Toronto Maple Leafs

How Important is Winning a Period in the NHL? by Alex Craig

By: Adam Sigesmund (@Ziggy_14)

Sometimes when I watch hockey on television, the broadcast will display a stat that makes me cringe. One of my (least) favourites is a stat like the one displayed just under the score in the screenshot below:

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Most of us have noticed these stats on broadcasts before. I imagine they are common because they match the game state (i.e. the Leafs are leading after the first period), so broadcasters probably believe we find them insightful. However, we are all smart enough to understand that teams should theoretically have a better record in games that saw them outscore their opponents in the first period. In this case, whatever amount of insight the broadcasters believe they are providing us with is merely an illusion. Perhaps they also saw value in the fact that the Leafs were undefeated in those 13 games, but that is not what I want to focus on today. 

More generally, my primary objective for this post is to shed light on the context behind this type of stat, mostly because broadcasts rarely provide it for us. Ultimately, I will examine 11 seasons worth of data to understand how the outcome of a specific period effects the number of standings points a team should expect to earn in that game. Yes, this means there will be binning*. And yes, I acknowledge that binning is almost always an inappropriate approach in any meaningful statistical analysis. The catch here is that broadcasters continue to display these binned stats without any context, and I believe it is important to understand the context of a stat we see on television many times each season.

* Binning is essentially dividing a continuous variable into subgroups of arbitrary size called “bins.”In this case, we are dividing a 60-minute hockey game into three 20-minute periods. 

A particular team wins a period by scoring more goals than their opponent. I looked at which teams won, lost, or tied each period by running some Python code through a data set provided by moneypuck.com. The data includes 13057 regular season games between the 2007-2008 and 2017-2018 seasons, inclusive. (Full disclosure: I’m pretty sure four games are missing here. My attempts to figure out why were unsuccessful, but I went ahead with this article because the rest of my code is correct, and 4 games out of over 13K is virtually insignificant anyways).  The table below displays our sample sizes over those eleven seasons:

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Remember that when the home team loses, the away team wins, so the table with our results will be twice as large at the table above. I split the data into home and away teams because of home-ice advantage; Home teams win more games than the visitors, which suggests that home teams win specific periods more often too. We can see this is true in the table shown above. In period 1, for example, the home team won 4585 times and lost only 3822 times. The remaining 4650 games saw first periods that ended in ties. 

We want to know the average number of standings points the home team earned in games after winning, tying, or losing period 1. This will give us three values: One average for each outcome of the first period. We also want to find the same information for the away team, giving us atotal of six different values for period 1. (This step is not redundant because of the “Pity Point”system, which awards one point to the losing team if they lost in overtime or the shootout. The implication is that some games result in two standings points but others end in three, so knowing which team won the game still does not tell us exactly how many points the losing team earned). Repeating this process for periods 2 and 3 brings our total to 18 different values. The results are shown below:

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The first entry in the table (i.e. the top left cell) tells us that when home teams win period 1, they end up earning an average of 1.65 points in the standings. We saw earlier that the home team has won the first period 4585 times, and now we know that they typically earn 1.65 points in the standings from those specific games. But if we ignore the outcome of each period, and focus instead on the outcomes of all 13057 games in our sample, we find that the average team earns 1.21 points in the standings when playing at home. (This number is from the sentence below the table —the two values there suggest the average NHL team finishes an 82-game season with around 91.43 points, which makes sense). So, we know that home teams win an average of 1.21 points in general, but if they win the first period they typically earn 1.65 points. In other words, they jumped from an expected points percentage of 60.5% to 82.5%. That is a significant increase.

However, in those 4585 games, the away team lost the first period because they were outscored by the home team. It is safe to say that the away team experienced a similar change, but in the opposite direction. Indeed, their expected gain decreased from 1.02 points (a general away game) to 0.54 points (the condition of losing period 1 on the road). Every time your favourite team is playing a road game and loses period 1, they are on track to earn 0.48 less standings points than when the game started; That is equivalent to dropping from a points percentage of 51% to 27%. Losing period 1 on the road is quite damaging, indeed. 

