Sports technology, often called Sportstech, is the use of digital tools, software, equipment, and systems to improve athletic performance, elevate fan engagement, and optimize sports business operations. It encompasses innovations such as artificial intelligence, machine learning, wearable technology, virtual and augmented reality, blockchain, and data analytics.
MobiDev is taking its part in developing innovative applications for the sports industry, as we’ve been working with professional sports organizations since 2019. And we’ve seen how rapidly the market has changed with the popularity of machine learning, augmented reality, smart wearables and other technologies.
In this article, we’ll take a look at the recent sports technology trends, and what you can extract for your own software product development from these cases.
Sports Technology Market Overview
According to Mordor Intelligence, the global sports technology market was valued at USD 22.86 billion in 2025, projected to reach USD 60.49 billion by 2030 at 21.49% CAGR. Key growth drivers include 5G stadium infrastructure, mandated player tracking, AI-powered video analytics, and esports platforms expanding at 26.5% CAGR.
Wearable sensors dominate at one-third of revenue, with professional teams accounting for 40% spend.
Football leads with a 28% share while cricket is accelerating to 23% CAGR. Direct-to-fan e-commerce transforms club revenue through dynamic pricing and personalized engagement, with consumer apps projected to grow above 27% annually.
Trend #1: AI coaching Applications
Among various ways AI is being used in the fitness and sports industry, computer vision is a prominent example of a transformative technology for sports apps, offering real-time analysis and immediate insights into user movements. With the introduction of pose estimation technology, the limitations for tracking human movements became less of an issue. Those computer vision models can achieve accurate perception of a movement pattern in a 3D space, and don’t require any wearables to be used.
AI coaches can leverage this capability to dissect player techniques, identify strengths and weaknesses, and provide targeted feedback for improvement. Moreover, it can be used by average users on their own – to prevent injury by detecting mistakes in doing exercises and irregularities in form or posture during training and rehabilitation.
Also, the objective performance metrics generated by computer vision eliminate subjectivity, providing accurate data crucial for training strategies and game plans. This approach is actively implemented to create systems that can provide professional athletes with accurate feedback on the performed movement, and support with corrective feedback and analytical summaries for longer sessions.
MobiDev has been involved in a project related to automating training procedures using human pose estimation. BeOne Sports is a live example of what can be called an AI coaching app, as it provides access to the feedback of professional athletes within your smartphone. Recently, BeOne Sports has partnered with Rice University to integrate BeOne Sports’ mobile motion-capture AI and advanced data analytics with Rice’s premier sports medicine and rehabilitation programs, to set a new standard for athlete care, injury prevention, and performance optimization.
Check out the video about how BeOne Sports created its product and how they succeeded on the market of sports applications.
Trend #2: Wearable Tech and Performance Analytics
Sports analytics algorithms play a pivotal role in computing user input to analyze different aspects of sports performance, training, sleep/rest balance, and achievements. Various devices are used in today’s sports technology to collect data and process it using AI. Wearable devices designed to gather information of general health indicators, physiological parameters, and performance analysis right during the game have become a wide-spread thing among sports organizations.
As research by the University of Calgary suggests, “AI applications have the potential to enhance traditional sports performance analysis methodologies. It has enabled video analysis to become faster, more precise, and comprehensive, supplanting time-consuming and potentially biased manual reviews.” The technological base for such systems involves different methods of computer vision paired with analytical algorithms meant to collect and process data about player performance. These can be either individual players or sports teams, and their interactions during the game.
Such systems are often integrated into sports analytics platforms and apps, enabling athletes and coaches to make data-driven decisions, optimize training strategies, and enhance overall performance and well-being. For instance, PlaySight developed a solution that utilizes optical sensors to observe the training court and recognize the actions automatically.
The underlying ecosystem includes numerous types of sensors and software components that analyze the reasons for injuries, or help to make data-driven decisions since such systems are meant to monitor millions of data points per game. The data entry point is not limited to just images, as other types of sensors are widely used, like Google’s Jacquard insole tracker for football. IoT devices, accelerometers, pressure sensors, gyroscopes and other approaches are all becoming increasingly popular, and are here to stay in the future.
Trend #3: Immersive Broadcasting with AR
Sports broadcasting is one of three main sources of income for sports organizations. The previous years when COVID interrupted sports events, and the shortening of attention spans brought about by TikTok imposed a problem for broadcasters of long games. People now spend less time watching the event. But as the popularity of Augmented reality technology grows, sports might claim get their attention again.
In 2023, Apple and Walt Disney Co announced their mutual work on integrating Apple Vision Pro into a broadcasting experience in augmented reality, making it available to watch up to 5 different games at once, allowing for picture-in-picture features and on-screen stats. MetaQuest VR headset launched a fairly similar feature called Xtadium. However, the use of augmented reality to watch games from different angles of a stadium and scroll through statistics in real-time is not limited to just that.
For example, ESPN, an American broadcaster, implemented an AR application that helps viewers to visualize data collected during the match. Virtual overlays help to navigate the game quickly and boosts viewer engagement. More importantly, it opens up new perspectives of AR application development for businesses that operate in the sports industry.
Trend #4: Video Assistant Referees
As video assistant referees (VAR) have been around for some time, and there are protocols to use such systems in sports, we can say for sure this trend is here to stay for the upcoming years. These tools rely on video recordings to analyze in-game situations and provide an unbiased opinion on whether there was an out in a football match, or if a penalty is needed.
Video assistant referees are already used in F1 car racing and other sports, bringing down the cost of judging the game. As artificial intelligence is fully capable of understanding the rules of sports and analyzing on-field events. But more importantly, VAR plays a crucial role in making unbiased decisions, since hot debates over the referee decisions are a long-standing part of any sport.
An automated system based on AI will not only help with making the right decisions during a sports competition but also instantly and convincingly visualize it for teams and fans, preventing stress and disputes. However, the requirements to implement VAR are very high because the decision provided by the system determines the fate of each match. All in all, VAR comes as a part of data analytics and visualization trends that are explicitly used in sports, and the number of use cases is only growing since AI algorithms have become more accessible and accurate.
Trend #5: AI and Wearables for Injury Prevention
Sports injuries remain a critical challenge worldwide, causing substantial physical, psychological and financial burdens. In European professional football alone, each club loses on average more than 150 playing days per season due to injury, with direct medical and salary costs exceeding €150 million annually, according to the research.
Following the growing trend of using artificial intelligence in healthcare, injury prevention with AI, became a thing since NFL and AWS announced their partnership to develop Digital Athlete. This is a groundbreaking technology that uses various sensors across a player’s protective suit to collect data and create a virtual representation of NFL players to predict possible injuries.
Similar systems have also emerged in different types of sports. For example, the NBA has collaborated with GE HealthCare and MedStar Health to implement injury prevention using data analysis and wearable sensors. This means the concept of aggregating biomechanical, physiological and general health data will continue to evolve in big sports, and possibly trickle down to amateur sports as well. With that, a separate type of system is growing on the market.
Rehabilitation after injuries sometimes can take months or even years of consistent and controlled exercise. Professional athletes usually have highly skilled physiotherapy or rehab specialists on their side. However, this may not be the case for amateur athletes who don’t have sponsorship.
