It is well known that the team scoring first has an advantage. Over the past two regular seasons, 2024-25 and 2025-26, teams that scored first won 66.5 percent of games.
That is the top-line number, but I wanted to go one level deeper. Does team strength, venue, or the timing of the first goal change that advantage? The short answer is yes: all three influence the winning percentage.
Most statistics below cover the previous two regular seasons unless otherwise noted. I will start with league-wide trends and finish with how the Seattle Kraken perform when scoring first. Let’s get into it.
Favorite or underdog?
Three buckets were created: Favorite, Toss-Up, and Underdog. Here is how each bucket was defined:
Favorite: Points percentage was at least .050 higher than the opponent.
Toss-Up: The difference in points percentage between the teams was less than .050.
Underdog: Points percentage was at least .050 lower than the opponent.
Over an 82-game season, a .050 difference in points percentage is equivalent to 8.2 standings points.
Overall, NHL teams that score first win 66.5 percent of games. Favorites, however, win 77.6 percent of games after scoring first. Toss-Up games closely match the overall win percentage, at 66.4 percent compared with 66.5 percent. That means Underdogs are pulling down the overall percentage. They still benefit from scoring first, but their win rate is only 2.9 percentage points above a coin flip.
The second chart looks at teams that scored first but later allowed the opponent to tie the game. The most interesting number to me is the 49.9 percent win rate for the Toss-Up bucket. That suggests the advantage of scoring first is essentially erased once a Toss-Up game is tied. Favorites still push through at a higher rate, while Underdogs struggle once their initial advantage disappears.
Home-ice advantage
A while back, I looked at whether home-ice advantage really exists in the NHL. In that analysis, home teams enjoyed a 5.5 percent boost in standings points compared with their performance on the road. A similar benefit appears when looking at teams that score first. Teams that score first at home win 6.6 percentage points more often than teams that score first on the road.
The same pattern holds after an equalizer. Home teams that score first and later allow the opponent to tie still win 5.1 percentage points more often than road teams in the same situation.
Now let’s combine home and road games with the Favorite, Toss-Up, and Underdog buckets.
The home-ice advantage is clear in every bucket. One interesting result is that Away Underdogs that score first still lose more often than they win. Another takeaway from both Home-versus-Away charts is that Home teams score first more often than Away teams. Even when Away teams score first, their win percentage is lower than that of Home teams scoring first.
When the first goal is scored
Now let’s look at when the first goal is scored and how that impacts the winning percentage.
The trend is clear: the later the first goal is scored, the more likely that team is to win. If a goal is scored in the opening minute, the opponent has 59 minutes to respond.
Keep in mind that the sample size also decreases significantly in each period. The overtime win percentage is 100 percent because the first overtime goal ends the game.
Period Splits with matchup role
The third-period sample sizes get small, but the trend is obvious in the first two periods. So maybe the trick to winning is to score first, but wait a while to do so?
First goal by five-minute increments
Let’s break each period down further into five-minute increments.
There is a lot going on in this chart, so the key is to look at each time bucket independently. The chart shows how many first goals were scored in each bucket. It also shows how often that team won, represented by the blue line, and how often the game was later tied, represented by the red line.
There were too few first goals in the third period to break them into five-minute increments. Because of that, all first goals after 40 minutes were grouped together.
There are several interesting trends. The blue line, representing teams that scored first and won, remains relatively flat until after the 25-minute mark. The red line, representing games that were later tied, also begins declining after 25 minutes. In other words, after the five-minute mark of the second period, the game result becomes harder to change based on which team scored first.
The blue and red lines cross in the 10-to-15-minute bucket. Before the 10-minute mark, games were tied again more often than the team scoring first ultimately won. The overall takeaway is that early first goals are less predictive of the final outcome than late first goals.
First goal vs making the playoffs
Scoring first improves a team’s chances of winning an individual game. Based on that, I wanted to see whether scoring first more frequently also translated into making the playoffs. For this section, we are looking only at data from the 2025-26 season.
