What are Corsi Metrics?

Corsi metrics are an essential tool in the world of hockey analytics. These metrics provide a way to measure shot attempt differential while at even strength play. Corsi metrics are derived by taking shots attempted for and dividing it by the shots that the opposing team attempts. This includes shots on goal, missed shots on goal, and blocked shot attempts towards the opposition’s net minus the same shot attempts directed at your own team’s net.

Understanding Corsi metrics is crucial for both individual performance analysis and team analysis. By measuring shot attempt differential, Corsi metrics provide a way to evaluate players and teams beyond traditional statistics like goals and assists. Corsi metrics are also useful in predicting future performance. Players and teams with high Corsi numbers are more likely to continue performing well in the future.

Corsi metrics have become an important part of hockey analytics in recent years. Advanced applications of Corsi metrics include adjusting for certain game situations, such as when the team is in a close game. Emerging trends in hockey analytics are also exploring new ways of using Corsi metrics to gain insights into player and team performance. As the use of analytics in hockey continues to grow, Corsi metrics will remain a key tool for understanding and evaluating performance.

Key Takeaways

  • Corsi metrics measure shot attempt differential while at even strength play in hockey.
  • Corsi metrics are important for both individual performance analysis and team analysis.
  • Advanced applications of Corsi metrics include adjusting for certain game situations, and emerging trends in hockey analytics are exploring new ways of using Corsi metrics to gain insights into player and team performance.

Understanding Corsi Metrics

Definition and Origin

Corsi metrics are a set of advanced statistics used in the game of ice hockey to measure shot attempt differential while at even strength play. This includes shots on goal, missed shots on goal, and blocked shot attempts towards the opposition’s net minus the same shot attempts directed at your team’s net. Corsi metrics were named after Jim Corsi, a former NHL goaltender coach who created them.

Corsi Number and Its Components

The Corsi number is a simple metric that measures the difference between the total number of shot attempts for and against a team while at even strength play. It is calculated by subtracting the number of shot attempts against (CA) from the number of shot attempts for (CF). A positive Corsi number indicates that a team has more shot attempts for than against, while a negative Corsi number indicates the opposite.

Corsi For and Against

Corsi for (CF) and Corsi against (CA) are the two components of the Corsi number. Corsi for is the total number of shot attempts a team takes while at even strength play, including shots on goal, missed shots, and blocked shots. Corsi against is the total number of shot attempts a team allows while at even strength play, including shots on goal, missed shots, and blocked shots.

Corsi metrics are used to evaluate puck possession and shot differential, giving an indication of a team’s overall performance. They are particularly useful in identifying which teams are generating more scoring opportunities and which teams are struggling to create chances.

Corsi in Context

Corsi is an advanced statistic used in ice hockey to measure shot attempt differential. It is a possession metric that considers all shot attempts, including shots on goal, missed shots on goal, and blocked shot attempts towards the opposition’s net minus the same shot attempts directed at your own team’s net. Corsi is a powerful tool for evaluating a team’s puck possession and shot differential, which are essential components of a successful hockey team.

Even Strength and Score Effects

Corsi metrics are typically measured during even-strength play, which means that they do not include power plays or penalty kills. This is because power plays and penalty kills can significantly affect shot attempts and therefore skew Corsi metrics. Additionally, Corsi metrics are often adjusted for score effects, which means that they take into account the fact that teams may play differently when leading or trailing in a game. For example, a team that is leading may play more defensively and take fewer shots, while a team that is trailing may take more risks and take more shots.

Adjusted Corsi

Adjusted Corsi is a variation of Corsi metrics that takes into account the quality of the shots taken. This is done by weighting each shot attempt based on its expected goal value, which is calculated based on shot location and quality. Adjusted Corsi metrics are often used to evaluate players and teams based on their ability to generate high-quality scoring chances.

Overall, Corsi metrics are a valuable tool for evaluating a team’s and player’s ability to possess the puck and generate shot attempts. By measuring shot attempt differential, even strength play, and score effects, Corsi metrics provide a comprehensive view of a team’s performance on the ice.

