Stanley Cup

How Important is Thanksgiving in Relation to Making the Playoffs? by Alex Craig

By: Ryan Reid

How early is too early when it comes to getting excited about a player or teams’ success early on in the season? While looking at Mikko Rantanen’s pace through 20 games and assuming he will score 130 points seems a bit ridiculous now (he is currently on pace for just over 100), the fact is that a 20 game sample size for teams as a whole is often very predictive of whether or not they will ultimately make the playoffs. In fact, over the past 5 seasons, 77.5% of teams that found themselves in a playoff position at American Thanksgiving went on to make the playoffs.

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Given the high predictability of holding a playoff spot at Thanksgiving, I believed that when other statistics are analyzed, they are likely to provide an even greater ability to predict which teams are playoff teams given various statistics collected at American Thanksgiving each year. 

With the help of machine learning, I hoped to be able to create a model to out predict the strategy of picking current playoff teams.

Process Used

In creating a machine learning model, I wanted to be able to classify whether a team could be best classified as a playoff team or not, given a variety of statistics collected on Thanksgiving. To do so, I used Logistic Regression within machine learning in order to classify and group variables as binary, 1 being a playoff team, and 0 being a non-playoff team. Through examining the past 11 years of team data from Thanksgiving (minus the lockout shortened season for obvious reasons) and classifying each team, I hoped to train my model to be able to accurately classify playoff teams.

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Within python I used the numpy, pandas, pickle, and various features within sklearn including RFE (Recursive Feature Elimination) and Logistic Regression packages to create the model. Pandas was used to import and read spreadsheets from within excel. Pickle was used to save my finalized model. Numpy was used in certain fit calculations. RFE was used to eliminate features and assign coefficients to the impact criteria was having on the decision of whether a team made the playoffs. Finally, Logistic Regression was used to assign a predicted shape to the model.

Criteria Valuation             

Starting off with all statistics I could collect for teams at Thanksgiving, I began to weed out less predictive variables until I landed on a group of 8. Using Recursive Feature Elimination (RFE), I was able to continually run the model and see which variables were deemed most predictive and should be included in the model. The factors as listed below were deemed most predictive, in order of importance 
to the model. 

While point percentage is the most predictive, other statistics like shooting percentage, save percentage, or goals for percentage provide a bigger picture perspective that allows for a better predictive capability for the machine learning model.

It has been determined that having higher shots for, shooting percentage, and save percentage all have a negative effect on whether or not you end up making the playoffs. For shooting percentage and save percentage, this is likely due to the fact that the model has identified a PDO like correlation in which teams with a lower save percentage and shooting percentage can be classified as “unlucky” and will eventually regress towards the norm. Additionally, the number of shots a team takes relative to the other team has a negative correlation with making the playoffs. This could be due to score effects that cause losing teams to typically generate more shots that are of lower quality. As the model shows, it is primarily high danger chances that are predictive of making the playoffs, not just any shot.

The Results

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Running the model, 81.25% or 13 out of 16 playoff teams in a playoff spot as of March 1stwere correctly classified as playoff teams. Furthermore, an additional 2 teams (Columbus and Colorado) sat only 1 point back of a playoff spot. In contrast, picking the playoff teams at Thanksgiving would only result in a 68.75% success rate or 11 out of 16 teams. Furthermore, 3 teams that were in a playoff position at Thanksgiving are no longer in the playoff race in comparison to only 1 team (Buffalo) predicted by the model. 

Outliers

Particularly interesting decisions made by the machine learning model include the decision to not pick the Rangers to make the playoffs, despite leading the Metro at Thanksgiving, and the choice to select Vegas to make the playoffs despite a slow start.

