One of the best tools to come out of the hockey analytics community recently is dCorsi. dCorsi is the difference between how a player performed and how a player was expected to perform.
The expected value is determined by a series of variables regressed by @SteveBurtch , including position, TOI/60, quality of teammate and competition, zone starts, and team. You can read more in Burtch’s write-up on the concept here .
Knowing what to expect from a player is very important when judging how a player performed. If a player drove possession, but played the easiest minutes on the team, it’s possible that the player is not achieving what he should. On the other hand, if a player doesn’t stand out as a possession driver, but he plays the hardest minutes on the team, he could be outperforming expectations.
dCorsi can be a bit of a challenge to understand, since it can go against what people generally believe about players. Shea Weber, Ryan Suter, and Anton Stralman are instances where the data conflicts with the popular understanding of the players.
I put together the visualization below to try to show how dCorsi can be used in player evaluation.
A few notes:
- The black bar indicates the expectation for the player
- The colored bars indicate how the player actually performed
- In Corsi For, if the player’s bar is above the black bar, the player exceeded expectations
- In Corsi Against, if the player’s bar is below the black bar, the player exceeded expectations.
- Green is good, red is bad
- The player bars are sized by the deviaton from expectation, good or bad. Big bar= big impact. Small bar= small impact
- You can use the third tab to add your own players to the list, and compare anyone you wish.
The data used in this post is from War-On-Ice