The new data allows you to map both Expected Goals (xG) and Expected Win (xW) throughout a game. With this information, you can see how a team’s fortunes turned in a game, or whether one team’s shooters were more efficient than the other.

The use of advanced stats is becoming more widespread throughout the hockey world, but let us run you through the basics of what you need to know.

Expected Goals (xG)

xG estimates the probability of scoring off each shot. Each shot is divided into one of three groups – High Danger, Mid Danger, and Low Danger – based on the probability that a shot from that area results in a goal.

The top 10% of shots based on their quality are labelled as High Danger. Only 1 of 10 shots, on average, is a High Danger shot, although 50% of goals scored in games are from these areas, and the average shooting percentage for High Danger shots is 26%.

The next 20% of shots based on their quality are labelled as Mid Danger. In total, 30% of goals scored in games are Mid Danger shots.

Low Danger shots comprise the remaining 70% of shots. Only 20% of goals are scored from Low Danger shots, with a shooting percentage of just 1.7%.

The probability of scoring off each shot is mathematically calculated from tens of thousands of shots registered from past games. It mostly depends on the location of the shots and several other factors. Its value reveals how many goals are scored from similar shots on average.

So, how do we read xG figures? Jan Morkes, chief data analyst at Czech Hockey, reveals the details.

“If a team loses while having a higher xG than their opponent, that means that the losing team’s shooters were less efficient, or that they had bad luck. This may be true for one game, but it is very rare to overperform on xG long-term, meaning that it can be a good way of predicting how a team will fare in the future,” Morkes said.

Expected Goals Story Widget

This season, the Champions Hockey League has launched a new Expected Goals Story Widget that allows you to follow the course of a game and see when a team began to have success.

In the widget, all shots (dots), goals (team logos), and collated Expected Goals value are mapped out, divided into periods.

Any period of dominance in the game can be noted by a steep rise of the xG line. If the line plateaus, that means that the team in question was having difficulties generating high-quality scoring chances.

Expected Win (xW)

xG data can also be used to calculate the Expected Win (xW, measured in percentage) of a team in a game given the chances they create. The xW percentage is measured by xG and the given time elapsed.

The Expected Win statistic is calculated based on hundreds of past hockey games and can be interpreted as the average percentage of times that a team with a similar xG result (at that point in the game) will win the game.

For example, an xW of 60% means that teams with a similar xG (both for and against) at the same point in a game won approximately 60% of those games.

Now that you have the basics down, let’s run through an example from the 2021/22 season.

As the above image shows, Rogle Angelholm had a higher xW and xG figure despite having less shots on goal in their GD1 game against EV Zug.

But that’s not all! We also have detailed heat maps that show you where the action took place and how the game evolved.

Additionally, the CHL also produces Game Impact visualizations for every game, allowing you to track the chances for and against that took place while an individual player was on the ice. 

This data can be truly revealing, as demonstrated below!

So how do we read this table? A value of 50% for an individual player means that his team had the exact same number of shot attempts as their opponent while he was on the ice.

A value higher than 50% means that while that player was on the ice, his team had more shot attempts than their opponent during 5-on-5 play.

If their value is below 50%, then the opponent produced a greater number of shot attempts while he was on the ice.

As a result, the image above shows that Sparta Prague’s fourth line was highly effective in their Game Day 2 shootout win over TPS Turku, and this played itself out in the course of the game, as both Zdeněk Doležal and Ladislav Zikmund found the back of the net.

Make use of our advanced stats widgets to get the inside track on your favourite team’s games throughout the 2021/22 season!