Calgary FlamesScience/Statistics

Understanding the stats in TWC’s About Last Night recaps

Our About Last Night articles try to provide a comprehensive overview of each and every Calgary Flames game. We dissect the key moments from the game, using a mix of what we saw on the ice and what the statistics told us about the game.

We know that stats are not everyone’s cup of tea, so I have written this primer to help you better understand what the numbers mean. This article is pulled heavily from our previous article: Making Sense of Advanced Stats.

This is the table that is at the top of each ALN. We will go through all of the headers below:

5v5 Score and venue adjustment (SVA)

This stat basically measures how players do at full strength (taking out the power play and penalty kill) and adjusted for the score and venue. The theory behind this metric is that teams with a lead take less shots depending on how many goals they are up. This has been quite definitively shown as seen in the chart below from TSN.

This image has an empty alt attribute; its file name is shots-per-60.jpg

This metric also adjusts for if a team is playing at home or away. The term home ice advantage is very real, and can be quantified. Whether it is because of the comfort of a home-cooked meal before the game, the crowd cheering the players on, or something else, teams at home tend to shoot more than when they are on the road. Micah Blake McCurdy (@IneffectiveMath on Twitter) studied the effects of home-ice advantage, and these results are used to adjust the performance of each team relative to whether they play at home.


Most simply, Corsi For Percentage counts the number of shot attempts by a team. This includes shots on net, missed shots (including shots that hit the post or crossbar), and blocked shots. Each of these is counted as a Corsi event, and we look at the percentage of the total attempts each team had.

The reason people look at Corsi for as opposed to just shots on net is it more accurately shows the flow of the game. For example, if a team has heavy offensive presence and directs five shots on the net, but two bounce off the post, two are blocked, and one goes wide, none of those will count as a shot on net. If you only look at shots on goal, it does not show the other offensive chances at all, even though some were probably even more dangerous than the ones that did hit the net, and can be misleading.

As well, if the opposing team takes a number of shots on net, but they are all from outside and hit the goalie square in the crest, they are all counted as shots on goal, but none of them had any chance of going in the net. This skews the results and makes it seem like one team controlled the game, when in reality it was more mixed.

Scoring chances for (SCF%) and High danger scoring chances for (HDCF%)

Scoring chances for is exactly what the name suggests- the number of real scoring chances a team has over the course of the game. Scoring chances are calculated using the below image from War On Ice.

In calculating SCF, shots from the yellow zone are assigned a score of one, pink assigned a score of two, and blue assigned a score of three. Rush chances and rebounds are assigned an additional point. A blocked shot lose one point. Scoring chances are counted as any event that is a score of two or above.

For example, only rebounds or rush chances from the yellow zone that hit the net would be counted as scoring chances. Any unblocked shots from the pink zone, a blocked rebound or rush chance from the pink zone, or any shot from the blue zone would also count as scoring chances.

High danger chances for are calculated as chances with a score of three or above. This is an unblocked chance from within the blue zone above, a blocked rebound or rush chance in the blue zone, or an unblocked rebound or rush chance from the pink zone. These are typically the most difficult shots for a goalie to save or a team to defend, and thus are most likely to go in.

expected goals for (XGF%)

Expected goals models can be complex but they provide a lot of value. In short, it measures each sequence and provides a value on each shot attempt on how likely it was to be a goal. The objective being that if you have more high value scoring sequences, you are more likely to score. The model measures factors such as shot type, how it was created (on the rush, rebound etc.), handedness of the shooter, location of the shot, and many more. Unlike scoring chances, this measure uses the whole scoring that led up to the chance. The xGF% measures the percentage of the total expected goals created by one team.

Natural Stat Trick does not share their model for how they weigh different factors to determine the weight of each scoring chance, but there are a number of models which are public, including Evolving Wild’s model, which you can read here. These models are not perfect and all vary from one another, as they are based on the way someone views different variables in relation to how they translate into goals.

Hopefully this helps you better understand the statistical metrics that we use in our About Last Night articles. If you do have any questions, please feel free to DM us on Twitter @wincolumnblog or on Facebook at The Win Column.

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