One reason behind this choice could have been New York’s low number of ROW. With a mere 8 ROW in 22 games, the New York Rangers sat atop the Metropolitan Division mainly in part to their 4-0 record in shootouts. Seeing that the New York Rangers were playing so many close games, the model likely discounted the strength of the Rangers. Additionally, the New York Rangers had the 4thlowest corsi for %, 6thlowest shots for %, 9thlowest scoring chance for %. As for points for %, the Rangers were ranked at an underwhelming 13th in the league, but led the Metro since the Metro was a weak division and the Rangers had more games played. Given the Rangers low valuation across all these supporting criteria, the machine predicted that they would not make the playoffs despite their stronger points for % at Thanksgiving. 

As for the Golden Knights, despite holding the 29thbest point % in the league, Vegas was among the top 4 in the league in shots for %, corsi for % and scoring chances for %. Additionally, Vegas had the league’s lowest PDO (SH% + SV%) at 95.66. Given all these things considered, the model likely believed it was only a matter of time before the Vegas Golden Knights began winning.

Flaws in the Model

While my machine learning model appears to have the ability to out predict the strategy of picking all playoff teams at Thanksgiving, two main limitations of the model as highlighted above is the inability of the machine to pick teams based on the given playoff format, and the lack of data at various game states. 

Unaware of the NHL’s current playoff format, the model picked 9 Eastern Conference teams, and only 7 Western Conference teams. Without a grasp on the alignment of divisions within the league, the model is at a disadvantage when picking teams, particularly when specific divisions or conferences are more “stacked” than others. Therefore, there is the potential of the model picking an otherwise impossible selection of teams to make the playoffs.

Furthermore, data collected to be fed into the model was only even-strength data. While this provides a decent picture of a team’s capability, certain teams that rely on their power play, as the Penguins traditionally have, may be disadvantaged and discounted. Finding a way to incorporate this data into the model would likely provide a fuller picture and a more accurate prediction.

Final Thoughts

While the model I have created is by no means perfect, it provides a unique perspective into not only the importance of the first 20 or so games of the season, but also what statistics beyond wins are important in attempting to classify a playoff team. While the model appears to out predict the strategy of selecting all playoff teams at Thanksgiving, it will be interesting to see in years to come if there is a continued ability to classify playoff teams given Thanksgiving stats.

***All statistics gathered from Natural Stat Trick


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Analysis: How 5 Elite Scorers Get Their Goals by Owen Kewell

By: Owen Kewell

There’s something beautiful about scoring a goal.

Goals are the building blocks that make up hockey success, both on the individual and team level. They are a single moment in time, a culmination of a series of plays that ends with one team’s attack successfully defeating the other’s defense.

Teams are forever searching to add goals to their lineup, and for good reason. Goals win games, playoff series and, eventually, championships.

Goal-scoring is a repeatable talent, and while certain NHLers are far better at it than others, each player does it their own way. Each scorer exhibits unique tendencies of shot type selection and shot location.

Alex Ovechkin, Evgeni Malkin, Connor McDavid, Nikita Kucherov, and Patrik Laine are five of the best scorers in the game. Of the 10 goal leaders for the 2017-18 season, these five players possess the highest career goals per game rates. They are the elite of the elite when it comes to putting the puck into NHL nets.

I wanted to explore how they each do it differently.

Elite Scorers 1.jpg

The above visualization separates by shot type to show how each player scored their goals in the 2017-18 season. Overall, the most popular shot type was wrist shot, followed by snap shot, slap shot, and finally backhand.

It should be noted that the ‘AVG (10+ G Forwards)’ represents a weighted average of the relevant shot rate among all forwards who scored 10 or more goals, weighted by the number of goals that they scored. It’s a way to quantify ‘normal’ rates for the league’s goal scoring forwards.

Let’s take a more detailed look at each of these five players.

 

Alex Ovechkin

Elite Scorers 3.jpg

It’s no secret that Alex Ovechkin is really good at scoring goals. Since breaking into the league, he’s won the scoring title 7 times and no one else has won it more than twice. Sitting at 607 career goals, Ovi continues to propel himself further up the list of all-time greats. His 0.605 goals per game ranks first league-wide, beating out all other forwards by at least 0.08 G/GP.

