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How to understand advanced metrics in hockey

What kind of advanced metrics are used in hockey?

Hockey, like many other sports, uses various advanced metrics to analyze player performance, team performance, and game strategies. Here are some examples of advanced metrics used in hockey:

  1. Corsi: This metric measures the total number of shots on goal, missed shots, and blocked shots for a team or player. It is used to evaluate puck possession and shot attempts.
  2. Fenwick: Similar to Corsi, Fenwick also takes into account blocked shots, but does not include missed shots. It is another indicator of a team or player’s ability to generate offensive opportunities.
  3. Expected Goals (xG): xG measures the quality of a scoring chance by analyzing factors such as the shooter’s location, angle, and the distance from the goal, as well as the location and movements of defenders and the goaltender. This metric gives a more accurate measure of a player or team’s offensive efficiency.
  4. Zone starts: This metric measures the percentage of times a player starts a shift in the offensive, defensive, or neutral zone. It is used to evaluate a player’s deployment and role on the team.
  5. Goals Above Replacement (GAR): GAR measures a player’s total contribution to their team’s goal differential compared to a replacement-level player. It takes into account a player’s offensive and defensive contributions, as well as other factors such as penalty-killing and faceoff performance.
  6. Save Percentage (SV%): SV% is a metric that measures a goaltender’s effectiveness in stopping shots on goal. It is calculated as the number of saves made divided by the total number of shots faced.

These are just a few examples of the many advanced metrics used in hockey. Each metric provides a different perspective on player and team performance, and together they provide a comprehensive picture of the game.

When did advanced metrics begin to be used in hockey?

Advanced metrics began to be used in hockey in the early 2000s. One of the first advanced metrics used in hockey was Corsi, which was introduced by Jim Corsi, a goaltending coach for the Buffalo Sabres. Corsi developed the metric in the late 1990s to help evaluate goaltender performance, but it was later expanded to include team and player analysis.

Around the same time, other advanced metrics such as Fenwick and Zone Starts were also developed by hockey analysts and researchers. As technology improved, more data became available to teams and analysts, leading to the development of even more advanced metrics like Expected Goals (xG) and Goals Above Replacement (GAR).

Today, advanced metrics are widely used by teams, analysts, and fans to evaluate player and team performance, develop game strategies, and make personnel decisions. The use of advanced metrics has become an important part of the modern game, and their impact on hockey analytics continues to grow.

Is the use of advanced metrics widespread amongst NHL teams?

Yes, the use of advanced metrics is widespread among NHL teams. Most NHL teams now employ a dedicated analytics department or analyst to help evaluate player and team performance using advanced metrics. These analysts work closely with coaches, scouts, and other team staff to provide insights and data-driven recommendations.

The use of advanced metrics has become increasingly important in the NHL, with many teams using them to evaluate player performance and make decisions regarding contracts, trades, and draft picks. Additionally, advanced metrics are used to develop game strategies, such as identifying weaknesses in an opponent’s defensive system and adjusting line combinations.

While not all teams have fully embraced the use of advanced metrics, the majority of NHL teams use them to some extent. The use of advanced metrics in the NHL is now considered a standard part of the game, and teams that do not use them risk falling behind their competitors in terms of player evaluation and game strategy.

What is the most important advanced stat in hockey?

It’s difficult to identify one single advanced stat as the most important in hockey, as different metrics are used to evaluate different aspects of the game. Some advanced stats, such as Corsi and Fenwick, focus on shot attempts and puck possession, while others, such as Expected Goals (xG) and Goals Above Replacement (GAR), look at offensive and defensive contributions.

That being said, one advanced metric that is often considered to be particularly important in evaluating player and team performance is Expected Goals (xG). xG is a metric that estimates the probability of a shot resulting in a goal based on various factors such as the location, angle, and type of shot, as well as the position and movements of the goaltender and defenders.

xG is important because it provides a more accurate measure of a player or team’s offensive efficiency than traditional statistics like goals and assists, which can be influenced by factors such as luck or the quality of teammates. By analyzing a player or team’s xG, analysts can gain a better understanding of their ability to generate high-quality scoring chances and how well they are taking advantage of those opportunities.

