AI in sports: current trends and future challenges

The AI technologies in sports now

According to the same PwC research, AI-based tools are already being deployed in virtually all major sports disciplines, such as American football, soccer, baseball, and cricket, but also non-professional leisure activities like grassroots sports.

Among these tools, we can count sensors, wearables, and computer vision-powered cameras to collect athletes' performance data. At the same time, natural language processing devices can leverage speech and text recognition to gather insights regarding the audience's sentiment.

All this information will be processed by machine learning (ML) and deep learning (DL) systems to create forecast models and assist coaches and managers through their decision-making, or to automate numerous processes related to broadcasting and fan experience.

How much money are we talking about?

Considering the holistic role of AI in sports, it should come as no surprise that the technology market linked to this sector was already worth $8.9 billion in 2018, and may reach $31.1 billion by 2024 with a compound annual growth rate (CAGR) of 20.63%, according to MarketsAndMarkets' report Sports Technology Market—Global Forecast to 2024.

Among the potential reasons for this sharp development, the researchers identified an increasing level of public involvement, the expanding demand for decision making but also a plain and simple growth in the number of sporting events. These factors lead sports companies to invest increasing sums in AI implementation, data analytics, and machine learning consulting.

AI in sports business

We’ve already mentioned the mind-boggling investments dedicated to implementing AI in sports. It goes without saying that we wouldn't face such large numbers if sports were a mere recreational activity rather than a business.

Actually, sports represent a true cornerstone of the entertainment industry. According to Statista, the global value of the sports market was approximately $471 billion in 2018, compared to $324 billion in 2011. Focusing on sports media rights alone, SportBusiness's 2020 Global Media Report estimated them to be worth $44.6 billion in 2020.

Therefore, it's impossible to ignore all those elements that make sport a full-fledged media product to sell, such as broadcasting, sports journalism, user experience in general, marketing strategies, and betting.

Experts, investors, managers, and other industry professionals seem to think so. According to the Current State of Sports Technology study by Scrum Ventures in 2019, 78% of sports professionals surveyed cited fan engagement technologies (e.g. live streaming, media distribution, content platforms) as the innovations with the greatest impact on sports in the following 12 months.

These high hopes were roughly reflected in investment prospects, with media and content platforms representing the main sphere of interest, followed by data, analytics, and biometrics in the second position and other relevant use cases shown in the following graph:

As you might expect, most of these technologies are closely related to artificial intelligence. Let's take a brief overview of some of the most interesting and promising AI use cases in the sports business, primarily intended as a media phenomenon.

Broadcasting and sports journalism

Artificial intelligence looks set to revolutionize the way sports storytelling is done. Using machine learning and deep learning algorithms, broadcasters can automate numerous videomaking operations, including camera shooting and zooming in on key field actions.
Fully automated sports production is already almost comparable to professionally edited videos. A similar solution was developed by IBM Watson for Wimbledon 2019. Their ML-based system was able to identify the highlights of each match by analyzing fan reactions, balls' trajectory and rebounds, and other types of acoustic and visual data. Based on these insights, the AI chose the most exciting shots to be broadcast on television and the Internet.

A similar trend is occurring with sports journalism, thanks to recent innovations in the field of natural language processing. Of course, news writing is unlikely to be 100% automated anytime soon. So, sports reporters, you can breathe a sigh of relief!
But something is already moving, with AI-powered tools integrated into content creation for the sports press. That's exactly what The Associated Press did in 2018, leveraging an AI-based platform to quickly turn NCAA basketball game data into brief news reports. Thanks to this system, it is possible to cover many more events while deploying fewer resources.

AI-enhanced audience experience

Automating the creation of written and video content can be seen as a way to improve audience experience. But it's not the only one. During sporting events, for example, AI can provide real-time subtitles in different languages based on the viewer's nationality.

It's also capable of offering fans additional stats and insights to enrich their experience. This feature, powered by machine learning algorithms, was implemented in 2019 during the Roland-Garros tennis tournament. Thanks to an innovative user interface, the spectators were able to follow the results live, enjoy in-depth match analyses, and understand which actions most influenced the outcome.

Last but not least, artificial intelligence ensures better customer support across a wide range of industries, including sports, via chatbots and other smart assistants. These tools have already been adopted by many sports teams and leagues, such as the NHL and NBA, to address fan questions regarding tickets, parking, and other organizational issues.

Marketing strategies

Offering a great user experience with artificial intelligence is one of the best ways to broaden your audience and, as a result, maximize advertising commissions.

But AI-powered systems don’t stop there. They can also improve marketing effectiveness by offering advanced targeting based on fan demographics, including media consumption behaviors, personal interests, shopping habits, and so on.

In this regard, Nielsen has already acquired some AI startups which develop such kind of tools. The goal is to offer its customers, specifically sports brands, suggestions and forecasts based on machine learning to better choose sponsorships and media rights investments.

Additionally, machine learning algorithms allow broadcasting companies to pinpoint game highlights by taking into account players’ actions and fans’ emotional responses. Such insights are invaluable for advertisers to choose the proper time for commercials and better grab the attention of their audiences.

Betting

If they had the gift of foresight, many people's first plan would probably be to win the lottery. Sports betting operators must have thought the same thing regarding machine learning's forecasting capabilities. As a result, high-tech companies like Sportlogiq have started selling analytical data to bookmakers in the United States, helping them set odds on bets.

Gambling is certainly a profitable and controversial component of the sports industry. But it is even more so when tools like AI come into play. After all, who would be able to draw the line between legitimate strategies and cheating?

Another problem is that reliable forecasts would require massive amounts of information, including historical data on individual and team performance, locations, scores, weather conditions, and so on. Many of these details are not public but lie in the hands of sports clubs.

Does it mean that we'll soon be dealing with a data market between clubs and bookmakers? Well, that's starting to be a realistic scenario, considering that the state of Illinois has approved legislation on sports betting and athlete data.