The application of artificial: Intelligence and big data in sports industry
Keywords:
Artificial intelligence, Big data, Sports industryAbstract
In the era of advanced technology, intelligent and innovative sports events have transformed the global sports industry, reshaping how businesses operate. Traditional sports events are now enhanced with artificial intelligence, incorporating automatic tracking systems for real-time technical analysis of player movements in football, basketball, and other games. During event broadcasts, simulation technology and virtual-real integration are widely utilized, enabling live streaming on various audio-visual platforms. Consequently, leveraging big data analysis has become a critical factor in the success of professional teams and sports marketing companies. This research examines the classification and significance of big data analysis and artificial intelligence in sports by reviewing relevant literature. It aims to provide insights into the applications and importance of big data in the sports industry, highlighting its role in shaping future developments. Additionally, this study explores how the advancement of intelligent sports technology can help industry-related companies refine their strategic development, enhance Taiwan’s global presence in sports, and showcase the research and innovation capabilities of Taiwanese sports enterprises on an international scale.
References
Brzostowski, K., & Szwach, P. (2018). Data fusion in ubiquitous sports training. Computing, 10, 1–14.
Chen, J. (2024). The application and development of artificial intelligence and high technology in sports events. Highlights in Business, Economics and Management Journal, 30.
Fergurson, A. (2020). How does IoT help connect fans to smart stadiums and arenas? Losant. Retrieved from https://www.losant.com/blog/how-is-iot-being-used-for-smart-stadium-and-smart-arenas.
Gasparetto, T., & Loktionov, K. (2023). Does the video assistant referee (VAR) mitigate referee bias in professional football? PLOS ONE, 18(11).
Guardian Sport. (2021). Manchester City suspend partnership with cryptocurrency start-up 3Key. The Guardian. Retrieved from https://www.theguardian.com/football/2021/nov/19/manchester-city-suspend-partnership-with-cryptocurrency-start-up-3key.
Huang, M. L., & Li, Y. Z. (2021). Use of machine learning and deep learning to predict the outcomes of Major League Baseball matches. Applied Sciences, 11(10), 4499.
Hung, C. C., Chang, C. J., & Chen, M. Y. (2020). Technology always comes from humanity: Application of intelligent technology in the fitness industry. Physical Education Journal, 53(2), 215–233.
Hutchins, B., Li, B., & Rowe, D. (2019). Over-the-top sport: Live streaming services, changing coverage rights markets, and the growth of media sport portals. Culture and Society, 41, 975–994. https://doi.org/10.1177/0163443719857623
Jain, A., & Bhatnagar, V. (2016). Olympics big data prognostications. International Journal of Rough Sets and Data Analysis, 3(4), 32–35.
Kamisalic, A., Fister, I., Turkanovic, M., & Karakatic, S. (2018). Sensors and functionalities of non-invasive wrist-wearable devices: A review. Sensors, 18(6), 1714.
Keshtkar Langaroudi, M., & Yamaghani, M. (2019). Sports result prediction based on machine learning and computational intelligence approaches: A survey. Journal of Advances in Computer Engineering and Technology, 5(1), 27–36.
MacInnes, P. (2022). Fan group ‘appalled’ by UEFA deal with cryptocurrency company. The Guardian. Retrieved from https://www.theguardian.com/football/2022/feb/15/uefa-deal-with-cryptocurrency-company-socios-fan-group-appalled.
Melander, B. A. (2016). Smart stadiums: An illustration of how the internet of things is revolutionizing the world. Sports and Entertainment Law Journal, Arizona State University, 6(2), 349–382.
Nieborg, D. B., Poell, T., & van Dijck, J. (2022). Platforms and platformization. The SAGE Handbook of the Digital Media Economy.
Patel, D., Shah, D., & Shah, M. (2020). The intertwine of brain and body: A quantitative analysis on how big data influences the system of sports. Annals of Data Science, 7, 1–16.
Petersen-Wagner, R., & Lee Ludvigsen, J. A. (2022). Digital transformations in a platform society: A comparative analysis of European football leagues as YouTube complementors. Convergence, 13548565221132705.
Popp, N., Simmons, J. M., & Smith, D. K. (2021). Understanding sport event ticket-type preference in a forced e-ticket environment. Sport, Business and Management: An International Journal, 11(3), 287–301. https://doi.org/10.1108/SBM-08-2020-0079
Sadowski, J. (2019). When data is capital: Datafication, accumulation, and extraction. Big Data and Society, 6(1), 205395171882054.
Santomier, J., & Hogan, P. (2013). Social media and prosumerism: Implications for sport marketing research. In Handbook of Research on Sport and Business (pp. 179–201). Edward Elgar Publishing.
Socios. (2022). Socios.com | Be more than a fan. Retrieved from https://www.socios.com.
Spaaij, R., & Thiel, A. (2017). Big data: Critical questions for sport and society. European Journal for Sport and Society, 14(1), 1–4.
Uhrich, S. (2022). Sport spectator adoption of technological innovations: A behavioral reasoning analysis of fan experience apps. Sport Management Review, 25(2), 275–299. https://doi.org/10.1080/14413523.2021.1935577
Van Dijck, J. (2021). Seeing the forest for the trees: Visualizing platformization and its governance. New Media and Society, 23(9), 2801–2819.
Watkins, B., & Lewis, R. (2014). Winning with apps: A case study of the current branding strategies employed on professional sport teams’ mobile apps. International Journal of Sport Communication, 7(3), 399–416.
Zuccolotto, P., Manisera, M., & Sandri, M. (2018). Big data analytics for modeling scoring probability in basketball: The effect of shooting under high-pressure conditions. International Journal of Sports Science & Coaching, 13(4), 569–589.
Published
How to Cite
Issue
Section
Copyright (c) 2025 Khelifi Salim
This work is licensed under a Creative Commons Attribution 4.0 International License.
Allows users to: distribute and copy the article; create extracts, abstracts, and other revised versions, adaptations or derivative works of or from an article (such as a translation); include in a collective work (such as an anthology); and text or data mine the article. These uses are permitted even for commercial purposes, provided the user: gives appropriate credit to the author(s) (with a link to the formal publication through the relevant URL ID); includes a link to the license; indicates if changes were made; and does not represent the author(s) as endorsing the adaptation of the article or modify the article in such a way as to damage the authors' honor or reputation. CC BY
