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Unformatted text preview: 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 [email protected] Virgilio A.F. Almeida Federal University of Minas Gerais 31270-901, Belo Horizonte Minas Gerais, Brazil [email protected] Antonio A.F. Loureiro Federal University of Minas Gerais 31270-901, Belo Horizonte Minas Gerais, Brazil [email protected] 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 rep- resent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statis- tics, 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 net- work metrics, such as clustering coeﬃcient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the im- portance 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 com- puting 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 large amount of statistical data are generated describing the performance of each player who played in the match. These statistics are used in the United States to move a betting market estimated in tens of billions Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. KDD’08, August 24–27, 2008, Las Vegas, Nevada, USA. Copyright 2008 ACM 978-1-60558-193-4/08/08 ...$5.00....
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This note was uploaded on 04/08/2010 for the course CS 420 taught by Professor Dawsonengler during the Spring '02 term at San Jose State.
- Spring '02