Data-Driven Transformation in Sports Sciences with Artificial Intelligence

Year 2025, Volume: 3 Issue: 2, 182 - 197, 17.10.2025

Murat Sarıkabak , Engin Vural

Abstract

The purpose of this study is to examine the impacts of artificial intelligence (AI) in sports sciences. Given the limited number of studies in the literature, this research aims to comprehensively address the contributions of AI to sports sciences. A qualitative research method was employed, and searches were conducted in databases such as the YÖK Thesis Center, Google Scholar, and PubMed using keywords including "Artificial intelligence and sports" and "AI applications in sports sciences." The obtained articles, books, and reports were selected according to specific criteria and analyzed using the document analysis method. The findings reveal that AI provides extensive contributions to sports sciences. AI is effectively utilized to evaluate athlete performance, optimize training processes, and predict injury risks. Additionally, it plays a role in verifying referee decisions, conducting game analyses, and developing team strategies. Through wearable technologies and sensors, athletes' biometric data is collected to create personalized training programs and enhance performance improvements. AI-based analyses in various sports disciplines (e.g., football, basketball, volleyball, swimming) assist in developing more effective strategies. However, challenges such as data security and ethical concerns remain. In conclusion, AI is revolutionizing sports sciences, and its more widespread use in the future will play a significant role in enhancing athlete performance. Future research should particularly focus on sports psychology and real-time applications. Furthermore, it is recommended that sports clubs, federations, researchers, and policymakers collaborate to take more comprehensive steps regarding the ethical use of AI applications and their effects on athletes.

Keywords

Artificial intelligence , Sports sciences , AI applications in sports

Yapay Zekâ ile Spor Bilimlerinde Veri Odaklı Dönüşüm

Abstract

Bu çalışmanın amacı, yapay zekânın spor bilimlerindeki etkilerini incelemektir. Literatürdeki sınırlı çalışmalar göz önüne alındığında, bu araştırma, yapay zekânın spor bilimlerine katkılarını daha kapsamlı bir şekilde ele almayı hedeflemektedir. Çalışmada nitel bir araştırma yöntemi kullanılmış ve YÖK Tez Merkezi, PubMed gibi veri tabanlarından “Yapay zekâ ve spor” ve “Yapay zekâ spor bilimleri uygulamaları” gibi anahtar kelimelerle tarama yapılmıştır. Elde edilen makale, kitap ve raporlar belirli kriterlere göre seçilip, doküman analizi yöntemiyle incelenmiştir. Bulgular, yapay zekânın spor bilimlerine geniş bir katkı sağladığını ortaya koymaktadır. Yapay zekâ, sporcuların performansını değerlendirmek, antrenman süreçlerini optimize etmek ve yaralanma risklerini tahmin etmek için etkili bir şekilde kullanılmaktadır. Ayrıca, hakem kararlarını doğrulamak, oyun analizleri yapmak ve takımların stratejilerini geliştirmek gibi alanlarda da yer almaktadır. Giyilebilir teknolojiler ve sensörler aracılığıyla sporcuların biyometrik verileri toplanarak kişiselleştirilmiş antrenman programları oluşturulmakta ve performans iyileştirmeleri sağlanmaktadır. Farklı spor branşlarında (futbol, basketbol, voleybol, yüzme vb.) yapay zekâ tabanlı analizler, daha etkili stratejiler geliştirilmesine yardımcı olmaktadır. Ancak, veri güvenliği ve etik kaygılar gibi engeller de bulunmaktadır. Sonuç olarak, yapay zekâ spor bilimlerinde dönüşüm yaratmaktadır ve gelecekte bu teknolojinin daha yaygın kullanımı, sporcuların performansını artırmada önemli rol oynayacaktır. Gelecekte yapılacak araştırmaların, özellikle spor psikolojisi ve gerçek zamanlı uygulamalar üzerine yoğunlaşması gerektiği söylenebilir. Ayrıca, spor kulüpleri, federasyonlar, araştırmacılar ve politika yapıcıların işbirliği yaparak, yapay zekâ uygulamalarının etik kullanımı ve sporcular üzerindeki etkileri konusunda daha kapsamlı adımlar atmaları önerilmektedir.

Keywords

Yapay zekâ , Spor bilimleri , Sporda yapay zekâ uygulamaları

Ethical Statement

Bu çalışma için etik kurul kararına gerek yoktur.

