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Artificial intelligence in sports: a business & technical overview
The adoption of AI in sports enables organizations to increase athlete performance and safety, optimize game
planning, ensure fair play, facilitate media coverage, and promote fan engagement.
With over five years of experience in AI consulting, Itransition
helps professional sports teams and companies adopt AI solutions to effectively nurture talent, strengthen
relationships with their fan bases, and boost business performance.
Applications of AI in sports
AI is transforming every aspect of sports, from the competition and game play to the show and entertainment
business. AI-powered tools are already being used in virtually all major sports disciplines, such as American
football, soccer, baseball, and cricket, as well as non-professional leisure activities like grassroots
sports.
Scheme title: AI in sports: main fields of application
Data source: pwc.com.au — Artificial Intelligence. Application to the Sports Industry
Recruiting
Artificial intelligence can influence the relationship between athletes and sports companies right from the
start. Specifically, AI-powered predictive modeling is used to analyze players’ performance data (speed,
agility, endurance, technique, playing style, etc.) and assess their athletic potential and market value
before a sports club decides to invest in them.
The adoption of tools powered with artificial intelligence can benefit players as well because they reduce
any bias during recruitment. Furthermore, AI software can analyze game footage from anywhere in the world,
increasing the chances of athletes getting noticed even if they live in remote locations or where certain
sports aren’t very popular.
Coaching & talent development
AI-based performance analytics software provides coaches with real-time insights to refine game strategies.
By processing data from wearable sensors and computer vision solutions, these systems identify player speed,
ball trajectories, and tactical patterns. AI software also allows teams to simulate in-game scenarios and make
data-driven tactical adjustments during matches that improve win rates.
At the same time, AI helps coaches shed light on their athletes’ strengths and weaknesses and thus create
more personalized training programs for performance optimization.
Athlete well-being management
For injury prevention, sports organizations can integrate AI into health wearable devices to monitor
biometric and biomechanical parameters like heart rate and muscle load in real-time. These systems detect
early signs of fatigue or biomechanical stress, allowing medical teams to intervene before injuries occur and
design personalized recovery protocols.
These solutions also provide coaches with data-driven insights to develop more balanced training plans,
adjusting the intensity and duration of each activity to avoid harmful overtraining and minimize the risk of
injury.
Refereeing
Video assistant referee systems (VAR) like Hawk-Eye and similar video-review or instant-replay systems have
been used in various sports for years to provide slow-motion highlights to human referees. AI-based computer
vision technology can detect game violations, such as handball offenses in football, way faster and more
accurately than traditional systems or humans, minimizing game flow disruptions and errors caused by
subjective officiating.
Broadcasting & journalism
AI is revolutionizing sports storytelling by automating key production tasks. Machine learning algorithms now
handle complex video editing, such as auto-zooming on key plays and generating instant game highlights. This
technology allows media outlets to produce professional-grade content at a fraction of the traditional cost
and time.
Thanks to recent innovations in the field of natural language processing (NLP) and generative AI, automation
is also finding its way into sports journalism, enabling the press to create brief news reports for hundreds
of matches and cover many more events with fewer resources. Furthermore, media companies can use
conversational AI systems to retrieve video footage and other sports content from large databases through
simple queries in natural language. The National Football League implemented a similar solution to search
for media assets in its vast library of athlete press interviews, fan-generated social media content, and
many other resources.
Fan engagement
AI software enhances the fan experience by delivering personalized content in real time. From multi-language
subtitles to AR-powered tactical breakdowns delivered via image overlays, AI solutions provide deeper
insights during live broadcasts, explaining complex team formations or estimating the probability of the
next move. AI can also be combined with virtual reality to integrate such insights into immersive match
simulations. These interactive elements keep fans engaged longer and create new monetization opportunities
for sports leagues.
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.
Advertising
Offering a great fan experience with artificial intelligence is significantly reshaping sports advertising
and, as a result, maximizes advertising commissions. ML-powered marketing solutions analyze viewer preferences, such as demographics, media consumption patterns, and shopping behavior, to segment
audiences and deliver personalized, relevant ads instead of generic ones.
