After digging into Lane Lambert’s impact on Kraken goal scoring in my last piece, I couldn’t shake the feeling that there was still more research to look at or areas to explore. Goal scoring tells part of the story, but it doesn’t tell us how teams are generating their chances. In this follow-up piece, I’m zooming in on shot quality: high‑danger, medium‑danger, and low‑danger attempts across Lambert’s coaching tenures, plus a look at expected goals to see whether the underlying process matches the results. If the first article raised good questions, this one aims to push a little deeper into the answers.
In that first piece, I looked at whether Lambert’s system might be suppressing Kraken goal scoring. The short version: the picture wasn’t nearly as straightforward as the raw totals suggested. Yes, Seattle’s scoring dipped in Lambert’s first season, and yes, several players saw their production slide. But when I compared his Kraken tenure to his time with the Islanders, and to the coaches who came before and after him, the results were mixed.
Lambert’s Islanders actually scored more than Barry Trotz’s and nearly as much as Patrick Roy’s, and individual player trends didn’t point to a consistent pattern either. The broader takeaway was that Seattle’s scoring struggles likely stem from a blend of roster construction, usage, and variance rather than a system that inherently throttles offense. That left one big question on the table: if the goals dried up, what was happening underneath the surface in terms of shot quality and expected goals?
Shot quality and expected goals primer
Before we get too deep into the weeds, it’s worth laying out why shot quality matters, and offering a quick primer for anyone who doesn’t spend time dabbling in the dark arts of hockey analytics. Not all shots are created equal. A wrist shot from the point with no traffic is not the same as a backdoor tap‑in rebound, and analytics try to capture that difference by categorizing attempts into high‑danger, medium‑danger, and low‑danger areas. High‑danger chances come from the slot and net‑front, where shooting percentages spike. Medium‑danger chances live in the middle of the ice, and low‑danger attempts are typically perimeter shots with a low probability of beating an NHL goalie.
Expected‑goals (xG) models take all of this into account: location, shot type, and timing relative to other events, to estimate how likely a shot was to become a goal. For this exercise, all shot‑quality and xG data comes from NaturalStatTrick.com, one of the most widely used public analytics sites in hockey. If the first article focused on what happened to Kraken scoring under Lambert, this section is about understanding how those chances were generated in the first place. To illustrate the value of high‑, medium‑, and low‑danger shots, take a look at the shooting percentages across those three categories.
Shortcomings of public models
It’s also worth acknowledging the limitations of the public shot‑quality and expected‑goals models used here. Sites like NaturalStatTrick do incredible work with the data they have, but that data is inherently limited. Public models only track a handful of events: shot attempts, their location on the ice, and timing relative to other recorded events.
What they don’t capture is often just as important: passing sequences, pre‑shot movement, traffic in front of the net, screens, odd‑man rushes, defensive breakdowns, or where the other nine skaters are positioned when the shot occurs. All of those factors meaningfully influence scoring probability, but they’re invisible to public datasets. So while these models give us a useful directional understanding of shot quality, they’re not a perfect representation of what happens on the ice.
High‑Danger chance rate per game
Let’s start with high‑danger chances per game for both the Islanders and Kraken across coaching tenures.
The trends look similar to the goal‑scoring analysis from last week’s Data Dump. Across both teams, the tenure with the most high‑danger chances per game was Lambert’s time with the Islanders, and the tenure with the fewest was Lambert’s time (so far) with the Kraken. That contrast is interesting and a reminder that context matters.
Medium‑danger chance rate per game
Now let’s take a look at medium‑danger chances per game.
The pattern is similar to high‑danger chances, with one notable difference: the drop from Bylsma to Lambert is smaller here than it was in the high‑danger category.
Because the impact is so minimal, I’m skipping a deep dive into low‑danger chances.
Putting it together with expected goals
Continuing with NaturalStatTrick data, here’s the expected goals per game by coaching tenure.
Seeing Lambert’s Kraken tenure produce the highest expected goals per game is a little surprising, which makes me theorize that the 2025–26 team was unlucky, or that the prior four seasons were a little lucky. The truth is probably somewhere in the middle.
