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Artificial Intelligence (AI) in Sports

Original Editor - Angeliki Chorti

Top Contributors - Angeliki Chorti, Ishita Pareek, Kim Jackson and Khloud Shreif  

Introduction

Artificial Intelligence (AI) - Definition

Artificial intelligence (AI) terminology commonly refers to the use of computers and technology to simulate intelligent behavior and critical thinking comparable to a human being. [1] John McCarthy was the first to report the term AI in an academic conference in 1956 as the science and engineering of making intelligent machines. [2]

Since then, we have witnessed substantial growth in AI, especially over the last decade, where the focus of investment has been on clinical applications in healthcare. [3] AI in healthcare and rehabilitation is transforming policies, health strategies and clinical practice. As a result, health professionals, are required to increase awareness on AI's applications in these fields to provide best practice patient care.[4]

In a similar way, AI in sports is refering and changing the application of technology to several facets of the sports sector, especially the elite sports. [5] The extension of AI into the sports sector is still in its infancy, but shows promise in predicting and preventing athlete injury, enhancing the precision and accuracy of injury assessment, optimising recovery plans, improving patient education, monitoring rehabilitation progress, and predicting return to play. [3][6]

AI is actually cited as the fourth industrial revolution and is considered the next frontier in medicine that is going to transform the field of orthopaedics and sports medicine. [7] Therefore, the adoption of AI by sports professionals and scientists is standing out in recent years as a priority since these emerging technologies and applications affect professional sports at various levels of sophistication. [8]

Some of the uses and applications of AI are described below.

AI Statistics in Sports and Gaming

1. Talent Identification and Acquisition

  • Recent Advances: AI-powered algorithms are being used by teams to analyse player performance data, genetics, and physical attributes to predict future success. Tools like AI-powered scouts and wearable technology help identify hidden talent in grassroots levels and across global competitions.

2. Game Analytics

  • Recent Advances: Real-time performance tracking through AI and machine learning helps teams analyse match data in seconds, offering insights into player positioning, team dynamics, and opponent strategies. Companies like Second Spectrum are using AI to provide advanced data visualisations for both teams and broadcasters.

3. Training and Coaching

  • Recent Advances: AI-based virtual coaching assistants can analyse player form, biomechanics, and endurance in real-time. With wearable tech, coaches receive immediate feedback on player conditioning, helping optimise individualised training plans. AI systems like HomeCourt (used in basketball) have enhanced player training by providing instant performance reviews.

4. Predictive Modeling

  • Recent Advances: Predictive AI models analyse vast amounts of historical match data to provide detailed forecasts on game outcomes, player performance, and injury risks. These tools are increasingly integrated with betting platforms and in sports strategy development. AI models like IBM Watson are used in major tennis tournaments to predict match results.

5. Strategy Improvement

  • Recent Advances: AI-driven simulations analyse different game scenarios to refine strategies. Teams like Liverpool FC and NBA franchises use machine learning to predict and counter opponent strategies in live games. Reinforcement learning is becoming a key tool in tactical decision-making and strategy optimisation.

6. Injury Prevention

  • Recent Advances: AI algorithms are being developed to predict injuries before they happen by analysing biomechanics, training load, and past medical data. Tools like Zone7 and Catapult provide early warnings about potential injuries, helping sports medical teams adjust training loads and prevent overexertion.

7. Fan Engagement

  • Recent Advances: AI chatbots and recommendation engines enhance fan experiences by providing personalised content and real-time information. Platforms like IBM Watson engage fans through personalised game highlights, player stats, and custom experiences during live events.

8. Immersive Experiences

  • Recent Advances: AI has brought immersive experiences like augmented reality (AR) and virtual reality (VR) into fan engagement and training. Virtual stadiums powered by AI, combined with real-time data feeds, allow fans to experience matches from unique angles and perspectives. In gaming, AI-based NPCs and environments provide a more immersive experience.

9. Evidence-Based Practice

  • Recent Advances: AI is enabling more data-driven, evidence-based decision-making in both sports and gaming. Machine learning models are used in rehabilitation, treatment planning, and performance tracking, ensuring the best practices are backed by robust data analytics and simulations.

[9][10]

AI Applications in Sports

  1. Player Performance
  2. Personalised Training
  3. Sports Nutrition
  4. Scouting and Recruitment
  5. Referee
  6. Ticketing
  7. Sports Predictions
  8. Automated Sports Broadcasting
  9. Advertising

[11]

AI and Tech Innovations at the Olympic Games

In April 2024, the International Olympic Committee (IOC) introduced the Olympic AI Agenda, reflecting the impact that AI is envisioned to deliver for sport. The Olympic AI Agenda is the third document of a series introduced in 2014 (Olympic Agenda 2020) and continued in 2021 with the Olympic Agenda 2020+5. The Paris 2024 Games were the first to implement AI technology to such a scale.