Additionally, AI solutions powered by machine learning algorithms can identify the most exciting in-game
actions based on fans' emotional response, enabling broadcast companies to better time commercials and
capture the attention of their audience for maximum engagement. This makes ads not only more personal but
also better timed and more effective.
Betting
Sports betting operators have long shown interest in the predictive analytics capabilities of machine
learning systems. 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.
Adopt a tailored AI solution with Itransition’s guidance
AI is reshaping the sports industry, providing opportunities for sports organizations to improve revenue and
fan engagement. However, to make the most of AI, organizations need to navigate certain adoption roadblocks
like infrastructure gaps or the absence of a formal AI implementation strategy.
Market overview
The global AI in sports market is set for explosive growth, with the primary market drivers being machine
learning, data analytics, and computer vision, which are becoming essential technologies for game analytics
and fan engagement.
The global AI in sports market size was valued at $10.6 billion in 2025 and is projected
to hit $49.9 billion by 2033, growing at a CAGR of 21.6% between 2026 and 2033.
The leading technologies driving AI in the sports industry were machine learning (29%),
data analytics (26%), computer vision (around 23%), and natural language processing (14%) in 2025.
AI in sports was the most widely adopted for game analytics (31%), fan engagement (22%), and predictive modeling for forecasting player performance, injuries, and operational outcomes (19%) in 2025.
As the majority of sports organizations already use AI and the growing number of fans is willing to pay more
for AI-enhanced viewing experiences, investing in the right AI solution will provide businesses with a
significant competitive advantage in the near future.
Over 80% of sports organizations now use artificial intelligence, with nearly
three-quarters of them reporting tangible value from AI initiatives.
Scheme title: Share of fans willing to pay more for AI-enhanced sports viewing experience
Data source: Capgemini
Adoption challenges
Despite the fact that AI is becoming increasingly prominent in sports, companies still face several barriers
to its successful implementation, from high upfront costs to a lack of in-house expertise.
High upfront costs, infrastructure gaps, poor data quality, and unclear business value can negatively impact the long-term profitability of AI
adoption in media, entertainment, and sports, and increase the AI project failure rate, currently
estimated between 30% to 80%.
The lack of in-house talent or expertise (23%) and insufficient budget or resources
(18%) are the main AI adoption challenges for sports organizations.
Real-life examples of artificial intelligence in sports
AI is integrated into diverse apps and devices, supporting sports scouting, athlete training, and officiating.
Among the notable examples of AI-powered solutions in sports are the NBA Global Scout app, Catapult One smart
vests, IBM Power Index and Match Insights, and the Gymnastics World Championships’ scoring system.
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 self-assess their skills (including wingspan, vertical leap,
and shooting ability) while performing specific exercises and 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 player recruitment campaigns.
It really helps with shooting. It records you then shows the percent you made, your arc height and
whether your shot is too flat, your release time. It’s a great way to know specifically what you need to
improve.
Olivier-Maxence Prosper
Student at NBA Academy Latin America
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 player workload and selection while preventing
overtraining and minimizing the 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.
Access to this type of technology is the logical step in giving athletes at all levels the tracking and
analytics capabilities that previously had been reserved only for professional athletes and
organizations.
Will Lopes
CEO at Catapult
IBM Power Index & Match Insights
Deployed both at 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 software. The same NLP technology is then
used to self-generate fact sheets and share such insights with the fan base.
Watson predicted results with pretty much 100% accuracy on day one of the tournament.
Kevin Farrar
Head of Sport Partnerships at IBM
Gymnastics World Championships’ scoring system
During the Artistic Gymnastics World Championships, the FIG 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.
This is a step toward the challenge of justice through technology.
Morinari Watanabe
President of the International Gymnastics Federation
AI adoption challenges & 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, from data integration complexity to AI
model overfitting and strict compliance regulations.
Issues
Recommendations
Integrating data from multiple sources
Most AI solutions for sports comprise multiple components, including different IoT sensors (cameras,
wearables, etc.) to collect visual and physiological data and an analytics system to process such
information.
