발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.4] A Study on Pro-baseball Player Nomination Prediction for High School Baseball Hitter Using K-Means Clustering Method |
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| 논문투고자 |
Young-Hwan Oh |
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| 논문내용 |
Recently, IT/BT companies, universities, and research institutes of government have developed Artificial Intelligence(AI) that plays a role as the brain of the Internet of Things (IoT) and Big Data as an opportunity for a new leap forward in the 4th industrial revolution and then they are doing our best to research and develop. A key method of Artificial Intelligence is machine learning, which uses large amounts of data to determine the rules of a cognitive process based on human experience, and finds out rules by itself through learning and reasoning, perceptual ability and natural language analysis ability. Machine learning learns, analyzes, and predicts various data using statistical and logical algorithms of mathematics. It is being actively used in various fields such as aviation, medical care, finance and marketing, transportation and logistics, and sports industry. In order to grow as a good hitter(field player) in baseball, they must have excellent strength, speed, agility, and a sense of play. And based on this, indicators(baseball statistics such as batting average(BA), slugging percentage(SLG), on-base percentage(OBP), and OPS must be excellent. We design and implement the K-Means clustering method to predict the professional baseball nominations of high school hitters using Tensorflow in this paper. We use the batter's slugging percentage and on-base percentage as learning data, implement prediction method, and evaluate performance. Based on this, We can predict whether a specific batter will be nominated or not for professional baseball. |
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