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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 talents, 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, 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 analysis software provides coaches with valuable insights to develop effective game
strategies. AI systems can analyze information gathered via wearable sensors and cameras, including passes,
goals scored, rebounds, player’s movement speed, and ball trajectories, to help identify opponents' playing
patterns and adjust game plans accordingly. Coaches can even use AI software to run simulations of potential
in-game scenarios, test tactical adjustments, and make more informed decisions during matches.
At the same time, AI helps coaches to shed light on their athletes’ strengths and weaknesses and thus create
more personalized training programs for performance optimization.
Athlete well-being management
Combined with health wearable devices, AI-driven systems can monitor biometric data and biomechanics
parameters, such as heart rate or muscle loading, to assess a player’s physical condition, spot potential
injuries or health problems at an early stage, and thus help physicians select suitable therapies for faster
recovery.
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) and similar video-review or instant-replay systems have been used in
various sports for years to provide slow-motion highlights to human referees, but they often slowed down the
game, creating frustration among fans and players. AI-based computer vision technology, on the other hand,
can detect game violations, such as handball offenses in football, way faster and more accurately than
traditional systems, minimizing game flow disruptions and subjective officiating errors.
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) 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 game footage and other sports content from large databases through
simple queries in natural language. The NFL 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
During sporting events, sports companies can use AI software to provide fans with real-time subtitles in
different languages based on their nationality or additional stats and deeper insights to enrich their
experience. AI can also be combined with augmented reality to display AI-generated tactical breakdowns via
image overlays, or with virtual reality to integrate such insights into 3D environments.
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
The global AI in sports market size was valued at $10.61 billion in 2025 and is projected
to hit $49.92 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%), predictive modeling for forecasting player performance, injuries,
and
operational outcomes (19%) in 2025.
According to fans surveyed globally, AI and other technologies will positively impact the advancement of
sports in areas such as training (mentioned by 70% of them),
sports medicine (70%), game strategy (70%), coaching (67%), injury prevention (65%), talent
acquisition (62%), and fan engagement (58%)
Sports fans consider real-time updates the top priority to improve engagement with GenAI
(selected by 34% of them), followed by personalized content (29%), unique insights (28%), and meaningful predictions (26%)
Scheme title: GenAI impact across sports use cases and organizations
Data source: PwC
Adoption challenges
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%
Key barriers to GenAI adoption in sports include the need to change previous ways of working (mentioned by 19% of respondents),
lack of clarity on potential use cases (17%), and insufficient funding to support GenAI development (14%)
Real-life examples of artificial intelligence 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 player recruitment 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 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.
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 software. 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.
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.
Challenge
Solution
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.
Avoiding overfitting and ensuring model robustness is crucial for effective sports applications. First,
you should train the solution on large and diverse data sets. For example, training data should include
data points collected from different matches, teams, tournaments, etc. To assess how the model performs on
different data sets, you can split training data into multiple subsets. 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 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.
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.
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 involving the Oakland Athletics’ adoption
of analytics techniques. 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.
The Paris 2024 Summer Olympics featured widespread use of AI in multiple usage scenarios, including motion
tracking for athlete performance analysis, highlight video generation in multiple languages, and social media
moderation.
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.
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. 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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