Introduction
Everyone is aware of how famous NBA players are in today’s world, they are athletes but hold the same value of celebrities. I have been a fan of basketball ever since I knew what it was, to learning it, to playing it, and to watching it, I can’t get enough of it. We all know the impact that social media has on our lives and even though we know how famous the NBA players are on social media platforms, I have always wondered how influential and well connected to each other they are and wanted to know if there is an effective and efficient way to showcase these facts and analysis. After the concepts learned in CSCC46, I found out that there is an optimal way to show this analysis in a concise, organized, professional manner.
Data & Analysis
A study was conducted by Chukwubueze Hosea Ogeleka with the aim of conveying insights into the Twitter dynamics of NBA players while investigating which NBA players are influential and how well connected the players are on Twitter. He wrote a Python script using Twitter API to generate connections between the players and a node in his project represents an NBA players Twitter account and if there is an edge from node A to node B, then there exists a relation between the two such that A follows player B on Twitter. He ended up with more than 400 nodes and 14000 edges. The data corresponds up till June 12, 2020, and once the data was gathered, he investigated the insights and results. Just as we learned in class about Degree Centrality including In-degree and Out-degree, Ogeleka did his analysis using these two measures to look at who is influential and how well connected the NBA players are.
As shown in the network above, you can see the bigger a player is, the higher the number of NBA Players that follow them. We see that Lebron James (@KingJames), Kevin Durant (@KDTrey5), Steph Curry (@StephenCurry30), and Damian Lillard (@Dame_Lillard) are some of the most followed players by their NBA colleagues which I would also expect.
Here we see that the players that have the most following consists of CJMcCollum, Dame_Lillard, JohnWall, and even though these are famous players and have high out-degrees, the names that dominate by this metric are necessarily not the most famous players of the NBA such as Lebron James and Steph Curry. One interesting analysis found by Ogeleka is that besides a few players like Lillard and Wall, the nodes with the highest out-degree tend to be younger than the nodes with the highest in-degree meaning the younger a NBA player is, the more likely they are to follow their colleagues on Twitter. He also reported 2 degrees of separation, the average shortest path is about 2 meaning on average NBA players are 2-degrees apart on Twitter and results the network in being well connected. It is amazing to see how the concepts taught in class are being applied to the connections between the sporting and social media worlds.
Furthermore, Ogeleka took a step further and factored in mutual relationships into the network, meaning he considered an edge between players A and B if they both follow each other resulting in a undirected network.
Here it is important to note that a player like jaytatum0 who did not have a high in-degree or out-degree actually has a relatively high number of mutual friends in the NBA on Twitter because a player following a lot of other players tends to have a lot more mutual friends.
Fun Fact: Here is the Betweenness Centrality of the NBA players.
Here we see Spidadmitchell and JohnWall have high betweenness centrality along with some other players.
Conclusion
I felt that this topic was important as I wanted to know the Twitter dynamics that NBA players are currently going through and to satisfy and see in detail what I had always thought about. In addition, we got to learn which NBA players have influence over their colleagues, who is well connected, and you can also get a sense of whose Twitter activity is most accessible to their colleagues. We saw many concepts learned in class in this study such as In-degree, Out-degree, Betweenness Centrality, and Shortest Path. It is great to see how networks can be obtained from events that occur in our daily lives.
References
Ogeleka, C. H. (2020, June 16). Social network analysis of current NBA players on Twitter. Medium. Retrieved October 25, 2022, from https://medium.com/analytics-vidhya/social-network-analysis-of-current-nba-players-on-twitter-b3fb9a741806