Watch this video to get an idea about what the Olympic AI Agenda was about.

[12]

There are five focus areas that relate to transforming sport and the Olympic Games with AI: [13]

  1. Supporting Athletes, Clean Competition and Safe Sport
  2. Ensuring Equal Access to the Benefits of AI
  3. Optimising Olympic and Paralympic Games Operations with a Focus on Sustainability
  4. Growing Engagement with People
  5. Driving Efficiency Across the Management of the IOC and Sport

For more information, you can find the Olympic AI Agenda document in the resources section.

The Future of AI Technology in Sports

AI has become a valuable tool for athletes, sports scientists and companies, organisations, and spectators. Its broad applications also leave a broad scope of implementation, which is likely to lead to even more interest in AI research and development for sports. However, despite the immense potential of these technologies for injury prediction, performance analysis, personalised training, and optimised management, one can not ignore challenges related to the complexity of sports dynamics, data quality, interpretability of produced models, the loss of the clinician-patient relationship and the multidimensional aspects of athletic performance. [14] Therefore, the use of AI may be better complementary rather than substitutional in sports. In addition, the result of AI application should be assessed by comprehensive cost-benefit analyses, taking into account factors such as initial investment, maintenance costs, and long-term outcomes before its braod implementation.[6] Future studies should also look at ethical issues, regulation and liability around the wide application of AI systems. [15]

The International Olympic Committee acknowledges that the future of AI in sport requires the collaboration of all its stakeholders across the Olympic Movement – "athletes, International Federations (IFs), National Olympic Committees (NOCs), Worldwide Olympic Partners, the International Paralympic Committee (IPC) and many other organisations " – this collaboration will ensure that these transformative technologies can be unlock their full potential without also ignoring potential risks. [16]

Resources

The Olympic AI Agenda (pdf form)

References

  1. ↑ Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019 Jul;8(7):2328-2331.
  2. ↑ Andresen SL. John McCarthy: father of AI. IEEE Intelligent Systems. 2002 Sept-Oct.; 17(5):84-85.
  3. ↑ 3.0 3.1 Desai V. The Future of Artificial Intelligence in Sports Medicine and Return to Play. Semin Musculoskelet Radiol. 2024 Apr;28(2):203-212.
  4. ↑ Alsobhi M, Khan F, Chevidikunnan MF, Basuodan R, Shawli L, Neamatallah Z. Physical Therapists' Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study. J Med Internet Res. 2022 Oct 20;24(10):e39565.
  5. ↑ Hammes F, Hagg A, Asteroth A, Link D. Artificial Intelligence in Elite Sports-A Narrative Review of Success Stories and Challenges. Front Sports Act Living. 2022 Jul 11;4:861466.
  6. ↑ 6.0 6.1 Gojanovic B. Editorial: Bridging the Gap: Artificial Intelligence in Sports Medicine and Musculoskeletal Rehabilitation. SEMS Journal 2023. Available from: https://sems-journal.ch/13651 [accessed 12/9/2024]
  7. ↑ Ramkumar PN, Luu BC, Haeberle HS, Karnuta JM, Nwachukwu BU, Williams RJ. Sports Medicine and Artificial Intelligence: A Primer. Am J Sports Med. 2022 Mar;50(4):1166-1174.
  8. ↑ Chmait N, Westerbeek H. Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists. Front Sports Act Living. 2021; 3: 682287.
  9. ↑ Chmait N, Westerbeek H. Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists. Front Sports Act Living. 2021 Dec 8;3:682287.
  10. ↑ Smaranda AM, Drăgoiu TS, Caramoci A, Afetelor AA, Ionescu AM, Bădărău IA. Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review. Sports (Basel). 2024 May 26;12(6):144.
  11. ↑ Hammes F, Hagg A, Asteroth A, Link D. Artificial Intelligence in Elite Sports-A Narrative Review of Success Stories and Challenges. Front Sports Act Living. 2022 Jul 11;4:861466.
  12. ↑ IOC. Olympic AI Agenda. Available from: https://olympics.com/ioc/olympic-ai-agenda [12/9/2024]
  13. ↑ IOC. Olympic AI agenda. Available from: https://stillmed.olympics.com/media/Documents/International-Olympic-Committee/AI/Olympic-AI-Agenda.pdf [accessed 12/9/2024]
  14. ↑ Reis FJJ, Alaiti RK, Vallio CS, Hespanhol L. Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives. Braz J Phys Ther. 2024 May-Jun;28(3):101083.
  15. ↑ Mylrea M, Robinson N. Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy (Basel). 2023 Oct 9;25(10):1429.
  16. ↑ IOC. Olympic AI Agenda. Available from: https://olympics.com/ioc/olympic-ai-agenda [accessed 24-8-2024]
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