Teams in bold on the left made the playoffs. The chart is sorted by the number of times each team scored first. I expected to see playoff teams bunched near the top, but they are spread fairly evenly throughout the chart.
Three teams won fewer than 50 percent of their games when scoring first: the Vancouver Canucks, Toronto Maple Leafs, and Chicago Blackhawks. All three finished in the bottom five of the league. Surprisingly, Chicago tied for eighth in the NHL by scoring first in 45 games.
This shows that simply scoring the first goal is not enough. What matters most is converting that early advantage into wins.
Where the Kraken rank
As you may have noticed in the previous section, the Kraken scored first in 41 games during the 2025-26 season. That is exactly half their games and tied for 16th in the NHL. It is a substantial improvement from 2024-25, when Seattle scored first 35 times and ranked 29th in the league.
Using the .050 winning-percentage difference for the three matchup buckets, the Kraken rarely qualified as a Favorite. Over the past two seasons, Seattle was a Favorite only 11 times and scored first in seven of those games. In the four games when Seattle did not score first, they went 1-3.
When the Kraken were a Favorite or Toss-Up and scored first, they won 22 of 28 games. They struggled as an Underdog, winning only 47.9 percent of the time after scoring first. Holding a lead is one area the Kraken can improve.
Kraken splits with home versus away
Seattle performed markedly better when scoring first at home than on the road. The Kraken were a few percentage points below the league-wide home win rate of 69.6 percent, while their road win rate was well below the 63.0 percent league average.
The Kraken left some points on the table here. Matching the league averages in these situations would be worth about six additional standings points. Holding leads is an area Seattle can improve, but doing so alone would not be enough to push them into playoff contention.
Kraken splits with matchup role
The sample sizes get smaller here, but the trend follows the rest of the league. Seattle is above average in win percentage during Favorite and Toss-Up games, while its overall percentage is dragged down by away games when the Kraken are Underdogs. This feels similar to how the Kraken struggled in the 2024-25 season during the second game of back-to-backs, when they went 0-12.
Does the first goal matter?
Overall, scoring first provides an advantage, but it is not a substitute for playing a full 60 minutes. The 2025-26 data did not show a clear relationship between how frequently a team scored first and whether it made the playoffs.
The 2026 Stanley Cup Final featured the Carolina Hurricanes and Vegas Golden Knights. Carolina ranked fourth in the league by scoring first in 49 games. The three teams ahead of Carolina were tied with 50 games. Vegas ranked 28th, scoring first only 34 times. Two very different paths to the Final.
I think the Kraken will be better than the general consensus expects this season. Even so, they will likely enter plenty of games as Underdogs. The good news is that falling behind is not necessarily decisive. If Seattle can tie the score, the game essentially resets to a 50-50 proposition.
If you have any comments or questions, please leave them below.
Blaiz Grubic
Blaiz Grubic is a contributor at Sound Of Hockey. A passionate hockey fan and player for over 30 years, Blaiz grew up in the Pacific Northwest and is an alumni of Washington State University (Go Cougs!). When he’s not playing, watching, or writing about hockey, he enjoys quality time with his wife and daughter or getting out on a golf course for a quick round. Follow @blaizg on BlueSky or X.
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.
Last week I posted a deep dive on the Seattle Kraken’s goal scoring over the 2024–25 season. Several of you asked to see the other side of the equation, so this week I’m doing another deep dive, this time into goals against. The publicly available data and tools are limited, so this breakdown will be a bit basic and theoretically incomplete, bur it gives us an idea of how the Kraken have trended.
Establishing a baseline
If there’s a broad area you can point to as the reason for the disappointing 2024–25 season, it’s the goals against. Without any high-end talent, the Kraken have had to lean heavily on team defense to be competitive. But this past season saw the Kraken allow the most goals per game since their inaugural year.
They finished 22nd in the league, giving up 3.20 goals per game. Compared to last year’s 2.83, that’s an increase of +0.37 goals per game.
Goals against by situation
Just like we approached Kraken goals for last week, let’s break down how teams scored against the Kraken.
Maybe a little surprising: the Kraken’s goals against while shorthanded were relatively low, just 0.54 per game. Here’s how the goals against by scenario compares across the league in 2024–25.