Another point of interest in these results, albeit an unsurprising one, is the presence of home-ice advantage in all scenarios. Regardless of how a specific period unfolds, the home team is always better off than the away team would be in the same situation.

I also illustrated these results in Tableau for those of you who are visual learners. The data is exactly the same as in the results table, but now it’s illustrated relative to the appropriate benchmark (1.21 points for home teams and 1.02 points for away teams).  

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Now, let’s reconsider the original stat for a moment. We know that when the Leafs won the first period, they won all 13 of those games. Clearly, they earned 26 points in the standings from those games alone. How many points would the average team have earned under the same conditions? While the broadcast did not specify which games were home or away, let’s assume just for fun that 7 of them were at home, and 6 were on the road. So, if the average team won 7 home games and 6 away games, and also happened to win the first period every time, they would have: 7(1.65) + 6(1.53) = 20.73 standings points. Considering that the Leafs earned 26, we can see they are about 5 points ahead of the average team in this regard. Alternatively, we can be nice and allow our theoretical “average team”to have home-ice advantage in all 13 games. This would bump them up to 13(1.65) = 21.45 points, which is still a fair amount below the Leafs’ 26 points. 

One issue with this approach is that weighted averages like the ones I found do not effectively illustrate the distributionof possible outcomes. All of us know it is impossible to earn precisely 1.65 points in the standings —the outcome is either 0, 1, or 2. An alternative approach involves measuring the likelihood of a team coming away with 2 points, 13 times in a row, given that all 13 games were played at home and that they won the first period every time. We know the average is 13(1.65) = 21.45 standings points, but how likely is that? It took a little extra work, but I calculated that the average team would have only a 3.86% chance to earn all 26 points available in those games. (I did this by finding the conditional probability of winning a specific game after winning the first period at home, and then multiplying that number by itself 13 times). Although the probability for the Leafs is a touch lower than this, since there is a good chance a bunch of those 13 games were not played at home, you should not allow such a low probability to shock you; 13 games is a small sample, especially for measuring goals. There is definitely lots of luck mixed in there. 

This brings us back to my original anecdote about cringing whenever I encounter this type of stat. Even if we acknowledge its fundamental flaw —scoring goals leads to wins, no matter when those goals occur in a game —the stat is virtually meaningless in a small sample. Goals are simply too rare to provide us with much insight in a sample of 13 games. Nevertheless, broadcasters will continue displaying these numbers without context. This article will not change that. So, the next time it happens, you can now compare that team to league average over the past eleven seasons. Even if the stat is not shown on television, all you need to know is the outcome of a specific period to find out how the average team has historically performed under the same condition. At the very least, we have a piece of context that we did not have before.

Playoff Preview: Toronto Maple Leafs vs. Boston Bruins by Anthony Turgelis

By: Kurt Schulthies

Monday May 13, 2013:

The city of Toronto was electric. Competing in the Stanley Cup Playoffs for the first time in 12 seasons, the Toronto Maple Leafs inched their way to game 7 against the heavily favoured Boston Bruins. Continuing an improbable run led by Phil Kessel, Nazem Kadri, James Van Riemsdyk, Cody Franson, Dion Phaneuf, and James Reimer.

I was with a dozen of my closest friends, sitting at the head of the table in a Shoeless Joe’s party room. Every detail of that night is vivid in my mind -- for what was about to come can only be described as demoralizing. The Leafs held a 3 goal lead with less than 11 minutes to go in regulation time.

The lead evaporated. The Bruins’ eventual overtime winner became an inevitability.

Without a word, I immediately got up from my seat and stormed out of the bar. I glanced over at the patrons -- and to this day, I have never seen so many people simultaneously unsure how to react.

Present Day

Toronto is a dramatically different team. Now led by their sophomore phenom Auston Matthews, the Leafs look for revenge against the team that crushed the hopes of an entire fanbase five years ago.  

Taking an analytics-focused view, let’s see how Toronto and Boston compare now.

