The Ultimate Guide to Important Hockey Stats: Know What to Look For

As one of the most physical and competitive sports in the world, hockey is a game of inches, millimeters, and seconds. For fans and analysts alike, understanding the key statistics that make up the game is crucial for evaluating performance, predicting outcomes, and making informed decisions about players and teams.

In this comprehensive guide, we’ll explore some of the most important hockey stats you need to know, from the basics like goals and assists to the more advanced metrics that go beyond traditional box scores. Whether you’re a die-hard fan or a casual observer, this guide will provide you with the knowledge and insight you need to take your hockey analysis to the next level.

So whether you’re a coach looking to optimize your team’s performance, a fantasy owner looking to gain an edge in your league, or just a fan who wants to appreciate the game on a deeper level, read on to discover the ultimate guide to important hockey stats.

Get ready to gain a new appreciation for the game of hockey and take your analysis to the next level. Let’s dive in!

The Basics: Understanding Key Hockey Stats

When it comes to hockey, statistics play a crucial role in determining a player or team’s success. Understanding the most important stats is essential for any fan or aspiring player who wants to gain a deeper understanding of the game. One of the most commonly used stats in hockey is plus-minus, which measures a player’s goal differential when they are on the ice. A player with a high plus-minus is often considered a valuable asset to their team.

Faceoff percentage is another critical stat in hockey. This statistic measures a player’s success rate at winning faceoffs, which can lead to more possession time and ultimately, more scoring opportunities. Winning faceoffs can be the difference between a team keeping possession of the puck or having to chase it down, which is why faceoff percentage is such an essential stat.

Finally, save percentage is a key statistic for goalies. This stat measures the percentage of shots a goalie saves, with a higher percentage indicating a more effective goalie. A goalie’s save percentage can be the difference between winning and losing a game, so it’s no surprise that this stat is closely monitored by coaches and fans alike.

The Basics: Understanding Key Hockey Stats

Goals, Assists, and Points

Goals, assists, and points are perhaps the most well-known statistics in hockey, and for a good reason. Goals are scored when a player puts the puck into the opposing team’s net, and assists are credited to the player who passed the puck to the goal scorer. Points, meanwhile, are awarded for both goals and assists, with one point for an assist and one point for a goal.

1. Goals: Scoring goals is the ultimate aim of the game. Goals are awarded when the puck crosses the goal line, and they are often the result of a combination of skill, teamwork, and a bit of luck.
2. Assists: Assists are often just as important as goals, as they require a player to have vision, creativity, and the ability to execute a pass to set up a scoring chance.
3. Points: Points are a combination of goals and assists and are a great indicator of a player’s overall offensive production. Players who consistently score points are often considered to be the most valuable members of their team.
4. Shots: The number of shots a player takes is another important statistic to consider. More shots typically lead to more goals and scoring opportunities, making this stat a valuable one for players and coaches alike.

While goals, assists, and points may be the most well-known stats in hockey, they don’t tell the whole story. Other statistics, such as time on ice, power-play goals, and shooting percentage, can be just as important in determining a player’s value to their team.

Offensive Hockey Stats: Identifying Game-Changers

When it comes to ice hockey, a sport built on speed, strength, and agility, there are certain players who have the ability to change the game in a split second. These players, often referred to as game-changers, possess a unique set of skills that allow them to score goals, set up plays, and dominate the competition. To identify these players, coaches and analysts often turn to advanced statistics and metrics that can provide insight into a player’s performance beyond the traditional goals and assists.

One such metric is the Corsi statistic, which measures a player’s shot attempt differential when they are on the ice. A positive Corsi rating indicates that a player’s team is controlling possession of the puck more often than their opponent when that player is on the ice, suggesting that the player is having a positive impact on their team’s offensive output.

Another important offensive statistic to consider is Expected Goals (xG). This metric assigns a probability to every shot attempt based on the location and other variables such as the angle and type of shot. The higher the xG value, the more likely the shot is to result in a goal. By looking at a player’s xG, coaches and analysts can get a better understanding of a player’s ability to create high-quality scoring chances and finish plays.

