How has artificial intelligence contributed to sports management? A scoping review of applications, challenges, and opportunities
Marco Vinicius Acioli da Gama, Jorge Eduardo Maciel, Yves Miranda, Carlos Augusto Mulatinho de Queiroz Pedroso
Abstract
Introduction: Artificial Intelligence (AI) is regarded as a central tool for the development of new management models. However, the integration of this technology faces barriers such as organizational technological maturity, ethical dilemmas, and the risk of excessive dependence, which may overshadow human intuition in decision-making processes. As the sports environment continues to evolve, examining how AI has been applied in research focused on sport management is essential to guide future directions and disseminate these applications to other contexts. Objective: To describe and critically review the existing literature on the use of artificial intelligence in sport management. Methods: A scoping review was conducted following the five methodological stages proposed by Arksey and O’Malley (2005). Searches were performed in the Web of Science, Scopus, and SportDiscus databases using English-language descriptors related to artificial intelligence and sport management. Data were organized in Microsoft Excel and analyzed using descriptive and thematic approaches. The VOSviewer software was used to identify patterns, occurrences, and emerging themes within the analyzed body of literature. Results: A total of 1,005 studies were identified. After screening and duplicate removal, only 13 studies were included in the final analysis. From a descriptive perspective, a growing debate on the topic has been observed since 2022, with a predominance of publications authored in China, as well as a prevalence of quantitative and predictive studies. Machine learning was the most frequently used technology, primarily aimed at predicting consumer behavior. Studies employing deep learning, neural networks, computer vision, and time series analysis were also identified. Discussion: The integration of AI reflects a paradigm shift in sport management, serving as a fundamental support for decision-making in clubs and federations, as well as in the formulation of public policies. However, the application of this tool is limited by challenges related to understanding complex human dimensions and the need for interdisciplinary collaboration. Conclusion: Artificial intelligence has become established as a strategic resource for sport management, enhancing predictive capacity and supporting decision-making processes. Nevertheless, the field still requires greater theoretical and methodological depth. This highlights the need for future studies to develop conceptual frameworks aligned with established sport management theories and to expand the empirical contexts analyzed, contributing to the sustainable and scientifically robust development of the field.
Keywords
References
Acar, O. A. (2024). Commentary: Reimagining marketing education in the age of generative AI. International journal of research in marketing, 41(3), 489-495.
Al-Buenain, A., Haouari, M., & Jacob, J. R. (2024). Predicting fan attendance at mega sports events—a machine learning approach: A case study of the fifa world cup qatar 2022. Mathematics, 12(6), 926.
Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32.
Barneva, R. P., & Hite, P. D. (2017). Information technology in sport management curricula. Journal of Educational Technology Systems, 45(3), 326-342.
Chen, J., & Xu, S. (2022). Research on the development of digital creative sports industry based on deep learning. Computational Intelligence and Neuroscience, 2022(1), 7760263.
Didas, M., & Mutajwaa, S. A. (2025). Opportunities and challenges of generative artificial intelligence models for Africa awareness: A systematic literature analysis. African Research Reports, 1(5), 341-362.
Dowling, M., Leopkey, B., & Smith, L. (2018). Governance in sport: A scoping review. Journal of Sport Management, 32(5), 438-451.
Gerke, A., Le Dean, R., & Bawack, R. (2025). Consumer adoption of artificially intelligence-supported devices for sports services: an empirical validation. International Journal of Sports Marketing and Sponsorship.
Herold, E., Singh, A., Feodoroff, B., & Breuer, C. (2024). Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response. Sport Management Review, 27(5), 793-816.
Johnson-Mann, C. N., Loftus, T. J., & Bihorac, A. (2021). Equity and artificial intelligence in surgical care. JAMA surgery, 156(6), 509-510.
Johnson, K. N., & Reyes, C. L. (2021). Exploring the implications of artificial intelligence. J. Int'l & Comp. L., 8, 315.
