Hockey

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.

Screen Shot 2019-03-05 at 4.27.36 PM.png

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.

Screen Shot 2019-03-05 at 4.33.56 PM.png

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

Screen Shot 2019-03-05 at 4.39.46 PM.png

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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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:

Picture1.png

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:

Picture2.png

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:

Picture3.png

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).  

Picture4.png

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.

Do Tired Defensemen Surrender More Rebounds? by Owen Kewell

By: Owen Kewell

Two thoughts popped into my mind, one after the other.

First, I wondered whether an NHL player’s performance fluctuated depending on how long they had been on the ice. Does short-term fatigue play a significant role over a single shift?

Second, I wondered how to quantify (and hopefully answer) this question.

The Data

Enter the wonderfully detailed shot dataset recently published by moneypuck.com. In it, we have over 100 features that describe the location and context of every shot attempt since the 2010-11 NHL season. You can find the dataset here: http://moneypuck.com/about.htm#data.

Within this data I found two variables to test my idea. First, the average number of seconds that the defending team’s defensemen had been on the ice when the attacking team’s shot was taken. The average across all 471,898 shots was 34.2 seconds, if you’re curious. With this metric I had a way to quantify the lifespan of a shift, but what variable could be used as a proxy for performance?

Fortunately, the dataset also says whether each shot was a rebound shot. To assess defensive performance, I decided to use the rate at which shots against were rebounds. Recovering loose pucks in your own end is a fundamental part of the job description for NHL defensemen, especially in response to your goalie making a save. Should the defending team fail to recover the puck, the attacking team could generate a rebound shot, which would often result in a goal against. We can see evidence of this in the 5v5 data:

Rebound shooting % is 3.6x larger than non-rebound shooting %

Rebound shooting % is 3.6x larger than non-rebound shooting %

The takeaway here is that 24.1% of rebound shots go into the net, compared to just 6.7% of non-rebound shots. Rebounds are much closer to the net on average, which can explain much of this difference.

I believe that a player’s ability to recover loose pucks is a function of their ability to anticipate where the puck is going to be and their quickness to get to there first. While anticipation is a mental talent, quickness is physical, meaning that a defender’s quickness could deteriorate over the course of their shift as short-term fatigue sets in. Could their ability to prevent rebound shots be consequently affected? Let’s plot that relationship:

No trendline graph.jpg

There’s a lot going on here, so let’s break it down.

The horizontal axis shows the average shift length of the defending defense pairing at the time of the shot against. I cut the range off at 90 seconds because data became scarce after that; pairings normally don’t get stuck on the ice for more than a minute and a half at 5v5. The vertical axis shows what percentage of all shots against were rebounds.

Each blue dot represents the rebound rate for all shots that share a shift length, meaning that there are 90 data points, or one for each second. The number of total shots ranges from 382 (90 seconds) to 8,124 (27 seconds). Here’s the full distribution:

Shot Rates.jpg

We can see that sample size is an inherent limitation for long shifts. The number of shots against drops under 1,000 for all shift lengths above 74 seconds, which means that the conclusions drawn from this portion of the data need to be taken with a grain of salt. This sample size issue also explains the plot’s seemingly erratic behaviour towards the upper end of the shift length range, as percentage rates of relatively rare events (rebounds) tend to fluctuate heavily in smaller sample sizes.

The Model

Next, I wanted to create a model to represent the trend of the observed data. The earlier scatter plot tells us that the relationship between shift length and rebound rate is probably non-linear, so I decided to use a polynomial function to model the data. But what should be this function’s degree? I capped the range of possibilities at degree = 5 to avoid over-fitting the data, and then set out to systematically identify the best model.

It’s common practice to split data into a training set and a testing set. I subjectively chose a split of 70-30% for training and testing, respectively. This means that the model was trained using 70% of all data points, and then its ability to predict previously unseen data was measured using the remaining 30%. Model accuracy can be measured by any number of metrics, but I decided to use the root mean squared error (RMSE) between the true data points and the model’s predictions. RMSE, which penalizes large model errors, is among the most popular and commonly-used error functions. I conducted the 70-30 splitting process 10,000 times, each time training and testing five different models (one each of degree 1, 2, 3, 4, and 5). Of the five model types, the 5th degree function produced the lowest root mean squared error (and therefore the highest accuracy) more often than the degree 1, 2, 3 or 4 functions. This tells us that the data is best modelled by a 5th degree polynomial. Fitting a normalized 5th degree function produced the following equation:

x  = shift length in seconds

x = shift length in seconds

This equation is less interesting than the curve that it represents, so let’s look at that:

Regression.jpg

What Does It Mean?

