—Technological advances have given rise to increasingly diverse sports data and applications. Among them, integrated sensors, big data and cloud computing are the most intriguing ones. Their combination with social media enables users to share their sports data, expanding their network. In this study, we will go through a screening analysis model that employed advanced mathematics, and derive our solution through the approximation theory. Our goal is to find a more accurate matching model for users of a big sports database to join a virtual competition. Compared with traditional sports rating methods, our desired model can more flexibly match each piece of sports data in a practical manner.
—Sports data matching, screening analysis, approximation theory, virtual competition.
K. L. Wang is with the National Taiwan Sport University, Taoyuan City, Taiwan 33301 ROC (e-mail: firstname.lastname@example.org).
K. C. Wang was with Fu Jen Catholic University, New Taipei City, Taiwan 24205 ROC. He is now with the Commerce Development Research Institute, Taipei City, Taiwan 10665 ROC (e-mail: email@example.com).
Cite: Kai-Li Wang and Kai-Chun Wang, "A Study on Sports Data Matching for Virtual Competitions," International Journal of Social Science and Humanity vol. 6, no. 9, pp. 706-709, 2016.