How to Leverage AI in Sportstech? Best Use Cases and Implementation Tips

According to recent estimates, the AI in sports market grew from $5.91 billion in 2023 to $7.20 billion in 2024 and is expected to continue growing at a CAGR of 23.52%, reaching $25.95 billion by 2030. As more and more sportstech companies successfully rely on AI software development, sports-related businesses that ignore the potential of artificial intelligence risk missing a competitive edge.

AI-powered sportstech solutions help sports-related businesses enhance the experience of sports enthusiasts, boost fan engagement, provide better workout support to their clients, and ensure better results in professional sports. 

In this article, we will discuss the most promising use cases for AI in sportstech, common challenges pertaining to AI sportstech implementation, and tips on solving these challenges.

How is AI used in sports?. Artificial intelligence algorithms can be applied to various aspects of sports, ranging from athlete performance enhancement to fan engagement. Various sportstech apps use machine learning algorithms, AI-powered data analytics, and computer vision to provide insights enhancing both athlete productivity and profitability of various sports organizations, such as athletic clubs, sports media, and international athletic associations.

Basically, there are three main directions for implementing artificial intelligence in sports industry:

• Enhancing the experience for users of sports streaming platforms
• Media and fan engagement, which is relevant to sports clubs, media organizations like ESPN, and international athletic associations
• Workout, training management, performance analytics, and injury prevention for athletes, sports enthusiasts, and coaches.

Let’s discuss AI implementations for all these purposes in more detail.

Streaming apps take a very important place in the global sportstech market. By cooperating with sports clubs and associations, streaming platforms help them raise money from PPV (pay-per-view) streams or monthly subscriptions. AI can facilitate such platforms raise even more solid funds by providing better user personalization and streaming quality.

AI-powered stream quality optimization

AI can help streaming apps improve the quality of their streams. Machine learning algorithms monitor network conditions, user device capabilities, and video playback performance in real time. They adjust video resolution, bitrate, and buffering dynamically to maintain a smooth viewing experience, even if the network is unstable. AI can also predict potential issues, like network congestion or device overload, and preemptively adjust the stream to prevent interruptions. As a result, users of streaming platforms get a better viewing experience with minimal lag and better higher video quality. 

Streaming recommendations automated with AI

AI automates streaming recommendations in sports apps by analyzing user behavior, preferences, and historical viewing data to suggest relevant streams. AI also considers factors like trending games, upcoming events, and popular content among similar users to provide individual suggestions. This improves user experience and enhances user engagement, which translates into higher user approval rates and popularity of a streaming platform. 

Translation and subtitles powered with AI

AI helps sports streaming platforms translate streams and provide subtitles in real time by using natural language processing (NLP) and speech recognition technologies. Automatic speech recognition (ASR) transcribes live commentary into text while machine translation models translate this text into different languages. AI can also learn sports-specific jargon and context to make translations more accurate. With efficient AI for sports technology scripts, such subtitles can appear in real time, ensuring excellent user experience. 

AI-enhanced fraud prevention and monitoring

AI secures sports streaming platforms from fraud. Such sport AI scripts analyze user behavior and identify unusual patterns, such as account sharing, unauthorized access, or piracy attempts. Such tools detect anomalies like multiple logins from different locations or abnormal viewing patterns, or monitor for illegal stream redistribution in order to block unauthorized streams through automated takedown mechanisms. Consequently, AI safeguards sports streaming platforms from fraud and privacy, preventing revenue loss. 

AI also has rich potential in terms of promoting and improving sports media and fan engagement applications. They also provide automation in content generation, which is very important in terms of managing large sports media platforms loaded with information, statistics, match results, highlights, etc.

AI bots for fan engagement and personalized user experience

According to Gartner’s research, organizations that focus on personalized messaging and content generation can expect 16% more impact on commercial outcomes than those that don’t. Tailored customer interactions, whether it goes about communication or content recommendations, can boost the popularity of sports media platforms and fan engagement resources connected to particular clubs, organizations, or athletes. AI can significantly boost the efficiency of such personalization. AI bots can handle customer communication and use analytical data to provide users of sports media platforms with tailored content based on their preferences. If a user gets relevant answers and content constantly, their satisfaction with a sports media platform grows exponentially.

