2nd African International Conference on Industrial Engineering and Operations Management

“STEAMS” Methodology of NBA Draft Player Position

Mason Chen
Publisher: IEOM Society International
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Track: High School STEM Poster Competition
Abstract

This paper adopts STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology.  The objectives of this paper are to introduce the benefits of integrating all 6 “STEAMS” elements, especially living in the Big Data World. NBA Draft Position case study was demonstrated to present this novel “STEAMS” concept as compared to current “STEM” or” STEAM” approach.  There are three core visions of this “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math”, and (3) integrate all six “STEAMS” elements.  Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of “Sports” Science Research and “Math” algorithms.  Separating the “Statistics” element can conduct more effective risk management and draw practical conclusions.  Due to the previous two benefits, integrating all 6 “STEAMS” elements is becoming a natural critical thinking way for most scientists and engineers striving in the modern Big Data era. Several techniques are used to help determine the NBA Player position and identify the similar NBA players for benchmarking objective. It’s critical and urgent for educators and teachers to migrate from their traditional STEM approach to the new “STEAMS” approach to educate our next generations in their early school learning and career development.

Published in: 2nd African International Conference on Industrial Engineering and Operations Management, Harare, Zimbabwe

Publisher: IEOM Society International
Date of Conference: December 7-10, 2020

ISBN: 978-1-7923-6123-4
ISSN/E-ISSN: 2169-8767