Measuring NBA Players' Mood by Mining Athlete-Generated Content

Abstract

Online athlete-generated content in social media has high potential to become the information source for both team managers and coaches to discern players' mood status and shaky performance before games. In the existing literature, either in psychology or sport analytics, there is a stream of research that investigated the relationship between athletes' mood and the individual sport performance, however, few of them discussed the causality from the social media perspective. In this study, we look deep into the Athlete-generated content (AGC) and aim to provide a more comprehensive framework to sport operators that incorporates players' social media content into their administrative decision-making process. We obtained a unique and extensive dataset of AGC for active NBA players (in the 2012-13 season) from Twitter and apply sentiment analysis technique to measure the general mood polarity of a player. The general mood was then incorporated into econometrics models to examine its effect on players' individual game performance. The results suggest that the mood of NBA player has significant effect on driving sport performance. This paper explores the possibility of using social media data to measure athletes' mood and predicting the sport performance.