Ovechkin loves slap shots, which should come as no surprise to anyone who’s watched Washington’s power play operate. His 17 slap shot goals were an uncontested 1st league-wide, with Steven Stamkos being the only other forward to score more than 7. Ovechkin’s slap shot is so powerful that it beats goalies clean even whey they know it’s coming, meaning that it can be unleashed without needing to be disguised.

Equally noteworthy, Ovechkin scored just 31% of his goals by wrist shot, which represents the lowest rate among all 32 players who scored 30+ goals.

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

The red areas in the above heat map show where Ovechkin shoots more frequently than the rest of the league. Ovechkin makes an absolute killing at the top of the left faceoff circle, often referred to as the ‘Ovi Spot’. This area lines up with Ovechkin’s average shot distance of 32.3 feet, which ranked in the 80th percentile among the league’s forwards.

Although it’s not reflected in the heat map, much of Ovechkin’s damage is done with the man advantage playing the left point. Of his 49 goals, 17 were scored on the power play, which ranked 2nd only behind a player further down this list. His remaining 32 were scored at even-strength, which again ranked 2nd in the league. Elite scoring across both special teams and even-strength situations throughout his career has propelled Ovechkin to the status of the league’s premier goal scorer.

 

Evgeni Malkin

Elite Scorers 5.jpg
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Despite being the second-best player on his team, Malkin has put together the resume of an elite goal scorer. He’s scored 75 goals in 140 games over the past two seasons, which converts to 44 goals over an 82-game season. His career goals per game of 0.472 ranks 6th among active forwards, placing him in elite company.

What makes Malkin dangerous is his offensive versatility; he can score from anywhere on the ice. Equal parts power and precision, Malkin possesses a variety of weapons. His snap shot goal rate clocks in at roughly double the league average (his 11 snap shot goals ranked 4th), but his middle-of-the-pack rates for wrist shots, slap shots and backhands speak to his balanced toolkit. Malkin does not rely on a single shot type to score goals, meaning that defenders must respect all shot types that Malkin credibly threatens. 

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Did I mention that Malkin can score from anywhere? The sea of red is the beauty of Evgeni Malkin. He’s one of the most complete offensive players in the league. In addition to his heavy shot, his slick puck-handling ability and power forward frame allow him to generate shots and scoring chances at elite rates in the low slot area. His shot distance ranked just inside the upper third league-wide, influenced both by his crease-area chances and his shot activity in the high slot.

Malkin joins Ovechkin as the only two players in the league to finish top-10 in both even-strength goals and power play goals. He scored 28 times at evens, ranking 7th, and 14 times with the man advantage, ranking 6th. Malkin is one of the game’s most dangerous players in the offensive zone, and his goal scoring abilities rank among the NHL’s elite.

 

Connor McDavid

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At this point, not much more needs to be said about Connor McDavid’s offensive game. His 108 points were enough for a second consecutive Art Ross (but not Hart) Trophy. He’s the been the league’s best forward for the last two years, and he’s only 21 years old.

But is he a goal scorer? While it’s true that McDavid has been viewed more as a set-up man than a finisher thus far in his young career, in 2017-18 we saw a transformation in McDavid’s offensive role. Compared to the year prior, McDavid scored 11 more goals and took 23 more shots. He became more of a trigger man, electing to attempt shots more often instead of looking to pass. This development calls to mind a young Sidney Crosby, who recorded seasons of 70 and 84 assists before breaking out for 51 goals in 2009-10.

McDavid prefers to score goals with his wrist shot. His 25 wrist shot goals ranked 3rd league-wide behind only Nathan MacKinnon and Eric Staal, while his rate of 61% ranked 9th among the 32 players who scored 30+ goals. He hardly ever takes slap shots, registering just 7 of these shots during the entire season, of which just 1 beat the goalie. Rather than rely on strength to generate power, McDavid creates offense thanks to generational skating and elite-level hands. He’s able to create and navigate space better than anyone else on the planet and puts himself into positions where a quick and accurate wrist shot is more than enough to beat the goalie.

