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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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A Study on Prediction of Baseball Game Based on Linear Regression

인공지능연구 / Korean Journal of Artificial Intelligence, (E)2508-7894
2019, v.7 no.2, pp.13-17
https://doi.org/https://doi.org/10.24225/kjai.2019.7.2.13
LEE, Kwang-Keun (Department of Social Welfare, Kyungdong University)
HWANG, Seung-Ho (Department of Medical IT Marketing, Eulji University)
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Abstract

Currently, the sports market continues to grow every year, and among them, professional baseball's entry income is larger than the rest of the professional league. In sports, strategies are used differently in different situations, and the analysis is based on data to decide which direction to implement. There is a part that a person misses in an analysis, and there is a possibility of a false analysis by subjective judgment. So, if this data analysis is done through artificial intelligence, the objective analysis is possible, and the strategy can be more rationalized, which helps to win the game. The most popular baseball to be applied to artificial intelligence to analyze athletes' strengths and weaknesses and then efficiently establish strategies to ease the competition. The data applied to the experiment were provided on the KBO official website, and the algorithms for forecasting applied linear regression. The results showed that the accuracy was 87%, and the standard error was ±5. Although the results of the experiment were not enough data, it would be possible to effectively use baseball strategies and predict the results of the game if the amount of data and regular data can be applied in the future.

keywords
Machine Learning, Linear Regression, Baseball Game

인공지능연구