NHL

# The Basics of Hockey Analytics: A Glossary of Terms

Ever wanted to learn the basics of hockey analytics? Get started right here with a glossary of the key definitions.

The concept of hockey analytics has been around the game for decades thanks to pioneers like former NHL head coach Roger Neilson. He was one of the first people in the industry to ever consider breaking down the finer points of the game by collecting data and using it to try to gain a competitive advantage over other NHL teams.

That said, all of the formulas and data that have been collected over the years have only come to the forefront recently, as more and more teams are building entire departments around hockey analytics to try to gain that extra edge. Young guns and industry like Kyle Dubas have found themselves going from the Ontario Hockey League right to two major league manager positions in the NHL because of the analytics trend. But it hasnâ€™t stopped there -- even bloggers have gotten a crack with big league clubs and an opportunity to make an impact.

It remains to be seen whether these shifts will translate to success in the NHL, as the story is still unfolding in front of our very eyes. For now though, let me introduce you to the basic concepts and terminology, as we here at numberFire work towards helping you master your daily and season-long fantasy leagues.

Class is in session -- welcome to the basics of hockey analytics.

### Corsi

Corsi is at the top of the list as the top hockey analytics term you should pay attention to. It works both as a measure of team and individual performance, but admittedly itâ€™s better for evaluating a team player, because the individualâ€™s performance is affected by the rest of the players on the ice.

To calculate Corsi, you add up the total number of shots a team or player has attempted and subtract the total number of shots allowed while that player was on the ice. Since most hockey formulas are meant to normalize various aspects of the game and keep things on a level playing field, youâ€™ve got to do this calculation in consideration of only 5-on-5 hockey.

Hereâ€™s the basic formula:

Corsi = Shots on net + shots that miss the net +blocked shots

The Stanley Cup-winning Los Angeles Kings and the Chicago Blackhawks were two of the best teams in the league in terms of Corsi, and 8 of the top-10 regular-season teams within the metric made the playoffs last year.

The formula basically tells you which team is controlling the puck more in the offensive zone. Of course, the more you have the puck in the other teamâ€™s zone, the less likely they are to score a goal.

### Fenwick

Fenwick is a Corsi-like calculation, except it removes blocked shots from the equation. It maintains that blocking shots is a skill, meaning, technically on the defensive side of the puck, a coach could put players on the ice that are good at voiding or blocking the other teamâ€™s attempts. Or, that Corsi isn't the most accurate measure of puck possession or performance, because you can still be the more dominant team in the game even if a lot of your attempts are being blocked.

### PDO

Unlike in baseball where the acronyms for analytics relate directly to the terms used in the acronym itself, a lot of the ones used in hockey donâ€™t actually directly translate into anything. PDO is actually a really simple calculation -- itâ€™s a team stat that quantifies the efficiency of a teamâ€™s shooting and ability to stop the opponentsâ€™ shot at even strength. Itâ€™s way easier to explain in a formula rather than in words:

Even Strength Team Shooting % + Even Strength Team Save % = PDO

Letâ€™s say that, on a given night, the Detroit Red Wingsâ€™ collective shooting percentage is 7% and the save percentage is .909. The equation would come out as follows:

.07 +.909 = .916, or 91.6% if you wanted to multiply by 100 to avoid working with decimals.

At the present moment, the Nashville Predators have the leagueâ€™s best PDO, according to SportingCharts.com -- 924 save percentage + 0.095 shooting % = 1.019 PDO

### Points Per 60 Minutes of Ice Time

Points per 60 minutes of ice time is pretty self-explanatory. You take the number of points a player has scored and divide it by his total ice time, and then multiply that number by 60.

Hereâ€™s the formula: Points/Time on Ice x 60

The reason why this formula is important in analytics is that it looks at the scoring rate of players without considering ice time. It essentially puts everybody on a level playing field no matter how much or how little they play.

Steven Stamkos of the Tampa Bay Lightning averaged 3.2 points per 60 minutes of ice time last year, which ranked him 5th in the league. Like many different calculations and types of data in hockey analytics, this one omits the fact that Stamkos only appeared in 37 regular-season games last year and gives him full credit for the production he was able to amass over a 60-minute average.

### Zone Starts

Zone starts calculate exactly what the name says -- the number of times a player starts his shift in one of the three zones on the ice. Of course, this calculation isnâ€™t as helpful when it comes to the neutral zone starts, but it can help a coach define what percentage of the time a player starts his shift in the offensive or defensive zone.

Itâ€™s calculated as follows:

Offense: Number of offensive zone starts/(offensive zone starts + defensive zone starts) = percentage of offensive zone starts

Defense: Number of defensive zone starts/(offensive zone starts + defensive zone starts) = percentage of defensive zone starts

It goes without saying that coaches would generally want their better defensive players having a higher percentage of defensive zone starts, while the offensive players handle offensive zone starts.

Obviously along with that, one might think that a defensive player who spends most of his zone starts in the defensive zone would have more blocked shots, and an offensive player would have better Corsi and Fenwick numbers.

So there you have it -- you have now learned a little bit about the basics of hockey analytics. Now, you can use some of these formulas on individual players to figure out who to select in fantasy, but like any other measure of evaluation, youâ€™d still want to factor in things like the cost of a player in a daily fantasy league with a cap, his home versus road performance, his recent performance against the opposing team, and any number of other factors.

If you want specific numbers for each player each day, remember to check out our daily hockey projections!