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

McDavid has figured out hockey’s (not-so) secret formula: if you get close to the net, you’re more likely to score. He's extremely effective at using his speed, hands, and vision to attack the most dangerous area of the ice. McDavid’s sub-20’ average shot distance is a testament to his elite ability to generate scoring chances from the crease and low slot area.

McDavid’s special teams split is intriguing. His 35 even-strength goals ranked first in the entire NHL, but his 5 power play goals tied him for 96th among forwards. This latter can be explained both by Edmonton’s league-worst power play and also McDavid’s primary role as a puck distributor on the top unit. If Edmonton’s power play improves, which is likely given regression to the mean, McDavid’s special teams goal-scoring could very well take a step forward and supplement his elite even-strength scoring totals. He is already the game’s best forward and he poses a legitimate threat to become the game’s best scorer sooner rather than later.

 

Nikita Kucherov

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A late 2nd round pick, Nikita Kucherov has emerged from relative anonymity to become one of the league’s most dangerous forwards. His 79 goals over the past two seasons place 3rd league-wide, and he was one of just three players to break 100 points in 2017-18.

While Kucherov’s absurdly accurate wrist shot remains his primary weapon (4th in wrist shot goals with 24), he is equally dangerous on the backhand. He scored 8 times (21% of all goals) on his backhand, ranking 2nd among 30+ goal scorers to Brad Marchand in both raw total and rate. Kucherov’s ability to score using wrist shots and backhands is all the more impressive considering that he shoots from further away than 93% of other forwards. He can be successful from this range without relying on the power of slap and snap shots due to his innate ability to find and exploit tiny gaps that goaltenders leave open. His shots are precise and accurate, and he excels at finding any available daylight.

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

An incredibly versatile player, Nikita Kucherov generates shots at elite rates all over the mid and high-slot. Rather than favour a specific shooting location, he elects to test the goalie from all areas of the offensive zone. This makes Kucherov unpredictable, which helps explain why his quick-release wrist shot and backhand are so devastating. He doesn’t shoot much from the crease area, but driving the net really isn’t part of how he creates offense.

Kucherov was more of a goal-scorer at even-strength than on the power play in 2017-18. He recorded 31 ES goals, one of just four players to crack 30, compared with 8 on the man advantage. He played more of a set-up role on Tampa Bay’s 3rd-ranked power play, registering 28 assists as he regularly sent cross-ice passes to Steven Stamkos (15 PP goals). Kucherov’s outstanding season cemented his status as one of the most dangerous goal scorers in the NHL, and at the prime age of 25 he’s as good a bet as any to repeat his offensive dominance next season.

 

Patrik Laine

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At just 20 years old, Patrik Laine is already among the game’s premier snipers. His 44 goals ranked 2nd league-wide in 2017-18, fueling the Jets to their franchise-best season. Laine’s biggest asset is his shot, which may very well be the best in the league. Among current NHLers with 50+ career goals, Patrik Laine’s career shooting percentage of 18.0% ranks 2nd behind only Paul Byron. Byron, meanwhile, had an average shot distance of 19.3 feet in 2017-18, least of all eligible forwards, while Laine’s average shot came from 36.1 feet, ranking in the 97th percentile. The kid can shoot the puck.

Laine’s weapon of choice is his snap shot, which he routinely uses to one-time pucks into the back of the net. His quick release and accurate shot placement resulted in 14 snap shot goals in 2017-18, which tied for the league lead with Phil Kessel. He also is a fan of the slap shot, with his 6 slap shot goals placing him in a tie for 4th among all forwards.

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Heat Map courtesy of Micah Blake McCurdy's website HockeyViz (https://hockeyviz.com)

Here we see Laine’s favourite shooting locations. A right-handed shot, Laine loves to one-time pucks from the high slot. The fact that he’s able to beat the goalie so consistently from so far away speaks to his talent as a shooter. Like Ovechkin, Laine’s shooting locations lack variety, but he’s so good from his spots that goalies have difficulty stopping the shot even if they can anticipate that it’s coming.

