Analytics with Alison: Expected Goals | Seattle Kraken (2024)

Not all shots are the same. Expected goals helps us understand shot quality on a variety of levels: by player, by line and by team

"Expected goals." It's a term you hear a lot in hockey analytics circles but what does it mean and more importantly, how do you use it?
Let's dig in.

WHAT is Expected Goals?

In the broadest sense, expected goals (xG) is a measure that seeks to address the concern that not all shots are created equal. xG considers a variety of factors and then mathematically assigns a value to each shot attempt that represents the probability of that shot becoming a goal. That value can come in one of two forms: it can be a percentage - which directly represents how likely a goal was to follow; or it can be a straight value which factors in probability. Terms like "expected goals" and xG can feel clunky, so I like to call this measure simply "shot quality."

HOW do you use Expected Goals?

Much like many data points, expected goals can be used to evaluate a lot of different things which is great - but that can also make it confusing so let's look at shot quality measures in action.

What an individual player creates

Shot quality can be added to together - so the sum total of the shot quality values for any shots a player takes tells you how much individual quality a player is contributing. Who leads in this stat for the Kraken? In 5-on-5 play, it's none other than Brandon Tanev. According to Evolving-Hockey.com, his 4.99 individual expected goals (ixG) is the highest on the team. If we look at his shot map, this makes sense.

Look at all those shots coming from the dangerous areas right around the goal and in the slot in the middle of the ice.

How an individual player contributes to his team

xG can measure what a player creates through their shot attempts, but what about what's going on for the entire team when any one player is on the ice? xG can measure that, too. In addition to Evolving-Hockey.com, other sites that track xG include NaturalStatTrick.com, MoneyPuck.com, and HockeyViz.com. These sites will calculate how much shot quality does a player's team generate when that specific player is on the ice (Expected Goals For, xGF, higher is better) as well as how much shot quality does an opponent generate when said player is on the ice (Expected Goals Against, xGA, lower is better). So now we can see if a player is helping drive play for his team and/or if a player is limiting quality chances against.
If you want to look at xG in isolation, again we can look at totals like we did for individual players but you may also see xG presented as a rated, or "per 60" stat, which basically accounts for how much ice time a player has in relation to what they contributed. Think of any per 60 stat as "leveling the playing field" and adjusting for the differences in ice time among players.
Another great way to consider both quality for and against together is to look at the overall percentage of shot quality generated. If a player has an xGF% of more than 50%, that means they are helping create an offensive advantage for their team as measured by shot quality. Haydn Fleury leads the Kraken in this measure with a 61.9 xGF% (that's also 32nd in the NHL).
What does a prime example of impacting team performance look like? Let's look at Jordan Eberle's hat trick performance against Buffalo. Yes, the goals were fantastic, but also, the shot chart below shows shot attempts for both teams from that game when Eberle was on the ice.

We see so many shots for the Kraken (top left) and just one shot against and that shot came from a very low risk area of the ice. That's a strong shot quality advantage for the Kraken when Eberle was playing.
Also note that just like we can look at how a team performs with a certain player on the ice, we can also apply the same idea to when a forward line or defensive pair is on the ice. We can also look at xG measures for a team as a whole.

WHY Expected Goals isn't perfect

Looking at shot quality makes sense, doesn't it? It feels right to evaluate each shot based on what was happening, who shot it, where it came from, and so on. But we have to remember a few things when it comes to xG. There are many public models and each is mathematically unique so understanding what's included in each is important. And some things we want to include, we can't just just. Most public models don't have access to passing data, for example, so that doesn't factor into shot quality. That's part of why we're excited about the xG values we
present in our Instant Analysis
. Sportlogiq is able to include some pre-shot movement considerations.
It's also important to remember that xG is NOT the only number that matters when it comes to player, line, or team evaluation. It's part of a bigger puzzle and should be treated as such. But going beyond just a shot count, expected goals gives us a great way to measure shot quality and gain a deeper understanding of what's happening with a player, line, or team on the ice.