발간년도 : [2024]
| 논문정보 |
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| 논문명(한글) |
[Vol.19, No.3] Professional Baseball Nomination Prediction Method for High School Baseball Players Using KNN(K-Nearest Neighbors) Classification Algorithm |
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| 논문투고자 |
Young-Hwan Oh |
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| 논문내용 |
The 4th Industrial Revolution is accelerating with the development of the Internet of Things(IoT) and Big Data, and artificial intelligence(AI) and machine learning(ML) are attracting attention as core technologies. AI technology will play a key role in various industries such as healthcare, education, manufacturing, finance, and services, and will have a positive impact on society as a whole through the development of new services, improved productivity, cost reduction, and reduction of human errors. Machine learning can strengthen the learning ability of artificial intelligence systems and help them make more accurate and effective decisions by analyzing amounts of data and identifying patterns. The criteria for a good pitcher in a baseball game are as follows: Basic evaluation indicators include wins, ERA(Earned run average), strikeout rate, and the sum of hits and walks allowed per inning(WHIP). In addition to these numbers, excellent ball control, powerful fastballs, various breaking balls, strong mentality, and leadership can also be criteria for evaluating a good pitcher. In this paper, we study the KNN(K-Nearest Neighbors) supervised learning classification model method to predict professional baseball nominations of high school pitchers using Google colab. To achieve this, we use the pitcher's ERA and WHIP as training data, implement prediction techniques, and evaluate performance. Based on this, it is possible to predict which pitcher will be nominated to professional baseball. |
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