# Relative importance of Corsi and the other 42%

Today apparently there was some discussion about the Avalanche and their non-interest in hockey analytics. In that discussion Corey Pronman wrote the following tweet:

I have seen the above logic from time to time. I think it dates back to something Gabe Desjardins wrote many years ago. I find the logic very odd though. Let me explain.

Let’s assume that the numbers are true. According to my math, that leaves 25% unaccounted for. I don’t really consider 25% insignificant but it is actually more significant than that.

Luck, or I prefer the term randomness, is a component that is outside the control of a general manager, a coach, a player or anyone else that could potentially influence the outcome of the game. Thus it is pointless to bring luck into the equation.  All that management and players for an NHL team really needs to worry about is what they can control. That is the non-luck fraction of winning or the other 60%.

Now, if Corsi is 35% of winning overall then it accounts for 58% of the controllable aspect of winning. That leaves 42% of what is controllable unaccounted for. If I were an owner of an NHL team, or an owner of a business of any kind, and my general manager told me that we are going to largely ignore 42% of  the controllable factors that lead to positive outcomes I’d be firing that general manager on the spot. It simply isn’t acceptable business practice to ignore 42% of what is within your control that produces good outcomes.

Here is the the real kicker though. The estimate that Corsi explains 35% of wins is based on historical data (and probably from several years ago). It does not necessarily mean it will be that way in the future. As teams become more aware of Corsi and possession it is certainly conceivable that the disparity across teams in corsi shrinks and thus the importance of Corsi as a differentiator among teams and as predictor of winning shrinks. If teams switch focus to Corsi those other factors might be the great differentiator of team talent and be the better predictor of success. It is easy to hop on the Corsi bandwagon now. The forward thinking teams and forward thinking hockey analytics researchers are those researching that other 42% to some significant degree.

Now, if you are a hockey analytics researcher raise your hand if you have spent ~60% of your research and analysis time on Corsi related issues and ~40% of your research time on non-Corsi related issues. If you are honest I suspect very few of you have raised your hand. The honest truth is those other factors have been unfairly downplayed and in my opinion that is very unfortunate.

1. dan says:

Good post David. You know I am on the side of shot quality’s value, however, luck or randomness is actually clioser to 70% of an outcome. Here’s how It can be shown.
This approach has been adapted from bburke’s e adv nfl stats website. The best teams win at most 65% of games also the best theoectical predictive models top out at. I will provide links. We know that randomness is solit equally since it is not skill based.Therefore to get around a 63% winbest skilled tm must hv ~30% skill and 1/2 of the luck component. or 70/2 which is 30+35 or 65%.
This also passes the rye test. I counted ~300 goals in games llast yr and morecthan 2 out of 3 were primarly the result of luck…bad bouncec etc etc. I have made this point before and no ine has refuted it .Perhaps someone will this time? – So Corsi’s value is fighting for the 35%. I like Alan ryders old breakdown goaltending 15% defense 40% offense 45%. If we div. by 3. We get goaltending 5% skill.(about the observed skill diff we see-btw)..Off. 15% skill and def. ~13%. So the next question is how much of off is Corsi and how much of def skill is from corsi….Not sure about this answer yet as seperating D skill very tough.. Dan

2. ed_finnerty says:

I think that your observation that Corsi will decline in predictive power over time is a good one. We are probably getting close to the top of the S curve on its value growth already.

As you say, we will need to find new measures that indicate an edge.