Clearly, the Kraken need to improve their defense on their 5-on-5 play which is a large percentage of their even-strength time on ice.
Penalty kill performance
From a goals-against-on-the-penalty-kill perspective, the Kraken look… alright. They allowed the 10th-fewest total power-play goals against, despite ranking 27th in the league on penalty kill percentage. It turns out that encouraging total is propped up by the fact Seattle was one of the most disciplined teams in the league.
Seattle was fifth in the NHL for fewest times shorthanded. So while the raw total looks fine, the percentage was below average, and we’ve already talked about how offseason additions should help improve that. Here’s how the Kraken’s PK volume and efficiency stack up league-wide.
Shots against volume
Now let’s look at the Kraken’s shots against per game.
Seattle ranked 17th in the league in shots against, which is again a little surprising, given their 27th-place finish in the standings. But as we all know, not all shots are created equal. So let’s dig into the quality of shots against: high, medium, and low danger.
Like the overall shot volume, the Kraken were right around the league average in high- and medium-danger shots allowed. That’s not bad at all, especially considering several 2025 playoff teams allowed more dangerous chances per game. The Kraken did allow more low-danger shots than most, but that’s not terribly concerning.
Team goaltending
Of course, the other critical element of team defense is goaltending. So let’s check in on team save percentage, broken out by strength.
As you might expect, Seattle’s team save percentage was on the low side, but still (somehow) above two playoff teams: Montreal and Carolina. Still, there’s a lot of room for improvement.
Here’s a look at the Kraken’s save percentages by shot danger level, per NaturalStatTrick.com:
Interestingly, the Kraken’s save percentage got worse relative to the rest of the league as the shots got easier. Spot-checking other teams reveals a lack of consistency. For example, Anaheim was 22nd in high-danger save percentage, fourth in medium-danger, and first in low-danger save percentage.
We can theorize that there’s something systemic at play here, maybe Anaheim’s defensive structure prevents cross-ice passes in medium- and low-danger areas but leaves low-danger shooters more open for cross-seam chances. The inverse might be true for the Kraken’s 2024-25 system implemented by Dan Bylsma.
Individual goalie save percentage
We all know Philipp Grubauer struggled this past season, and the numbers back that up. But let’s put it in context by looking at save percentages for all goalies who played 25+ games.
Yeah… no sugarcoating that one.
Just for good measure, let’s look at Grubauer’s save percentage by shot quality.
It’s a pretty poor performance across the board.
Outlook for 2025-26
Aside from the obvious need for improvement from Grubauer, the Kraken are actually middle of the pack in a lot of defensive categories. As mentioned before, the additions of Ryan Lindgren and Fredrick Gaudreau should help the penalty kill.
There’s also hope that Lane Lambert can implement systems that reduce both the volume and quality of shots the Kraken face, which, in theory, should cut down on goals against.
Final thoughts
All in all, the Kraken’s goals against story in 2024–25 is a mixed bag. Some areas, like overall shot volume and penalty kill goals allowed, aren’t as dire as the standings might suggest, while other areas, like 5-on-5 goals allowed and backup goaltending, clearly have room for improvement. With some key roster tweaks already in place and a new coaching staff behind the bench, there’s at least some reason for cautious optimism heading into 2025–26 that things will get better. The defense doesn’t need to become elite overnight, but if the Kraken can bring the goals against down even a little, they’ll give themselves a much better shot at turning things around.
I’ve been eager to dive into the Seattle Kraken scoring this season, and the 4 Nations Face-Off break provided the perfect opportunity to analyze some key metrics without the data changing every few days. In this exploration, I’ll examine a couple areas: how the Kraken rank across the NHL in scoring metrics and the factors that contribute to scoring (e.g., shots). Additionally, I’ll compare the 2024-25 Seattle Kraken to prior seasons.
Average goals scored
It’s no secret that the Kraken struggled to find the back of the net last season, ranking 29th in the league in goals for per game with just 2.61. This year, they’ve shown significant improvement, ranking 15th in the league and averaging .34 more goals per game.
