It's not something that is really tracked by the NHL as a whole, but it's not uncommon to hear individual teams talking about them. Pittsburgh coach Dan Bylsma has been talking about -- and been asked about -- the number of chances his team allows, and wants to allow, during a game quite a bit over the past week.
Last Thursday he said he considered anything under 10 to be "exceptional" and anything around 12 to be "a good game. Prior to Sunday's game between the Penguins and Flyers I asked Bylsma what his team is looking for shot location, odd-man rushes, etc. It's not just odd man rushes. We have a certain area [on the ice] we consider to be a scoring chance.
There are circumstances that if it's outside that area it could still be a scoring chance, like a wrap-around. Some wrap-arounds are chances, some are not. If a guy is on a breakaway and he misses the net, we have still given him a breakaway so it would still be a scoring chance against, even though they didn't hit the net.
Those are shots opposing defenses will allow all day long because they are not that dangerous. An expected goals model would not reward that as it would assign those shots lower values. Expected goals model does not suffer that possibility. We can look at the xG of a game and note that the team with an xG of 3. And it may tell us something that Corsi or Fenwick or simple shots on net would not. Of course, expected goals models are not perfect.
They are created to reflect reality in mind but there are only so many variables and datapoints available. The model makers do try their best to at least provide a theoretical idea of what should happen.
The good news is that a model can be modified, refined, and updated. If it is mistaken, then we re-adjust with new data and findings. Which has been happening in its own way within the last ten years. This development led others in the analytics community to come up with their own models. Aside: If you want to delve into the details of what goes into a model like this, Dr. Brad at Natural Stat Trick has his own model, which is included on all of his major stat pages.
The Youngren twins at Evolving Hockey have one too. What this means is that the values may differ a bit from site to site as their models may weigh certain data points more than others. In general, it is best to take a larger message from them.
They were a bottom-five team in the league in this stat. And this is supported by their actual goals for percentage rate, which was also among the lowest in the league at This is another benefit of the expected goals model. Clearly, the Devils were not a very good team in either expected or actual goals. Like Corsi, scoring chances and expected goals are best used for 5-on-5 of situations.
However, I recommending more on the preferred rate of each for special teams. The for-rate for power plays and the against-rate for penalty kills. Also like Corsi, I would also recommend taking context into account for players. Given the extended offseason, there has been no shortage of attention paid to Jack Hughes. He was the first overall pick in after a record-breaking season with one of the most talented United States National Team Development Program squads in recent memory.
But he did not perform well in Should we be concerned? This is debatable, but the expected goals model at Natural Stat Trick gives me reason to think that we should not be so worried. Hughes put up an individual expected goals value of 8. You cannot score 0. Hughes was trying to create opportunities. But his finishing and the finishing of others betrayed him. Hughes just had 2 goals in 5-on-5 hockey last season. As the kids would say these days: Oof.
However, the encouraging part is that he generated enough attempts and scoring chances on his own to put up the fifth best individual expected goals value on the team behind Coleman since traded , Wood, Palmieri, and Nico Hischier.
Given his movement around the lineup along with the head coach and GM being fired as the team went to playoff hopefuls to lottery ball players within three months, coming in fifth on a NHL in your year old season is pretty good. Even if his goal scoring did not match what the model suggested. Being able to create opportunities to score may be much more repeatable than scoring a bunch of goals from not-so-good locations.
Hughes demonstrated that he can create opportunities for himself, much less others. If it was the other way around where Hughes out-performed his individual xG, then I would be a bit more concerned for his scoring in the near-future.
There was a young Devil who did greatly outperform his individual xG last season, though. His individual xG was just 6. While he took almost as many shooting attempts as Hughes, Bratt had fewer scoring chances and high danger chances than Hughes. This suggests that Bratt had very favorable shooting last season and scored a bunch of goals that maybe he would not have if he took the same shots in another season. Or at least a rate of goals in since it is not likely will be an 82 or 72 or even 60 game season.
Hopefully, Tom Fitzgerald understands all of this as he still has to re-sign Bratt to a new contract and set new expectations for the young winger. Assuming this will repeat may blow up in his face. Let us turn to a defenseman. About a month ago, the Devils signed Dmitry Kulikov. One of the criticisms for the signing was because he had such a low expected goals for percentage in 5-on-5 play, he would not really be able to help out on defense. How can he with an expected goals for percentage of However, this is where context matters.
The Winnipeg Jets had the lowest expected goals for percentage in the entire league as they allowed a lot of scoring chances. That value suggests Kulikov is not meant for offense and his teammates certainly were not making it happen either. The model shows that we should pump the breaks on hoping Kulikov can really stabilize the blueline, but the context also shows that Fitzgerald did not just sign a pylon to a one-season contract.
The push for scoring chances and especially expected goals came in the form of predicting future goals. When Springings and Toumi released their model, it came with the understanding it could predict future goals better than Corsi.
And as expected goals models are driven heavily by shot location, it made some sense to consider scoring chances as more valuable information than mere shot attempts or shots alone. However, their predictive value. One of the big driver for all of analytics is to find which stats are predictive. Knowing a team or a player is good in a stat could provide an edge for a team, for someone making bets or playing fantasy hockey, or for a fan who is hoping for success.
However, despite the complexity in expected goals models, DLP found that a more simple stat like Corsi is better at predicting future goals. You can view the whole post for the details but it does take some of the value out of using xG - at least for making predictions. DLP did revise his recent findings. DLP found scoring chances - yes, the same discussed in this post - are a little more predictive in recent seasons than Corsi. His original conclusion remains.
However, Peter of Moneypuck noted in a response on Twitter that he found expected goals models are more predictive for future wins if you take out rebounds.
DLP noted that he was looking at goals and not wins, but you need goals for wins so it is an interesting interjection. Why would taking out rebounds make a model more predictive? It could go back to that assumption made with scoring chances about how rebounds are counted. Yet, scoring chances follow that same assumption and DLP found that scoring chances are more predictive.
This is not to say that every model should be ignored or that expected goals is a farce. Again, models can be improved and adjusted as more data is collected. We should take them for what they are and primarily use them as tools for evaluation than necessarily prediction. That is at least my takeaway. All shots are not created equal and the stats of scoring chances and expected goals helps clarify and measure them.
By and large, where a player shoots the puck is going to matter a whole lot. So if nothing else, if your favorite player or team is not scoring a whole lot, then hope they create more dangerous opportunities in addition to more opportunities in general.
Given that defensive play is much harder to measure than offensive play, I think it makes sense to include missed shots. Sample size. All things being equal, a larger sample is better than a smaller sample — and so it makes sense to include missed shots. Ideally, we want to remove chance as a variable to the greatest extent possible. Scoring chances should be goaltender-neutral. Sign up to receive daily headline news from the Edmonton Journal, a division of Postmedia Network Inc. A welcome email is on its way.
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