Zone Entries is another statistic that can provide valuable insights into a player’s offensive contributions. This metric tracks how many times a player carries or passes the puck into the offensive zone, giving an indication of a player’s ability to gain possession and create scoring opportunities.

In addition to these advanced statistics, traditional metrics such as goals and assists should not be ignored when identifying game-changers. While they may not provide the full picture of a player’s performance, they can still offer valuable insights into a player’s offensive impact.

Ultimately, identifying game-changers requires a combination of both traditional and advanced statistics. By analyzing these metrics, coaches and analysts can gain a better understanding of a player’s offensive contributions and identify those players who have the ability to change the game in an instant.

Shots on Goal and Shooting Percentage

Player	Shots on Goal	Shooting Percentage
John Doe	25	20%
Jane Smith	18	22%
Michael Lee	30	15%
Sarah Johnson	12	25%
David Garcia	20	18%
Alex Rodriguez	16	19%

When it comes to analyzing a team’s performance in a soccer match, one of the most important statistics to look at is shots on goal. Shots on goal refer to any shot attempt that is aimed directly at the goal and would have gone in if it were not for a save by the opposing goalkeeper. A high number of shots on goal is generally an indicator of a team’s offensive prowess.

However, simply taking a lot of shots does not guarantee success. It’s also important to look at shooting percentage, which is the percentage of shots on goal that actually result in a goal. For example, if a team takes 10 shots on goal and scores twice, their shooting percentage would be 20%.

Looking at the table above, we can see that Jane Smith has the highest shooting percentage at 22%, despite having fewer shots on goal than John Doe and Michael Lee. This indicates that Jane is an accurate shooter who is able to convert a higher percentage of her shots into goals.

On the other hand, while Michael Lee has the highest number of shots on goal at 30, his shooting percentage is only 15%, which suggests that he may be taking lower-quality shots or struggling to finish his chances.

Ultimately, analyzing shots on goal and shooting percentage can provide valuable insights into a team’s offensive performance and help coaches and analysts make strategic decisions about tactics and player selection.

Defensive Hockey Stats: Measuring a Team’s Defensive Prowess

Defensive hockey stats are essential when it comes to measuring a team’s defensive prowess. One of the most important stats in hockey is the team’s goals-against average (GAA). This stat represents the average number of goals the team allows per game. A low GAA is a sign that a team has a strong defense that can prevent the opposing team from scoring.

Penalty kill percentage is another important defensive stat in hockey. It represents the percentage of penalties a team kills off without allowing a goal. Teams that have a high penalty kill percentage have a better chance of winning games, as they are able to prevent the opposing team from scoring on power plays.

Blocked shots are also a key defensive stat in hockey. Teams that have players who are willing to sacrifice their bodies to block shots are more likely to prevent the opposing team from scoring. A high number of blocked shots is a sign that a team has a strong defensive mindset and is committed to preventing the other team from scoring.

Takeaways are a defensive stat that measures a team’s ability to take the puck away from the opposing team. Teams that have players who are skilled at taking the puck away from the other team are more likely to prevent scoring chances and create turnovers.

Finally, hits are a defensive stat that measures a team’s physicality. A team that delivers a lot of hits is often more intimidating and can wear down the opposing team over the course of a game. Hits can also be an indication of a team’s defensive play, as they can prevent the other team from gaining momentum and scoring opportunities.

Defensive Hockey Stats: Measuring a Team’s Defensive Prowess

Save Percentage and Goals Against Average

Save percentage and goals against average are two of the most important stats when it comes to measuring a team’s defensive performance. Save percentage represents the percentage of shots on goal that a goaltender saves, while goals against average represents the average number of goals that the goaltender allows per game. These two stats go hand in hand when evaluating a team’s defense.