Karimi, J., Pashaie, S., & Golmohammadi, H. (2024). Artificial Intelligence (AI) and the Future of Sports Marketing: Exploring New Challenges and Opportunities. Journal of Advanced Sport Technology, 8(4), 65-79.
Kumar, R., Saxena, S., Kumar, V., Prabha, V., Kumar, R., & Kukreti, A. (2024). Service innovation research: a bibliometric analysis using VOSviewer. Competitiveness Review: An International Business Journal, 34(4), 736-760.
Lee, J. L., Choi, S. H., Jeong, S., & Ko, N. (2025). Generative AI in sport advertising: effects of source-message (in) congruence, model types and AI awareness. International Journal of Sports Marketing and Sponsorship.
Liang, H. (2022). An intelligent prediction for sports industry scale based on time series algorithm and deep learning. Computational Intelligence and Neuroscience, 2022(1), 9649825.
Liu, Y., Dong, B., & Zeng, X. (2022). Healthy and sustainable development of sports economy based on artificial intelligence and mental model. Frontiers in Psychology, 13, 956682.
Melnychenko, O. (2020). Is artificial intelligence ready to assess an enterprise’s financial security? Journal of Risk and Financial Management, 13(9), 191.
Momenifar, F., Pourranjbar, M., & Farazyani, F. (2024). The impact of artificial intelligence on sports marketing: applications and limitations. Journal of New Studies in Sport Management, 5(1), 1066-1080.
Munoz-Macho, A. A., Domínguez-Morales, M. J., & Sevillano-Ramos, J. L. (2024). Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review. Frontiers in Sports and Active Living, 6, 1383723.
Nalbant, K. G., & Aydın, S. (2022). Literature review on the relationship between artificial intelligence technologies with digital sports marketing and sports management. Indonesian Journal of Sport Management, 2(2), 135-143.
Nguyen, J. K., Karg, A., Valadkhani, A., & McDonald, H. (2022). Predicting individual event attendance with machine learning: a ‘step-forward’approach. Applied Economics, 54(27), 3138-3153.
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan-um aplicativo da web e móvel para revisões sistemáticas. Syst Rev, 5(1), 210-210.
Qian, T. Y., Li, W., Gong, H., Seifried, C., & Xu, C. (2025). Experience is all you need: a large language model application of fine-tuned GPT-3.5 and RoBERTa for aspect-based sentiment analysis of college football stadium reviews. Sport Management Review, 28(1), 1-25.
Sabiston, C. M., Vani, M., de Jonge, M., & Nesbitt, A. (2022). Scoping reviews and rapid reviews. International Review of Sport and Exercise Psychology, 15(1), 91-119.
Sobreiro, P., & Santos, A. (2017). Abordagem para a previsão do abandono num ginásio.
Thomas, G., Gade, R., Moeslund, T. B., Carr, P., & Hilton, A. (2017). Computer vision for sports: Current applications and research topics. Computer Vision and Image Understanding, 159, 3-18.
Tsiskaridze, R., Reinhold, K., & Jarvis, M. (2023). Innovating HRM recruitment: A comprehensive review of AI deployment. Marketing i menedžment innovacij, 14(4), 239-254.
Wu, C.-W., Shieh, M.-D., Lien, J.-J. J., Yang, J.-F., Chu, W.-T., Huang, T.-H., . . . Chen, Y.-T. (2022). Enhancing fan engagement in a 5G stadium with AI-based technologies and live streaming. IEEE Systems Journal, 16(4), 6590-6601.
Yan, B., & Xing, X. (2024). Research on the Innovative Model of Artificial Intelligence Technology Facilitating Sports Management in Colleges and Universities. Applied Mathematics and Nonlinear Sciences, 9. doi:10.2478/amns-2024-1806
Zhang, T., Zhang, M., & Luo, J. (2025). Review of the Application of Artificial Intelligence (AI) Exercise Training in Improving Cognitive Function in the Elderly Population. Quality in Sport, 38, 58019-58019.
Submitted date:
12/24/2025
Accepted date:
02/15/2026
