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 tries to take the individual player's puck-possession successes and failures (passes, dekes, puck-battles, etc) to get to the same point.
This is a first attempt at seeing what impact Corsi Relative Quality of Competition numbers would have on player's even-strength risk/reward ratings.
Habs Eye on the Prize's Andrew Berkshire explains Corsi as follows:
Corsi - is a +/- statistic for a player/team that measures all shot attempts, including misses and blocked shots, directed for and against the team/player being measured per 60 minutes.
Andrew explains Corsi Relative Quality of Competition as follows:
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
Silversevens.com explains the calculation for Corsi Relative Quality of Competition as follows:
Corsi Rel QoC is the weighted Relative Corsi Number of a player's opponents.
For example, if a player plays 30% of the time against five players with a relative corsi of +1.5, 35% of the time against five players with a relative corsi number of +0.2, and 35% of the time against five players with a relative corsi number of -2.1 then:
Corsi Rel QoC = (0.3 * 5 * 1.5) + (0.35 * 5 * 0.2) + (0.35 * (5 * (-2.1)) = -1.075
The top graph is a visual representation of each Montreal Canadiens even-strength risk/reward rating as calculated using my system, while the graph below that is a visual representation of each player's ES risk/reward rating after including Corsi relative quality of competition into the calculation.
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. Because of 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.
Quality of competition had a big impact on 5 Montreal Canadiens defensemen. Two of those players saw their even-strength risk/reward rating improve substantially, while 3 others saw it drop.
Josh Gorges and PK Subban faced the highest quality of competition. As such, Subban saw his risk/reward rating go from to to 1.78 to 1.92. Josh Gorges also saw his ES rating improve substantially; going from 1.39 to 1.67.
Tomas Kaberle, Frederic St. Denis, and Yannick Weber were all hurt by the inclusion of quality of competition into their risk/reward ratings. Kaberle's rating went from 1.57 to 1.46. This dropped him from third among Habs defensemen to fourth. St. Denis rating went from 1.37 to 1.21, while Weber's dropped from 1.13 to 0.98.
Quality of competition had a substantial impact on 8 Montreal Canadiens forwards. Six of those players saw their even-strength risk/reward ratings improve substantially, while 2 others saw them drop.
Tomas Plekanec's rating went from an already impressive 1.24 to 1.42. Brian Gionta's rating went from 1.11 to 1.26. Rene Bourque's rating went from 0.67 to 0.80. Travis Moen's number went from 0.88 to 1.02. Ryan White's rating improved from 0.97 to an impressive 1.24, and Lars Eller's rating jumped from a 1.46 to 1.57. Eller had the top rating among forwards prior to the inclusion of quality of competition numbers, as well as after their inclusion.
Petteri Nokelainen and Brad Staubitz both saw their risk/reward ratings drop the most. The inclusion of Quality of competition saw Nokelainen's rating go from 0.90 to 0.69, while Staubitz's rating dropped from 0.50 to 0.34.
Keep in mind this is only an experiment. I will continue to research other forms of advanced stats in the attempt to find the most realistic and representative results possible.