• Journal of Intelligence and Information Systems
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• 제28권1호
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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.

• Information Systems Review
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• 제22권2호
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• pp.137-166
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• 2020

While South Koreans overseas travelling rate has been increased every year, domestic travelling rate has been at a standstill for several years. The purpose of this study is to analyze domestic traveling styles of Koreans according to their generations in order to provide generation-specific traveling services. For this purpose, we categorized the survey respondents into four different generations, which are Millennium (age 19~34), X generation (35~54), Baby Boomer (55~64) and senior by following the criterions of the Korea National Tourism Organization. After then, we analyze factors related to travel preparation process, the actual traveling activities and satisfaction after the travel. In this study, 16,713 data collected by the Ministry of Culture, Sports and Tourism are used. The results of this study show that Korean people tends to acquire domestic traveling information from their own or acquaintances past experiences. Also, they do not prefer the organized trip for domestic travels, thus do not buy package products a lot. In addition, natural scenery, rich in cultural heritage, and convenient accommodation are the most important determinant factors affecting the overall travel satisfaction of level for all generations. The traveling characteristics for each generation are as follows. Millennium get traveling information from the internet a lot, and more specifically, they refer portal sites and social network services (SNS) in many cases. Also, they tend to travel in summer peak season to popular destinations and pursues active traveling experiences. Generation X has similar traveling patterns with Millennium, however they major transportation method is using their own car. Also, transportation convenience and satisfactory leisure activity are important factors affecting the overall satisfaction level to Generation X. On the other hand, Baby boomer generation has a greater emphasis on appreciation of nature, visiting famous restaurants, and relaxation, rather than actively participating experiencing programs. They travel evenly in summer and spring/fall season to many different areas instead of focusing on popular tourist spots. In addition, shopping and eating delicious food are the important factors affecting the overall satisfaction level for them. Lastly, Senior generation has similar characteristics with Baby boomer in many ways, however, they travel a lot on the same day using public transportations or car rental service. They prefer spring and autumn trips rather than summer peak season, and tend to buy packaged travel products a lot compared with other generations. If these different traveling characteristics of each generation are considered for organizing and customizing tourism services, it is expected that domestic tourism satisfaction level will be ultimately increased.

• Journal of the Korean Applied Science and Technology
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• 제37권4호
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• pp.870-877
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• 2020

The purpose of this study is to compare and analyze the effects of stepping-in-place tempo and roundhouse kick types on response time. Fifteen males participated in this experiment. All have over ten years of experience and hold a forth dan(degree) black belt in taekwondo. The task is when the participants are doing stepping in place they respond to the light stimulus as fast as they can do roundhouse kick. Five different stepping in place tempos (100, 120, 140, 160, and 180 bpm) and four different types of roundhouse kick(front leg body roundhouse kick, front leg head roundhouse kick, back leg body roundhouse kick, and back leg head roundhouse kick) were used. Three measurements were taken for each of the different combinations of conditions for a total of 60 measurements. For data analysis, two-way ANOVA with repeated measures was used and pair-wise comparisons were performed using bonferroni statistics. The results show that there was significant difference interaction effect between stepping in place tempo and roundhouse kick type in the response time. And, there were significant difference in main effect of response time in accordance with stepping in place tempo and roundhouse kick type. The response time of roundhouse kick was the fastest at 160 bpm of stepping in place tempo, but there was no significant difference between 140 and 160 bpm. Front leg body roundhouse kick was the fastest. And, the response time was the fastest when front leg body roundhouse kicked at 140 bpm of stepping in place tempo. Stepping in place tempo between 140 and 160 bpm is the most effective to optimize the response time. And, More effective response time was front leg roundhouse kick as compared with back leg roundhouse kick and, body roundhouse kick as compared with head roundhouse kick. The findings in this study will provide useful information for performance improvement and will help with strategy for taekwondo competition.