The regression appears to generally do a good job of fitting the data. Our r-squared value of 0.826 tells us that ~83% of the variance in ‘Rebound %’ is explained by defensemen shift length, which is encouraging. Let’s talk more about the function’s shape.

Rebound rate first differences decrease at first as the rate stabilizes, and then increase further

Rebound rate first differences decrease at first as the rate stabilizes, and then increase further

As defense pairings spend more time on the ice, they tend to surrender more rebound shots, meaning that they recover fewer defensive zone loose pucks. Pairings who are early in their shift (< 20 seconds) surrendered relatively few rebound shots, but there's likely a separate explanation for this. It's common for defensemen to change when the puck is in other team’s end, meaning that their replacements often get to start shifts with the puck over 100 feet away from the net they're defending. For a rebound shot to be surrendered, the opposing team would need to recover possession, transition to offense, enter the zone and generate a shot. These events take time, which likely explains why rebound rates are so low in the first 15-20 seconds of a shift.

We can see that rebound rates begin to stabilize after this threshold. The rate is most flat at the 34 second mark (5.9%), after which the marginal rate increase begins to grow for each additional second of ice time. This pattern of increasing steepness can be seen in the ‘Rebound Rate Increase’ column of the above chart and likely reflects the compounding effects of short-term fatigue felt by defensemen late in their shifts, especially when these shifts are longer than average. The sample size concerns for long shifts should again be noted, as should the accompanying skepticism that our long-shift data accurately represent their underlying phenomenon.

The main strategic implications of these findings relate to optimal shift length. The results confirm the age-old coaching mantra of ‘keep the shifts short’, showing a positive correlation between shift length and rebound rates. Defensemen shift lengths should be kept to 34 seconds or less, ideally, since the data suggests that performance declines at an increasingly steep rate beyond this point. Further investigation is needed, however, before one can conclusively state that this is the optimal shift length.

Considering that allowing 4 rebound shots generally translates to a goal against, it’s strategically imperative to reduce rebound shot rates by recovering loose pucks in the defensive zone. Better-rested defensemen are better able to recover these pucks, as suggested by the strong, positive correlation between defensemen shift length and rebound rates. While further study is needed to establish causation, proactively managing defensive shift lengths appears to be a viable strategy to reduce rebound shot rates. 

Any hockey fan could tell you that shifts should be kept short, but with the depth of available data we're increasingly able to figure out exactly how short they should be.

In Search of Similarity: Finding Comparable NHL Players by Owen Kewell

By: Owen Kewell

The following is a detailed explanation of the work done to produce my public player comparison data visualization tool. If you wish to see the visualization in action it can be found at the following link, but I wholeheartedly encourage you to continue reading to understand exactly what you’re looking at:

https://public.tableau.com/profile/owen.kewell#!/vizhome/PlayerSimilarityTool/PlayerSimilarityTool

NHL players are in direct competition with hundreds of their peers. The game-after-game grind of professional hockey tests these individuals on their ability to both generate and suppress offense. As a player, it’s almost guaranteed that some of your competitors will be better than you on one or both sides of the puck. Similarly, you’re likely to be better than plenty of others. It’s also likely that there are a handful of players league-wide whose talent levels are right around your own.

The NHL is a big league. In the 2017-18 season, 759 different skaters suited up for at least 10 games, including 492 forwards and 267 defensemen. In such a deep league, each player should be statistically similar to at least a handful of their peers. But how to find these league-wide comparables?

Enter a bit of helpful data science. Thanks to something called Euclidean distance, we can systemically identify a player’s closest comparables around the league. Let’s start with a look at Anze Kopitar.

Anze Kopitar's closest offensive and defensive comparables around the league

Anze Kopitar's closest offensive and defensive comparables around the league

The above graphic is a screenshot of my visualization tool.

With the single input of a player’s name, the tool displays the NHL players who represent the five closest offensive and defensive comparables. It also shows an estimate of the strength of this relationship in the form of a similarity percentage.

The visualization is intuitive to read. Kopitar’s closest offensive comparable is Voracek, followed by Backstrom, Kane, Granlund and Bailey. His closest defensive comparables are Couturier, Frolik, Backlund, Wheeler, and Jordan Staal. All relevant similarity percentages are included as well.

The skeptics among you might be asking where these results come from. Great question.

