If you’re a fan of hockey, you’re probably familiar with some of the most commonly used statistics in the sport. But have you ever wondered what the “S” in hockey stats stands for? In this deep dive, we’ll explore the meaning of S and its significance in hockey analytics.
From the basics of hockey stats to the evolution of advanced analytics, we’ll cover it all. We’ll also take a closer look at S and compare it to other popular stats like Corsi and Fenwick. By the end of this article, you’ll have a better understanding of what S represents in hockey stats and why it’s important for evaluating player performance.
Table of Contents
The Role of Statistics in Hockey
Statistics have become an essential component of hockey analysis, helping teams and fans better understand player performance, identify areas of improvement, and make informed decisions.
At a basic level, statistics like goals, assists, and plus-minus provide a quick snapshot of a player’s performance. But as the game has evolved, so too have the stats. Advanced analytics like Corsi, Fenwick, and Expected Goals have become increasingly popular, providing a more comprehensive look at a player’s impact on the ice.
Corsi and Fenwick
Corsi and Fenwick are two of the most commonly used advanced analytics in hockey. These stats measure shot attempts, regardless of whether the shot actually hits the net.
- Corsi: A player’s Corsi measures the total number of shot attempts (shots on goal, missed shots, and blocked shots) that their team generates while they are on the ice.
- Fenwick: Similar to Corsi, Fenwick measures the total number of unblocked shot attempts that a player’s team generates while they are on the ice.
Expected Goals
Expected Goals (xG) is another advanced stat that has gained popularity in recent years. Rather than simply tracking the number of goals a player scores, xG takes into account the quality of the shot attempt.
Factors like shot location, shot type, and whether the shot was a rebound or a one-timer are all considered in determining a player’s xG. This stat provides a more accurate reflection of a player’s scoring ability, and can be used to identify players who may be overperforming or underperforming based on their shot quality.
Overall, the role of statistics in hockey continues to grow, providing a wealth of information for teams and fans alike. As the game evolves, so too will the stats, offering new and exciting ways to evaluate player performance and gain a deeper understanding of the sport we love.
Hockey Stats 101: Understanding the Basics
Statistics play a crucial role in the world of hockey. Not only do they help teams evaluate player performance, but they also provide insights into team strategies and overall gameplay. In this article, we will explore the basics of hockey stats and how they are used in the sport.
When it comes to hockey stats, there are a few key terms that every fan should know. First and foremost is the plus-minus statistic, which measures the number of goals a player is on the ice for while their team is either shorthanded or at even strength. A player with a high plus-minus rating is considered to be a valuable asset to their team.
Goals and Assists
One of the most fundamental stats in hockey is goals scored, which is pretty self-explanatory. However, it’s worth noting that goals can be further broken down into even-strength goals (which are scored while both teams have the same number of players on the ice), power-play goals (which are scored while the opposing team has a player in the penalty box), and shorthanded goals (which are scored while the player’s team is shorthanded). Another important stat is assists, which measures the number of times a player has helped set up a goal scored by a teammate.
Faceoffs and Shots
Faceoffs are a critical part of hockey, as they determine which team gains possession of the puck at the start of each play. Faceoff win percentages are often used to evaluate a player’s skill in this area. Another key stat is shots on goal, which measures the number of times a team has taken a shot that would have gone in the net if not for the opposing team’s goaltender.
- Save percentage is a stat used to evaluate goaltenders, and measures the percentage of shots on goal that the goaltender stops.
- Penalty minutes track the number of minutes a player spends in the penalty box due to penalties.
By understanding these basic hockey stats, fans can gain a better appreciation for the sport and its players. Whether you’re a seasoned fan or just starting to get into hockey, tracking these stats can make the game even more exciting to watch.
Advanced Hockey Statistics: Going Beyond Goals and Assists
Hockey is a complex sport, and so are the statistics that come with it. While goals and assists are important metrics to measure a player’s offensive output, there are a host of advanced statistics that can give a more nuanced understanding of a player’s performance.
These advanced statistics are often used by coaches, scouts, and analysts to evaluate players and teams. Understanding them can help fans gain a deeper appreciation for the sport and the players they watch. Here are some of the most important advanced hockey statistics:
Corsi
Corsi, also known as Corsi For, is a statistic that measures shot attempt differential. This means it takes into account all shots on net, missed shots, and blocked shots for a team while a certain player is on the ice. A positive Corsi number means that the team is generating more shot attempts than their opponent. This is a good indicator of offensive zone time and puck possession.
Fenwick
Fenwick, also known as Fenwick For, is similar to Corsi, but it only takes into account shots on net and missed shots. This statistic is often used in conjunction with Corsi to get a more complete picture of shot attempt differential. Like Corsi, a positive Fenwick number indicates that the team is generating more shot attempts than their opponent.
PDO
PDO is a statistic that combines a team’s shooting percentage and save percentage while a certain player is on the ice. The average PDO is around 100, so any number above or below that indicates a team or player may be getting lucky or unlucky. PDO can be a useful tool to evaluate a player’s performance, but it should be used in conjunction with other statistics.
