• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.978-984
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• 2020

In this study, the correlation between sports records and weather data was analyzed using the big data analysis method. To this end, data was collected by API and crawling, data was processed, statistics were performed, and data visualization was performed. The subject of this study was a player who entered the regular at-bat among outfielders in the 2019 KBO League. In addition, meteorological data were analyzed by using the unpleasant index and above 70 and below 70. As a result of the study, in the various hitting indicators, which are the records that pitchers intervene, the higher the unpleasant index, the better the outfielder's record, but pitchers, walks, pitches, pitching success rates, pitches per turn, pitches per game From the records of the back, it was found that the outfielder made the pitcher difficult. It is expected that this study will help the development of the sports data industry and the performance of baseball players, baseball teams, and coaching staff.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.169-175
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• 2011

Since 2009, a new method of computing the percentage of victories is being used in the regular league of the Korean professional baseball. This method produced enormous results from the first year of application, and also had an effect on the team standings in 2010. In this paper, we have examined the effects this method had on the Korean professional baseball in 2009 and 2010. We also have discussed what the Korea Baseball Organization need to complement in using this method and suggested complementary measures.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.13-17
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• 2019

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.

• Journal of Industrial Convergence
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• v.19 no.4
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• pp.17-21
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• 2021

Recently, in the field of sports, the use of data in conducting games, planning seasons, and operating teams has increased significantly. Also, in order to develop better players, it has become necessary to use data to accurately analyze their performance. Therefore, in this study, various data about rookie players was collected and pre-processed in order to analyze and visualize their performance. Additionally, an analysis was conducted to determine at least how many opportunities should be given to foster rookie players. Then, a data analysis method was presented for nurturing athletes by using data in the field of sports. It is expected that this study will contribute to fostering rookie players by utilizing data.

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.

• Journal of Industrial Convergence
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• v.18 no.4
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• pp.61-66
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• 2020

Recently, attempts have been made to use data to operate games, seasons, and teams in professional baseball. Therefore, in this study, we collected baseball game records and analyzed the relationship between on-base rate and pitching count induction, and this was defined as the third record for non-game factors such as discomfort index, which includes the weather application data. When the discomfort index was over 75, the pitcher's induction of pitching was high, and when the discomfort index was less than 69.9, the on-base rate was high, but when the discomfort index was 70 or more and less than 75, the batter's on-base performance was the lowest. Through the results of the study, it could be inferred that the discomfort index, the batter's on-base rate, and the number of induction pitches are related, and that it is highly likely to be related to the pitcher's performance. Through this study, we could see the possibility of defining a cumulative/ratio record defined as the primary record and a saver metric defined as the secondary record, and a third, tertiary record linking data outside the game.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.99-110
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• 2016

This study examined the factors affecting the team identification of professional baseball fans. The factors were divided into the fan factor, game factors, and other factors. For the analysis, two investigators visited stadium three times in total and a total number of 297 spectators were sampled using a convenience sampling method from three baseball teams. For the fan factor, a fan who began supporting his/her team from childhood or after childhood was used as a dummy variable. The interaction effects between the fan factor and other variables were investigated to offer a stereoscopic understanding about the role of kids marketing. In addition, three game factors and four non-game factors were analyzed. The results regarding fan variable and interaction effects were obtained. Fans from their childhood have much stronger team identification, and show interaction effects with the players. Regression analysis revealed player, promotion and fan service, price, and regional connection to have positive relations with team identification. This study is especially meaningful in a sense that it has proposed positive results regarding marketing to children, and the results will contribute to both the academic field and the industry.