Abstract
Bu çalışma, yapay zekâ uygulamalarının spor teknolojisindeki bilimsel eğilimlerini ortaya koymak amacıyla, Web of Science Core Collection’da 2018–2025 (Ağustos’a kadar) arasında yayımlanan 164 yayını kapsayan bir bibliyometrik inceleme sunmaktadır. Eş-yazarlık ağları, anahtar kelime eş-görünümü, atıf örüntüleri ve bibliyografik bağlantılar analiz edilmiştir. Bulgular, yayın hacminde hızlı bir artış olduğunu ve çalışmaların özellikle makine öğrenmesi, giyilebilir teknolojiler, bilgisayarla görme ve performans analitiği temalarında yoğunlaştığını göstermektedir. Disiplinlerarası iş birliği güçlenmekle birlikte, spor bilimleri odaklı dergilerin katkısı teknoloji dergilerine kıyasla görece sınırlı kalmaktadır. Elde edilen genel desen, yöntem ve metrik çeşitliliği ile dış doğrulama eksikliği nedeniyle, literatürdeki kanıtın sahaya aktarımında belirgin boşluklar bulunduğunu göstermektedir. Çalışmanın çıkarımı olarak, alanın sürdürülebilir ilerlemesi için çok merkezli ve dış doğrulamalı deneysel ve gözlemsel çalışmaların artırılması, açık veri ve kod paylaşımının benimsenmesi, ortak metrik ve raporlama standartlarının kullanılması ve yapay zekâ çıktılarının doğrudan performans göstergeleriyle ilişkilendirilmesi önerilebilir. Ayrıca etik ve adalet ilkelerinin sistematik biçimde gözetilmesi ve spor bilimci, mühendis iş birliğinin kurumsal düzeyde güçlendirilmesi beklenmektedir. Bu çerçevede çalışma, mevcut eğilimleri ve öncelikli gelişim alanlarını görünür kılarak gelecekteki araştırmalar için pratik bir yönlendirme sunar.
Keywords
Bilim Haritalama Veri Analitiği Bibliyometri Perfformans Analitiği Giyilebilir Teknolojiler
References
- An, R. (2025). Artificial intelligence in health and sport sciences: Promise, progress, and prudence. Journal of Sport and Health Science, 14, 101054. https://doi.org/10.1016/j.jshs.2025.101054
- Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
- Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York. Springer.https://doi.org/10.1117/1.2819119
- Bonidia, R. P., Rodrigues, L. A., Avila-Santos, A. P., Sanches, D. S., & Brancher, J. D. (2018). Computational intelligence in sports: A systematic literature review. Advances in Human-Computer Interaction, 2018, 3426178. https://doi.org/10.1155/2018/3426178.
- Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317.
- Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002.
- Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203. https://doi.org/10.1016/j.joi.2010.10.008
- Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2023). Conceptual structure and current trends in artificial intelligence, machine learning, and deep learning research in sports: A bibliometric review. International Journal of Environmental Research and Public Health, 20(1), 173. https://doi.org/10.3390/ijerph20010173.
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133,285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
- Escamilla, R. F., Marshall, B., & McMahon, M. (2023). Artificial intelligence and machine learning in human movement and performance analysis. Sports Biomechanics, 22(2), 189–204. https://doi.org/10.1016/j.bjpt.2024.101083
- Fister, I., Munivrana, G., & Fister Jr, I. (2021). Artificial intelligence in sports: A review. Expert Systems with Applications, 175, 114797. https://doi.org/10.1016/j.eswa.2021.114797 .
- Glänzel, W., & Schubert, A. (2004). Analysing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Springer. https://doi.org/10.1007/1-4020-2755-9_12.
- Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103
- Leydesdorff, L., & Nerghes, A. (2017). Co-word maps and topic modeling: A comparison using small and medium-sized corpora. Journal of the Association for Information Science and Technology, 68(4), 1024–1035. https://doi.org/10.1002/asi.23740.
- Liu, W., Yan, C. C., Liu, J., & Ma, H. (2017). Deep learning-based basketball video analysis for intelligent arena application. Multimedia Tools and Applications, 76(23), 24983–25001. https://doi.org/10.1007/s11042-016-4217-0.
- Mariappan, S., & Durai, C. (2024). In sports science, perform a bibliometric analysis on artificial intelligence. Shanlax International Journal of Arts, Science and Humanities, 11(3), 56–60. https://doi.org/10.34293/sijash.v11i3.6895
- Mulet-Forteza, C., Genovart-Balaguer, J., Mauleon- Mendez, E., & Merigó, J. M. (2019). A bibliometric research in the tourism, leisure and hospitality fields. Journal of Business Research, 101, 819–827. https://doi.org/10.1016/j.jbusres.2018.11.029
- Nassis, G. P., Massey, A., Jacobsen, P., Brito, J., Randers, M. B., Castagna, C., & Mohr, M. (2020). Elite football of 2030 will not be the same as that of 2020: Preparing players, coaches, and support staff for the evolution. Scandinavian Journal of Medicine & Science in Sports, 30(6), 962–964. https://doi.org/10.1111/sms.13681.
