工商管理学毕业论文--英文翻译.doc

英文翻译资料 Can Complex Network Metrics Predict the Behavior of NBA Teams? Pedro O.S. Vaz de Melo Federal University of Minas Gerais 31270-901, Belo Horizonte Minas Gerais, Brazil olmo@dcc.ufmg.br Virgilio A.F. Almeida Federal University of Minas Gerais 31270-901, Belo Horizonte Minas Gerais, Brazil virgilio@dcc.ufmg.br Antonio A.F. Loureiro Federal University of Minas Gerais 31270-901, Belo Horizonte Minas Gerais, Brazil loureiro@dcc.ufmg.br ABSTRACT 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. Categories and Subject Descriptors H.2.8 [Information Systems]: database management— Database Applications, Data mining; G.3 [Mathematics of Computing]: Probability and Statistics—Statistical computing General Terms Theory 1. INTRODUCTION The United States National Basketball Association (NBA) was founded in 1946 and since then is well known for its efficient organization and for its high level athletes. After each game played, a lar