Artificial Intelligence in Sports: Opportunities, Challenges, and Future Directions

Artificial intelligence (AI) is increasingly used in sports for movement analysis, performance monitoring, injury research, talent identification, officiating support, and fan services [23]. This review assesses how well the current evidence supports these claims and what uncertainties remain. The strongest evidence is with respect to specific measurement tasks. For instance, a validation study of markerless pose estimation in 5 participants reported mean absolute joint angle errors of 9.7 ± 4.7 degrees for athletic movements and 9.0 ± 3.3 degrees for sports movements [1]. A separate study found that VAR improved decision accuracy from 92.1% to 98.3% across 2,195 matches [2] (again, this refers to video-assisted refereeing, not AI). Prediction-based applications are less settled. Talent-identification studies report ROC-AUC values from about 0.58 to 0.96, but calibration is rarely reported, external validation is uncommon, and 59% of studies were judged at high risk of bias [3]. A return-to-sport review also found that most included studies were rated unclear or high risk in the analysis domain, primarily because external validation remained absent [4]. The data suggest that AI can serve as a measure or decision-support tool, provided it's validated for the specific application. There are no apparent broad predictive or clinical benefits. The next step is to validate this independently, report it properly, involve the practitioner, and set clear rules for using athlete data. This is a narrative review rather than a systematic review, and the sources we've used are not exhaustive. They should not be interpreted as a full representation of the literature that's available

artificial intelligence; sports technology; performance analysis; athlete health; sports analytics; injury prediction; talent identification; data governance

1. T. Fukushima, P. Blauberger, T. G. Russomanno, and M. Lames, “The potential of human pose estimation for motion capture in sports: a validation study,” Sports Engineering, vol. 27, no. 1, May 2024, doi: 10.1007/s12283-024-00460-w. [Google Scholar] [Crossref]

2. J. Spitz, J. Wagemans, D. Memmert, A. M. Williams, and W. Helsen, “Video assistant referees (VAR): The impact of technology on decision making in association football referees,” Journal of Sports Sciences, vol. 39, no. 2, pp. 147–153, Aug. 2020, doi: 10.1080/02640414.2020.1809163. [Google Scholar] [Crossref]

3. Q. Tang, X. Wei, and B. Tan, “The Role of Machine Learning in Talent Identification for Team Sports: A Systematic Review,” Journal of Sports Science and Medicine, vol. 25, no. 1, pp. 58–83, 2026, doi: 10.52082/jssm.2026.58. [Google Scholar] [Crossref]

4. R. Satija and D. Dkhar, “Predictive accuracy of machine learning and markerless gait analysis for return-to-sport following lower extremity injury: a systematic review and meta-analysis,” Frontiers in Sports and Active Living, vol. 8, Jun. 2026, doi: 10.3389/fspor.2026.1862870. [Google Scholar] [Crossref]

5. J. W. Kwon, “Athlete data sovereignty: addressing the legal and policy gaps in sports technology,” Frontiers in Sports and Active Living, vol. 7, p. 1742484, Dec. 2025, doi: 10.3389/fspor.2025.1742484. [Google Scholar] [Crossref]

6. W. X. Zhao, H. Zheng, and Q. Zhang, “Modern Advances in Artificial Intelligence Across the Athletic Domain,” Concurrency and Computation Practice and Experience, vol. 37, Apr. 2025, doi: 10.1002/cpe.70068. [Google Scholar] [Crossref]

7. D. Zhou, J. Keogh, Y. Ma, R. K. Tong, A. R. Khan, and N. R. Jennings, “Artificial intelligence in sport: A narrative review of applications, challenges and future trends,” Journal of Sports Sciences, pp. 1–16, Jun. 2025, doi: 10.1080/02640414.2025.2518694. [Google Scholar] [Crossref]

8. L. Dawson, S. A. McErlain-Naylor, G. Devereux, and M. Beato, “Practitioner Usage, Applications, and Understanding of Wearable GPS and Accelerometer Technology in Team Sports,” Journal of Strength and Conditioning Research, vol. 38, no. 7, pp. e373–e382, 2024, doi: 10.1519/JSC.0000000000004781. [Google Scholar] [Crossref]

9. T. Luczak, R. F. Burch, E. C. Lewis, H. Chander, and J. E. Ball, “State-of-the-art review of athletic wearable technology: What 113 strength and conditioning coaches and athletic trainers from the USA said about technology in sports,” International Journal of Sports Science & Coaching, vol. 15, no. 1, pp. 26–40, Nov. 2019, doi: 10.1177/1747954119885244. [Google Scholar] [Crossref]