There’s an important caveat: over the final month of last season, after Vince Dunn got hurt, Alex Wennberg got traded, and the Kraken dropped out of the playoff race, their scoring declined significantly, bringing their 2023-24 goals for average down for the full campaign. So, the increase in scoring this season is a little inflated. Even so, they’re currently averaging more goals per game than five teams that hold playoff spots.
Goal scoring scenarios
To further analyze the Kraken’s goal scoring, let’s examine the goal scoring scenarios over the last four seasons.
We can see improvements in even-strength scoring, particularly in 5-on-5 goals, but the numbers still fall short of the 2022-23 season, when the Kraken qualified for the playoffs as a wild-card team. One area where the team could make significant strides is in contributions from the power play.
Power-play scoring
A prime opportunity for improvement lies in the power play. The Kraken currently rank 25th in power-play conversion, with a 18.1 percent success rate. Historically, the Kraken have struggled on the power play, but this season’s 18.1 percent conversion rate is the lowest since the inaugural season’s 14.5 percent.
We’ll discuss him further shortly, but the loss of Jordan Eberle just 17 games into the season undoubtedly hindered the power play. At the time of his injury, Eberle was playing 54 percent of the Kraken’s power-play minutes.
Shot volume and quality
Another key aspect to consider when evaluating the Kraken’s goals scored per game is shot volume and quality. The team ranks 27th in shots per game, averaging 26.84 shots per contest.
To better understand the team’s shot quality, let’s break down 5-on-5 shots by high, medium, and low danger for each season.
The struggles with scoring last season were largely attributed to a lack of shots in high-danger areas, and unfortunately, that trend appears to have continued this season, with high-danger shots per game actually decreasing from last year’s numbers. Furthermore, the Kraken’s 5-on-5 medium-danger shots per game have also declined compared to last season.
Players’ scoring output
While a more in-depth analysis of individual goal scoring is needed, a preliminary look reveals which players are scoring more or less frequently over the last three seasons, as well as how their current output compares to their three-year averages.
At first glance, it’s clear that Vince Dunn, Jaden Schwartz, and Eeli Tolvanen are experiencing above-average goal production. Eberle was on pace for his best goal-scoring season ever before suffering an injury just a month into the season. Conversely, Andre Burakovsky and Jared McCann are below their three-year averages. It’s important to note that Shane Wright’s decline is likely attributed to his small sample size of just eight games in the 2023-24 season.
Further analysis is necessary, as factors like ice time and power-play time can significantly impact these numbers.
Assessment
Overall, the Kraken have increased their goal production from last season, but there’s still room for improvement. The two most significant areas for growth are power-play production and generating more high-danger shots. While these improvements are easier said than done, the loss of Eberle since mid-November and Dunn’s injury that kept him out of the lineup for 20 games has undoubtedly hurt, particularly given their contributions on the power play. Adding another goal scorer during the offseason would be ideal, but it’s not a necessity, considering several playoff-bound teams are scoring fewer goals than the Kraken this season.
This brief analysis of scoring metrics provides a general direction, but there’s certainly an opportunity to delve deeper into advanced analytics for a more comprehensive understanding.
With all the downtime in the offseason, I have decided to dig into NHL play-by-play data. One question that has been bouncing around my head is how the “long change” affects scoring rates. In the NHL, the long change is in effect during the second period, when teams are defending the goal on the far side of the ice, and players have to skate farther to get to the bench from the defensive zone.
It is only a difference of about 50 feet compared to changing lines in the first and third periods, but this makes it harder for teams to swap out lines on the fly. Players can get stuck on the ice, and shifts can turn into marathons, tiring out defenders. I’ve been curious if it changes scoring rates, though, so let’s take a closer look at how teams score during the first, second, and third periods to see if the long change has an impact.
Data is from all the regular-season games from the 2023-24 NHL season. In total, 8268 goals were scored and analyzed. Let’s dive in!!
Goals scored
Breaking the goals down by period shows that the most goals are scored in the third period. Since the long change is only in effect during the second period, you would think that in the first and third periods, the scoring would be similar.
