• Journal of Digital Convergence
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• v.17 no.2
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• pp.463-473
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• 2019

This study aimed to investigate the relationship between soccer games of Korea vs. Japan and emotions of anti-Japan and anti-Korea, empirically. For that, this study selected 2,400 comments from Naver and 5CH where people could write their SNS comments on EAFF E-1 football championship 2017. The study results got by frequency analysis and one-way ANOVA were as follows. First, Korean showed amity with own team and hostility to the opponents, and stronger hostility toward Japan. Japanese showed hostility to own team, and it was especially strong when vs. Korea. Second, Korean showed stronger hostility toward Japan than others. Japanese showed stronger hostility to own team when vs Korea. From those results, this study could conclude that soccer games of Korea vs. Japan could be a field to express those emotions rather than effect on the emotions of anti-Korea and anti-Japan. By the empirical method of this study on the emotions of anti-Japan and anti-Korea unlike advance studies, this could receive favorable evaluation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.102-110
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• 2019

Today's professional sports teams are in a highly competitive environment with no prospects in sight. A that was considered the favorite to win a championship can suffer from a severe slump, a team that was expected to be at the bottom can achieve victory. This unexpected situation is continuing to repeat. It is also a sign of the need to look at the use of human resources and strategies in the sports industry. Accordingly, this study looks at Daegu FC soccer team, Korea's first team for citizens. A case study on human resources innovation was done. This study introduces an argument for ordinary resources (AOR) from Frey et al. (2015) for a theoretical review and a case analysis of domestic professional football. This study determines the direction of how Daegu FC should proceed. The results of the research are as follows: first, the success of harmonizing strategic and mundane resources, and second, belief in coaches. The main contribution of this research is that it presents the importance of human resources, which is one of basic tactics that sports teams should utilize. In the sports industry, where the temporality is underlined for its clear beginning and ending, teams are more likely to use diverse sports tactics to obtain superior grades, which enriches the basis of their tactics.

• Journal of Korea Entertainment Industry Association
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• v.15 no.6
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• pp.153-163
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• 2021

This study has a main purpose that closely examines the structural relationship among A Study on the Structural Relationship of Achievement Goal Propensity, Sports Confidence, and Perceived Performance Using the Mentoring System of High School Leaders. In order to achieve it, the sample from high school student participants were accumulated by using convenience sampling that chose taekwondo championship in national. The samples were 500 people. and we excluded unsuitable sheet like that were decided as untruthful one and an omission and unclear. So the final samples were 465 people. On the basis of that, The statistical techniques, spss 24.0 and amos 24.0 program have been used for the analysis as per research purpose. The statistical analysis used in the research for a data analysis includes frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structure equation model analysis. The research has drawn the following conclusions, based on the above mentioned research method and its procedures: First, The mentoring influences the achievement goal orientation. Second, The mentoring influences the sport confidence. Third, The achievement goal orientation influences the perceived performance. Fourth, The sport confidence influences the perceived performance.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.112-126
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• 2012

The theme is to analyze the time-series changes of design elements on golf courses and golf country club and its factors influencing to the changes with regard to 81-membership country club located in the Capital Area of Seoul, created in the periods from 1964 until 2011. The research methods is to analyze the time-series changes of environmental factors influencing to the changes of the elements on the golf course by book review, and to compare and speculate the results of the analysis on the environmental factors with the time-series changes on the golf course elements of statistical analysis as like regression analysis. The research results were as follows. The first, the environmental factors influencing to the change of golf course elements were analyzed as the five elements of the golf-related policies and regulations, the economy, the numbers of golfers, PGA and KPGA golf tournaments, the golf instruments. The second, the type and scale of the location of the golf course were showing trends of transformation from flat ground to mountainous one and from small scale to large one. The third, it was analyzed that he golf course elements as like the sizes, length, numbers of golf course elements as like fairway, green, bunker, teeing ground and pond were influenced by the law and regulation, the increase of amateur golfers, the promotion of techniques of golfers, the increase of the chance of foreign course designer's involvement to domestic market through the opening of PGA and KPGA tournaments etc. The fourth, the promotion of golf instruments and the flying distance were the factors influencing to the numbers of bunker, the lengths of holes. The fifth, it was revealed that the trends of increase of sizes of ponds influenced by enacting environmenal friendly laws and regulations, considering of landscaping, reflecting of design trends followed by the opening of PGA tournaments. Finally, it was proposed that the further research would be introduced with regard to the qualitative analysis onto the changes and influential factors of golf course design.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.