10. C. Leckey, N. van Dyk, C. Doherty, A. Lawlor, and E. Delahunt, “Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis,” British Journal of Sports Medicine, vol. 59, no. 7, pp. 491–500, Nov. 2024, doi: 10.1136/bjsports-2024-108576. [Google Scholar] [Crossref]

11. M. Klemp, M. Bassek, M. G. Caparrós, L. Bakhtiar, and D. Memmert, “The use of machine learning in performance analysis in invasion games: Umbrella review of reviews,” Journal of Sports Sciences, pp. 1–28, Mar. 2026, doi: 10.1080/02640414.2026.2636863. [Google Scholar] [Crossref]

12. R. Bunker and T. Sušnjak, “The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review,” Journal of Artificial Intelligence Research, vol. 73, pp. 1285–1322, 2022, doi: 10.1613/jair.1.13509. [Google Scholar] [Crossref]

13. J. G. Claudino, D. de O. Capanema, T. V. de Souza, J. C. Serrão, A. C. M. Pereira, and G. P. Nassis, “Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review,” Sports Medicine - Open, vol. 5, no. 1, p. 28, Jul. 2019, doi: 10.1186/s40798-019-0202-3. [Google Scholar] [Crossref]

14. C. L. Mușat et al., “Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods,” Diagnostics, vol. 14, no. 22, p. 2516, Nov. 2024, doi: 10.3390/diagnostics14222516. [Google Scholar] [Crossref]

15. J. Yuan, Q. Zeng, J. Li, Z. Cong, and Y. Zhang, “Machine learning applications in sports injury prediction: A narrative review,” Science Progress, vol. 108, no. 4, Oct. 2025, doi: 10.1177/00368504251385956. [Google Scholar] [Crossref]

16. B. T. Naik, M. F. Hashmi, and N. D. Bokde, “A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions,” Applied Sciences, vol. 12, no. 9, p. 4429, Apr. 2022, doi: 10.3390/app12094429. [Google Scholar] [Crossref]

17. W. Li et al., “A review of artificial intelligence for sports: Technologies and applications,” Intelligent Sports and Health, vol. 1, no. 3, pp. 113–126, Jun. 2025, doi: 10.1016/j.ish.2025.05.001. [Google Scholar] [Crossref]

18. F. Hammes, A. Hagg, A. Asteroth, and D. Link, “Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges,” Frontiers in Sports and Active Living, vol. 4, p. 861466, Jul. 2022, doi: 10.3389/fspor.2022.861466. [Google Scholar] [Crossref]

19. T.-K. Davidson, S. Barrett, J. Toner, and C. Towlson, “Professional soccer practitioners’ perceptions of using performance analysis technology to monitor technical and tactical player characteristics within an academy environment: A category 1 club case study,” PLoS ONE, vol. 19, no. 3, Mar. 2024, doi: 10.1371/journal.pone.0298346. [Google Scholar] [Crossref]

20. H. V. Eetvelde, L. D. M. Mendonça, C. Ley, R. Seil, and T. Tischer, “Machine learning methods in sport injury prediction and prevention: a systematic review,” Journal of Experimental Orthopaedics, vol. 8, no. 1, p. 27, Jan. 2021, doi: 10.1186/s40634-021-00346-x. [Google Scholar] [Crossref]

21. J. S. M. Mora, R. A. B. Medina, V. E. Q. Molina, and E. H. H. Rincon, “Beyond the conventional: Artificial intelligence in identifying risk factors in sports injuries. A scoping review,” International Journal of Medical Informatics, vol. 214, p. 106408, Mar. 2026, doi: 10.1016/j.ijmedinf.2026.106408. [Google Scholar] [Crossref]

22. A.-R. Abdel-Fattah et al., “YOLOv8 computer vision for automated offside detection in professional football validated through supervised learning,” Scientific Reports, May 2026, doi: 10.1038/s41598-026-52668-4. [Google Scholar] [Crossref]

23. Rahmani, M., Majedi, N., Hemmatinejad, M., & Jamshidi, A. (2024). Application of Artificial Intelligence in the Sports Industry: A Review Article. 2(2), 20–27. https://doi.org/10.61838/kman.aitech.2.2.4 [Google Scholar] [Crossref]

24. N. Nakano et al., “Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple Video Cameras,” Frontiers in Sports and Active Living, vol. 2, p. 50, May 2020, doi: 10.3389/fspor.2020.00050. [Google Scholar] [Crossref]