The third period has its own special circumstance, though, as this is the time for teams that are losing to make a last-ditch effort to try and get the equalizer. This causes a surge in empty-net goals, which throws the numbers off a bit. There were 446 empty-net goals in the third period, which actually means 2349 goals were scored in the third period with a goaltender in the net. Surprisingly, 2349 goals is the exact number of goals scored in the first period as well. For this exercise, empty net, overtime, and shootout goals will be excluded.
This shows there is quite a boost in scoring during the second period, to the tune of a 17.2 percent increase or 403 goals. So this answers the base question, scoring rates are increased by a pretty significant percentage when the long change is in effect.
Additional data
Let’s take a look at the data one click down and consider how the goals are scored and if there are any trends. With periods one and three both having 2349 goals scored.
Playing situation
Depending on the point of the game, teams can be at even strength, on a manpower advantage, have their goalie pulled, or be shorthanded.
The main contributor for the second-period increase in goal scoring (307) is even strength, which is where the majority of all NHL goals are scored. Power-play goals were also up by 149 in the second period, which is the biggest category percentage jump at 30.9 percent. Due to more opportunities for the goaltender to be pulled in the third period, this causes a decrease in other goals. “Other” goals includes shorthanded, penalty shot, and goaltender-pulled goals.
Overall, you see 307 additional goals at even strength, 149 additional goals on the power play, and a decrease of 53 goals in other situations. That works out to an increase of 403 goals during the long change period.
Penalties
With power-play goals seeing the biggest category jump, let’s look at the number of penalties called.
Period two saw a 21.4 percent jump in the number of penalties called. What is interesting here is that is less than the percentage jump in the number of power-play goals scored (30.9 percent). This indicates there is a higher conversion rate of power plays during the second period.
Shot data
With more goals being scored, let’s look at the type of shots that are leading to goals. The shot data has been sorted into six buckets, consisting of wrist shot, snap shot, tip-in, slap shot, backhand, and other.
The wrist shot is by far the preferred shot selection of the NHL and accounts for 47.5 percent of all NHL goals scored. The wrist shot also saw a bump of 21.8 percent in the second period compared with the first and third periods, good for 240 goals. The tip-in goal was 22.2 percent more frequent in the second period, accounting for 70 goals.
Next, looking at the number of shot attempts, I expected to see a similar trend of an increase to go along with the added goals.
This is not the case, though, and shot attempts remain pretty flat between all periods. There is a small uptick of 3.7 percent, but that is a far cry from a 17.2 percent increase in goals. Perhaps this somewhat points to the increase in goals coming in situations where the goaltender has a vested interest in holding onto the puck to stop play so his teammates can get a line change.
Assist data
When a player scores a goal, it can be unassisted, have one assist, or two assists.
Unassisted goals are a singular effort to get the goal.
One-assist goals require one pass before the goal. Situations like 2-on-1’s, one-timers, and rebound goals result in one-assist goals.
Two-assist goals require two passes before the goal. This is the most common scoring scenario and accounts for 77.8 percent of all NHL goals. A team controlling the puck in the offensive zone can result in two-assist goals.
With the long change, defensive teams can get stuck out on the ice, if the offensive team is controlling the puck in its zone. The assist data supports this, as the number of two-assist goals increased by 332 goals and 18.2 percent. One-assist goals increased by 16.6 percent and unassisted goals by 4.4 percent. This indicates teams are getting setup in the zone and connecting on more passes leading to an increase in two-assist goals.
Kraken data
Unfortunately, the Seattle Kraken were not good at scoring second-period goals. They are one of only two teams that does not realize this boost in offense. The other team is the Edmonton Oilers, but since they were fourth in the league in scoring overall, this fault gets swept under the rug. All 30 other teams score more goals in the second period than they do in the first period.
Team
Period 1 Goals
Period 2 Goals
Period 3 Goals
Seattle Kraken
72
65
74 (65 wo/ empty net goals)
The Seattle Kraken were the fourth-worst team in the NHL in goals scored with 214 goals on the season. Most teams have figured out how to capitalize on the long change and trap defending teams in their zone. As goal scoring was the Kraken’s main issue, improving on the long-change scoring could be a target for improvement in the upcoming season.
