APPLICATION OF ARTIFICIAL INTELLIGENCE IN SPORTS MANAGEMENT: ENHANCING DECISION-MAKING AND PERFORMANCE OPTIMIZATION
Abstract
Artificial intelligence (AI) has become a key tool in sports management, enabling the optimization of athletic performance, more accurate decision-making, and improved organizational efficiency.
Objectives: The aim of this research is to analyze the application of AI in sports management, including performance analysis, outcome prediction, training optimization, sports event management, sports marketing, and financial analysis.
Methodology: The study employs data analysis and a literature review, utilizing both quantitative and qualitative approaches to identify key trends and the effects of AI on sports processes.
Results: AI contributes to reducing operational costs and increasing decision-making accuracy. In performance analysis, it allows the monitoring of biomechanical parameters, while machine learning algorithms improve result prediction accuracy by up to 85%. In sports marketing, AI supports content personalization, and in financial management, it aids in more precise budget planning.
Discussion: Although AI provides numerous advantages, challenges such as implementation costs, the need for expert personnel, and ethical concerns remain critical factors for future development.
Conclusion: AI is expected to continue enhancing sports management through more efficient decision-making, performance optimization, and the advancement of financial and marketing strategies
Downloads
References
Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied Computing and Informatics, 15(1), 27–33. https://doi.org/10.1016/j.aci.2017.09.005
Case technologies of universal learning actions in physical education of junior schoolchildren / N. Mischenko, M. Kolokoltsev, M. Tyrina [et al.] // Journal of Physical Education and Sport. – 2023. – Vol. 23, No. 3. – P. 589-595. – DOI 10.7752/jpes.2023.03073.
Comprehensive program for flat foot and posture disorders prevention by means of physical education in 6-year-old children / E. Romanova, M. Kolokoltsev, A. Vorozheikin [et al.] // Journal of Physical Education and Sport. – 2022. – Vol. 22, No. 11. – P. 2655-2662. – DOI 10.7752/jpes.2022.11337.
Fister, I., Jr., Ljubič, K., Suganthan, P. N., Perc, M., & Fister, I. (2015). Computational intelligence in sports: Challenges and opportunities within a new research domain. Applied Mathematics and Computation, 262, 178–186. https://doi.org/10.1016/j.amc.2015.04.004
García, L., Martínez, J., & Rodríguez, A. (2024). Using artificial intelligence to enhance sports event management: A focus on cost reduction and audience satisfaction. Event Management, 28(1), 33–45.
Johansson, E., Peterson, M., & Karlsson, L. (2024). Combining traditional statistics and AI for more accurate sports outcome predictions. Journal of Sports Analytics, 10(1), 99–108.
Lee, H., Choi, Y., & Kim, S. (2023). AI-driven training optimization: Improving athlete recovery time and training efficiency. Sports Engineering, 26(4), 371–380.
Rong, X., & Xiang, Y. (2014). Intelligent algorithms in sports event logistics: Planning schedules, managing resources, and engaging audiences. International Journal of Sports Management & Technology, 5(2), 100–109.
Schumaker, R. P., Solieman, O. K., & Chen, H. (2010). Sports knowledge management and data mining. Annual Review of Information Science and Technology, 44(1), 115–157. https://doi.org/10.1002/aris.2010.1440440110
Smith, J., Taylor, A., & Brown, R. (2023). Artificial intelligence in sports biomechanics: Reducing injury risk and enhancing athletic performance. Journal of Sports Science & Medicine, 22(4), 123–130.
The use of "COMBI" training method for developing technical competence in 7-8-year-old football players / P. Kryzhevsky, N. Mischenko, M. Kolokoltsev [et al.] // Journal of Physical Education and Sport. – 2022. – Vol. 22, No. 1. – P. 153-159. – DOI 10.7752/jpes.2022.01019.
Tyupa, P., & Vorozheikin, A. (2021). Substantiation of the need to develop a methodology for the formation of an individual manner of conducting a competitive combat among athletes in hand-to-hand combat. Health, Physical Culture and Sports, 23(3), 49-56. Retrieved from http://hpcas.ru/article/view/10467. DOI: 10.14258/zosh(2021)3.07
Vorozheikin, A.V., & Volkov, A.P. (2021). Research of sports activity motivation at different stages of longterm training girls in the form of sport handfight. Health, Physical Culture and Sports, 21(1), 57-69. Retrieved from http://hpcas.ru/article/view/9436. DOI: 10.14258/zosh(2021)1.07
Wang, Y., & Li, X. (2023). Data-driven fan engagement: AI applications in sports marketing strategies. International Journal of Sports Marketing and Sponsorship, 24(2), 123–140.
An author should not normally publish manuscripts describing essentially the same research in multiple journals or publication venues. Such redundant publication is generally considered to constitute unethical publishing behavior, and if discovered may result in a manuscript under consideration being rejected, or a published article being retracted.
Authors of manuscripts reporting on original research should present an accurate account of the work performed, accompanied by an objective discussion of its significance. Underlying data should be represented accurately in the manuscript. The manuscript should contain sufficient detail and references to permit others to replicate the work. The fabrication of results and the making of fraudulent or knowingly inaccurate statements constitute unethical behavior and may be cause for rejection or retraction of a manuscript or published article.
