My college professor used to say that statistics without analytics is like knowledge without action. You can have all the information in the world, but what will you do with it?
More importantly, if you understand the purpose, will you be able to interpret it correctly and make the right decision?
The goal of analytics is to analyze available data and identify all the correlations, trends and patterns that will affect the decision-making process, and, thus, improve the performance or achieve better results.
is not a new category.
Many sports, like baseball and golf, have been successfully implementing it for some years. Technological breakthroughs in the last several decades have contributed to the development and popularity of sports analytics.
With the introduction of the hawk-eye system in 2005, tennis has become another sport that makes an analysis based on the statistical data provided by hawk-eye tracking software. Today, most big sports teams have an analytics expert, which is a rapidly growing profession
Even though sports analytics have been part of decision making in many sports for a long time, Roger Neilson, a former hockey coach and game innovator, was one of the first to use analytics to assess players and their on-court performance. In 2014, the National Hockey League (NHL) started to implement detailed analytical approach using advanced hockey statistics
Data analytics are playing an important role in hockey because of the following:
- Building a better and more successful team – the objective of every hockey team is to achieve better results. Team or single player performance can be evaluated with the use of available quantitative and qualitative data. The playing strategy and evaluation of it is also largely dependent on the statistical data.
- Competition – statistical data of most hockey teams is usually available online. There are countless sites and forums dedicated to sports analytics, and hockey game and equipment analysis. Teams use this data for the comparison, and to identify opponent’s strengths and weaknesses. If interpreted right, sports analytics can become a team’s competitive advantage.
- Plans – hockey teams use analytics to make conclusions, implement necessary changes and plan for the future.
- Prevent mistakes – analytics will provide you with a detailed analysis of a team's performance – what was done well and what went wrong. The goal is to minimize mistakes and achieve better cohesion for future games.
- On-court decisions – special devices and sensors enable hockey coaches and teams to have real-time data during the game. They'll analyze this data to make decisions or change game plans instantly on the court.
Standard Sports Analytics in Hockey
The most important parameter in hockey is a puck possession. Common advanced statistics
used by hockey teams are Corsi, Fenwick, PDO, and Zone Starts, which all relate in some form or another to puck possession.
Named after a former hockey coach
, Corsi represents the sum of total shots on goal. It's a most common stat used in hockey by coaches and sports journalists. Corsi (aka SAT – shot attempts) includes the following statistics: shots at goal, shots attempted that missed the net and shot attempted that were blocked.
A positive Corsi number indicates that team spends more time in the offensive than the defensive zone (on the attack), while negative means that team spends more time in the defensive zone compared to the offensive zone (trying to defend).
Corsi can be measured for both individual players and teams.
How does Corsi affect coaching decisions? Corsi is a great source of information for team strategy decision, essentially diagramming with data how players will score or defend against their opponent.
When it comes to single player, Corsi above 50% tells that a player is outplaying his competition, while anything below indicates that opponent is performing better. Low Corsi number points out that a teammate has a bad impact on his team performance.
Fenwick (aka USAT – unblocked shot attempts) is similar to Corsi. However, Fenwick like Corsi includes shots at the goal and shots attempted that missed the net, the primary difference is Fenwick excludes shot attempts that were blocked.
Contrary to Corsi which is focused on a short period, Fenwick is usually aimed at a longer game period. High Fenwick number tells us that our team is getting more scoring chances than the opponent. Hockey coaches use this statistic to determine team’s overall ability to create offensive opportunity.
Furthermore, Fenwick is often used to predict team’s future success, because teams with constant high Fenwick percentage are playing on a very high level against their opponents.
PDO, created by an analytical expert Vic Ferrari, is the sum of team's shooting percentage and save percentage.
Shooting percentage measures goals compared with shots on goals taken while saving percentage measures goals allowed compared with shots on goals against. However, this statistic has often been criticized for being based on nothing but luck. It has been used by hockey teams and coaches and is an excellent way to see if a player's success/failure is maintainable over an extended period.
Finally, Zone Starts indicates how many faceoffs a player starts in every zone of the ice, measuring the percentage of time when a player starts in the offensive zone. A Zone Start ignores starts in the neutral zone, and begins when a player starts his shift in a zone (i.e. faceoffs).
Zone Start % provides insight into how a coach views a player, does he put them on the ice when in the offensive zone more often than the defensive? If so he values that player more for their offensive than defensive skills.
Zone Starts is an important stat on a player's overall point totals.
Today, many companies are developing hockey analysis software and video analysis of the game. These software solutions are very detailed and provide high-quality statistical and visual data. With the rapid development of technology, it will be interesting to see what will future bring in this department.