The triggerman for the Jets’ 5th-ranked power play, Laine lead all NHLers with 20 power play goals in 2017-18. He would routinely patrol the space between the left half-wall and left point, making himself open to cross-seam passes and one-timing his quick snapshot on net. His 24 even-strength goals tied for 20th in the league, so he’s no slouch at 5-on-5 scoring either.

Since breaking into the league, Laine has used his generational shot to pick apart opposing goalies. The odds-on favourite to inherit Ovechkin’s throne as best goal-scorer is the league, the sky’s the limit for a kid who potted 44 goals in just his second season in the league.

 

Conclusion

So there we have it; the modus operandi of five of the game’s elite. While Ovechkin, Malkin, McDavid, Kucherov, and Laine possess a shared gift for putting the puck in the net, they achieve it with vastly different sets of techniques, skills, and strategies. There is no uniform way to score a goal across the league, but all that matters is that it goes in.

With goals representing the currency of the NHL, goal-scorers are among the most valuable assets out there. Scoring goals wins you games, playoff series, and, as 32-year old Alex Ovechkin and 31-year-old Evgeni Malkin know, Stanley Cup championships. Kucherov (25), McDavid (21), and Laine (20) have not yet won hockey’s ultimate prize but given their relative youth and their otherworldly ability to put the puck in the net, they might not be far away.

 

Data courtesy of Hockey Abstract (http://hockeyabstract.com/testimonials), Natural Stat Trick (https://naturalstattrick.com), and NHL.com (https://nhl.com).

Shot heat maps courtesy of Micah Blake McCurdy’s wonderful visualization website HockeyViz (https://hockeyviz.com).

Does Goalie Rest Help Win a Cup? by Owen Kewell

By: Owen Kewell

On Thursday night, two third period goals scored in quick succession proved to be all that the Washington Capitals needed to defeat the Vegas Golden Knights. In doing so, they became champions, and the core built around Alex Ovechkin finally earned the right to lift the Cup after years of bitter playoff disappointment.

At some point in the Cup Final, I recall reading that both Braden Holtby and Marc-Andre Fleury played relatively few regular season games compared to most starting goalies. I looked it up, and it’s true. Holtby ranked 18th among goalies in TOI this past season, while Fleury came in at 25th.

The two goalies who made it furthest in the 2018 playoffs had a relatively light regular season workload. Could this be more than coincidence? Could a lighter workload directly translate into improved playoff performance? My first thought on the matter was that a goalie who played fewer regular season games would experience less fatigue, and so would be better suited for a long and grueling playoff run. Intuitively, this theory is pleasantly logical, but does it hold any merit?

The Data

To tackle this question systematically, I examined the number of regular season games played by starting goalies of all playoff teams dating back to the 2007-08 season. I defined a playoff run’s starting goalie as the goalie who played the most minutes for that team in that playoff run. I grouped the goalies by the number of series that their teams won, thus separating goalies by degree of playoff success. I then looked at the number of regular season games played by the goalies in each group.

Cup-winning goalies tend to play 7-9 fewer regular season games 

Cup-winning goalies tend to play 7-9 fewer regular season games 

The numbers in the coloured boxes show the median GP value for all starting goalies whose teams won the number of playoff series found on the horizontal axis. It’s worth noting that I prorated games played for the lockout-shortened 2013 season as if it were a standard 82 game season.

Interestingly, when we group by degree of playoff success, we can see that the goalies who went on to win the Stanley Cup generally played fewer regular seasons games than did the goalies who went on to be eliminated at one point or another. This certainly supports the hypothesis that having your starter play fewer games would help your chances in the playoffs. Let’s take a closer look at these Cup-winning goalies.