• Save percentage: A goaltender with a high save percentage is able to make more saves and keep the score close, even when the team is facing a lot of shots on goal.
• Goals against average: A low goals against average is a sign that the goaltender is able to prevent the opposing team from scoring, which in turn helps the team win games.
• Team defense: While the goaltender plays a big role in a team’s defensive performance, it is important to note that team defense also plays a significant role. A team that is committed to playing strong defense and preventing the opposing team from getting scoring chances will have better save percentages and lower goals against averages.
• Importance of defense: Save percentage and goals against average are key stats that demonstrate the importance of a team’s defense in hockey. Teams that have strong defensive players and a solid goaltender are more likely to win games and make it far in the playoffs.
• Improving defensive performance: To improve defensive performance, teams can focus on improving their penalty kill, blocking more shots, and taking the puck away from the opposing team more often.
• Conclusion: Save percentage and goals against average are crucial stats in hockey, as they demonstrate a team’s ability to prevent the opposing team from scoring. While the goaltender plays a big role, team defense also plays a significant role in a team’s defensive performance.

Blocked Shots and Plus/Minus

Two other key defensive hockey stats to look at are blocked shots and plus/minus. Blocked shots are when a player gets in the way of an opposing player’s shot attempt, preventing it from getting to the net. This can be an important factor in a team’s defensive strategy, as it can limit the number of scoring chances the opposing team gets.

The plus/minus stat is a measure of a player’s impact on the game, as it takes into account goals scored for and against while that player is on the ice. A plus rating indicates that the player’s team has scored more goals than the opposition while they were on the ice, while a minus rating indicates the opposite. This stat can be a good indicator of a player’s overall defensive ability.

When looking at a team’s blocked shots and plus/minus stats, it’s important to consider the context of the game. For example, a team that is trailing by multiple goals may not have as many blocked shots, as they may be taking more risks in order to generate offense. Similarly, a player who is often used in offensive situations may have a lower plus/minus rating despite being a strong defensive player.

Overall, while blocked shots and plus/minus are important defensive hockey stats to consider, they should be viewed in conjunction with other stats and within the context of the game.

Advanced Analytics: Going Beyond Traditional Stats

In the era of big data, traditional statistical methods alone are no longer enough to gain insights from large and complex datasets. Advanced analytics techniques are being used to extract meaning from vast amounts of data, and companies are increasingly turning to these techniques to gain a competitive edge.

One of the key benefits of advanced analytics is its ability to identify patterns that would be difficult or impossible to uncover using traditional statistical methods. This includes identifying relationships between variables, detecting anomalies, and even predicting future outcomes with a high degree of accuracy.

Another important aspect of advanced analytics is the ability to incorporate data from a wide range of sources. This can include unstructured data such as text, images, and social media posts, as well as structured data from databases and spreadsheets. By combining and analyzing data from different sources, organizations can gain a more complete understanding of their customers, products, and markets.

Machine learning is one of the most widely used techniques in advanced analytics. By training algorithms on large datasets, machine learning can be used to identify patterns and make predictions with a high degree of accuracy. This has applications in a wide range of industries, from healthcare to finance to marketing.

Data visualization is another important aspect of advanced analytics. By presenting data in a visual format, it becomes easier to identify patterns and relationships. This can be especially useful when presenting findings to non-technical stakeholders, who may not have the same level of expertise in data analysis.

Corsi and Fenwick

Corsi and Fenwick are two metrics commonly used in the sport of hockey to evaluate the performance of a team or player. These metrics have gained significant popularity in recent years among coaches, scouts, and fans alike. Corsi is a metric that measures the total number of shot attempts (on goal, missed or blocked) by a team or player, while Fenwick is a similar metric that excludes blocked shots.

The use of these metrics has allowed for a more in-depth analysis of a team’s performance beyond just goals scored and allowed. The idea behind Corsi and Fenwick is that the more a team or player possesses the puck and creates shot attempts, the more likely they are to score and win games. This concept has been supported by statistical analysis, which has shown a strong correlation between Corsi/Fenwick and winning percentages.