All these elements can rely on different communication protocols and technologies
to exchange data and typically handle various data types and formats (including ongoing real-time data
streams). If such components don't interact efficiently, the resulting analyses will be inaccurate.
Communication between IoT devices and the data analysis platform can be enabled through 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.
Addressing AI model overfitting
During the artificial intelligence and machine learning model training process, AI algorithms process
large sets of sports-related data to identify patterns and relationships across data points. 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.
To solve the problem of AI model overfitting in sports, developers need to prioritize data diversity.
Training algorithms on varied datasets spanning different leagues, weather conditions, and player
demographics ensures the model generalizes well to new game scenarios. Cross-validation across multiple
subsets is also essential for maintaining accuracy. Additionally, when working with datasets that have a
vast number of descriptive features, it is recommended to select the most relevant ones rather than using
the entire feature set. This will make the model more flexible.
Ensuring regulatory compliance
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 personal data collection, storage, and processing.
Make sure your AI tool processes data in compliance with major data management standards and regulations
applicable to the sports industry, including GDPR. Organizations need to adhere to data minimization
principles, ensuring only necessary biometric data is collected. Furthermore, Article 22 protections
mean that automated decisions affecting an athlete’s career have to be complemented with human
oversight to prevent legal and ethical risks.
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.
Drawing on our comprehensive AI expertise, we support sports organizations in adopting AI-powered solutions
safely and effectively, unlocking the benefits of smart automation and advanced analytics.
Our consultants can help you design, implement, and scale AI-powered software tailored to your needs,
providing advisory guidance at every stage of your AI project to overcome potential roadblocks and maximize
the value of the resulting solution.
We deliver AI solutions combining optimal performance with strict adherence to your industry’s quality
standards and data management regulations, or enhance existing AI tools in line with emerging tech trends
and business requirements.
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 every facet of the sports industry, 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 derive real-time insights. To streamline the adoption
of AI sports solutions, consider relying on Itransition's expert guidance.
The history of AI in sports dates back to the early 2000s, popularized by the “Moneyball” case study
involving the Oakland Athletics’ adoption of analytics techniques. Since then, the field has evolved from
simple statistical analysis of game outcomes to real-time player tracking and injury risk forecasting enabled
by machine learning and computer vision, driven by massive increases in computing power and data
availability.
For the 2026 Winter Olympics, Alibaba Cloud delivered upgraded AI-powered Real-Time 360º Replay systems that
generate immersive replays. The system isolates athletes from complex backgrounds such as snow and ice,
enabling three-dimensional reconstructions of key moments in 15-20 seconds. The IOC also launched “Olympic AI
Assistants”, the first large-language-model-based system in Olympic history, powered by Alibaba’s Qwen
models. The Olympic AI assistants provided fans with multilingual conversational support and real-time event
information on the IOC’s website, as well as personalized audio guides in the Olympic Museum in Lausanne.
Additionally, AI assistants equipped with multilingual translation capabilities help NOC staff find
documents, policy information, and grant guidelines through natural language commands.
FIFA 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. For the 2026 World Cup, FIFA
introduced a suite of AI tools called “Football AI”. The innovations include Football AI Pro for simplified
data reporting, AI-enabled 3D player avatars for precise offside decision-making, and an updated Referee
View solution for creating smooth footage from the referee’s camera.
Although AI-powered sports analytics help athletes gain a competitive advantage by providing insights on
movement patterns, tactics, and performance metrics, its full potential can be limited by technological and
operational barriers. For example, data quality and availability remain significant challenges, with
incomplete or inconsistent datasets reducing the accuracy and reliability of insights. According to
researchers from Ben-Gurion University of the Negev, AI algorithms can reproduce social and economic biases
inherited from training data. Researchers claim that when assessing athletes, AI systems can use indirect
markers like residency or educational background rather than those related to athletic ability, thereby
exacerbating inequalities during sports scouting. Moreover, AI cannot
fully capture and interpret human emotions and interactions, such as player morale or team chemistry that
still require human judgment.
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