Hockey analytics is a term used to describe the analysis of data and statistics to gain a better understanding of the game of ice hockey. The trend of analytics in hockey has grown in recent years, with teams and analysts using advanced statistics to provide deeper insights into players and teams.
In this Hockey Analytics 101 guide, we will dive deeper into a variety of advanced statistics used in hockey and how they can be used to evaluate player and team performance.
Corsi and Fenwick
Corsi and Fenwick are two of the most commonly used advanced statistics in hockey analytics.
Corsi is a statistic that measures shot attempts, regardless of whether the shot was on goal or missed the net. This includes all shots directed at the net, including blocked shots that do not result in a shot on goal. Corsi is named after Jim Corsi, a former NHL goaltender coach who was one of the first to use this metric.
Fenwick is a similar metric to Corsi, but only counts shots that are directed at the net. In other words, Fenwick excludes blocked shots from its calculations.
Both Corsi and Fenwick are used to evaluate puck possession and team performance. A team with a high Corsi or Fenwick percentage typically controls the game’s pace, keeping the puck in the offensive zone and generating more scoring opportunities.
Expected Goals (xG)
Expected goals (xG) is a relatively new metric used in hockey analytics. xG measures the quality of scoring chances by evaluating various factors, such as shot location, shot type, and whether the shot was taken off a rebound or a one-timer. The metric is expressed as a probability between 0 and 1, where 0 represents an impossible goal, and 1 represents a guaranteed goal.
One of the key benefits of xG is that it provides a clear picture of whether a team or player is generating quality scoring opportunities. A player with a high xG total but a low actual goal total may be underperforming relative to his expected output and may be considered unlucky. Similarly, a team with a high xG total but a low actual goal total may be considered to be playing well but not getting the breaks.
PDO
PDO is another statistic used in hockey analytics to evaluate team and player performance. PDO is the sum of a team’s shooting percentage and save percentage, expressed as a decimal. Typically, teams with a PDO over 100 are considered to be “luckier” than those with a PDO under 100.
PDO is a useful statistic for evaluating whether a team or player is performing above or below their expected level. A team with a lower PDO may be considered underperforming, while a higher PDO could signal a team is overperforming.
Zone Starts
Zone starts are often used in hockey analytics to evaluate a player’s usage and role within a team. Zone starts refer to the percentage of a player’s shifts that start in the offensive zone, neutral zone, or defensive zone.
Offensive zone starts generally mean that a player is being used in a more offensive role, while defensive zone starts are generally associated with less offensive-minded players. Evaluating a player’s zone starts can provide insight into his role within the team and help identify players who are being used in specific situations.
Conclusion
Advanced statistics are becoming increasingly important in hockey analytics and in evaluating player and team performance. A thorough understanding of these metrics can provide a more accurate and complete picture of the game and can help identify areas for improvement.
Some key metrics in hockey analytics include Corsi and Fenwick for evaluating puck possession, expected goals for measuring the quality of scoring chances, PDO for evaluating whether a team is performing above or below their expected level, and zone starts for evaluating a player’s usage and role within a team.
As the trend of analytics continues to grow in hockey, expect to see more focus on these and other advanced metrics that provide deeper insights into the game.