 

A Brief Word on Distance

The idea of distance, specifically Euclidean distance, is crucial to the analysis that I’ve done. Euclidean distance is a fancy name for the length of the straight line that connects two different points of data. You may not have known it, but it’s possible that you used Euclidean distance during high school math to find the distance between two points in (X,Y) cartesian space.

Now think of any two points existing in three-dimensional space. If we know the details of these points then we’re able to calculate the length of the theoretical line that would connect them, or their Euclidean distance. Essentially, we can measure how close the data points are to each other.

Thanks to the power of mathematics, we’re not constrained to using data points with three or fewer dimensions. Despite being unable to picture the higher dimensions, we've developed techniques for measuring distance even as we increase the complexity of the input data.

 

Applying Distance to Hockey

Hockey is excellent at producing complex data points. Each NHL game produces an abundance of data for all players involved. This data can, in turn, be used to construct a robust statistical profile for each player.

As you might have guessed, we can calculate the distance between any two of these players. A relatively short distance between a pair would tell us that the players are similar, while a relatively long distance would indicate that they are not similar at all. We can use these distance measures to identify meaningful player comparables, thereby answering our original question.

I set out to do this for the NHL in its current state.

 

Data

First, I had to determine which player statistics to include in my analysis. Fortunately, the excellent Rob Vollman publishes a data set on his website that features hundreds of statistics combed from multiple sources, including Corsica Hockey (http://corsica.hockey/), Natural Stat Trick (https://naturalstattrick.com) and NHL.com. The downloadable data set can be found here: http://www.hockeyabstract.com/testimonials. From this set, I identified the statistics that I considered to be most important in measuring a player’s offensive and defensive impacts. Let’s talk about offense first.

List of offensive similarity input statistics

List of offensive similarity input statistics

I decided to base offensive similarity on the above 27 statistics. I’ve grouped them into five categories for illustrative purposes. The profile includes 15 even-strength stats, 7 power-play stats, and 3 short-handed stats, plus 2 qualifiers. This 15-7-3 distribution across game states reflects my view of the relative importance of each state in assessing offensive competence. Thanks to the scope of these statistical measures, we can construct a sophisticated profile for each player detailing exactly how they produce offense. I consider this offensive sophistication to be a strength of the model.

While most of the above statistics should be self-explanatory, some clarification is needed for others. ‘Pass’ is an estimate of a player’s passes that lead to a teammate’s shot attempt. ‘IPP%’ is short for ‘Individual Points Percentage’, which refers to the proportion of a team’s goals scored with a player on the ice where that player registers a point. Most stats are expressed as /60 rates to provide more meaningful comparisons.

You might have noticed that I double-counted production at even-strength by including both raw scoring counts and their /60 equivalent. This was done intentionally to give more weight to offensive production, as I believe these metrics to be more important than most, if not all, of the other statistics that I included. I wanted my model to reflect this belief. Double-counting provides a practical way to accomplish this without skewing the model’s results too heavily, as production statistics still represent less than 40% of the model’s input data.

Now, let's look at defense.

List of defensive similarity input statistics

List of defensive similarity input statistics

Defensive statistical profiles were built using the above 19 statistics. This includes 15 even-strength stats, 2 short-handed stats, and the same 2 qualifiers. Once again, even-strength defensive results are given greater weight than their special teams equivalents.

Sadly, hockey remains limited in its ability to produce statistical measurements of individual defensive talent. It’s hard to quantify events that don’t happen, and even harder to properly identify the individuals responsible for the lack of these events. Despite this, we still have access to a number of useful statistics. We can measure the rates at which opposing players record offensive events, such as shot attempts and scoring chances. We can also examine expected goals against, which gives us a sense of a player’s ability to suppress quality scoring chances. Additionally, we can measure the rates at which a player records defense-focused micro-events like shot blocks and giveaways. The defensive profile built by combining these stats is less sophisticated than its offensive counterpart due to the limited scope of its components, but the profile remains at least somewhat useful for comparison purposes.

 

Methodology

For every NHLer to play 10 or more games in 2017-18, I took a weighted average of their statistics across the past two seasons. I decided to weight the 2017-18 season at 60% and the 2016-17 season at 40%. If the player did not play in 2016-17, then their 2017-18 statistics were given a weight of 100%. These weights represent a subjective choice made to increase the relative importance of the data set’s more recent season.

Having taken this weighted average, I constructed two data sets; one for offense and the other for defense. I imported these spreadsheets into Pandas, which is a Python package designed to perform data science tasks. I then faced a dilemma. Distance is a raw quantitative measure and is therefore sensitive to its data’s magnitude. For example, the number of ‘Games Played’ ranges from 10-82, but Individual Points Percentage (IPP%) maxes out at 1. This magnitude issue would skew distance calculations unless properly accounted for.

