Hockey statistics are traditionally simple with limited analytical value, especially for individual assessment. The National Hockey League provides Play-By- Play and Time-On-Ice files for every game, recording game events and players present during them. These are combined to derive Corsi, Fenwick, and shot differentials, now commonly used in the hockey analytics world. Using regression and data visualization we examined their value in explaining win percentage and goal percentage, demonstrating the value of full-strength tied Corsi particularly. We also examined the effects of score on shot attempts, and the effects of special teams and home advantage on win percentage. Finally, we created a binary transaction database of players and Corsi events and applied association rule learning to clearly measure individual performance and player chemistry, demonstrating significant potential for use in management and coaching.