AI in sports: top use cases, real-life examples & adoption challenges
November 15, 2023
The adoption of artificial intelligence in sports helps increase athlete performance and safety, enhance game planning, and promote fan engagement.
Discover how AI experts can apply this technology to benefit coaches, team managers, players, and media professionals in a variety of use cases. Additionally, explore the real-life examples, pay-offs, and challenges of adopting AI in the sports industry.
AI in sports: market trends & stats
CAGR of the AI in sports market from 2020 to 2030
Allied Market Research
the potential boost to team performance from adopting AI in sports
Research and Markets
of respondents consider tech physical sport augmentation a key market force
PwC
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.
Adopt a tailored AI solution with Itransition’s guidance
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
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.
Integration
Issues
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.
Recommendations
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
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.
