Hello all Please bear with me, it's my first post in this sub-forum and I don't really have a strong background in statistics. But I was just thinking about something and was wondering if an analysis such as the one I will describe below has been performed. And if not, whether it would be useful to do it. Background So from my understanding, we have the following: Corsi (for/against) - includes shots, missed shots, blocked shots Fenwick (for/against) - includes shots, missed shots Shots (for against) - self-explanatory, only shots that hit the net Goals (for/against) Wins (based on differential between goals for and against) Analysis With the above noted, we can see that as we decrease the granularity, we are removing one variable at each step (i.e. Fenwick removes blocked shots from Corsi, shots removes missed shots from Fenwick, etc). So what I am wondering is, has there been an analysis of the efficiency percentages of these stats? See below for an example - let's say only for events FOR. Fenwick efficiency % = Fenwick FOR / Corsi FOR Shot efficiency % = Shots FOR / Fenwick FOR Shooting % = Goals FOR / Shots for (yes I know this stat is tracked already) Potential Use Once we have the efficiency percentages above, it would allow us to disaggregate between the different levels of puck possession and shooting which lead to a goal. If you look at one team, you could see how much posession they generate, what percentage of puck possession results in shots directed to the net, what percentage of those events actually hit the net, and what percentage of those go in for goals. As it relates to predicting future performance, this is where the statistics bit would come in, that I am not qualified for. I'm assuming you would have to run a regression/analysis on each of the metrics and see their correlation and consistency over time? I think the toughest part would be to determine how much the metrics correlate closest to scoring goals or keeping them out? And how much of it is pure randomness? Also I guess how they correlate with each other. But if we can figure out what "stabilised" values are for each metric (is that possible?) then I think it could be a great indicator of future performance. I'm not sure about this part. But what spurred my thought was, if you just use Corsi close 5v5 as a predictor for future results, I think it is flawed, as it doesn't give credit for quality and efficiency of scoring, just possession. If you look at Pittsburgh last year they were 18th in Corsi For %, but because they have such talented and creative players, they were more effective in making the most of the possession they have. And I think it's clear that they are better than 18th in the league.