In this thesis, we aimed to obtain a better understanding of the public and private Twitter/Xpersonas of a professional basketball team (the 2022-2023 NBA Spurs). We used the data to form a social network and utilized statistical network analysis methodology to study our data. We studied this network to understand how an average NBA team’s players represent themselves to the public as opposed to studying the top teams/players in the NBA. We found that stochastic block models group the players correctly into two groups but mishandles the classification of some players due to their network structure. Analysis with Exponential- family random graph models (ERGMs) reinforce the idea that players often tend to keep their social media presence professional. Players also rated higher on an ESPN metric of performance tended to be more involved with social media.