One of the benefits of using Corsi and Fenwick is that they provide a more comprehensive picture of a team’s performance, beyond just the outcomes of individual games. By looking at shot attempts and possession metrics, coaches and scouts can identify areas where a team may need to improve, such as generating more offensive zone time or reducing the number of shot attempts allowed in their own end.

It’s worth noting that Corsi and Fenwick are not without their limitations. For example, they do not take into account the quality of the shot attempts or the situation in which they were generated. However, these metrics are still a valuable tool for evaluating a team’s overall performance and identifying areas for improvement.

In conclusion, Corsi and Fenwick are two important metrics in the sport of hockey that have allowed for a more detailed analysis of a team’s performance. By tracking shot attempts and possession metrics, coaches and scouts can gain insights into a team’s strengths and weaknesses and make informed decisions about strategy and player personnel.

• Corsi and Fenwick provide a more comprehensive picture of a team’s performance
• Coaches and scouts can identify areas where a team may need to improve
• These metrics have gained significant popularity in recent years
• There is a strong correlation between Corsi/Fenwick and winning percentages
• Corsi measures total shot attempts, while Fenwick excludes blocked shots
• These metrics are not without their limitations, but still valuable for evaluating performance

Expected Goals and High-Danger Scoring Chances

Expected Goals (xG) and High-Danger Scoring Chances (HDSC) are two crucial statistics in modern hockey analytics. xG measures the quality of a team’s scoring chances based on factors such as shot location, shot type, and game situation. HDSC, on the other hand, is a more specific metric that only takes into account scoring chances that are considered high-danger, meaning they have a greater chance of resulting in a goal.

These two stats are valuable because they provide a more accurate picture of a team’s offensive performance beyond just goals scored. For example, a team that has a high number of quality scoring chances and a high xG but low actual goals scored may be experiencing some bad luck or facing a hot goaltender. Conversely, a team that has a low xG and few HDSC but a high number of goals scored may be experiencing some good fortune.

When it comes to evaluating players, xG and HDSC can help identify those who are making the most of their opportunities and creating quality scoring chances. For example, a player who has a high number of HDSC and a high xG but a low number of goals may be due for some positive regression in their scoring output.

While these stats are still relatively new in hockey, they have quickly become an important part of how teams and analysts evaluate player and team performance.

Zone Starts and PDO

In hockey, the zone starts statistic refers to the location on the ice where a player begins a shift. It’s an important metric because it can affect a player’s performance and statistics. For example, if a player frequently starts their shift in the offensive zone, they may have more opportunities to score goals or generate scoring chances. Conversely, if a player frequently starts their shift in the defensive zone, they may be more focused on preventing goals against.

PDO stands for “save percentage plus shooting percentage”. It’s a statistic that has been found to be a reliable predictor of future team success in hockey. Essentially, PDO is a measure of a team’s shooting and save percentage while at even strength. It’s calculated by adding a team’s even-strength shooting percentage to their even-strength save percentage. A team’s PDO will generally regress towards a mean of 100 over time, so teams with an exceptionally high or low PDO may be expected to see some regression in their results over time.

There is some debate over whether zone starts can affect a player’s PDO. Some argue that players who frequently start in the offensive zone may have a higher shooting percentage and thus contribute to a higher team PDO. However, others argue that the impact of zone starts on PDO is negligible and that PDO is more indicative of overall team performance.

Making the Most of Hockey Stats: How to Incorporate Them Into Your Analysis

If you’re a hockey fan, you’ve probably heard of stats like corsi, fenwick, expected goals, high-danger scoring chances, zone starts, and PDO. These advanced metrics go beyond the traditional stats like goals and assists, and can provide valuable insights into a player’s performance and a team’s strategy.

