Abstract
Integrating analytics into sport management curricula has become increasingly crucial as the sport industry embraces data-driven decision-making. However, many programs struggle to bridge the gap between theoretical knowledge and practical application in analytics education. This paper presents a comprehensive approach to teaching sport analytics that emphasizes hands-on experience with data using the R programming language and RStudio. The course structure and content described herein focus on leveraging the tidyverse ecosystem and National Football League (NFL) data to immerse students in conducting sport analytics. By progressing through modules on data manipulation, visualization, web scraping, and statistical analysis, students acquire both the technical skills and analytical thinking necessary for success in the evolving sport industry. The paper also addresses challenges in implementing such a course, including time limitations, access to high-quality data, and computing resources. Despite these obstacles, the proposed approach fills a critical gap in sport management education by providing students with practical, industry-relevant skills. This paper contributes to the literature on sport management education by demonstrating how sports data can be effectively utilized to teach both sport-specific analytics concepts and general data science skills, thereby better preparing graduates for the changed needs of the modern sport industry.
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Recommended Citation
Congelio, B. (2024). Integrating a Practical Sport Analytics Course into Sport Management Education. The COSMA Journal. Retrieved from https://scholarworks.bgsu.edu/cosma/vol1/iss1/7Included in
Curriculum and Instruction Commons, Educational Assessment, Evaluation, and Research Commons, Sports Management Commons, Sports Studies Commons