AI-powered media content creation

AI-powered content creation in sports media apps automates the generation of articles and summaries by analyzing game data and player statistics in real time. AI scripts can use significant loads of data and their analytical capabilities to create match reports, recaps, or social media posts relevant to different categories of sports enthusiasts.  By using natural language processing (NLP), AI generates human-like commentary and insights, which helps sports media platforms keep users engaged without requiring constant manual input. Therefore, AI brings significant automation to the domain of sports media by allowing such companies to scale content production while maintaining excellent quality and up-to-date coverage for fans.

Sports highlights generated with AI

AI helps sports media platforms automatically prepare highlights by analyzing live game footage and identifying key moments, such as goals, turnovers, or record-breaking plays. Such a feature relies on computer vision combined with machine learning algorithms that detect patterns, crowd reactions, and signature player movements. AI isolates the most exciting sequences and compiles these clips into highlight reels. Such a feature helps sports media platforms keep the users engaged by producing more efficient athletic highlights, often in real time.

In addition, there is a growing trend with finding celebrity lookalikes in the crowd visiting sports events. AI algorithms can use biometric features of people on the stands to find celebirties that look similarly to them. This feature provides an additional “show component” to sports streams and highlights, enhancing fan engagement. 

In addition, a growing trend in sports events is the use of AI to find celebrity lookalikes among the crowd. By analyzing biometric features of spectators in the stands, AI algorithms can identify visitors who resemble famous personalities. This feature adds a fun and interactive “show component” to sports broadcasts and highlights, which enhances fan engagement and entertainment.

AI advisors and computer vision are widely implemented in the sports domain. In particular, NFL has developed its own computer-vision-based solution that detects early signs of potential injuries and significantly helps NFL clubs in injury prevention. However, the use of AI sports analytics tools in workout planning and injury prevention goes beyond professional competition. Many sports clubs and fitness apps actively use such technologies to help both athletes and sports enthusiasts improve their workouts and prevent injuries. The most common use cases for such technologies are outlined below.

AI-powered performance analytics and injury prevention for sports enthusiasts

AI-powered performance analytics is widely applied in personal fitness and running apps. Such algorithms analyze users’ workout data to provide individual insights and progress tracking. Such solutions use machine learning to monitor metrics like pace, heart rate, calorie burn, and correctness of exercise performance, adapting training sessions based on individual goals. Such algorithms also provide real-time feedback that helps users adjust training intensity in order to maximize efficiency and reduce injury risk. Besides, machine learning algorithms can learn over time from the data they receive, which allows them to offer smarter user recommendations and progress reports.

Management of fitness clubs and other sports sections with AI

It is also worth mentioning the role of artificial intelligence in sport and fitness club management. AI can help you automate tasks like scheduling by analyzing peak hours and user data to automatically offer fitness club visitors the most relevant booking options for training sessions. Also, by analyzing member data, such scripts can predict churn, helping sports clubs retain clients through targeted engagement. In addition, AI-driven customer service tools, like chatbots, can handle customer inquiries and bookings, providing customers with support and enhancing their overall experience. 

Personalized workout plans and virtual AI trainers

In some cases, AI-powered fitness apps work as useful coach assistants. A regular fitness app user can rely on efficient AI scripts providing individual workout plans, important tips on training, and useful training materials. Such AI trainers should rely on significant loads of general coaching knowledge, as well as users’ personal data and historical data from their previous workouts. If AI sports analytics is configured properly, a generative AI model can provide tailored coaching services to users in different sports, ranging from weightlifting to jogging.

AI-powered workout gamification

Gamification is a very popular trend in fitness app development, as it helps keep users engaged. While gamification features in sportstech may range from rivalries between players to augmented reality challenges in running apps. Sports artificial intelligence automates the creation of various gamification app components. In particular, AI-powered workout gamification in fitness apps users personalized challenges, rewards, and virtual achievements to make exercise more engaging. Sports AI scripts analyze user behavior and performance and tailor goals and rewards to suit individual fitness levels, ensuring the challenges are motivating but achievable. AI algorithms can be also used for creating adaptive leaderboards, competitions, or integrating with virtual environments for more engaging and competitive user experience. The key point of using ML algorithms is that such tools can enable hyper-personalization and automation to the sportstech apps’ gamification component.

