There has always been goals, assists and points.
Over the years, the NHL and fans alike started keeping track of real-time stats, which included hits, blocked shots and faceoff wins/losses. The way we kept tabs on the game changed, which led to more and more real-time stats, including missed shots, giveaways and takeaways.
For the past decade, the game’s statistics engines have evolved even more.
Why Advanced Statistics?
The eyeball test has always been reliable when evaluating a player, right?
While many would argue that simply watching a player or team has proven to be successful in the past, advanced statistics allow coaches, scouts, fans and media to see a whole other level of performance.
Advanced statistics can also help emphasize the importance of a player. For example, hockey experts have tried to express the significance of a defensive defenseman for decades, but have come up short (other than saying, “just watch him play!”). This new analytical process can show why that defensive defenseman is vital to team success.
In the beginning of advanced statistics, the NHL simply did not accept these as valid arguments. However, teams are starting to hire personnel based on their analytical presence. The league has even added a new section to its statistics page, highlighting the important analytics that general managers and coaches use to determine who their best players are in each situation.
Note: NHL.com describes Advanced Stats as “Enhanced Stats.”
Current St. Louis Blues goaltending coach Jim Corsi holds the distinction of being named after one of these evolved statistics. Actually developed by Edmonton Oilers blogger Tim Barnes, Corsi statistics were originally calculated to measure the workload of a goaltender. It was to determine not how many shots a goaltender faces, but how many shots he is reacting to, which can cause just as much stress to the goalie’s mindset as an actual shot on goal.
Corsi numbers are similar to plus/minus (your team’s goals scored while on the ice vs. goals against your team while on the ice). The difference, however, is that Corsi measures shots, not goals.
These shots are not just the ones that reach the goalie or the back of the net, either. Along with shots on goal, Corsi measures missed shots and blocked shots. For example, if you are on the ice while the other team blocks two of your teammates’ shots, you would receive a plus-2 for that shift. However, if in the same shift, the other team receives a shot on goal, you would receive a minus-1. Your total for that shift would be a plus-1.
Some additional notes on Corsi:
- Corsi can be expressed as a percentage. Obviously, the higher the team’s or player’s percentage, the more they are controlling play. Anything above 50 percent is considered great, while anything above 60 percent is considered elite.
- The NHL’s best team in terms of Corsi in 2015-16 was the Los Angeles Kings (56.37%).
- The NHL’s best player in terms of Corsi in 2015-16 was Drew Doughty (537).
- The NHL calls Corsi stats Shot Attempts (SAT). They measure Shot Attempts For (SAT For) and Shot Attempts Against (SAT Against), providing fans the ability to sort each on their stats site.
Fenwick statistics, named after Calgary Flames blogger Matt Fenwick, is simply a variation of Corsi. Only shots on goal and missed shots are calculated for the Fenwick number, which is believed to be a better indicator of solid scoring chances. The idea is that shots can be taken from bad areas and blocked easily by defenders, eliminating the shot from being counted as a scoring chance. Fenwick only looks at the team’s or player’s smart shot-taking decisions.
Note: Like Corsi, Fenwick is only calculated in even-strength play. The reason for this is simple: a player who is a penalty-kill specialist would generate a low Corsi or Fenwick stat, due to his inability to play sustained minutes in the offensive zone. It would skew the numbers of defensive specialists who receive top PK minutes.
Some additional notes on Fenwick:
- Like Corsi, Fenwick can be expressed as a percentage. Again, above 50% is considered great while above 60% is elite.
- The NHL’s best team in terms of Fenwick in 2015-16 was the Los Angeles Kings (56.18%).
- The NHL’s best player in terms of Fenwick in 2015-16 was Drew Doughty (1,351).
- The NHL calls Fenwick stats Unblocked Shot Attempts (USAT). They measure Unblocked Shot Attempts For (USAT For) and Unblocked Shot Attempts Against (USAT Against), providing fans the ability to sort each on their stats site.
In laymen’s terms, PDO statistics measure the luckiness of a team. Quite simply, it is the sum of a team’s shooting percentage and save percentage.
The mean for this statistic is right at 1,000. Although different defensive systems and different goalies mean different numbers, a player’s luck is measured by how many times pucks enter either net when he is on the ice. A player whose PDO is well above 1,000 should expect to regress back down eventually, as he has been finding good luck in his time on the ice.
The belief is that a high PDO will fall back down and a low PDO will bump back up over time. This is a nice statistic to use when determining team success after a free-agent signing or trade acquisition. If a player has a high PDO with his former team, can that same number be replicated when he joins his new team?
Some advanced stats experts consider this the most noteworthy statistic.
Note: PDO is also only measured during even-strength play.
Some additional notes on PDO:
- PDO is not an acronym for anything. It is the online handle over many platforms of its creator, Brian King.
- The NHL’s best team in terms of PDO in 2015-16 was the New York Rangers (1,025).
- The NHL’s best player in terms of PDO in 2015-16 was Yanni Gourde (2,000). However, Gourde only appeared in two games for the Tampa Bay Lightning.
- The best PDO player to play in over 20 games was Jonathan Drouin (1,094).
- The best PDO player to appear in over 60 games was Erik Haula (1,050).
- The NHL calls PDO stats SPSV%. They provide fans the ability to sort each on their stats site.
Zone Starts may be the easiest advanced statistic to understand. It is simply the ratio between how many faceoffs a player is in the offensive zone for as opposed to the defensive zone. Usually, the NHL’s elite-level forwards have a high Zone Starts statistic.
This statistic is directly correlated to Corsi, as players with high Zone Starts begin their shifts in the offensive zone, usually leading to more offensive chances. Inversely, a player with low Corsi will often have low Zone Starts because he begins his shifts in the defensive zone.
When evaluating a player, it is important to look at both Corsi and Zone Starts. If a player has a high Zone Starts stat but a low Corsi, chances are that he is hurting the team offensively more than helping it.
However, Zone Starts has become increasingly insignificant, as it has been determined that more than half of shifts begin on the fly and not with a faceoff.
Some additional notes on Zone Starts:
- Technically, the NHL does not keep track of Zone Starts. However, the league’s stat site does account for Offensive Zone Faceoffs (OZFO) and Defensive Zone Faceoffs (DZFO).
- The NHL’s highest-ranking team in terms of OZFO in 2015-16 was the Los Angeles Kings (1,409).
- The NHL’s highest-ranking team in terms of DZFO in 2015-16 was the Columbus Blue Jackets (1,315).
- The NHL’s highest-ranking player in terms of OZFO in 2015-16 was Drew Doughty (641).
- The NHL’s highest-ranking player in terms of DZFO in 2015-16 was Adam Larsson (660).
- OZFO and DZFO can be sorted on the NHL’s stats site.
Feature photo courtesy Buffalo Sabres | Flickr
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