Thematic Intelligence: Artificial Intelligence in Sport

Thematic Intelligence: Artificial Intelligence in Sport

Summary

AI offers the sports sector several advantageous use cases, mainly concerning improving efficiency by speeding up the decision-making process for referees, and increasing fan engagement on social media and during broadcast, for example with the use of graphics and statistics. The sports industry’s adoption of AI will focus on computer vision and machine learning. As yet, the application of generative AI in sports is still in its infancy, with no killer use case found so far.

The global AI market will be worth over $1 trillion by 2030

GlobalData defines artificial intelligence (AI) as software-based systems that use data inputs to make decisions on their own. Recently, machine-learning (ML) algorithms (e.g., OpenAI’s GPT-4) and computing power have developed significantly, enabling AI to solve problems in real time. According to GlobalData forecasts, the total AI market will be worth over $1 trillion in 2030, having grown at a compound annual growth rate (CAGR) of 39% from $103 billion in 2023.

Sports AI use focuses on machine learning, computer vision

The sports industry’s adoption of AI will focus on computer vision (CV) and machine learning (ML). Successful applications of ML have helped the decision-makers at sports companies, broadcasters, and leagues find underlying trends in vast datasets. This analysis informs their strategy, on and off the pitch. A greater breadth of data is available so ML models can gain deeper insight into tactics and strategy. CV is mainly used in training, officiating, performance analysis, and injury prevention. Many teams using this technology have reported a decline in lost days due to injury despite many sports, like soccer, straining players’ health with a high volume of matches.

A killer use case for generative AI in sports has not been found

Generative AI will likely disrupt every business across every sector in the coming years and is the fastest-growing advanced AI technology. One example of generative AI adoption in sports is IBM’s WatsonX AI and data platform, which included a feature called Catch Me Up for the 2024 Wimbledon tennis tournament. This provided AI-generated player stories and match analysis, with features such as pre-and-post-match player cards displaying recent performances to keep fans updated with the players’ progress throughout the tournament.

There is untapped potential for generative AI in sports. It could support chatbots that give fans additional, in-depth insights into their favorite leagues or clubs. It could also be used in sports betting to create customized models that generate odds for specific scenarios. As other industries find profitable use cases for generative AI, the sports sector will be a follower, not a leader. This has traditionally been how the industry operates.

Leaders and laggards

Below is a list of some of the leaders and laggards in AI within the sports industry.

Sporting federations

Leaders: International Automobile Federation (FIA), National Basketball Association (NBA), National Football League (NFL), PGA Tour

Laggards: International Federation for Equestrian Sports (FEI), International Ski and Snowboard Federation (FIS), World Aquatics.

Key Highlights

• The sports industry is typically slow to adopt new technologies. Sport is different from most industries as technology will not change how the sector operates, though it may change how sport is consumed. However, in other industries, AI could engender wholesale changes in operations. Sports companies, teams, and leagues generally wait to see successful, profitable adoption of a new technology before investing heavily themselves.
• The main application of AI in sports has been using machine learning models to analyze large datasets, which can help inform strategy on and off the pitch. More recently, machine learning has provided in-depth statistics for sports broadcasters, synthesizing large datasets. Computer vision is another subset of AI. Historically, it has been successful in the sports industry. Generative AI has not had a similar impact on the sports industry yet. However, developments are increasingly likely around betting and generating media content.
• Increased AI adoption could help prevent injuries and protect players’ wellbeing. Player safety is crucial for sports clubs. An injury to a star player could hamstring a club’s commercial strategy, damage a club’s bottom line, and harm the career and earnings of the player. AI can help tackle this through injury prevention.
• AI in the sports industry can benefit sponsors and those seeking a sponsor. Now there is a greater depth of data available, sports companies can tailor their sponsorship requirements accordingly. For instance, AI could analyze the demographics and interests of an audience and feed the insights to a prospective sponsor. This will help it decide whether a sponsorship opportunity is suitable.

Scope

• The “Artificial Intelligence in Sport” thematic intelligence report gives you an in-depth insight into the impact of AI in sport, including key players, challenges, and market size and growth forecasts. The report elucidates the growing importance of AI in the sports sector along with the progress made by the leading companies to integrate it. These detailed analyses are critical in developing effective business plans to gain a competitive edge.

Reasons to Buy

This report -

• Discusses the challenges the sports industry faces and how AI can be used to help address them.
• Evaluates the impact of AI in the sports sector, including various use cases and case studies.
• Benchmarks leading AI vendors, and leading sports companies based on their adoption of AI.