1. NHL CORSI STATISTICS – DATA MINING Max Schutzman ISC 110 Professor Wenderholm

2. What is Data Mining ■The process of analyzing data in the interest of finding patterns to provide useful information. ■This information can deliver methods an organization can implement to increase revenues or reduce costs. ■It is primarily used by companies with a strong consumer focus. –Retail –Communication –Financial –Marketing

3. Example of Data Mining ■Imagine a supermarket owner analyzing transaction data. ■It was discovered that three of every four consumers who bought soda all bought potato chips. –Soda Potato Chips (75%) ■The owner can now devise a strategy to increase revenues based on this pattern. ■One might be storing the products in the same location or creating a discount when both the items are purchased.

4. What are Corsi Statistics ■Advanced NHL stats that goes way beyond goals and assists, it’s almost like a measure of puck possession. ■The equation: –(Goals For + Shots on Target For +Missed Shots For + Blocked Shots Against) – (Goals Against + Shots on Target Against + Missed Shots Against + Blocked Shots For) –This is for an individual player during his time on the ice, per 60 minutes. ^^^ ■This statistic better indicates a player’s performance during the course of a game, month, or season.

5. Erik Karlsson’s Corsi Number (2014-15 Season) GoalsShots on Target Missed Shots Blocked Shots For 2.0429.011.612.9 Against 3.0429.110.316.0 Corsi Number = (2.04 + 29.0 + 11.6 + 16.0) – (3.04 + 29.1 +10.3 + 12.9) = 3.27 Karlsson had a 3.27 Corsi number in the 2014-15 season.

6. How does Corsi and Data Mining Relate ■Expert NHL analysts are hired by NHL teams to pour over data provided by Corsi statistics. ■They use the found information to construct a game plan against a particular team. –Line Combinations –Matchups ■Basically, they analyze data to find different ways to achieve success. ■That’s what data mining is, but in this case it deals with sports.