발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.4] High School Baseball Player's BA(batting average) and OPS(on-base plus slugging) Correlation Prediction Model Using Keras Supervised Learning |
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| 논문투고자 |
Young-Hwan Oh |
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| 논문내용 |
Recently, as an opportunity to take a new leap forward in the great change of the 4th industrial revolution, we are doing our best to develop artificial intelligence, which plays the role of the brain of the Internet of Things (IoT) and big data. AI is already being used in various fields such as healthcare, finance, manufacturing and transportation and is expected to infiltrating more deeply into our life and science in the future. Machine learning, which is the basis of AI, uses statistical and logical algorithms of mathematics to learn, analyze and predict using massive amounts of various data. Batting ability(of baseball) is one of the most important one for a baseball player. In particular, among the statistical data of batting average, slugging percentage, and OPS(On-base Plus Slugging) are the most important criteria for a baseball-fielder. The run-scoring ability is to run the base after hitting the ball, that is determined by the baseball player's speed, agility and baseball wits. In addition, defensive ability is the one to catch and throw the ball in the case of a in-fielder or out-fielder. Defensive ability is determined by a baseball player's dexterity, accuracy and judgment. In addition, teamwork minds are important because baseball is a team sport. Theses are determined by a baseball player's coordination, communication and leadership. In this paper, we study the Keras supervised learning prediction model method for predicting high school hitters' professional baseball nominations using Tensorflow. To this end, batters' batting average and OPS are used as learning data, and prediction model are implemented and performance is evaluated. Based on this one, it is possible to predict which hitters will be drafted in professional baseball player. |
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