Corsi as a Predictive Tool

Corsi and Future Performance

Corsi is a valuable metric in predicting future performance in ice hockey. Teams with a higher Corsi percentage tend to win more games over the course of a season. This is because Corsi measures a team’s ability to control possession of the puck, which is crucial in determining a team’s success. By measuring all shot attempts, Corsi provides a more complete picture of a team’s contribution to their success.

Corsi is also useful in predicting individual player performance. Players with a high Corsi rating tend to have a higher number of goals and points. This is because a high Corsi rating indicates a player’s ability to generate scoring opportunities for themselves and their teammates.

Corsi vs. Traditional Statistics

Traditional statistics such as goals and points are important in evaluating player and team performance. However, they do not provide a complete picture of a player or team’s contribution to their success. Corsi provides additional insight into a team’s ability to control possession of the puck, which is crucial in determining a team’s success.

Corsi is a more consistent metric than traditional statistics. Traditional statistics can be influenced by factors such as luck, while Corsi is a more reliable indicator of a team or player’s performance over time.

In summary, Corsi is a valuable predictive tool in ice hockey. It provides insight into a team’s ability to control possession of the puck and is a more consistent metric than traditional statistics. Teams and players with a high Corsi rating tend to have a higher number of wins, goals, and points, making it a valuable metric in evaluating performance.

Corsi and Team Analysis

Team Corsi and Game Strategy

Corsi metrics can be used to evaluate a team’s game strategy, particularly in terms of their offensive zone play. A team with a high Corsi rating is generally considered to have better puck possession, which can lead to more scoring opportunities. This is because Corsi measures all shot attempts, including missed shots and blocked shots, which can still indicate a team’s ability to control the puck and generate scoring chances.

Teams that have a strong offensive game plan will often have a higher Corsi rating, as they are able to maintain possession of the puck in the offensive zone and generate more shot attempts. This can be achieved through a variety of strategies, such as cycling the puck, making quick passes, and creating traffic in front of the net. On the other hand, teams with a weaker offensive game plan may struggle to generate shot attempts and have a lower Corsi rating.

Evaluating Team Performance

Corsi metrics can also be used to evaluate a team’s overall performance. By measuring a team’s shot attempts and comparing them to their opponents, Corsi can provide insight into a team’s ability to control the game and generate scoring opportunities.

Teams with a high Corsi rating are generally considered to be better performers, as they are able to maintain possession of the puck and generate more scoring chances. This can be an indication of a team’s offensive prowess, as well as their ability to defend against their opponents’ offensive attacks.

On the other hand, teams with a low Corsi rating may struggle to control the game and generate scoring opportunities. This can be an indication of a weak offensive game plan, poor defensive play, or a combination of both.

Overall, Corsi metrics can be a useful tool for evaluating a team’s performance and game strategy. By measuring a team’s shot attempts and comparing them to their opponents, Corsi can provide valuable insight into a team’s ability to control the game and generate scoring opportunities.

Corsi and Individual Performance

Player Corsi Impact

Corsi metrics can be used to evaluate the impact of individual players on their team’s performance. By analyzing a player’s Corsi number, it is possible to determine how much they contribute to their team’s possession of the puck and shot attempts.

For example, Connor McDavid, one of the NHL’s top players, has consistently posted high Corsi numbers throughout his career. This suggests that he is an effective player at maintaining possession of the puck and generating shot attempts for his team.

On the other hand, Alexander Ovechkin, another top NHL player, has historically had lower Corsi numbers. This suggests that he may not be as effective at maintaining possession of the puck and generating shot attempts as McDavid.

Corsi and Player Evaluation

Corsi metrics can also be used as a tool for evaluating individual player performance beyond just goals and assists. By considering a player’s Corsi number, coaches and analysts can gain a more complete picture of their impact on the game.

For example, a player with a high Corsi number may be contributing significantly to their team’s possession of the puck and generating shot attempts, even if they are not scoring goals or getting assists. Similarly, a player with a low Corsi number may be struggling to maintain possession of the puck and generate shot attempts, even if they are scoring goals or getting assists.

Overall, Corsi metrics provide a valuable tool for evaluating individual player performance and assessing their impact on their team’s success.

