## Sports Magazine

By Kicks @Chrisboucher73
No single system can produce one number that accurately reflects all aspects of a player's performance. But, by combining multiple systems, and including all possible tangible aspects of an individual player's performance, we can push advanced stats in the right direction; a direction that provides insight as well as context. I've tracked every preseason, regular season and playoff game the Montreal Canadiens have played since December 31st, 2010
In a constant effort to provide as much context as possible to my data-generated player tracking system, I've looked into more traditional "advanced-stats" metrics.
Fenwick and Corsi numbers take a global picture of what occurs when a player is on the ice (shots attempted for and against), and uses those numbers to rate individual players. My system tracks individual player's puck-possession successes and failures (passes, dekes, blocked passes, shots, etc) with a similar goal in mind.
The graph is a visual representation of each Montreal Canadiens player's even-strength risk/reward rating as calculated using my system, while the other bar is a visual representation of each player's ES risk/reward rating after including Corsi relative quality of competition into the calculation. Relative Corsi quality of competition - a measure of the average relative Corsi score of the opponents a player faces, weighted against the ice time played against each player
Even-strength rating represents how many more successful events than failed events a player produced per-minute of even-strength ice-time. Over 70 specific puck-possession plays are tracked during a game; producing upwards of 1200 separate points of data per-team, per-game. Most events are tracked as either successful or failed.
In order for the calculation to work, and for the numbers to make sense, I've divided each Canadiens player's Corsi Rel QoC number by 5. Not only does this help minimize the impact on the original number, it also relates better to the reality of my system; Corsi uses team numbers while a player is on the ice. In order to accomplish this it multiplies the value by 5 to represent the five skaters on the ice. My system tracks individual events taking place against individual players. As such, it does not need to by multiplied by 5.
The quality of competition numbers used in this calculation can be found here. Special thanks to ExtraSkater.com for the incredible work they do. The ratings shown here represent each players ratings from both regular season and playoff games.

Josh Gorges, Tomas Plekanec, and Brian Gionta faced the highest relative quality of competition. As such, their even-strength risk/reward ratings improved the most with the qualcomp adjustment. Other defensemen with improved risk/reward ratings include PK Subban, Andrei Markov, and Alexei Emelin.  Tomas Plekanec and David Desharnais have faced the toughest competition among Montreal centres. This is apparent in their adjusted risk/reward. Plekanec's adjusted rating is particularly impressive, and does a great job of relating the high quality of competition number 14 faces each game. Among wingers, Brendan Gallagher, Brian Gionta, and Max Pacioretty have the highest adjusted risk/reward ratings.That said, it is actually Gionta's rating that improves the most when quality of competition is taken into account.George Parros has the lowest even-strength risk/reward rating both before and after the quality of competition adjustment. Among wingers, Brandon Prust and Dale Weise see their ratings drop significantly after the adjustment. Context is an important aspect of player evaluation. The inclusion of quality of competition numbers into the calculation of a player's risk/reward rating helps produce just that context.