of athletes’ and spectators’ personal data, especially health-related, and the prevention of unfair advantage gained by organisations with large financies able to employ such technologies to boost their own performance. These are, however, already being tackled with growing regulation, as well as innovative, scaled and more affordable technologies.
Finally, as AI technologies are changing the way we practice and experience sports, some are asking the question – how much space will remain for the human? Here we must remember that it’s the human imperfections as well as sudden displays of spectacular athletes’ brilliance that spike the most thrill, controversy, and emotions on and off field. This is hardly to change. Although AI may be able to improve many aspects of the game, it can never fully replace the thrill of the unknown.