• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.117-129
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• 2023

Organizations must seek change in order to adapt to environmental changes and achieve better performance. However, despite this obvious statement, empirical analysis has been almost non-existent due to the difficulty of manipulating organizational performance or change. In this study, we overcame these limitations and analyzed the causes and effects of organizational change by assuming a professional sports team as a venture company, which is relatively easy to objectively measure and evaluate organizational change or performance. We systematically collected and preprocessed traditional and advanced metrics of National Basketball Association (NBA) statistics along with preprocessed trade data from eight years of regular seasons (2014~2015-2021~2022) to analyze our research model. Assessment of process macro model 7 derives the following empirical result. The results of the empirical analysis depict that NBA teams with low organizational performance in the previous season are more likely to make organizational changes through player trades to improve performance. Into the bargain player trades mediate the static relationship between the winning percentage in the previous season and the winning percentage in the current season. However, the indirect effect of a team's previous season's performance on player trades appears to vary depending on the current situations and context of each NBA team. Teams that made the playoffs in the previous season tend to make fewer trades than teams that did not and the previous season's performance is highly correlated with the current season's performance. On the other hand, teams that did not make the playoffs in the previous season tend to make a relatively larger amount of player trades in total, and the mediating effect of trades vanishes in this case. In other words, teams that did not make the playoffs in the previous season experience a larger change in performance due to trades than teams that made the playoffs, even if they achieved the same winning percentage. This empirical analysis of the inverse relationship between organizational change and the performance of professional sports teams has both theoretical and practical implications in the field of sports industry and management by analyzing the fundamentals of organizational change and the performance of professional sports teams.

• The Journal of Bigdata
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• v.7 no.1
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• pp.49-61
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• 2022

In sports, the proportion of data analysis in team management such as team strategy planning and marketing is increasing. In KBO(Korea Baseball Organization) league, in particular, plans such as recruiting players and fostering players are established to devise team strategies for the next year, such as FA and trade, at the end of a season. For these reasons, it is very important to predict players' performance for the next year. In this study, the target was limited to only the batter and tried to find out how to predict whether the performance of the next year will improve. As a standard record for rising and falling, OPS(On-Base Plus Slugging), which is easy to calculate and has a high relationship with team score, was used. In this study, 40 years of regular season data from 1982 to 2021 were used as data, and 11 machine learning classification models were used as experimental methods. Predicting the rise and fall of OPS, RBF SVM, Neural Net, Gaussian Process, and AdaBoost were more accurate than other classification models, and age did not significantly affect accuracy.

• Exercise Science
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• v.21 no.2
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• pp.183-190
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• 2012

This study is to evaluate the availability of cardiorespiratory fitness measurement by 20 m shuttle run test based upon energy contribution rates of elite athletes in different sports type. Sixty-seven elite athletes attending K national university participated in this study. They were divided by three groups based upon sports type, composed of Anaerobic Group (sprint, jumps, weightlifting, throw; n=35), Aerobic Group (medium-long distance; n=9), and Combat Sport Group (judo; n=23). 20 m shuttle run test was conducted by Leger et al.(1982) method and calculating acceleration using measured shuttle run repetitions was conducted by Brewer et al.(1988) method. To test the usefulness of VO₂max, graded exercise treadmill test was conducted and standing long jump and 50 m run were measured as power fitness factors. Z-jump was used for measuring power, agility, and muscular endurance. Standing long jump and 50 m run of Anaerobic Group (AnG) was significantly higher than that of Aerobic Group (AeG) and Combat Sport Group (CG) (p<0.05). However, Z-jump of CG was significantly higher than that of AnG and AeG(p<.05). There was a higher correlation of 20 m shuttle run test and VO₂max in AnG(r= 0.577, p<.0001) and CG(r= 0.760, p<.0001). Otherwise, there was a low correlation of 20 m shuttle run test and VO₂max in AeG. There was no significant group difference to test the availability of 20 m shuttle run test and there was a reduced error when converting 20 m shuttle run results into VO₂max. This study examined the usefulness of 20 m shuttle run test by converting 20 m shuttle run repetition results into VO₂max calculation, which showed reduced error. Therefore, this study confirmed that it would be needed to convert 20 m shuttle run results into VO₂max for universal and practical use in the field without dividing sports type.