Eric Tulsky has previously shown that defensemen have very little control over their opponents on-ice shooting percentages, by demonstrating the extremely low repeatability in the statistic. Recently, Travis Yost expanded on this revolutionary information with showing that on-ice save percentage repeatability is even lower when reducing the impact of goaltender skill level differences; which makes sense when a defender with Ondrej Pavelec is going to have a higher probability of repeating a low save percentage, much like the opposite would be true with Tuukka Rask behind them. This leaves a defender’s influence on shot metrics as their primary impact in improving the team’s chance in winning the game. Tyler Dellow then pushed it one step further by stating the best method of evaluation then is using a defenseman’s impact on a team’s Corsi%.
But, there is one other primary factor: how a defender impacts the opposition. The two are not exactly one in the same, even though they are related:
The link between the two effects is definitely there, although it is not overwhelming. One major flaw to Corsi% that has always bothered me when comparing players of two separate teams is possibility of team effects. Being on a good possession team with lots of depth penalizes a players relCorsi% and benefits their Corsi% for the most part. The opposite can be true with playing on a poor possession team with very little depth. This is why most analysts take both metrics into consideration when evaluating true talent levels.
Another way to look at the difference is by placing the two statistics on the same axis, and then sort by players in descending effect on team Corsi%:
There is a noticeable trend downward and therefore some link, but two players who have equal effect over their team do not necessarily have an equal effect on their opponents. Some of this may be derived from contextual differences, such as linemates, matchups, zone deployment and team effects. However, theses nuances exist between players of similar effects on team or opponents.
So, which one then is a better evaluator of talent then?
The answer is likely the one where the conditions are most similar between players. Which one is the best remains to be seen, but I would hypothesize that probably a hybrid version may end up being the most likely option.
Until we do further research, here is the top and worst 10 (where #1 is the most extreme) defensemen with 500+ minutes cumulative between 2011-14 in different shot metrics:
Combined effects is just a player’s deltaTeamCorsi% + (-deltaOpponentCorsi%)
I have placed all defensemen in the sample on a google documents for your viewing pleasure.
5 thoughts on “Evaluating Defensemen using their effect on both team and opponent Corsi%”
Are Muzzin and Martinez really that good? Is it possible that this analysis is amplifying QoC or QoT effects quite a bit? I admit to not seeing them play that much hockey but something about this doesn’t quite site right with me.
Eventually I’d like this to take those factors into account, but currently they do not.
Muzzin probably benefits a bit from being the supporting defender in his pairing with Doughty. Martinez, from games I’ve seen him, is quite good.
I’m pretty sure from memory, Muzzin has great C.O.R.S.I.S. without Doughty, and he actually bumps Doughty’s numbers up a bit as well. One thing he does that you can see with the eye is that he jumps into the play more often than Doughty when the full-season back-to-back Stanley Cup Champion Los Angeles Kings are cycling the puck and have sustained possession down low.
I don’t think he’s better than Doughty, but I think his over aggressiveness is undervalued (and when he makes mistakes, those are overvalued).
Corsi is a name, not an acronym (although maybe you’re making a joke) haha.