That being said, it’s important to note that hockey is a complex game, and no single metric can provide a complete picture of player or team performance. Rather, it’s the combination of multiple metrics and factors that provide a comprehensive understanding of the game.

Are the critics of the use of advanced metrics in hockey?

Yes, there are some critics of the use of advanced stats in hockey. Some critics argue that advanced stats are overemphasized and can lead to a devaluation of other important factors in the game, such as physicality, intangibles, and leadership.

Others argue that advanced stats can be misleading or misinterpreted, and that relying too heavily on them can lead to flawed decision-making. For example, some players may have low Corsi or xG numbers despite being valuable contributors in other areas of the game, such as defensive play or faceoff success.

There are also concerns about the accuracy and reliability of the data used to calculate advanced stats, as well as the potential for bias or manipulation in the analysis.

Despite these criticisms, the use of advanced stats in hockey continues to be widespread and is considered an important tool for evaluating player and team performance. While they are not a perfect or complete solution, advanced stats provide valuable insights and can help inform decision-making in areas such as roster construction, game strategy, and player development.

Who are the most vocal critics of advanced metrics in hockey?

The most vocal critics of advanced stats in hockey tend to be individuals who have a more traditional view of the game and may not fully understand the value of advanced metrics. These critics include some players, coaches, and analysts who believe that the use of advanced stats is overemphasized and that traditional statistics and scouting methods are more reliable.

For example, former NHL coach Don Cherry has been a vocal critic of advanced stats and analytics in hockey, arguing that they devalue the importance of physicality and toughness in the game. Former NHL player and current analyst Brian Burke has also been critical of advanced stats, arguing that they can be misleading and do not accurately capture the full impact of a player’s contributions to a team.

Are advanced metrics as important in hockey as they are in in other sports?

Advanced metrics are becoming increasingly important in hockey, but their importance may not be on the same level as in some other sports. For example, advanced metrics have played a significant role in baseball for many years, and in recent years, they have become increasingly important in basketball and football as well.

Hockey, on the other hand, has been slower to fully embrace the use of advanced metrics, and while they are now widely used and accepted, they may not be as central to the sport as in other sports. This is in part because hockey is a fast-paced, dynamic game with a lot of inherent variability and randomness, which can make it more challenging to accurately measure and analyze certain aspects of the game.

Which pro hockey players have surprisingly good advanced metrics?

There are several NHL players who have surprisingly good advanced metrics, meaning their advanced stats suggest they are contributing more to their team’s success than their traditional stats might indicate. Here are a few examples:

  1. Ryan Nugent-Hopkins, Edmonton Oilers – Nugent-Hopkins has consistently posted strong Corsi and Fenwick numbers throughout his career, indicating that he is an effective player at driving possession and generating scoring chances.
  2. Jaccob Slavin, Carolina Hurricanes – Slavin is one of the NHL’s most underrated defensemen, and his advanced stats reflect this. He consistently posts high Corsi and Fenwick numbers, indicating that he is a strong possession player and a key contributor to his team’s success.
  3. Casey Cizikas, New York Islanders – Cizikas is a fourth-line center who doesn’t put up big point totals, but his advanced stats suggest he is a valuable contributor to his team’s success. He consistently posts strong Corsi and Fenwick numbers and is one of the NHL’s best players at generating scoring chances and suppressing opposition chances.
  4. Colton Parayko, St. Louis Blues – Parayko is a big, physical defenseman who is known for his defensive play, but his advanced stats suggest he is much more than just a shutdown defenseman. He consistently posts strong Corsi and Fenwick numbers and is a key contributor to his team’s possession game.
  5. Zach Hyman, Edmonton Oilers – Hyman is known for his hard work and physical play, but his advanced stats suggest he is much more than just a grinder. He consistently posts strong Corsi and Fenwick numbers, indicating that he is an effective possession player who generates scoring chances for his team.

These are just a few examples of NHL players who have surprisingly good advanced metrics. There are many other players who may not get as much recognition for their contributions but are valuable contributors to their team’s success nonetheless.


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