References

• Aarons, M. F., Vickery, W., Bruce, L., Young, C. M., & Dwyer, D. B. (2024). Barriers to coach decision-making during Australian football matches and how it can be supported by artificial intelligence. International Journal of Sports Science & Coaching, 19(1), 41–52.
• Aini, G. (2020). Yapay zekânın yargısal uygulamasına ilişkin araştırmanın özeti. Çin Çalışmaları, 9(1), 14.
• Ambler, W. (2021, June 7). NBA big goes big on technology. Catapult. Access link: https://www.catapult.com/blog/nba-big-goes-big-on-technology.
• Araújo, D., Couceiro, M., Seifert, L., Sarmento, H., & Davids, K. (2021). Artificial intelligence in sport performance analysis. Routledge.
• Atiković, A., Kamenjašević, E., Nožinović Mujanović, A., Užičanin, E., Tabaković, M., & Ćurić, M. (2020). Differences between all-around results in women’s artistic gymnastics and ways of minimizing them. Baltic Journal of Health and Physical Activity, 12(3), 80–91. https://doi.org/10.29359/BJHPA.12.3.08
• Ayyıldız, E. (2018). Amerika Basketbol Ligi (NBA) maç sonuçlarının yapay sinir ağları ile tahmini. Gaziantep Üniversitesi Spor Bilimleri Dergisi, 3(1), 40–53.
• Beal, R., Norman, T. J., & Ramchurn, S. D. (2019). Artificial intelligence for team sports: A survey. Knowledge Engineering Review, 34, e22. https://doi.org/10.1017/S0269888919000225
• Beygmohammadloo, V., Gharakhani, D., & Naderinasab, M. (2024). Development of football tactics and strategies driven by artificial intelligence. AI and Tech in Behavioral and Social Sciences, 2(2), 1–6.
• Chan, K. M., Ha, S. C., Fong, D. T., & Chan, K. M. (2014). Analysis of ankle inversion sprain injury mechanism from accidental injury cases captured in televised basketball matches. Journal of Foot and Ankle Research, 7(1), A30. https://doi.org/10.1186/1757-1146-7-S1-A30
• Chmait, N., & Westerbeek, H. (2021). Artificial intelligence and machine learning in sport research: An introduction for non-data scientists. Frontiers in Sports and Active Living, 3, 682287. https://doi.org/10.3389/fspor.2021.682287
• Connor, M., Beato, M., & O'Neill, M. (2022). Adaptive athlete training plan generation: An intelligent control systems approach. Journal of Science and Medicine in Sport, 25(4), 351–355. https://doi.org/10.1016/j.jsams.2021.10.011
• Creswell, J. W. (2013). Nitel araştırma yöntemleri (M. Bütün & S. B. Demir, Çev.). Siyasal Yayın Dağıtım.
• Dai, X., & Li, S. (2021). Application analysis of wearable technology and equipment based on artificial intelligence in volleyball. Mathematical Problems in Engineering, 5572389. https://doi.org/10.1155/2021/5572389
• Dorrer, M. G., Popov, A. A., & Tolmacheva, A. E. (2020). Building an artificial vision system of an agricultural robot based on the DarkNet system. IOP Conference Series: Earth and Environmental Science, 548(3), 032032. https://doi.org/10.1088/1755-1315/548/3/032032
• Dowsett, B. (2025, February 8). The NBA is turning to wearable sensors to prevent player injuries. Access link: FiveThirtyEight. https://fivethirtyeight.com/features/the-nba-is-turning-to-wearable-sensors-to-prevent-player-injuries/
• Ekiz, D. (2009). Bilimsel araştırma yöntemleri. Anı Yayıncılık.
• Garofolini, A., Oppici, L., & Taylor, S. (2020). A real-time feedback method to reduce loading rate during running: Effect of combining direct and indirect feedback. Journal of Sports Sciences, 38(21), 2446–2453. https://doi.org/10.1080/02640414.2020.1788288
• Goodfellow, I., Bengio, Y., Courville, A., & Bach, F. (2017). Deep learning (Adaptive Computation and Machine Learning Series). MIT Press.
• Karasar, N. (2012). Bilimsel araştırma yöntemi (23. baskı). Nobel Yayın Dağıtım.
• Karetnikov, A. D. (2019). Application of data-driven analytics on sport data from a professional bicycle racing team [Master’s thesis]. Eindhoven University of Technology.
• Krause, L. (2019). Exploring the influence of practice design on the development of tennis players [Doctoral dissertation]. Victoria University.
• Li, B., & Xu, X. (2021). Application of artificial intelligence in basketball sport. Journal of Education, Health and Sport, 11(7), 54–67. https://doi.org/10.12775/JEHS.2021.11.07.005
• Liu, S. (2021). Application research of artificial intelligence in swimming. In M. Atiquzzaman, N. Yen, & Z. Xu (Eds.), Big data analytics for cyber-physical system in smart city (pp. 1–10). Springer. https://doi.org/10.1007/978-3-030-67051-2_1
