“Advanced stats” has become a kind of buzz word around the hockey world. It’s emerging into the NHL as it did years ago with the MLB. Hockey teams have entire departments for analytics, and it has become a war (pun intended) of sorts between two sides: The Data Nerds vs. Old School Hockey. What I intend to do is break down some of the common stats used in analytics to help people get introduced into the world of NHL analytics.
Understanding NHL Analytics
When I first wanted to get into NHL analytics, the hardest part was finding a resource that helped me understand not just what the stats meant, but what a “good” version of the stat was. It’s relatively common knowledge that scoring 30 goals in a season is a good season, but what’s good in terms of Corsi or expected goals?
“Advanced stats” is often a misleading term. This is because, at their roots, a lot of the common stats being talked about are extremely simple in nature. There are some complex ways of looking at the context at times, or some large calculations involved, but the base stats aren’t that “advanced”.
One final precursor before getting into the content: the majority of these numbers are to measure 5v5 in the NHL. When talking about powerplays and penalty kills, it becomes a different beast.
What is Corsi? This is probably the most common stat to be heard when referring to NHL advanced stats. To sum up in the most basic terms: it’s measuring the number of chances. Corsi measures shots on goal, shots wide, and blocked shots. It tends to paint a bigger picture of the entire game than just the traditional count of “shots on goal”.
There is both Corsi for (CF) and Corsi against (CA). Because of this, Corsi can be shown as a differential (C± or C+/-), or as a percentage (CF%). With that, the most common use of expressing Corsi is through CF%. It is the simplest to understand and puts it into a context that looking at raw Corsi for or against doesn’t. Individual Corsi (iCF) is also a calculatable stat. It can tell you how many shot attempts a single player has taken. Corsi in this context, however, is rarely used.
Now, what should you look for when looking at this stat? Traditionally, anything over 50% is seen as good. Take this with a grain of salt, however. This is the threshold that you should look for over a large body of work. In a single game, the difference between 47% and 51% should not be used to say “this player had a good game and this player had a bad game”. In the context of just one game, both these players could be seen as the middle of the pack. As the sample size becomes larger, such as over an 82 game season, the differences between 51% and 47% are much more significant.
Fenwick is basically the same idea as Corsi, however, it does not count the blocked shots in the stat. Only including shots on goal and shots wide gives credit the idea that blocked shots are intentional and could be a part of a coach’s system. Many of the ideas of Corsi apply to Fenwick. Fenwick for percentage (FF%), Fenwick plus-minus (F± or F+/-).
Relative to Team
Both Corsi and Fenwick can be depicted as relative to the rest of their team. It’s a rather simple way to see how the player drives play compared to teammates. It is measured by taking a player’s on-ice Corsi for percentage and subtracting the team’s Corsi for percentage without said player on the ice.
For example, we can look at Natural Stat Trick line tool and see that the Carolina Hurricanes had a CF% of 53.48% without Jordan Staal on the ice. With Staal on the ice, the Hurricanes had a CF% of 56.35%. This would give Staal a relative CF% of 2.87. This value can also be expressed as a negative should a player’s CF% be lower than his teams. The same formula could be applied to Fenwick.
*Note, PDO is not an acronym for anything. It is simply just PDO.*
PDO is an odd stat, and actually isn’t used that often, but its goal is to measure “luck” in hockey. That calculation is simply to measure either a team’s or player’s luck. It is simply the teams save percentage plus their shooting percentage. In terms of individual players PDO, we look at the on-ice shooting percentage and save percentage.
The idea behind PDO is that a team or player will typically average out a total of 100.0 over a full season once hot streaks and cold streaks have levelled out this is where the majority of players will stand.
The issue with PDO, however, is teams and players that are above average, such as Auston Matthews in 2017-18 who finished with 104.8, are expected to be higher than the expected 100.0. Better teams, and players, are expected to have higher shooting percentages and save percentages due to the fact that they just have higher skilled players, and this is where the stat falls short.
Zone starts evaluate how many shifts a player starts in the offensive zone versus the defensive zone. This can be used to look at context of usage and how it affects a player. Such as players that start in the offensive zone more frequently could be expected to have higher numbers in stats like Corsi. The inverse of this is also true. Many of these measurements do not give the full picture, however, Evolving Hockey has begun track this along with on the fly zone usage. This captures a more complete picture than before.
Next time in Understanding NHL Analytics: A Beginners Guide we will look at expected goals. If you have any questions, feel free to reach out on Twitter.
Embed from Getty Images