Regular season workload of Cup-winning goalies

Regular season workload of Cup-winning goalies

Of these 11 goalies, only 2 appeared in 60 or more regular season games: Jonathan Quick’s 69 games in 2011-12, and Marc-Andre Fleury’s 62 games in 2008-09. Comparatively, this rate of 2/11 is quite low:

Cup-winning goalies reach 60+ GP less frequently than any other group

Cup-winning goalies reach 60+ GP less frequently than any other group

Only 18.2% of Cup-winning goalies reached 60+ GP, while 47.2% of all playoff starters reached the same threshold. The difference between the two figures is stark, but let’s remember that sample size is a crucial piece of context. Due to the nature of awarding a title, we can only glean a single data point per season. As such, we have just 11 data points, and that’s including 2017-18 Braden Holtby.

We can’t ignore the possibility that Group 4’s low rate of 18.2% was caused by chance. If we were to simulate 11 random trials that each independently had a 47.2% chance of producing a certain outcome, as we established is league average for hitting 60 GP, the binomial distribution tells us that there’s a 4.8% chance that 2 or fewer of the trials would produce the desired outcome. In other words, there’s a 4.8% chance that the observed statistical phenomenon can be completely explained by random chance.

Shifting perspective, this also means that there’s a 95.2% chance that the result is not entirely attributable to chance, and there’s that at least some form of relationship that exists between a goalie’s workload and their likelihood of winning a Stanley Cup. The results, though produced in a small sample size, certainly suggest that a goalie being well-rested contributes to their ability to lead their team to a championship.

So I Should Rest My Goalie, but When?

This was my follow-up question. Accepting that a well-rested goalie is an ingredient in the Stanley Cup recipe, does it matter when that rest happens during the season?

To highlight patterns in the workload of the same 11 Cup-winning goalies, I split each of their regular seasons into thirds (Games 1-27, 28-55, and 56-82) using schedule data from https://www.hockey-reference.com. For each section of games, I examined the starter’s proportion of their team’s total goaltending minutes. For example, in Games 1-27 of Washington’s 2017-18 season, Holtby played 1162:34, which was 71.5% of all TOI for Washington goalies. The chart below shows data for all goalies, including a group median.

Cup-winning goalies tend to have their lightest workload in the season's middle third 

Cup-winning goalies tend to have their lightest workload in the season's middle third 

Cup-winning starters tend to play a larger proportion of their team’s minutes during the first third (Games 1-27) and the last third (Games 56-82) of the regular season. Comparatively, during Games 28-55, they tend to play about 7% less frequently. The emphasis on the beginning and end of the season is logical: a team must win games early to build a comfortable position in the standings, and a team must win games late to enter the playoffs firing on all cylinders.

This chart suggests that the best time to rest a starting goalie is during the middle third of the season. This is not an inflexible rule, however, as we can see that there are many ways to structure rest over the course of a season and experience playoff success. Holtby, for what it’s worth, was at his busiest during the middle third of this past season and was still able to remain sharp throughout the playoffs.

Conclusion and Takeaways

Over the last decade, we’ve seen well-rested goalies lift the Stanley Cup more often than not. The empirical data support the notion that resting starters more frequently, particularly in the middle third of the season, will increase the likelihood of playoff success. This means that NHL coaching staffs with championship aspirations could gain an advantage by proactively managing their starter’s workload throughout the season.

Over-reliance on a starting goalie induces fatigue and invites the risk of said goalie being unable to maintain their performance over a two-month playoff run. While teams with strong starting goalies have tendencies to lean on them heavily throughout the regular season, this may be detrimental to championship aspirations. If a coach truly wanted to maximize their team’s Stanley Cup chances, they must ensure that their starting goalie is rested enough to maintain physical and mental focus over an extended playoff run. If this can be done, the team will be one step closer to hockey’s ultimate prize.

All data taken from Natural Stat Trick (https://www.naturalstattrick.com/) unless otherwise specified.

