AI in sports: top use cases, real-life examples & adoption challenges
November 15, 2023
The adoption of artificial intelligence in sports helps increase athlete performance and safety, enhance game planning, and promote fan engagement.
Discover how AI experts can apply this technology to benefit coaches, team managers, players, and media professionals in a variety of use cases. Additionally, explore the real-life examples, pay-offs, and challenges of adopting AI in the sports industry.
AI in sports: market trends & stats
CAGR of the AI in sports market from 2020 to 2030
Allied Market Research
the potential boost to team performance from adopting AI in sports
Research and Markets
of respondents consider tech physical sport augmentation a key market force
PwC
Artificial intelligence can influence an athlete's career right from the start. Specifically, AI-powered software is used to process historical data on players' performance to predict their potential and market value before a sports club decides to invest in them.
The adoption of tools based on artificial intelligence can benefit players as well because they reduce any bias during recruitment and help find hidden talent even in countries where a specific sport is not particularly practiced.
Coaching
Performance analysis and forecast modeling can have an even greater impact when it comes to developing athletes’ training programs and playing tactics. With the help of wearable sensors and AI-powered cameras, it's possible to collect a multitude of data, including passes, goals scored, rebounds, player’s movement speed, ball trajectories, shot accuracy.
This information will then be processed by AI systems to ensure useful insights for coaches. At the same time, machine learning can also be used to identify opponents' playing patterns and discern their strengths and weaknesses.
Healthcare
The analytical and predictive capabilities of AI also find application in medical diagnostics, and health is a fundamental factor in determining the performance and well-being of athletes. AI-powered systems can screen physiological and biochemical parameters, such as blood pressure or limb symmetry, to assess a player’s condition, spot any injuries or health problems, and select suitable therapies for faster recovery.
Collecting information through various sensors is essential for such analyses. The absolute protagonists in this regard are health wearables thanks to their portability and increasingly low costs, which make them more and more popular not only among professional athletes but also amateurs.
AI referees
Video assistant referee systems (VAR) have been used in football for years to provide slow-motion highlights to human referees, but they slowed down the game. Fortunately, the latest algorithms are addressing this problem by making these tools faster and more accurate in detecting game violations.
Similar solutions have been adopted in football to detect offsides and handball offenses, in tennis to automate line calls, in figure skating and artistic gymnastics to collect scoring data, and in many other sports to identify various types of misconduct.
Broadcasting & journalism
Artificial intelligence looks set to influence sports storytelling. Using machine learning and deep learning algorithms, broadcasters can automate numerous video editing operations, including camera shooting and zooming in on key field actions. Fully automated sports production is already almost comparable to professionally edited videos.
Thanks to recent innovations in the field of natural language processing (NLP), automation is also finding its way into sports journalism, with the press relying on generative AI to create brief news reports for hundreds of matches. This enables media organizations to cover many more events while deploying fewer resources.
Fan experience
During sporting events, AI can provide fans with real-time subtitles in different languages based on their nationality or additional stats and insights to enrich their experience.
Furthermore, artificial intelligence enables clubs and sports event management companies to offer better customer support 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. However, AI can also improve marketing effectiveness with advanced targeting based on fan demographics, including media consumption behaviors, personal interests, and shopping habits.
Additionally, machine learning algorithms allow broadcast companies to pinpoint game highlights by factoring in player actions and fans' emotional responses. Such insights are invaluable for advertisers to time commercials and better capture the attention of their audience.
Betting
Sports betting operators have long had their eyes on machine learning’s predictive capabilities. So much so that high-tech companies like Sportlogiq have started selling analytical data to bookmakers in the United States, helping them set odds on bets.
However, 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 such details are not public but lie in the hands of sports clubs, so the increasing use of AI in betting leads to an intensification of data trading between different sports-related organizations.
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Real-life examples of AI in sports
NBA Global Scout app
Powered by artificial intelligence, The NBA Global Scout mobile app serves a dual purpose. First, it can analyze videos uploaded by users, helping them to self-assess their skills (including wingspan, vertical leap, and shooting ability) while performing specific exercises and to identify areas of improvement. Secondly, the training platform enables players around the globe to showcase their talent and potentially get drafted into the NBA, thus complementing resource-intensive recruiting campaigns.
Catapult One smart vests
Some of the football teams in the English Premier League have started using GPS vests by Catapult to monitor athletes during training and matches. These smart wearables can measure several metrics, such as distance, sprints, and speed, to help coaches optimize team selection and player workload while minimizing overtraining and injury risk. The related AI-powered application can also track athletes via heatmaps to assess whether their play aligns with the team's tactical objectives.
IBM Power Index & Match Insights
Deployed both in Wimbledon and the US Open, IBM's advanced data analytics solutions leverage AI to rank players' momentum and predict results based on explainable win factors. These include previous win-loss ratio and win margin, rank differential, court surface, and injury status, complemented with fan sentiment collected from social media through natural language processing. The same NLP technology is then used to self-generate fact sheets and share such insights with the fan base.