The Evolution of Hockey Analytics
Hockey has always been a sport that values statistics. From goals and assists to saves and penalty minutes, numbers have always played a role in evaluating player performance. However, in recent years, the emergence of hockey analytics has changed the game. These new statistics and metrics provide a deeper understanding of player and team performance, helping coaches and front offices make more informed decisions.
The origins of hockey analytics can be traced back to baseball, where statistical analysis has been a key component of the game for decades. In the early 2000s, a group of hockey enthusiasts began to apply these same principles to hockey, looking beyond traditional statistics to better understand the game. Over time, these efforts led to the creation of advanced statistics like Corsi, Fenwick, and Expected Goals, which have become staples in the world of hockey analytics.
The Rise of Advanced Statistics
- Corsi – A metric that tracks the total number of shot attempts (including shots on goal, missed shots, and blocked shots) taken by a team while a particular player is on the ice. This statistic is used to evaluate a player’s ability to generate offensive opportunities and drive play.
- Fenwick – Similar to Corsi, but excludes blocked shots from the calculation. This statistic provides a more accurate representation of a player’s offensive contributions, as blocked shots are often the result of good defensive play.
- Expected Goals – A metric that uses shot location data to predict the likelihood of a particular shot resulting in a goal. This statistic provides insight into a player’s ability to create high-quality scoring chances, rather than simply taking a lot of shots.
The Impact on the Game
The use of advanced statistics has had a significant impact on the way the game is played and evaluated. Coaches and front offices now have access to more detailed and nuanced data, allowing them to make more informed decisions when it comes to player acquisition, line combinations, and tactical adjustments. Additionally, the rise of analytics has led to the hiring of dedicated analytics staff, with many NHL teams now employing data scientists and analysts to help interpret and apply these new metrics.
Overall, the evolution of hockey analytics has brought a new level of sophistication and insight to the sport, and shows no signs of slowing down anytime soon.
S vs. Corsi vs. Fenwick: Which Statistic Should You Use?
Statistics are an integral part of any sport, and hockey is no exception. There are a variety of advanced statistics available to hockey analysts and fans, but which ones should you pay attention to? Three of the most popular statistics used in hockey analytics are S%, Corsi, and Fenwick.
S% is simply the percentage of shots that a team or player takes that result in a goal. It is a straightforward statistic that can be useful in evaluating a player’s or team’s offensive abilities. However, it does not take into account shot quality or shot location, which can be important factors in determining a player’s or team’s offensive success.
Corsi
Corsi is a more advanced statistic that takes into account all shot attempts, including those that miss the net or are blocked. It is calculated as the difference between a team’s shot attempts and its opponent’s shot attempts while a particular player is on the ice. This statistic gives a more complete picture of a player’s or team’s offensive and defensive abilities. It is also a good predictor of future success, as teams and players with high Corsi numbers tend to win more games over time.
Fenwick
Fenwick is similar to Corsi, but it only takes into account unblocked shot attempts. This statistic is used to measure a player’s or team’s ability to create scoring chances while preventing the opposing team from doing the same. It is a good indicator of puck possession and can help evaluate a player’s or team’s overall defensive abilities.
While all three of these statistics can be useful in evaluating a player’s or team’s performance, it ultimately depends on what you are looking for. S% is great for evaluating offensive production, while Corsi and Fenwick provide a more complete picture of a player’s or team’s overall performance. Whether you are a casual fan or a serious hockey analyst, understanding these statistics can help you gain a deeper appreciation and understanding of the game.
Frequently Asked Questions
What is S in hockey stats?
S is a statistic used in hockey analytics to measure a player’s shot attempt differential while the player is on the ice. The calculation of S is based on the difference between a player’s team’s total shot attempts and their opponents’ total shot attempts while the player is on the ice. This statistic is used to evaluate a player’s ability to control play and generate scoring chances.
How is S different from Corsi and Fenwick?
S is similar to Corsi and Fenwick in that all three statistics are used to evaluate a player’s ability to control play. However, S only takes into account shot attempts, while Corsi includes all shot attempts (including blocked shots) and Fenwick only includes unblocked shot attempts. Each of these statistics has its own strengths and weaknesses, and it is up to the analyst to determine which statistic best suits their needs.
What is a good S percentage?
There is no definitive answer to what a good S percentage is, as it can vary depending on the team and situation. Generally, a player with a high S percentage is considered to be effective at controlling play and generating scoring chances. However, it is important to take into account the quality of competition faced and the player’s role on the team.
Can S be used to predict future performance?
While S can be a useful statistic for evaluating a player’s current performance, it is not necessarily a good predictor of future performance. A player’s S percentage can be influenced by a variety of factors, such as the quality of their linemates or the quality of competition faced. Additionally, small sample sizes can make it difficult to draw meaningful conclusions from S percentages over short periods of time.