On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Forecasting in the NBA and other team sports: Network effects in action
ACM Transactions on Knowledge Discovery from Data (TKDD)
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The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.