Abstract

In this study, we examine the relationship between anxiety and athletic performance, measuring pre-game anxiety in a corpus of 12,228 tweets of 81 National Basketball Association (NBA) players using an anxiety inference algorithm, and match this data with certified NBA individual player game performance data. We found a positive relationship between pre-game anxiety and athletic performance, which was moderated by both player experience and minutes played on the court. This paper serves to demonstrate the use case for using machine learning to label publicly available micro-blogs of players which can be used to form important discrete emotions, such as pre-game anxiety, which in turn can predict athletic performance in elite sports. Based on the results, we discuss these findings and outline recommendations for athletes, teams, team leaders, coaches, and managers.

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