NHL: An introduction to advanced stats

The acceptance of advanced stats in the NHL has been one of the biggest stories of the 2014 NHL off season. The Toronto Maple Leafs and the Edmonton Oilers both started advanced stats departments in order to better analyze player value and to evaluate future talent.

Not everyone is familiar with advanced stats, so misnomers like “Money Puck” have been thrown around when describing the NHL advanced stats movement. This of course is in reference to the book and movie “Money Ball”, which first put advanced stats into the spotlight.

The reason this is a misnomer is because “Money Ball” is trying to get the highest talent for the lowest price. Due to the salary inequity in baseball, small market teams cannot compete with bigger markets at free agency. Instead, these smaller market teams turned to advanced stats to find value.

In hockey, advanced stats exist to evaluate and analyze talent. Scouts use advanced stats to evaluate future prospects, while coaches and GMs can utilize them with the current roster. Salary really isn’t the focus when it comes to advanced stats in the NHL.

I have made it pretty clear in my previous articles that I am more interested in the visual aspects of hockey. I was never a big statistics fan, but once I started playing fantasy hockey and reading up on advanced stats, my interest grew. It is very easy for hockey fans to be scared away by advanced stats; the name alone sounds like a difficult college course. However, it is very simple to gain a basic understanding of advanced stats. I was terrible at math in both high school and college, and even I have what I would consider a handle on some of the values and how they are used to evaluate talent.

In this post I will introduce some of the basic advanced stats you may come across. I will cover what they evaluate and what we can learn about a player from analyzing them. I will also share some of the sites I use to get the statistics (something that has become more difficult with ExtraSkater.com going dark, after the creator was hired by the Toronto Maple Leafs). By the end of this article you should be able to understand Corsi, Relative Corsi, and PDO.

To sum up Corsi in one simple sentence, it’s the difference in shots attempted by the player’s team and the shots attempted against the player’s team (usually Corsi is applied to 5 on 5 play only). The same could be applied to individual players.

The Silver Seven blog put together an easy formula to break down Corsi:

Corsi Number = (Shots on Target For + Missed Shots For + Blocked Shots Against ) – (Shots on Target Against + Missed Shots Against + Blocked Shots For)

To actually use Corsi to evaluate a player’s value to his team, you need to compare the player’s individual Corsi to that of the entire team. This is referred to as Relative Corsi. The formula for Relative Corsi is pretty logical:

Relative Corsi Number = Corsi Number of player – Corsi Number of Team when player not on ice

If you do not want to sit there with your pencil and paper and calculate these stats yourself, Behind The Net’s Player Breakdown is a great tool to view and compare these stats for every NHL roster. They do all of the calculations, all you have to do is understand what the data means.

With the departure of Extra Skater, a new site that is attempting to take its place is War-On-Ice.com, that also has a Player Breakdown. There are some differences in how each site breaks down the data. When you look at Corey Perry on Behind The Net, his Relative Corsi is listed as 10.6. On War-On-Ice, Perry’s Relative Corsi is 4.41%. They are measuring the same stat, but Behind The Net tells you that when Perry is on the ice, the Ducks average 10.6 more shots. War-On-Ice tells you that they average 4.41% more shots with Perry on the ice.

The shortcoming with Corsi and Relative Corsi when looking at individual players is that it does not account for the other players on the ice. Fortunately, we have stats that measure that as well. CorsiRelQoC and CorsiRelQoT measure the Relative Corsi of the opposition players, as well as the Relative Corsi of the players teammates that are on the ice with him. This way, you can look at a players Relative Corsi, and then by looking at the CorsiRelQoC and CorsiRelQoT, you can examine whether he plays against stronger or weaker players or is helped or hindered by stronger or weaker team mates.

The last thing that Corsi does not take into account is luck. PDO is the addition of shooting percentage and save percentage at even strength.

That seems simple enough, but what does PDO really measure? It is often described as a stat to measure a team’s overall “luck”, and in a way it does. PDO measures regression and sustainability. The theory behind PDO and regression is that a team or player’s baseline of play is a 1.00 PDO (sometimes the stat is expressed without the decimal). If a team has a PDO that is >1.00, then they are over performing and if their PDO is <1.00, they are under-performing. The sustainability part of PDO refers to the fact that a team can only over or under perform for so long. Eventually, they will naturally regress or improve to the baseline of 1.00.

PDO can then be used to follow several trends on many different scales in hockey. For example, you can track a player or team’s progress throughout the season and follow their hot and cold streaks. (One tool that comes in handy for this is the Chart Builder over at SportingCharts.com). PDO can show which players or teams traditionally start slow and then increase their performance in the second half of the season. PDO on the individual player scale can also be a useful tool for fantasy hockey managers, as it can identify which players you should buy low or sell high on.

I know this post threw a lot of information out into a short amount of space. However, if you take a look at the sites linked in this article, and re-read the formulas and descriptions, everything should make some sense. Always remember that advanced stats are not perfect. We can try to eliminate as much error or noise in the data, but hockey is inherently an unpredictable sport. No stat in the world can predict that a third liner on a scoring drought will be in the right place at the right time to score a game winning goal. However, advanced stats can help you see the game in a different way, and provide a numerical break down of some of the things that occur on the ice.

If you are into fantasy hockey, advanced stats can help you make informed decisions about adding and dropping players, as well as trades. You can track a player’s streak, as well as determine whether his offensive production is the result of high skill, or just the right combination of line mates and opposition. Advanced stats are not the end-all and be-all of analyzing the game of hockey, but that is no excuse to not have a basic understanding of what they mean.

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