To solve this problem, I proportionally scaled all data to range from 0 to 1. 0 would be given to the player who achieved the stat’s lowest rate league-wide, and 1 to the player who achieved the highest. A player whose stat was exactly halfway between the two extremes would be given 0.5, and so on. This exercise in standardization resulted in the model giving equal consideration to each of its input statistics, which was the desired outcome.

I then wrote and executed code that calculated the distance between a given player and all others around the league who share their position. This distance list was then sorted to identify the other players who were closest, and therefore most comparable, to the original input player. This was done for both offensive and defensive similarity, and then repeated for all NHL players.

This process generated a list of offensive and defensive comparables for every player in the league. I consider these lists to be the true value, and certainly the main attraction, of my visualization tool.

Not satisfied with simply displaying the list of comparable players, I wanted to contextualize the distance calculations by transforming them into a measure that was more intuitively meaningful and easier to communicate. To do this, I created a similarity percent measure with a simple formula.

Similarity Formula.jpg

In the above formula, A is the input player, B is their comparable that we’re examining, and C is the player least similar to A league-wide. For example, if A->B were to have a distance of 1 and A->C a distance of 5, then the A->B similarity would be 1 - (1/5), or 80%. Similarity percentages in the final visualization were calculated using this methodology and provide an estimate of the degree to which two players are comparable.

 

Limitations

While I wholeheartedly believe that this tool is useful, it is far from perfect. Due to a lack of statistics that measure individual defensive events, the accuracy of defensive comparisons remains the largest limitation. I hope that the arrival of tracking data facilitates our ability to measure pass interceptions, gap control, lane coverage, forced errors, and other individual defensive micro-events. Until we have this data, however, we must rely on rates that track on-ice suppression of the opposing team’s offense. On-ice statistics tend to be similar for players who play together often, which causes the model to overstate defensive similarity between common linemates. For example, Josh Bailey rates as John Tavares’ closest defensive comparable, which doesn’t really pass the sniff test. For this reason, I believe that the offensive comparisons are more relevant and meaningful than their defensive counterparts.

 

Use Scenarios

This tool’s primary use is to provide a league-wide talent barometer. Personally, I enjoy using the visualization tool to assess relative value of players involved in trades and contract signings around the league. Lists of comparable players give us a common frame through which we can inform our understanding of an individual's hockey abilities. Plus, they’re fun. Everyone loves comparables.

The results are not meant to advise, but rather to entertain. The visualization represents little more than a point-in-time snapshot of a player’s standing around the league. As soon as the 2018-19 season begins, the tool will lose relevance until I re-run the model with data from the new season. Additionally, I should explicitly mention that the tool does not have any known predictive properties.

If you have any questions or comments about this or any of my other work, please feel free to reach out to me. Twitter (@owenkewell) will be my primary platform for releasing all future analytics and visualization work, and so I encourage you to stay up to date with me through this medium.

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&nbsp;

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&nbsp;

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/)

The Stanley Cup Formula: An Investigation Through Machine Learning by Scott Schiffner

By: Owen Kewell

NHL seasons follow a formulaic plotline.

Entering training camp, teams share a common goal: win the Stanley Cup. The gruelling 82-game regular season separates those with legitimate title hopes from those whose rosters are insufficient, leaving only the sixteen most eligible teams. The attrition of playoff hockey gradually whittles down this number until a single champion emerges victorious, battle-tested from the path they took to win hockey’s top prize. Two months off, then we do it all again.

Teams that have won the Stanley Cup share certain traits. Anecdotally, it’s been helpful to have a dominant 1st line centre akin to Sidney Crosby, Jonathan Toews or Anze Kopitar. Elite puck-moving defensemen don’t hurt either, nor does a hot goalie. Delving deeper, though, what do championship teams have in common?

I decided to answer this question systematically with the help of some machine learning.

Some Background on Classification

Classification is a popular branch of supervised machine learning where one attempts to create a model capable of making predictions on new data points. We do this by building up, or ‘training’, the model using historical data, explicitly telling the model whether each past data point achieved the target class that we’re trying to predict. In the context of hockey, this data point could be some number of team statistics produced by the 2015 Chicago Blackhawks. The target here would be whether they won the Stanley Cup, which they did.