Advanced Applications of Corsi

Integrating Corsi with Other Metrics

Corsi is a valuable metric that can be used in conjunction with other advanced statistics to gain a deeper understanding of team and player performance. One such metric is Fenwick, which is similar to Corsi but excludes blocked shots. By comparing Corsi and Fenwick, analysts can gain insight into a team’s defensive strategy and ability to prevent shots on goal.

Expected goals (xG) is another metric that can be used in combination with Corsi to gain a more comprehensive understanding of a team’s performance. xG models take into account the quality of a team’s shots on goal, providing a more accurate assessment of their ability to score. By comparing Corsi with xG, analysts can identify teams that are over or underperforming based on the quality of their shots.

Corsi in Advanced Statistical Models

Corsi can also be used in advanced statistical models to predict future performance. One such model is the Corsi for percentage (CF%), which measures the percentage of shot attempts a team generates compared to their opponents. This metric is a good indicator of a team’s overall performance and can be used to predict future success.

Another advanced statistical model that uses Corsi is the xG model. By combining Corsi with xG, analysts can create a more accurate model for predicting future performance. This model takes into account a team’s ability to generate quality shots on goal, as well as their ability to prevent their opponents from doing the same.

Overall, Corsi is a valuable metric that can be used in a variety of ways to gain insight into team and player performance. By integrating Corsi with other metrics and using it in advanced statistical models, analysts can gain a more comprehensive understanding of a team’s strengths and weaknesses, and make more accurate predictions about their future performance.

Corsi in Practice

Corsi metrics have become an important tool for measuring a team’s puck possession and shot differential in the game of ice hockey. In practice, Corsi is calculated by adding the total number of shots on goal, missed shots, and blocked shots attempted by a team while at even strength play. This number is then subtracted from the total number of shot attempts directed at the team’s own net.

Real-World Examples

One of the most well-known examples of Corsi metrics in practice is the work done by Vic Ferrari, who created the website “Corsi Hockey League Stats” in 2007. The website tracks Corsi statistics for every NHL team and player, providing valuable insights into a team’s performance on the ice.

Another example of Corsi metrics in practice is the work done by the analytics team at Moneypuck. They use Corsi as one of the key metrics in their player evaluation models, helping teams to identify players who are strong puck-possession players and can contribute to a team’s overall success.

Limitations and Critiques

While Corsi metrics have become an important tool for measuring a team’s performance, they are not without their limitations and critiques. One of the main criticisms of Corsi metrics is that they rely heavily on sample size. In other words, the more data that is collected, the more accurate the Corsi metric becomes.

Another limitation of Corsi metrics is that they do not take into account the quality of the shots taken. For example, a team may take a lot of low-quality shots, which would inflate their Corsi number but not necessarily lead to success on the ice.

Despite these limitations, Corsi metrics have become an important tool for measuring a team’s puck possession and shot differential in the game of ice hockey. They have been used successfully by teams such as the Buffalo Sabres, Detroit Red Wings, New York Islanders, and Arizona Coyotes to evaluate their performance and make strategic decisions.

Emerging Trends in Hockey Analytics

Hockey Analytics is constantly evolving, with new metrics and trends emerging every season. One of the most significant developments in recent years has been the rise of microstats and player tracking. These metrics provide a more in-depth understanding of player performance, allowing coaches and analysts to identify strengths and weaknesses more accurately.

Microstats and Player Tracking

Corey Sznajder is one of the pioneers of microstats in hockey analytics. He manually tracks every event in every NHL game, including shot assists, zone entries, and zone exits. His work has been invaluable in providing a more detailed picture of player performance, beyond traditional stats like goals and assists.

Shot assists, in particular, have become a popular metric in recent years. They track the player who made the pass immediately preceding a shot attempt, giving credit to players who contribute to the offensive play beyond just scoring goals. This metric is especially useful in identifying playmakers who might not show up on the scoresheet.

Zone entries and exits are also critical metrics in player tracking. They measure the number of times a player carries the puck into or out of the offensive zone, providing insights into a player’s ability to generate offensive chances or break up opposing attacks.

