Glen Miller writes about Advanced Hockey stats. His posts will appear HERE. Check back frequently to see updates.
Advanced stats and the use of statistical analysis in sports are growing everyday. What started out as a fringe movement in baseball in the nineties has taken root in other sports, including hockey. And just like in baseball, hockey has their own group of smart and dedicated fans who continue to invest time developing new stats and in finding new ways to adapt and apply some old ones.
You know it’s starting to be taken seriously when members of NHL front offices talk about advanced stats. I was once told by the Scouting Coordinator of an NHL team that advanced stats are another “tool in the toolbox,” when it comes to player evaluation. Clearly clubs are beginning to look at statistical analysis as a viable player evaluation tool.
Of course just knowing these stats exist means nothing if we don’t understand what they really mean and can offer. It’s one thing to read that Derek Stepan led the Rangers in five-on-five “Relative Corsi” last season among Blueshirts playing at least 60 games with a mark of 10.4. It’s entirely another thing to know what the heck that even means.
Sometimes the acronyms used and the stats themselves are downright confusing. Never fear though, for that is why I am here. I won’t lie and say I have spent countless hours developing and deciphering advanced stats for NHL teams but I have spent countless hours studying and understanding many of them; at least to an extent. I am here to explain what some of the more commonly used advanced stats mean, how they are arrived at and during the season try to apply them in relation to the New York Rangers.
It’s important to first-of-all understand the difference between a counting and a rate stat. A counting stat is something that can be added to (or even subtracted from in the case of +/-) during the season such as; goals, assists, goaltender wins, etc.
A rate stat is how often a player does something versus how many opportunities he’s had to do it. For example; a goaltender’s save percentage is a rate stat (calculated by dividing the number of shots he’s stopped, or saved, by the total number of shots faced). So are a netminder’s Goals Against Average (GAA), a skater’s shooting percentage and incidentally, a skater’s Relative Corsi.