• McCabe, A., & Trevathan, J. (2008). Artificial intelligence in sports prediction. In Fifth International Conference on Information Technology: New Generations (ITNG 2008) (pp. 1194–1197). IEEE. https://doi.org/10.1109/ITNG.2008.256
• Mooney, R., Quinlan, L. R., Corley, G., Godfrey, A., Osborough, C., & O’Laighin, G. (2017). Evaluation of the Finis SwimsenseR and the Garmin Swim™ activity monitors for swimming performance and stroke kinematics analysis. PLOS ONE, 12(2), e0170902. https://doi.org/10.1371/journal.pone.0170902
• Nadikattu, R. R. (2020). Implementation of new ways of artificial intelligence in sports. Journal of Xidian University, 14(5), 5983–5997. https://doi.org/10.37896/jxu14.5/649
• O'Leary, Z. (2017). The essential guide to doing your research project (3rd ed.). SAGE Publications Inc.
• Poulios, P., Serlis, A., Groumpos, P. P., & Gliatis, I. (2021). Artificial intelligence and data processing in injury diagnosis and prevention in competitive sports: A literature review. MOJ Orthopedics & Rheumatology, 13(2), 34–37. https://doi.org/10.15406/mojor.2021.13.00544
• Pu, Z., Pan, Y., Wang, S., Liu, B., Chen, M., Ma, H., & Cui, Y. (2024). Orientation and decision-making for soccer based on sports analytics and AI: A systematic review. Journal of Automatica Sinica, 11(1), 37–57. https://doi.org/10.1109/JAS.2023.123807
• Ramkumar, P. N., Luu, B. C., Haeberle, H. S., Karnuta, J. M., Nwachukwu, B. U., & Williams, R. J. (2022). Sports medicine and artificial intelligence: A primer. The American Journal of Sports Medicine, 50(4), 1166–1174.
• Richter, C., O’Reilly, M., & Delahunt, E. (2021). Machine learning in sports science: Challenges and opportunities. Sports Biomechanics, 23(8), 961–967. https://doi.org/10.1080/14763141.2021.1910334
• Rommers, N., Rössler, R., Verhagen, E., Vandecasteele, F., Verstockt, S., Vaeyens, R., Lenoir, M., D’hondt, E., & Witvrouw, E. (2020). A machine learning approach to assess injury risk in elite youth football players. Medicine & Science in Sports & Exercise, 52(8), 1745–1751. https://doi.org/10.1249/MSS.0000000000002305
• Seyidoğlu, H. (2016). Bilimsel araştırma ve yazma el kitabı. Güzem Can Yayınları.
• Soğut, T., & Baytaş, E. (2022). Futbolda küresel konumlandırma sistemi (GPS) ve performans analizi. Akdeniz Spor Bilimleri Dergisi, 5(1), 151–165. https://doi.org/10.38021/asbid.1082339
• Tepe, C., Erdim, M., & Eminoğlu, İ. (2020). Myo bileklik ile gerçek zamanlı protez kol kontrolü. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 184–193. https://doi.org/10.31590/ejosat.779672
• Toledo, A., Sookhanaphibarn, K., Thawonmas, R., & Rinaldo, F. (2012). Personalized recommendation in interactive visual analysis of stacked graphs. International Scholarly Research Notices, 2012, 1–8. https://doi.org/10.5402/2012/389540
• Tong, Y., & Ye, L. (2023). Yapay zekâ algoritmasına dayalı spor sağlığı izleme yönetim sistemi. Fizikte Sınırlar, 1, 1141944.
• Uluca, M., Yel, K., Güzel, S., & Çakır, Z. (2024). Yapay zekâ ve drone teknolojileri ile spor etkinlikleri gözlem ve analizinde güncel yaklaşımlar. Dede Korkut Spor Bilimleri Dergisi, 2(2), 47–70.
• Wang, H. (2021). Neural network-oriented big data model for yoga movement recognition. Computational Intelligence and Neuroscience, Article ID 4334024, 1–10. https://doi.org/10.1155/2021/4334024
• Wei, S., Huang, P., Li, R., Liu, Z., & Zou, Y. (2021). Exploring the application of artificial intelligence in sports training: A case study approach. Complexity, 2021, Article ID 4658937. https://doi.org/10.1155/2021/4658937
• Yang, D., Oh, E.-S., & Wang, Y. (2020). Hybrid physical education teaching and curriculum design based on a voice interactive artificial intelligence educational robot. Sustainability, 12(19), Article 8000. https://doi.org/10.3390/su12198000
• Yolgörmez, A. C., & Tütüncü, O. (2023). 2022 FIFA Katar Dünya Kupası’nda gerçekleşen gollerin analizi. Spor Bilimleri Dergisi, 34(3), 107–117. https://doi.org/10.17644/sbd.1243811
• Zhang, H., Li, Z., Li, Y., Li, H., & Yang, Z. (2021). A decision support system for volleyball movement recognition based on edge computing and machine learning. IEEE Transactions on Computational Social Systems, 8(2), 423–434.
• Zhang, R., Wu, L., Yang, Y., Wu, W., Chen, Y., & Xu, M. (2020). Multi-camera multi-player tracking with deep player identification in sports video. Pattern Recognition, 102, Article 107260. https://doi.org/10.1016/j.patcog.2020.107260