Gymnastics World Championships’ scoring system
During the Artistic Gymnastics World Championships, the IFG adopted a deep learning-based system developed by Fujitsu which can track athletes' movements thanks to multiple laser sensors and computer vision technology. The solution visualizes gymnasts’ performance via 3D models and extracts key scoring data, enhancing judges' decision-making. To further improve its accuracy, the system was "trained" with digital scans of each athlete before the competition.
The benefits of AI in sports
AI adoption considerations & tips for sports companies
Given the complex architectures of sports-oriented AI solutions and their reliance on data, organizations implementing this technology can face a number of adoption challenges.
Integration
Issues
AI algorithms are the brain of a sports data analytics system. A typical AI solution is based on a multi-layered architecture which also includes IoT sensors (cameras, wearables, etc.) to collect visual and physiological data, a network layer to transmit such information, and an integration layer to aggregate and store the data sets for analysis.
All these elements can rely on different communication protocols and technologies to exchange data and typically handle various data types and formats (including ongoing data streams collected in real-time). If such components don't interact efficiently, the resulting analyses will be inaccurate.
Recommendations
Communication between IoT devices and the data analytics platform can be enabled by configuring application programming interfaces (APIs). You can leverage cloud platforms, such as Amazon API Gateway, Cloud Data Fusion API, or Azure API Management, to facilitate this process. To convert multiple communication protocols, however, you may need to use data virtualization techniques or create a middleware architecture, such as an ESB.
Furthermore, you should integrate heterogeneous data from different sources via ETL pipelines (consider using AWS Glue, Azure Data Factory, or other cloud data integration tools) and consolidate it into data storage acting as a single source of truth. In this regard, you can opt for time-series databases due to their ability to handle data streams, or NoSQL databases and data lakes for their flexibility.
Overfitting & generalization
Issues
After processing large data sets, AI algorithms will build models trained to recognize patterns or relationships across data points, which can be leveraged for analytical and forecasting purposes. A common issue you can face, however, is so-called overfitting, or when an AI model performs well with training data but struggles to generalize to different game scenarios or new players.
Recommendations
Avoiding overfitting and ensuring model robustness is crucial for effective sports applications. You can prevent this occurrence by splitting training data into multiple subsets for cross-validation and reducing the set of features considered by the model, selecting only the most relevant attributes. This will make the model more flexible.
Resistance to change
Issues
Some athletes and coaches can be reluctant for artificial intelligence to make decisions affecting their jobs and careers. At the same time, executives may not be willing to take investment risks for AI deployment.
Recommendations
Adopt an effective communication strategy to promote innovation and digital literacy while educating sports professionals on the benefits of AI for the industry. As for top management buy-in, consider a progressive AI implementation, starting with reasonable investments in small-scale use cases to achieve quick results and entice stakeholders.
Compliance
Issues
Sports analytics and data trading for betting purposes can offer immense financial benefits to clubs and broadcasters. For instance, the NCAA signed a 10-year contract with a UK IT company to collect and sell sports data to media corporations.
However, this clashes with various attempts to legislate on sports data, especially in sensitive matters such as healthcare data security for athletes. In this regard, the GDPR defines requirements for collecting, storing, and processing personal information.
Recommendations
Make sure your AI solution is built and used in compliance with major data management standards and regulations applicable to the sports industry. For example, the GDPR established the data minimization principle, which limits the use of data based on relevance and necessity. Also, Article 22 prohibits making decisions based solely on automated processing if they have significant effects on a person, including an athlete's career.
In addition, you should protect your software, related IoT sensors, and data assets with a variety of cybersecurity features (data encryption, IoT device authentication, security information and event management, etc.) to prevent breaches and leaks.
Reinventing sports with AI
Although technology and science have influenced sports since the beginning, in recent years AI and big data have boosted this trend. Today, algorithms play a key role in the entire sports life cycle, from athlete recruitment and training to performance analysis, from audience experience to media and management. On the other hand, AI’s data-driven nature can clash with increasingly strict legislation and require the deployment of complex, interconnected tech ecosystems to fuel real-time analyses. To streamline the adoption of sports-oriented AI solutions, consider relying on Itransition's expert guidance.
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FAQ
Does FIFA use artificial intelligence?
FIFA has implemented semi-automated offside technology (SAOT), which relies on AI-powered computer vision to track players and the ball. The system was also used during the 2022 World Cup.
When was AI introduced in sports?
Sports organizations actively started exploring the potential of AI, data science, and data analytics about two decades ago, also prompted by the famous "Moneyball" case study. However, the role of AI has become more prominent in recent years, fostered by growing access to data, increasing computing processing power, and the rise of machine learning.
Was AI technology used at the Olympics?
Deloitte highlighted how the 2020 Summer Olympic Games in Tokyo were characterized by the pervasive use of AI in a variety of embodiments, including robots, drones, wearable sensors, computer vision-powered cameras, and machine learning-based sports analytics. Artificial intelligence has even been used to create the theme song for the Olympics.
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