Machine Learning Models for Predictive Fan Engagement in Sports Events

Authors

  • Shyamakrishna Siddharth Chamarthy Scholar, Columbia University, Sakthinagar 2nd Ave, Nolambur, Chennai - 600095,
  • Murali Mohana Krishna Dandu Scholar, Texas Tech University, San Jose, CA 95134, murali.
  • Raja Kumar Kolli Scholar, Wright State University, CO, 80104, USA,
  • Dr Satendra Pal Singh Ex-Dean, Gurukul Kangri University, Haridwar, Uttarakhand
  • Prof.(Dr) Punit Goel Research Supervisor , Maharaja Agrasen Himalayan Garhwal University, Uttarakhand,
  • Om Goel Independent Researcher, Abes Engineering College Ghaziabad

DOI:

https://doi.org/10.36676/jrps.v11.i4.1582

Keywords:

Machine learning, fan engagement, sports events, predictive models, data analysis, marketing strategies, fan behavior

Abstract

In the realm of sports, fan engagement has become a pivotal element for enhancing the overall experience and maximizing revenue opportunities. This paper explores the application of machine learning models to predict fan engagement during sports events, offering insights that can inform marketing strategies, game-day operations, and fan interaction initiatives. Utilizing historical data on fan behavior, attendance, social media interactions, and game statistics, various machine learning algorithms—such as regression analysis, decision trees, and neural networks—are employed to develop predictive models. These models are designed to identify patterns in fan engagement and forecast future behaviors, allowing teams and event organizers to tailor their marketing efforts and improve fan experiences.

Additionally, the study highlights the importance of real-time data analysis, enabling stakeholders to make informed decisions during events. By integrating these predictive models with live data feeds, sports organizations can dynamically adjust their engagement strategies, ensuring they resonate with fans' preferences and expectations. The findings underscore the potential of machine learning to revolutionize fan engagement, transforming passive spectators into active participants. Ultimately, this research contributes to a deeper understanding of how technology can enhance the sports experience, fostering loyalty and increasing overall satisfaction among fans. The implications of these models extend beyond individual teams, providing a framework that can be adapted across various sports disciplines, ultimately enriching the landscape of sports marketing and fan interaction.

References

Achen, R. M., & Brann, D. (2016). The Role of Social Media in Sports: How Social Media Shapes Fan Engagement and Interaction. Journal of Sports Marketing & Sponsorship, 21(3), 32-45.

Baker, T. A., & Green, C. M. (2016). Using Predictive Analytics to Forecast Attendance in Sports Events. International Journal of Sports Management and Marketing, 16(1-2), 65-82.

Filo, K., & N. A. (2015). A Comprehensive Approach to Understanding Fan Loyalty in Sport. Sport Management Review, 18(1), 1-15. DOI: https://doi.org/10.1016/j.smr.2014.11.001

García, A., & Sánchez, J. (2020). Big Data and Machine Learning in Sports: A Review. Journal of Sports Analytics, 6(2), 65-85.

Hamari, J., & Sjöblom, M. (2017). The Relationship Between Esports Engagement and the Consumption of Video Game Content. Computers in Human Behavior, 68, 213-222.

Huang, Y., & Johnson, R. (2017). Enhancing the Game-Day Experience Through Real-Time Feedback: An Examination of Fan Satisfaction. Journal of Sport Management, 31(2), 121-134.

Klein, M. R., & Smith, J. (2020). Personalized Marketing in Sports: The Impact on Fan Engagement and Attendance. Sports Marketing Quarterly, 29(3), 134-145.

Koo, D. M., & Kim, J. (2018). Predicting Fan Engagement in Sports Using Machine Learning Techniques: An Empirical Study. International Journal of Sports Science & Coaching, 13(4), 675-688.

Schultz, B. J., & Kauffman, R. J. (2019). Analyzing Consumer Behavior in Sports: Machine Learning Applications in Marketing Strategies. Journal of Business Research, 103, 56-67.

Stier, J., & Welling, J. (2019). The Integration of Machine Learning into Customer Relationship Management in Sports Organizations. Journal of Sports Analytics, 5(3), 187-202.

Teng, Y., & Hsu, Y. (2020). The Impact of Game-Day Experience on Fan Engagement: A Study of Professional Sports Teams. Sport Management Review, 23(2), 264-275.

Wiggins, B. E., & Lough, N. (2018). Data Analytics and Fan Engagement: How Sports Organizations Can Benefit. Journal of Applied Sport Management, 10(3), 34-47.

Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.

Singh, S. P. & Goel, P., (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127-130.

Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh DOI: https://doi.org/10.32804/IRJMSH

Goel, P. (2016). Corporate world and gender discrimination. International Journal of Trends in Commerce and Economics, 3(6). Adhunik Institute of Productivity Management and Research, Ghaziabad.

Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf

"Effective Strategies for Building Parallel and Distributed Systems", International Journal of Novel Research and Development, ISSN:2456-4184, Vol.5, Issue 1, page no.23-42, January-2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf

"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.96-108, September-2020, https://www.jetir.org/papers/JETIR2009478.pdf

Venkata Ramanaiah Chintha, Priyanshi, Prof.(Dr) Sangeet Vashishtha, "5G Networks: Optimization of Massive MIMO", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.389-406, February-2020. (http://www.ijrar.org/IJRAR19S1815.pdf )

Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491 https://www.ijrar.org/papers/IJRAR19D5684.pdf

Sumit Shekhar, SHALU JAIN, DR. POORNIMA TYAGI, "Advanced Strategies for Cloud Security and Compliance: A Comparative Study", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf )

"Comparative Analysis OF GRPC VS. ZeroMQ for Fast Communication", International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February-2020. (http://www.jetir.org/papers/JETIR2002540.pdf )

Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf

"Effective Strategies for Building Parallel and Distributed Systems". International Journal of Novel Research and Development, Vol.5, Issue 1, page no.23-42, January 2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf

"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 9, page no.96-108, September 2020. https://www.jetir.org/papers/JETIR2009478.pdf

Venkata Ramanaiah Chintha, Priyanshi, & Prof.(Dr) Sangeet Vashishtha (2020). "5G Networks: Optimization of Massive MIMO". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.389-406, February 2020. (http://www.ijrar.org/IJRAR19S1815.pdf)

Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491. https://www.ijrar.org/papers/IJRAR19D5684.pdf

Sumit Shekhar, Shalu Jain, & Dr. Poornima Tyagi. "Advanced Strategies for Cloud Security and Compliance: A Comparative Study". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf)

"Comparative Analysis of GRPC vs. ZeroMQ for Fast Communication". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February 2020. (http://www.jetir.org/papers/JETIR2002540.pdf)

Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. Available at: http://www.ijcspub/papers/IJCSP20B1006.pdf

Downloads

Published

31-12-2020

How to Cite

Shyamakrishna Siddharth Chamarthy, Murali Mohana Krishna Dandu, Raja Kumar Kolli, Dr Satendra Pal Singh, Prof.(Dr) Punit Goel, & Om Goel. (2020). Machine Learning Models for Predictive Fan Engagement in Sports Events. International Journal for Research Publication and Seminar, 11(4), 280–301. https://doi.org/10.36676/jrps.v11.i4.1582

Most read articles by the same author(s)

1 2 > >>