- Souaifi, M., Dhahbi, W., Jebabli, N., Ceylan, H. İ., Boujabli, M., Muntean, R. I., & Dergaa, I. (2025). Artificial intelligence in sports biomechanics: A scoping review on wearable technology, motion analysis, and injury prevention. Bioengineering, 12(8), 887. https://doi.org/10.3390/bioengineering12080887
- Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406.
- Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3.
- Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629.
Abstract
This study presents a bibliometric analysis covering 164 publications released in the Web of Science Core Collection between 2018 and 2025 (up to August) with the aim of revealing scientific trends in artificial intelligence applications within sports technology. Co-authorship networks, keyword co-occurrence, citation patterns, and bibliographic links were analysed. Findings indicate a rapid increase in publication volume, with studies particularly concentrated on machine learning, wearable technologies, computer vision, and performance analytics. While interdisciplinary collaboration is strengthening, the contribution of sports science-focused journals remains relatively limited compared to technology journals. The overall pattern obtained, along with the lack of external validation due to methodological and metric diversity, indicates significant gaps in translating evidence from the literature to the field. As a conclusion of the study, it is recommended that multi-centred and externally validated experimental and observational studies be increased, open data and code sharing be adopted, common metrics and reporting standards be used, and artificial intelligence outputs be directly linked to performance indicators for the sustainable advancement of the field. Furthermore, the systematic consideration of ethical and fairness principles and the institutional strengthening of collaboration between sports scientists and engineers are expected. Within this framework, the study provides practical guidance for future research by highlighting current trends and priority areas for development.
References
- An, R. (2025). Artificial intelligence in health and sport sciences: Promise, progress, and prudence. Journal of Sport and Health Science, 14, 101054. https://doi.org/10.1016/j.jshs.2025.101054
- Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
- Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York. Springer.https://doi.org/10.1117/1.2819119
- Bonidia, R. P., Rodrigues, L. A., Avila-Santos, A. P., Sanches, D. S., & Brancher, J. D. (2018). Computational intelligence in sports: A systematic literature review. Advances in Human-Computer Interaction, 2018, 3426178. https://doi.org/10.1155/2018/3426178.
- Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317.
- Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002.
- Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203. https://doi.org/10.1016/j.joi.2010.10.008
- Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2023). Conceptual structure and current trends in artificial intelligence, machine learning, and deep learning research in sports: A bibliometric review. International Journal of Environmental Research and Public Health, 20(1), 173. https://doi.org/10.3390/ijerph20010173.
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133,285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
- Escamilla, R. F., Marshall, B., & McMahon, M. (2023). Artificial intelligence and machine learning in human movement and performance analysis. Sports Biomechanics, 22(2), 189–204. https://doi.org/10.1016/j.bjpt.2024.101083
- Fister, I., Munivrana, G., & Fister Jr, I. (2021). Artificial intelligence in sports: A review. Expert Systems with Applications, 175, 114797. https://doi.org/10.1016/j.eswa.2021.114797 .
- Glänzel, W., & Schubert, A. (2004). Analysing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Springer. https://doi.org/10.1007/1-4020-2755-9_12.
- Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103
- Leydesdorff, L., & Nerghes, A. (2017). Co-word maps and topic modeling: A comparison using small and medium-sized corpora. Journal of the Association for Information Science and Technology, 68(4), 1024–1035. https://doi.org/10.1002/asi.23740.
- Liu, W., Yan, C. C., Liu, J., & Ma, H. (2017). Deep learning-based basketball video analysis for intelligent arena application. Multimedia Tools and Applications, 76(23), 24983–25001. https://doi.org/10.1007/s11042-016-4217-0.
- Mariappan, S., & Durai, C. (2024). In sports science, perform a bibliometric analysis on artificial intelligence. Shanlax International Journal of Arts, Science and Humanities, 11(3), 56–60. https://doi.org/10.34293/sijash.v11i3.6895
- Mulet-Forteza, C., Genovart-Balaguer, J., Mauleon- Mendez, E., & Merigó, J. M. (2019). A bibliometric research in the tourism, leisure and hospitality fields. Journal of Business Research, 101, 819–827. https://doi.org/10.1016/j.jbusres.2018.11.029
- Nassis, G. P., Massey, A., Jacobsen, P., Brito, J., Randers, M. B., Castagna, C., & Mohr, M. (2020). Elite football of 2030 will not be the same as that of 2020: Preparing players, coaches, and support staff for the evolution. Scandinavian Journal of Medicine & Science in Sports, 30(6), 962–964. https://doi.org/10.1111/sms.13681.
- Souaifi, M., Dhahbi, W., Jebabli, N., Ceylan, H. İ., Boujabli, M., Muntean, R. I., & Dergaa, I. (2025). Artificial intelligence in sports biomechanics: A scoping review on wearable technology, motion analysis, and injury prevention. Bioengineering, 12(8), 887. https://doi.org/10.3390/bioengineering12080887
- Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406.
- Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3.
- Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Sports Activity Management |
| Journal Section | Research Article |
| Authors | |
| Submission Date | August 29, 2025 |
| Acceptance Date | November 13, 2025 |
| Publication Date | December 22, 2025 |
| Published in Issue | Year 2025 Volume: 6 Issue: 3 |
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