Analytics from professional sports competitions for coaches and athletes

AI scripts can use technologies like computer vision to enhance athlete performance analytics. By recording sports events, like basketball matches, boxing bouts, running competitions, etc., AI AI-powered sportstech tools can analyze statistical data on athlete performance, including focus on its strengths and weaknesses. This information is relevant to coaches who can use AI-powered insights to determine useful training strategies or performance tactics that improve the athlete’s results. 

While AI brings great opportunities and improvements to the sportstech domain, not all sportstech platforms manage to implement AI features properly in their solutions. Numerous challenges are the main reasons why some businesses are unable to implement proper AI in their solutions. Make sure to check the main AI adoption challenges in sportstech to be prepared.

The IBM Global Study reveals that 33% of companies believe that the lack of AI skills and expertise is a significant problem preventing them from AI implementation. Despite the abundance of ready-to-go large language models (LLMs) and generative AI integration opportunities, implementing quality AI scripts for a sportstech app requires strong technical expertise.

That’s why it is important to rely on the most skilled technical teams with a proven record of successful AI development and integration projects. Experienced AI developers will build, configure, and customize efficient AI scripts tailored to your business needs.

AI relies on data, and the sportstech domain is associated with great loads of poorly-structured data of questionable quality. Common reasons behind poor data quality in sportstech include device sensor inaccuracies, inconsistent data collection methods, and inadequate data standardization across different platforms. While data complexity is a critical challenge in 25% of AI implementation cases, it is definitely a problem that needs to be addressed.

If you feel insecure about your data organization, you may opt for a technical audit that will help you understand the peculiarities of your data architecture. Also, rely on data architects capable of organizing your data and modernizing your data infrastructure. While this may look like an additional step for your AI sports implementation project, this will definitely pay off. If your AI scripts rely on quality data that is properly structured, it will provide more efficient and relevant outputs.

Ethical concern is a significant challenge for 23% of companies implementing AI. This is especially relevant to fitness apps. First, they should provide users with personalized fitness plans that don’t lead to injuries or any other health issues. Secondly, there is a question of data security and privacy relevant to the apps that process personal user data, sometimes even health-related. Many regulations may hinder sportstech companies from feeding large sets of private data to AI. For example, AI developers need to consider GDPR / ССРА principles that introduce some limitations on data exposure to AI.

The best solutions for this challenge involve thorough planning and an audit that will help the company identify and find the best solutions for the main ethical concerns. Security and compliance audits will be very useful in case you feel insecure about the safety risks associated with AI implementation.

According to Deloitte, less than 36% of companies feel that AI fulfills their goals to a large or very large extent. The problem largely builds upon the fact that many businesses fail to clearly define the goals being pursued with AI implementation or the ways AI can help them achieve those goals. Issues commonly emerge with the solutions’ strategic focus and proper implementation. Some sportstech companies can just implement an AI feature that doesn’t bring the desired outcome. Or there are no performance indicators to measure the outcomes of such an implementation at all.

That’s why it is important to dedicate time to strategic planning. Sportstech companies can start AI implementation with a thorough market research and outlining long-term goals and long-term planning. In this case, they will be able to gain a better strategic focus and maximize the value of AI implementation.

Studies reveal that AI may show bias in up to 38.6% of cases as it may perceive unchecked facts as “common sense.” Such biases may lead to poor recommendation from your sportstech AI solutions, especially when it comes to training tips provided by fitness apps. 

A certain way to mitigate the problem of AI bias is to invest in training and fine-tuning your AI models. You will need AI specialists to handle such AI model development and optimization. However, such an approach will pay off because it will boost the quality of your AI-powered recommendations, enhancing the overall user experience. 

AI in sportstech unlocks diverse opportunities for different sports-related organizations and businesses. Such algorithms are applied across three key areas: personalized workouts and athlete support, media and fan engagement, and sports streaming platforms. Sportstech AI enhances athlete performance and helps sports organizations through machine learning, data analytics, and computer vision technologies. AI-powered systems offer tailored workout plans, injury prevention insights, and gamification features to engage users. In media and streaming, AI automates content creation, improves fan engagement, optimizes stream quality, and prevents fraud, helping organizations improve efficiency and user experience.

Do you want to leverage the power of AI in sportstech? We are a company with solid experience in AI development and implementation and a notable player in the domain of sportstech app development with our product, Leostep, shortlisted for the prestigious Sports Technology Awards 2024. You may check out our portfolio of sportstech apps.

Contact us to leverage the power of AI in your sportstech applications!