AI in sports: top use cases,
real-life examples & adoption challenges

AI in sports: top use cases, real-life examples & adoption challenges

November 15, 2023

Applications of AI in sports

As pointed out by PwC, AI embraces every aspect of sports, from the competition and game play to the show and entertainment business. Also, AI-based tools are already deployed in virtually all major sports disciplines, such as American football, soccer, baseball, and cricket, but also non-professional leisure activities like grassroots sports.

Scheme title: AI in sports: main fields of application
Data source: — Artificial Intelligence. Application to the Sports Industry

Datafromwearables&gameequipmentSensorydatafromDatafromMachine&deeplearning(think)othersystemsperipheraldevices(sense)Media&fanexperienceManagement&operationsTalentID&selectionPost-gameanalysisIn-gameactivityPre-gamepreparationAnalysis & feedback Recovery Injury managementFan relationship
managementNews & contentFactory sportseSportsSports bettingMedia rightsTalent identification Talent selectionCompetition management Club & team management Venues, events & ticketingSponsorship MerchandisingPaymentsUmpiringSpecialist coachingTraining & coachingNutrition Physical Biomechanics (skill/ technique) MentalInjury management Strategic & tactical game planningTeam selection


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.

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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.

The benefits of AI in sports


Training and game strategy optimization
based on data analytics and resulting real-time insights

More efficient injury prevention,
diagnoses, and rehabilitation through anomaly detection

Data-driven, bias-free decisions
that ensure a fairer athlete's career progression

More accurate refereeing decisions
thanks to IoT sensors and computer vision

Sports democratization
and new career opportunities via app-based talent scouting

Superior fan engagement
and augmented user experience with AI-based highlights

Increased revenues
for sports and media companies via AI-driven marketing

Automation of time-consuming processes,
including AI-generated fact sheets and videos

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.



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.


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.

Reinventing sports with AI

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.

Reinventing sports with AI

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