The Future of Corsi

Corsi has been a popular metric in hockey analytics for over a decade. However, the metric has its limitations, and analysts are constantly looking for ways to improve it. Evolving Hockey has been at the forefront of this effort, developing new metrics like xG and ixG that provide a more accurate picture of a team’s offensive performance.

Expected Goals (xG) is a metric that takes into account the quality of a team’s shots, rather than just the number of shots. It assigns a probability to each shot attempt based on factors like shot distance, shot angle, and the presence of defenders. This metric provides a more accurate picture of a team’s offensive performance, as it rewards teams that generate high-quality scoring chances.

Intelligent Expected Goals (ixG) is a more advanced version of xG that takes into account the player who took the shot, as well as the player who made the pass. This metric provides even more insight into a team’s offensive performance, as it rewards players who contribute to the offensive play beyond just taking shots.

As hockey analytics continues to evolve, we can expect to see more advanced metrics like xG and ixG become increasingly popular. These metrics provide a more accurate picture of a team’s performance, allowing coaches and analysts to make more informed decisions.

Frequently Asked Questions

How is Corsi utilised in evaluating player performance in hockey?

Corsi is used to evaluate player performance in hockey by measuring the number of shot attempts directed at the net while a player is on the ice. This includes shots on goal, missed shots, and blocked shots. The Corsi metric is a useful tool for evaluating a player’s offensive and defensive abilities. A player with a high Corsi rating indicates that they are generating more shot attempts than their opponents while on the ice.

What distinguishes Corsi from Fenwick in ice hockey analytics?

Corsi and Fenwick are both advanced metrics used in ice hockey analytics, but they differ in the way they account for blocked shots. Corsi includes blocked shots in its metric, while Fenwick only includes shots on goal and missed shots. This means that Corsi provides a more complete picture of possession and shot generation, while Fenwick is a more accurate representation of scoring chances.

In what ways does Corsi contribute to understanding team possession?

Corsi is a key metric in understanding team possession in ice hockey. It measures the total number of shot attempts directed at the net by a team while a player is on the ice. A team with a high Corsi rating indicates that they are generating more shot attempts than their opponents, which translates to more possession and scoring opportunities.

What steps are involved in accurately tracking Corsi during a game?

Accurately tracking Corsi during a game involves counting all shot attempts directed at the net, including shots on goal, missed shots, and blocked shots, for each team while a player is on the ice. This requires careful observation and recording of each shot attempt, as well as the ability to distinguish between shots that are on target and those that miss the net.

How does Corsi relate to expected goals (xGF) in hockey statistics?

Expected goals (xGF) is a statistic that estimates the number of goals a team is expected to score based on the quality and quantity of their shot attempts. Corsi is a key component in calculating xGF, as it provides information on the total number of shot attempts directed at the net. A team with a high Corsi rating is more likely to generate high-quality scoring chances, which translates to a higher xGF.

Can Corsi be considered a reliable indicator of a team’s offensive capabilities?

Corsi can be considered a reliable indicator of a team’s offensive capabilities, as it measures the total number of shot attempts directed at the net while a player is on the ice. A team with a high Corsi rating indicates that they are generating more shot attempts than their opponents, which translates to more possession and scoring opportunities. However, Corsi should be used in conjunction with other metrics to provide a more complete picture of a team’s offensive and defensive abilities.


Leave a comment

Free Betting Tips, Direct to Your Inbox

Sign Up Today to Join Betting Gods for FREE and Receive Betting Tips Direct to Your Inbox Every Morning

Not Sure Who to Join?

These are the Top Performing Tipsters in May

In Form

Premier Greyhound Tips

1,081 Winners Since October 2014
Total Profit:£34,820.10

£305.44

Per Month

27.34%

Win Rate

13.35%

ROI

£425.00

This Month

BSP Profits

108 Winners Since January 2023
Total Profit:£3,159.47

£197.47

Per Month

30.77%

Win Rate

36.01%

ROI

£89.13

This Month

In Form

Systematic Betting

432 Winners Since September 2022
Total Profit:£1,568.02

£74.67

Per Month

38.66%

Win Rate

3.64%

ROI

£507.00

This Month