Sufficiently robust classification models can identify a number of statistical trends that underpin the phenomenon that they’re observing. The models can then learn from these trends to make reasonably intelligent predictions on the outcome of future data points by comparing them to the data that the classifier has already seen.

Building a Hockey Classifier

We can apply these techniques to hockey. We have the tools to train a model to learn which team statistics are most predictive of playoff success. To do this, we must first decide which stats to include in our dataset. To create the most intelligent classifier, we decided to include as many meaningful team statistics as possible. Here’s what we came up with:

team stats.jpg

It’s worth noting that we engineered the ‘Div Avg Point’ feature by calculating the average number of points contained by all teams in a given team’s division. The remaining statistics were sourced from Corsica and Natural Stat Trick. An explanation of each of these stats can be found on the glossaries for the two websites.

Our dataset included 210 data points: 30 teams per season over the 7 seasons between 2010-11 and 2016-17. Each data point included team name, the above 53 team stats, and a binary variable to indicate whether the team in question won the Cup. Using this data, we trained nine different models to recognize the statistical commonalities between the 7 teams whose seasons ended with a Stanley Cup championship. The best-performing model was a Logistic Regression model trained on even-strength data, and so all further analysis was conducted using this model.

Results: Team Stats that Matter Most

To evaluate which team stats were most strongly linked to winning a Cup, we created a z-score standardized version of our team data. We then calculated the estimated coefficients that our logistic regression model assigned to each team stat. The size of these coefficients indicates the relative importance of different team stats in predicting Stanley Cup champions. The 5-highest ranking team stats can be seen below:

top 5 team stats.jpg

Of all team statistics, ‘Goals For Per 60 Minutes’, or GF/60, is most predictive of winning a Stanley Cup. Of the 7 champions in the dataset, 4 ranked within the top 5 league-wide in GF/60 in their respective season, with 2016-17 Pittsburgh most notably leading the league in the statistic. Impressive results in ‘High Danger Chances For’ and ‘Team Wins’ both strongly correlate to playoff success, while ‘Scoring Chance For Percentage’ and ‘Shots on Goal For Percentage’ round out the top 5.    

What Does It Mean?

Generating a list of commonalities among past champions allows us to comment on what factors impact a team’s likelihood of going all the way. Most apparent is the importance of offense. It is more important to generate goals and high-danger chances than it is to prevent them, as GA/60 and HDCA rank 36th and 13th among all statistics, respectively (their corollaries are 1st and 2nd). In the playoffs, the best team offense tends to trump the best team defense, which we saw anecdotally in last year’s Pittsburgh v Nashville Final. If you want to win a Stanley Cup, the best defense is a good offense.

offense vs defense.jpg

We can see that a team’s ability to generate scoring chances, both high-danger and otherwise, is more predictive of playoff success than their ability to generate shots. Although hockey analytics pioneers championed the use of shot metrics as a proxy for puck possession, recent industry sentiment has shifted towards the belief that shot quality matters more than shot volume. The thinking here, which is supported by the above results, is that not all shots have an equal chance of beating a goalie, and so it is more important to generate a shot with a high chance of going in than it is to generate a shot of any kind. Between a team who can consistently out-chance opponents and a team who can consistently out-shoot opponents, the former is more likely to win a hockey game, and therefore playoff series.  

Application: The 2017-18 Season

A predictive model isn’t very helpful unless it can make predictions. So let’s make some predictions.

By feeding our model the team stats produced by the recently-completed 2017-18 regular season, we can output predictions of each team’s likelihood of winning the 2018 Stanley Cup. Since this is the fun part, let’s get right to the probability estimates for all 31 NHL teams:

probability estimates.jpg

The rankings above essentially indicate how similar each team’s season was to the regular season of teams that went on to win it all. In doing so, they hope to identify the teams most likely to replicate this success The model favours the Boston Bruins to win the 2018 Stanley Cup, predicting a victory over the Nashville Predators in the Final.

The above data highlights a few curiosities. Notably, we can see that some non-playoff teams had 5-on-5 numbers that were relatively comparable to past Cup champions. Specifically, the Blues, Stars, and Flames played 5-on-5 hockey well enough this season to qualify for the playoffs. The Blues and Flames can attribute their disappointingly long off-seasons to the 30th and 29th-ranked power plays, respectively. The Stars’ implosion is more of a statistical anomaly, and while conducting an autopsy would be interesting it would be better served as a subject for another article.

The lowest-ranked teams to have made the playoffs in the real world are the New Jersey Devils and the Washington Capitals. While their offensive star power might have been enough to get these squads to the dance, the model predicts a quick exit for them both.