However, if you’re new to hockey analytics, it can be overwhelming to know where to start. Here are some tips to help you incorporate these stats into your analysis:

Understand what each stat measures: Before you start analyzing a player or team’s performance using these metrics, it’s important to understand what each stat measures. Take some time to research and learn about each metric so that you can interpret the data accurately.

Use multiple stats to get a complete picture: No single stat can tell you everything you need to know about a player or team’s performance. Instead, use a combination of stats to get a more complete picture. For example, you might look at a player’s corsi and expected goals to see how often they’re generating shot attempts and how likely those attempts are to result in goals.

Consider context: Stats can be misleading if you don’t consider the context in which they were produced. For example, a player’s corsi might be high because they’re taking a lot of low-quality shots from outside the scoring area. Look beyond the numbers and consider factors like a player’s linemates, zone starts, and opponents to get a more accurate picture of their performance.

Use stats to identify strengths and weaknesses: Advanced stats can help you identify a player or team’s strengths and weaknesses. For example, if a team is generating a lot of high-danger scoring chances but isn’t converting them into goals, they might need to work on their finishing skills. Alternatively, if a player has a low corsi but a high expected goals, they might be generating quality scoring chances despite not generating a lot of shot attempts.

Don’t ignore the eye test: While advanced stats can provide valuable insights, they don’t tell the whole story. It’s important to also watch games and use the eye test to evaluate a player or team’s performance. Stats can be a useful tool for confirming or contradicting what you’re seeing on the ice.

Comparing and Contrasting Players with Similar Stats

Player Name	Average Points Per Game	Average Rebounds Per Game
John	22.5	7.5
Sarah	22.5	7.0
Mike	22.0	7.5
Chris	22.0	7.0
Lisa	21.5	7.5
Alex	21.5	7.0

When it comes to sports, stats are the backbone of the game. Players are judged on their average points per game and average rebounds per game, among other things. However, sometimes players with similar stats can be difficult to compare and contrast.

Take John and Sarah, for example. They both have an average of 22.5 points per game, but John has a slightly better average of rebounds per game, at 7.5 compared to Sarah’s 7.0. On the other hand, Mike and Chris also have similar stats, but Chris has a slightly better average of points per game at 22.0 compared to Mike’s 21.5, while Mike has a slightly better average of rebounds per game at 7.5 compared to Chris’s 7.0.

Then, there are players like Lisa and Alex, who both have an average of 21.5 points per game, and Lisa has a slightly better average of rebounds per game at 7.5 compared to Alex’s 7.0.

It can be challenging to compare and contrast players with similar stats. While stats are important, other factors like teamwork, communication, and leadership can also play a role in a team’s success.

As fans, we often get caught up in the numbers, but it’s important to remember that there’s more to the game than just stats. Nevertheless, comparing and contrasting players with similar stats can still be an exciting exercise, and it’s always interesting to see how different players stack up against each other.

Looking Beyond the Numbers: The Importance of Context

• Statistics are important, but they only tell part of the story. Without context, they can be misleading and even harmful.
• Data can be manipulated to support almost any argument, but it’s up to us to look beyond the numbers and understand the context in which they were collected.
• Research findings are often reported without proper contextualization, leading to misunderstandings and misinterpretations.
• Facts without context can be weaponized and used to spread misinformation and propaganda.
• Metrics can be useful in measuring progress and success, but they must be analyzed in the appropriate context to be truly meaningful.

Context is everything when it comes to understanding data and statistics. Take for example a study that found a correlation between ice cream consumption and crime rates. At first glance, this seems like a bizarre finding. However, when you add the context that both ice cream consumption and crime rates increase during the summer months, it becomes clear that there is no causal relationship between the two.

It’s important to remember that context can be subjective and dependent on a variety of factors such as culture, geography, and history. What may be considered normal or acceptable in one context may be completely different in another.

When analyzing data or research findings, it’s essential to consider the contextual factors that may have influenced the results. This includes factors such as sample size, demographics, and methodology. Without taking these factors into account, the findings may not be applicable or generalizable to other contexts.

Frequently Asked Questions