25. Z. Xu et al., “Smart Textiles for Personalized Sports and Healthcare,” Nano-Micro Letters, vol. 17, no. 1, p. 232, Apr. 2025, doi: 10.1007/s40820-025-01749-6. [Google Scholar] [Crossref]

26. Asiabar, M. G., Asiabar, M. G., & Asiabar, A. G. (2025). Examining Legal Challenges of Modern Technologies in Sports Arbitration: A Case Study of VAR in Football. https://doi.org/10.14293/pr2199.001551.v1 [Google Scholar] [Crossref]

27. M. G. Asiabar, M. G. Asiabar, and A. G. Asiabar, “Legal Challenges of Wearable Technology Use in Sports Competitions: A Case Study of European Football Leagues,” Nov. 15, 2024. doi: 10.20944/preprints202411.0558.v2. [Google Scholar] [Crossref]

28. S. Colyer, M. Evans, D. Cosker, and A. Salo, “A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System,” Sports Medicine - Open, vol. 4, no. 1, p. 24, Jun. 2018, doi: 10.1186/s40798-018-0139-y. [Google Scholar] [Crossref]

29. R. Blythman, M. Saxena, G. Tierney, C. Richter, A. Smolić, and C. Simms, “Assessment of deep learning pose estimates for sports collision tracking,” Journal of Sports Sciences, vol. 40, no. 17, pp. 1885–1900, Sep. 2022, doi: 10.1080/02640414.2022.2117474. [Google Scholar] [Crossref]

30. Mencarini, E., Rapp, A., Tirabeni, L., & Zancanaro, M. (2019). Designing Wearable Systems for Sports: A Review of Trends and Opportunities in Human–Computer Interaction. IEEE Transactions on Human-Machine Systems, 49(4), 314–325. https://doi.org/10.1109/thms.2019.2919702 [Google Scholar] [Crossref]

31. M. T. Islam et al., “Advancements in functional smart and wearable textiles for sportswear applications,” RSC Advances, vol. 15, no. 58, pp. 50206–50232, Jan. 2025, doi: 10.1039/d5ra07231j. [Google Scholar] [Crossref]

32. S. C. Michalski, A. Szpak, and T. Loetscher, “Using Virtual Environments to Improve Real-World Motor Skills in Sports: A Systematic Review,” Frontiers in Psychology, vol. 10, p. 2159, Sep. 2019, doi: 10.3389/fpsyg.2019.02159. [Google Scholar] [Crossref]

33. Rebelo, A., Martinho, D. V., Valente‐dos‐Santos, J., Coelho‐e‐Silva, M. J., & Teixeira, D. (2023). From data to action: a scoping review of wearable technologies and biomechanical assessments informing injury prevention strategies in sport. BMC Sports Science Medicine and Rehabilitation, 15(1), 169. https://doi.org/10.1186/s13102-023-00783-4 [Google Scholar] [Crossref]

34. Peake, J. M., Kerr, G., & Sullivan, J. P. (2018). A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Frontiers in Physiology, 9, 743. https://doi.org/10.3389/fphys.2018.00743 [Google Scholar] [Crossref]

35. Waśkiewicz?, Z., Słomka, K. J., Grzywacz, T., & Juras, G. (2025). Ethical Bias in AI-Driven Injury Prediction in Sport: A Narrative Review of Athlete Health Data, Autonomy and Governance. AI, 6(11), 283. https://doi.org/10.3390/ai6110283 [Google Scholar] [Crossref]

36. Oonk, A., Kempe, M., Lemmink, K., & Buurke, T. J. W. (2025). Examining the concurrent validity of markerless motion capture in dual-athlete team sports movements. Journal of Sports Sciences, 1–12. https://doi.org/10.1080/02640414.2025.2497678 [Google Scholar] [Crossref]

37. Migliaccio, G. M., Padulo, J., & Russo, L. (2024). The Impact of Wearable Technologies on Marginal Gains in Sports Performance: An Integrative Overview on Advances in Sports, Exercise, and Health. Applied Sciences, 14(15), 6649. https://doi.org/10.3390/app14156649 [Google Scholar] [Crossref]

38. Robertson, S., Kremer, P., Aisbett, B., Tran, J. & Cerin, E. (2017). Consensus on measurement properties and feasibility of performance tests for the exercise and sport sciences: a Delphi study. Sports Medicine - Open 3. https://doi.org/10.1186/s40798-016-0071-y [Google Scholar] [Crossref]

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