Investigating the Disappearance of Vegas’ First Line by Owen Kewell

By: Owen Kewell

The Golden Knights kept finding ways to pull it off. Driven by all-world goaltending, an opportunistic counter-attack, and the desire to prove the rest of the hockey world wrong (especially their former teams), the group that James Neal affectionately dubbed the ‘Golden Misfits’ put together a Cinderella run through the Western Conference and into the Stanley Cup Final.

Only midnight appears to be approaching faster than anticipated.

After a 6-2 loss at the hands of the Washington Capitals yesterday, the Golden Knights find themselves searching for answers as their first elimination game in franchise history looms. The last three games, which Vegas has lost by a combined score of 12-5, featured a team that appeared much different from the group we saw roll their way through the Western Conference and into a 1-0 Stanley Cup Final lead.

So what’s different?

Goaltending is the obvious answer. After posting a save percentage above .930% for each of the first three rounds, Fleury’s mark is a paltry .845% through four games in the Final. Anyone could point out that Fleury needs to be better, and while it’s not wrong, it’s not particularly insightful.

Instead, I wanted to investigate the play of Vegas’ other big guns, who have been similarly subpar in their recent string of losses. I’m referring to the Knights’ three-headed monster of a top line, which features William Karlsson between Jonathan Marchessault and Reilly Smith. These three have been catalysts for their team’s offense all season and are similarly 1-2-3 in team scoring for these playoffs.

The table below compares all-situations production of Vegas’ top line during the first 16 playoff games, which includes Rounds 1-3 and Game 1 of the Cup Final, versus their production in the last 3 games.

Graphic 1.jpg

We can clearly see that the group’s production has dropped off. While the trio was averaging well over one goal and three points per game through the first 16 games, they’ve managed only one goal and four points total in the last three games. Goals are low-frequency events by nature, though, so to properly evaluate their play in a sample as small as three games we need to look at the higher-frequency plays that lead to goals. The table below reflects even-strength play where Vegas’ 1st line is on the ice together.

Graphic 2.jpg

A few numbers jump out from the above table. While the top line is generating significantly more shot attempts than previously, they are producing fewer shots on goal. This means that a higher proportion of the line’s shot attempts are being blocked, and those that aren’t being blocked are missing the net more often. Only 38.3% of the line’s shot attempts in the last three games are reaching the net, which is down more than 10% from the previous 16 games.

Graphic 3.jpg

Elsewhere, the line’s event rates are down across the board. Per 60 minutes, Marchessault, Smith, and Karlsson are generating 4.86 fewer scoring chances, 1.19 fewer high danger chances, and 1.64 fewer goals than they did in the previous 16 games. Much of the reduced scoring can be explained by a decrease in the unit’s on-ice shooting percentage, but the line’s decreased scoring chance generation remains a worrying red flag.

Offensive production, or a lack thereof, does not exist in a vacuum. I would be remiss if I did not acknowledge the work that Matt Niskanen and Dmitry Orlov have done in neutralizing Vegas’ top line. This pairing has been heavily leaned upon to shut down Vegas’ stars, especially in Games 3 and 4 when Washington had last change as the home team. Using William Karlsson and Dmitry Orlov as proxies for Vegas’ 1st line (VGK L1) and Washington’s 1st pairing (WSH P1), we can see what proportion of VGK L1’s 5-on-5 minutes were played against WSH P1 in each game thus far.

Graphic 4.jpg

Vegas’ lone victory came in the only game where their top line was able to play most of their even-strength minutes away from Washington’s top shutdown pairing. Since then, VGK L1 has seen a healthy dose of Orlov and Niskanen, and their production has suffered.

Whether attributable to a lack of execution or stellar opposing defense, the play of Vegas’ first line has been insufficient in their last three games. Their goal-scoring is down by more than half, fewer shot attempts are reaching Braden Holtby, and the line isn’t producing scoring chances at their usual rate.

For Vegas to begin climbing out of the hole they find themselves in, their top line will need to reverse these trends and find a way to produce. If they don’t manage to do so, the strike of midnight might be right around the corner.

All statistics courtesy of Natural Stat Trick (https://www.naturalstattrick.com/)