For the heck of it, let’s also look at average goals per game by season versus expected goals per game by season.
Truth be told, it’s not as simple as “lucky” or “unlucky,” but the public models do suggest the Kraken deserved more goals than they scored under Lambert last season.
What we learned
I am not sure we gained any additional insight with this additional analysis. What we did get was a baseline: Lambert’s Kraken generated enough expected goals to deserve more than they got, but they also produced the fewest high-danger chances in the league. That’s a tough combo. Call it unlucky, call it unsustainable, call it whatever you want, but if the Kraken want to score more this season, they’ll need to spend a lot less time on the perimeter and a lot more time in the places where goals are actually scored.
What this shows me is that our GM’s have not assemble the talent with the mental skills to get into scoring areas. If they buy players that play a board game and are not good at keep posession to get it off the boards, then, no matter who is behind the bench, it ain’t gonna happen. No one want to come to
seattle for many reasons, like the talent we have and had except in the first season where we had untelligent vetern players. Then we let them go for low draft picks.
Analyzing the scoring chance data under Lambert definitely gives you pause. On one hand, the high-danger chances were surprisingly low, but on the other, the expected goals metrics weren’t actually that terrible. It feels like the Kraken were just dealing with some brutal bad luck on finish, even if the underlying process looked decent.
By the way, I was actually pulling together a quick breakdown of these team charts for a group chat recently and used https://decksyai.com/word-to-ppt/ to turn my raw notes and stats into slides. Saved me a ton of time formatting everything manually, definitely worth bookmarking if you ever present hockey analytics.
Anyway, it’s way too early to write Lambert off. Once shooting percentages regress to the mean, those numbers should bounce back.
Wow! Just saw Tolvanen signed for $1.5m x 1 with the Rangers.
It’s stuff like this that really drives home how little the fans – and the media – know. Although I’m sure there will be raft of commentary about how “obvious” this is.
That’s it? Whoa, the Rangers got a bargain, and Tolvy got robbed.
Daryl I could be completely ignorant about this but I’m curious how tolvanen signing a 1.5 one year contract shows how little the fans and media know? Just curious what I’m missing here.
Regarding the signing I feel like Tolvanen was not getting the offers he felt he deserved, decided to take a very low one year deal to showcase himself outside of the LL system. Really not a bad decision, he’s got better than average chances of putting up better numbers and getting a decent contract moving forward.
All of the talk I heard around Tolvanen was much more dollars and term… but apparently a league full of GMs working with professional staffs concentrating on contract efficiency didn’t see it that way.
This just seems like a clear example of the fan and media perception – based on the limited information they have – being obviously disconnected from the reality. I think that’s something that happens way too often. I know I’m almost always wrong.
…also, I agree with your “showcase” take – to a degree. The fact that not only is it a “prove it contract”, it’s also just $1.5m with no trade protection is curious. If he wasn’t getting the dollars or term he wanted then sign a one-year… but $1.5m? with a struggling team?
It really sounds to me like the consensus around the league on Tolvanen might be very different than the perception from the outside.
Yeah I agree, if you asked me before he signed I would have said he’s worth at least double what he signed for. For a showcase type situation he would want a team he felt like he could be getting the most ice time, struggling team fits that mold pretty well. Although not not struggling in the same way as the kraken…LL has a very different approach.
John, I just wanted to drop in here to say I really enjoyed your writing & how you broke all of this down.
In an era of AI slop, it is really refreshing and great to see someone who is good at their craft & can still think critically on their own (and please don’t tell me this is mostly AI- that will convince me there is nothing left worth reading)
Well Tolvinen signing means Lamberts scomg metrics will now go up. For or someone with a good hard shot could not hit the net. Well actualy the only net he hit was the one above the boards. One rarely saw him in front of the net to try for tip ins or shot. I am so glad he is gone now. I wish the Rangers all the worst.
Great write-up with practical and easy-to-understand details. Quick online games are perfect when you want a short challenge without complicated rules. You can try spacebar game, where the main goal is to press the keyboard spacebar quickly and improve your score with every attempt.