Previous sport management research has demonstrated the positive relationship between political skill and personal career outcomes, but research addressing the question of how leader social effectiveness (i.e. political skill) influences the commitment and satisfaction of subordinates is lacking. This study sought to determine if leader (athletic director) political skill influences subordinate (head coach) evaluations of leader effectiveness, in turn influencing subordinate job satisfaction and commitment. Surveys were completed by interscholastic athletic directors ( n = 250) and representative subsets of head coaches ( n = 806) in the United States. Structural equation modeling was used to analyze the data. Political skill was shown to have a positive impact on evaluations of leader effectiveness. Leader effectiveness also acts as a mediator between political skill and employee job satisfaction and affective organizational commitment. Thus, political skill appears to be an important contributor to subordinate perceptions of leadership effectiveness, job satisfaction, and organizational commitment.
The popularity of analytical research specializing in forecasting of March Madness saw an increase in the past decades. While the influence of nongame statistics on the game outcome has become a great interest in sports analytics, little research has focused on situational factors in predicting sports tournament outcomes. Therefore, this study is to examine the use of different machine learning algorithms, including artificial neural network (ANN), k‐nearest neighbors (kNN), support vector machine (SVM), logistic regression, and random forest (RF), to forecast the winning in a matchup between any two given teams during the March Madness tournaments. Our data include 1370 observations with 685 tournament games from 2006 to 2007 to 2016 to 2017 seasons. The results show that neural networks outperformed all other classifiers (67% of accuracy), followed by SVM (65%), kNN (63%), logistic regression (63%), and RF (61%).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.