Looking back to the 2022-23 season, the Kraken had success scording during the second period, improving their offensive production by 24.4 percent in that period, to 107 goals.
Team 2022-23
Period 1 Goals
Period 2 Goals
Period 3 Goals
Seattle Kraken
86
107
87 (72 wo/ empty net goals)
Wrap up
I had alway heard the long change was difficult on defending teams, but had yet to see any numbers to back it up. There is definitely a surge in scoring during the second period. First-year Kraken coach Dan Bylsma has some work cut out to maximize on the scoring opportunities on the long change.
One note, this data was looking at regulation data, but note that overtime follows the same pattern. For first overtime in the playoffs, the teams have to deal with the long change in addition to already playing three periods of hockey.
If you made it through this article, thank you for reading; you might just like numbers as much as I do. If you have any thoughts, suggestions, or other takes, feel free to make a comment below. You can follow me on the X at @blaizg.
It’s been a while since we’ve had a good old-fashioned Data Dump, but that doesn’t mean I haven’t been actively scouring the publicly available data for insights. I spent the better part of my weekend rebuilding my data pipelines so that I could condense things into a few charts to share what I found. This will be all about the Stanley Cup Playoffs to date.
Stanley Cup Playoff series length
This year’s Stanley Cup Playoffs seemed to start out really slowly, with several teams winning their first two games of the series, and we all started to worry that we would see a lot of sweeps. As luck would have it, several teams turned it around to give us some competitive series and a couple of Game 7’s. That said, only three of the eight series went longer than five games, thus bringing down the average games per series.
By the looks of the chart, maybe we were just spoiled the two prior years, which had longer series than usual in Round 1.
I do have some bad news, though. The second round of the last three Stanley Cup Playoffs saw lower average series lengths than the first round.
I would not put too much stock in this since 2019 and 2020 had an average series length of six or more games in the second round.
Stanley Cup Playoff scoring
After a few low-scoring games over the weekend, I wanted to examine how goal scoring in Round 1 compared to the regular season and previous season’s playoffs.
After a string of regular seasons in which the average goal scoring exceeded six goals per game, these playoffs have dipped to under six goals per game for the first time since the 2021 Stanley Cup Playoffs. It’s important to note that playoff averages only include goals scored by the top 16 teams in the league, as half the teams don’t qualify.
Playoff teams have been scoring less in the postseason so far than they did in the regular season.
Another aspect of playoff goal scoring I like to examine is the percentage of goals scored on the power play. I’ve heard a lot about the Edmonton Oilers’ power play, so I was a little surprised to see the Rangers at the top of the list for the highest percentage of goals scored via power play.
Blocked shots
We often hear about the commitment of players to blocking shots in the playoffs. The eye test suggests that shot blocking goes up in the postseason, but is this common narrative true?
So yes, blocked shots do historically go up in the playoffs, but only marginally. I would have expected a higher increase in the playoffs.
Here is a look at a team-by-team breakdown of blocked shots in the playoffs compared to the regular season.
Hitting in the Stanley Cup Playoffs
Another aspect of the game that changes is the number of hits in playoff games. This change can be most noticeable in Game 1 of a series, when teams come out with a lot of adrenaline and look to “set the tone” physically. Here’s a look at the average hits per game per team.
Let’s not forget that out-hitting does not necessarily translate to winning. We examined this during the regular season in the last Monday Musings chart of the week. Here are the winning percentages when you are out-hit (negative differential) versus out-hitting (positive differential) your opponent.
In my definition, a negative differential occurs when the opponent has four or more hits than your team, whereas a positive differential means your team has four or more hits than the opponent.
More playoff Data Dumps to come
I plan to do a couple more Data Dumps throughout the playoffs. The next area I am going to investigate is the roster construction, but if there is anything else you want me to look into, let me know in the comments, and I will see what I can do. In the meantime, enjoy the playoffs.
