Peer-to-peer networks often use incentive policies to encourage cooperation between nodes. Such systems are generally susceptible to collusion by groups of users in order to gain unfair advantages over others. While techniques have been proposed to combat collusion, our lack of understanding of user collusion in existing systems makes evaluating such mechanisms difficult. In this paper, we report analysis and measurement results of user collusion in Maze, a large-scale peer-to-peer file sharing system with a point-based incentive policy. We search for the existence of colluding behavior by examining complete user logs of the entire system, and use a set of collusion detectors to identify several major collusion patterns. In addition, we evaluate how proposed reputation policies would perform in Maze, and identify reasons why they might miss their objectives. Our results are generally applicable to largescale peer-to-peer systems, and can help guide the design of more robust incentive schemes.