Offensive Matchup

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All data used is courtesy of Corsica and NaturalStatTrick

The Leafs are superior to the Bruins in every major offensive category. Toronto is one of the highest paced teams in the league, relying on their high-end offensive talent to best opponents. Boston had a similarly strong offensive season, but failed to generate a significant amount of high danger scoring chances per 60 minutes of play. This can likely be attributed to the Bruins' slower paced style of play.

                               Toronto                                                                       Boston

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The visuals above show the league rank of each forward in 5v5 primary points per 60 minutes. This metric is highly repeatable year over year, and gives a somewhat accurate depiction of a player’s offensive prowess. However, numbers are somewhat skewed by factors such as the quality of their linemates and the quality of competition faced.

The first thing that stands out about the Leafs’ chart is Auston Matthews. He ranks first league wide in 5v5 P1/60. Fans can expect him to be a constant threat, and the biggest ‘X-factor’ player in the series. Boston is led by what is likely the league’s most dominant first line. It is one of the only lines that is capable of dominating the overpowering combination of Auston Matthews and William Nylander.

Heat maps created and available on HockeyViz.com

Heat maps created and available on HockeyViz.com

Toronto is incredible at generating high danger scoring chances. This metric is much more predictive of goal scoring than stats such as ‘shots’. In contrast, Boston is far below league average at generating scoring chances right in front of the net, but remain a threat in the high slot. Toronto outperforms metrics such as Corsi for and scoring chances due to their admirable scoring talent, and high number of odd man rushes per game. Boston has slightly above average shot quality, meaning they likely score near their expected results according to Corsi and scoring chances.

Defensive Matchup

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                 Boston

Zdeno Chara - Charlie McAvoy

Torey Krug - Kevan Miller

Matt Grzelcyk - Adam McQuaid

               Toronto

Morgan Reilly - Ron Hainsey

Jake Gardiner - Nikita Zaitsev

Travis Dermott - Roman Polak

Boston has been an excellent defensive team this season, beating Toronto in every major defensive category. The Bruins are one of the best shot suppression teams in the NHL, forcing teams to shoot from unfavourable scoring positions. In contrast, the Leafs allow a high concentration of dangerous scoring chances from the slot, leading to a much worse defensive performance. Shots against location heat maps for each team can be seen below:

Heat maps created and available on HockeyViz.com

Heat maps created and available on HockeyViz.com

Toronto gives up a lot of high danger chances, leading to a higher expected goals against per game. It also means the team underperforms metrics such as corsi and scoring chances. Boston, in contrast, is excellent at shot suppression. This leads to outperforming metrics such as corsi and scoring chances, and results in a very low expected goals against per game.

Goaltending Matchup

Both the Leafs and Bruins boast top tier goaltenders with Frederik Andersen and Tuuka Rask. Using a goalie comparison tool created by Tyler Kelley (@DocKelley41), we are able to compare each goalie by key metrics:

Compare other goalies at: https://public.tableau.com/profile/tyler7457#!/vizhome/GoalieTool/2017-18ComparisonTool

Compare other goalies at: https://public.tableau.com/profile/tyler7457#!/vizhome/GoalieTool/2017-18ComparisonTool

For more on what each metric means, read here. The values on the x-axis of the graph are the percentile ranks that each of their stats fall on. Frederik Andersen is near the top of the charts with his Goals Saved Above Average. This is unsurprising considering the aforementioned shaky Leafs defense and the great play of Andersen so far this year. The stat highlights that if an average goalie were to be placed in the Leafs net in front of Andersen, they would be expected to concede a lot more goals. By this metric among others, it appears Andersen has a small edge over Tuuka Rask this season.

Prediction

The team statistics would suggest the Boston Bruins are the favourites in this series. However, in head-to-head matchups in the Toronto Maple Leafs have been the better team with a 7-1-0 record in 8 games over the past 2 seasons. This series should be a war, and one of the most likely first round matchups to go to 7 games. With that being said, my final prediction is Leafs in 7 games.


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