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 imple...
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.
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.
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제안 방법
The predictive model applied Azure Machine Learning Studio's linear regression algorithm (Kang Min-soo, 2018). The reason for this is that the results of the analysis were easy to understand through visualization functions such as statistics and graphs. The results of the analysis were estimated at 30% by 70% in order to avoid overconformity, a phenomenon where learning is so well done in the analysis process, but less accurate in the test data or actual application, the total result accuracy is 87%, and sampling error is ±5%.
성능/효과
The results of the analysis were estimated at 30% by 70% in order to avoid overconformity, a phenomenon where learning is so well done in the analysis process, but less accurate in the test data or actual application, the total result accuracy is 87%, and sampling error is ±5%.
billion-a-year salary 3. starting pitchers 4. For more than 10 years, left-handed pitchers have been set at five conditions The reason why we set the conditions is that we set up veterans to exclude variables outside the game as much as possible. The reason is that rookie players are influenced by environmental or psychological variables, but because veteran players are more experienced and show consistent performance by adjusting the variables to the fullest extent possible.
참고문헌 (11)
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KBO (2019). Retrieved May 22, 2019, from https://www.koreabaseball.com/Default.aspx
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Kim, W. (2017). Retrieved May 22, 2019, from https://news.joins.com/article/21205179 JoongAng IIbo
Oh, Y.H., Kim, H.H., Yoon, J.S., & Lee, J.S. (2014). A Study on the Establishment of the Forecasting Model for Korean Professional Baseball by Using Data Manning. Journal of Industrial Engineering, 40(1), 8-17.
Park, D.S., & Kim, H.J. (2016). A Study on the Data Reduction Methods through the Analysis of Baseball Data. Journal of the Korea Telecommunications Society Congress,12, 244-245
Shin, G.S., & Lee H.C. (2014). The Winning Factors and Pattern Analysis of Korean Professional Baseball by Using the R Program, Journal of the Korea Industrial Engineering Association Chungye Joint Academic Contest, 11, 819-824
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