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Sports Ed 3.5: Establishing the value of data-driven sports development programs for universities through machine learning models

Published: 09 April 2021 Publication History

Abstract

Sports have evolved from the exposition of athletes’ pure talent to an industry that generates jobs and entertainment. In the Philippines, athletes from different Universities and Colleges are the primary source of talents in amateur and professional leagues especially in the field of volleyball and basketball. As such, schools are developing their physical and athletic programs to establish their respective competitive advantage. However, student-athletes from universities with insufficient resources are often neglected due to inadequate exposure. Furthermore, there is no framework that secures the statistical data of student-athletes which can be used for future appraisal of player's talent and athletic skills. This study aims to propose a conceptual framework of “Sports Ed 3.5” for universities and collegiate athletic associations to develop an athlete information system for sports analytics. In addition, the framework aims to define the philosophy that will govern all the stakeholders in the utilization of an athlete's information for flexible decision-making. Players’ dataset from the Philippine Basketball Association's website was retrieved to demonstrate on how an athletic information system can support the athletes, coaches, and stakeholders of sports. Supervised algorithms were used to illustrate the value of data and machine learning models in athletic development programs. Future research direction and challenges are discussed to implement the proposed “Sports Ed 3.5” model for Philippine schools and universities.

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cover image ACM Other conferences
ICIT '20: Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart City
December 2020
266 pages
ISBN:9781450388559
DOI:10.1145/3446999
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 April 2021

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ICIT 2020
ICIT 2020: IoT and Smart City
December 25 - 27, 2020
Xi'an, China

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View all
  • (2024)Stars and Spikes: Analyzing Fan Attraction to Star Players in the Philippine Professional Volleyball LeagueJournal of Interdisciplinary Perspectives10.69569/jip.2024.03122:8Online publication date: 2024
  • (2024)Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical EducationInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.33796120:1(1-16)Online publication date: 14-Feb-2024
  • (2023)Examining the Relationship of Teacher and Peer Belonging to Rural Attachment and Community Aspirations Among Diverse Rural YouthThe Rural Educator10.55533/2643-9662.134544:4(43-58)Online publication date: 1-Oct-2023

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