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@article{ART002496996},
author={Young Hwan Oh},
title={High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2019},
volume={14},
number={4},
pages={381-390},
doi={10.34163/jkits.2019.14.4.007}

TY - JOUR
AU - Young Hwan Oh
TI - High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method
JO - Journal of Knowledge Information Technology and Systems
PY - 2019
VL - 14
IS - 4
PB - Korea Knowledge Information Technology Society
SP - 381
EP - 390
SN - 1975-7700
AB - Recently, studies on artificial intelligence such as AlphaGo and machine learning have been actively conducted. In statistics, linear regression is a regression method that models the linear correlation between dependent variable y and one or more independent variables y. Generally, a linear regression model is established using least square method. In other words, linear regression analysis is a method of modeling the relationship between independent variables, dependent variables, and constant terms. There is a simple linear regression method that models the relationship between two variables and a multiple linear regression method based on two or more independent variables. In a baseball game, the pitcher must be good at ball speed, pitching control, pitching balance, etc., in order to get good results when dealing with batter. High school baseball player’s pitching speed is important factor to grow as excellent pitcher. Also, pitcher's ball speed is one of the important factors that determine the winning or defeat of the baseball game. In this paper, we use the Deep Learning Framework(Tensorflow) to measure ball speed of pitcher among high school baseball players and use it for athlete 's exercise and training rehabilitation. In this study, we generate training data about stride and speed of the pitcher and perform the linear regression prediction method using the gradient-descent method which is the optimization algorithm.
KW - Artificial intelligence;Machine-learning;Linear regression;Tensorflow;Gradient descent algorithm
DO - 10.34163/jkits.2019.14.4.007
ER -

Young Hwan Oh. (2019). High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method. Journal of Knowledge Information Technology and Systems, 14(4), 381-390.

Young Hwan Oh. 2019, "High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method", Journal of Knowledge Information Technology and Systems, vol.14, no.4 pp.381-390. Available from: doi:10.34163/jkits.2019.14.4.007

Young Hwan Oh "High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method" Journal of Knowledge Information Technology and Systems 14.4 pp.381-390 (2019) : 381.

Young Hwan Oh. High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method. 2019; 14(4), 381-390. Available from: doi:10.34163/jkits.2019.14.4.007

Young Hwan Oh. "High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method" Journal of Knowledge Information Technology and Systems 14, no.4 (2019) : 381-390.doi: 10.34163/jkits.2019.14.4.007

Young Hwan Oh. High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method. Journal of Knowledge Information Technology and Systems, 14(4), 381-390. doi: 10.34163/jkits.2019.14.4.007

Young Hwan Oh. High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method. Journal of Knowledge Information Technology and Systems. 2019; 14(4) 381-390. doi: 10.34163/jkits.2019.14.4.007

Young Hwan Oh. High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method. 2019; 14(4), 381-390. Available from: doi:10.34163/jkits.2019.14.4.007

Young Hwan Oh. "High-School Baseball Pitcher’s Pitching Speed Prediction Using Linear Regression Analysis Method" Journal of Knowledge Information Technology and Systems 14, no.4 (2019) : 381-390.doi: 10.34163/jkits.2019.14.4.007