In this paper, we use k-means clustering to define distinct player types for each of the three positions on a National Hockey League (NHL) team and then use regression to determine a quantitative relationship between team performance and the player types identified in the clustering. Using NHL regular-season data from 2005-2010, we identify four forward types, four defensemen types, and three goalie types. Goalies tend to contribute the most to team performance, followed by forwards and then defensemen. We also show that once we account for salary cap and playing-time information, the value of different player types may become similar. Lastly, we illustrate how to use the regression results to analyze trades and their impact on team performance.