A Computer-Generated Bracket:

2018bracket.jpg

For fun, I’ve filled out the above bracket using the class probability rankings generated by our model. Of the 8 teams who have won or are winning their first-round playoff series, the model picked 7 of them as at the winner, with Philadelphia being the exception. While it’s far too early to comment on the model’s accuracy, as only a single playoff series has been completed, it’s an encouraging start.

Limitations of the Analysis

The above results must be considered in the appropriate context. The model was trained and tested using only 5-on-5 data, which would explain the lack of love for teams with strong special teams like Pittsburgh and Toronto. The model is also blind to the NHL’s playoff format. Due to the NHL’s decision to have teams play against their divisional foes during the first two playoff rounds, teams in strong divisions have a much harder road to winning a Cup. Consider that Minnesota’s path to the conference final would likely involve Winnipeg and Nashville in the first two rounds, who finished 2nd and 1st in NHL standings in the regular season. Divisional difficulty is not reflected in the probabilities listed above, though incorporating divisional difficulty either probabilistically or through a strength of schedule modifier could be areas of further analysis.

A final limitation of the model is that it is trained using only 7 champions. In an ideal world, we would have access to dozens or hundreds of Stanley Cup positive instances, but due to the nature of the game there can only be one champion per year. We considered extending the dataset backwards past 2011 but ultimately decided against doing so. The NHL is different today than it was in the past. Training a model on a champion from 2000 tells us little about what it takes to have success in 2018. Using 2010-11 onwards represented a happy medium in the trade-off between data relevance and quantity.

What next?

Winning a Stanley Cup remains an inexact science. While it’s valuable to identify trends among past winners, there is no guarantee that what’s worked before will work again. It’s a game of educated guesses.

I believe that the most legitimate way to build a Stanley Cup winner is a combination of the past and the future. Analyzing historical data to identify team traits that are predictive of a championship is half the battle. The rest is anticipating what the future of the NHL will look like. The champions of the next few years will be lead by managers who are best able to identify what it’ll takes to win in the modern NHL. While the above framework approaches the first half in a systematic way, the latter remains much harder to crystallize.

In the meantime, let’s turn to what’s in front of our eyes. The playoffs have been tremendously entertaining thus far, and that’ll only pick up as teams are threatened by elimination. Let’s enjoy some playoff hockey. Let’s see which playing styles, tactics, and matchups seem to work. Let’s learn.

Even if your team gets eliminated, just remember that this season’s playoffs are just a couple months away from being data points to train next season’s model.

Then we do it all again.

Playoff Preview: Winnipeg Jets vs. Minnesota Wild by Scott Schiffner

By: Owen Kewell and Scott Schiffner

The calm before the storm.

The brackets have been setup, the matchup strategies developed, and the razors hidden away. For the first time since June, playoff hockey is here. We are mere hours from the puck drop that’ll kick off the 2017-18 Stanley Cup Playoffs, the starting pistol for a two-month long marathon where only one team can cross the finish line. In anticipation of this, we at the Queen’s Sports Analytics Organization decided to tee up the matchups featuring Canadian teams. We start with the Winnipeg Jets, who will play host to the Minnesota Wild on Wednesday night. The first round playoff series between the Central division rival Winnipeg Jets (2nd, 52-20-10) and the Minnesota Wild (3rd, 45-26-11) is an exciting matchup that is sure to feature a high level of speed, talent, and physicality from both sides. Both squads have enjoyed productive seasons, with the Jets posting the best record of any Canadian team, finishing with 114 points.

Offensive Matchup

Winnipeg enters the series with the reputation of having one of the most lethal forward groups in the league. Lead by a rejuvenated Blake Wheeler (91 points) and 44 goals from sophomore winger Patrik Laine, the Jets possess high-end offensive firepower that has torched the league for the better part of the season. Minnesota, meanwhile, enjoyed strong seasons from Eric Staal (76 points), Mikael Granlund (67 points) and Jason Zucker (64 points). Let’s take a quick look at some summary statistics from the regular season.

Stats from Corsica.hockey

Stats from Corsica.hockey

The Jets scored 23 more goals than the Wild over the season, though much of this can be explained by their superior power play. Jets skaters had a higher shooting percentage, though the difference is too small to reasonably infer superior shooting ability. The Jets outperformed the Wild at generating shot attempts and scoring chances, though the Wild were able to create more high-danger scoring chances. While individual point totals suggest Winnipeg has more high-end forwards, we can examine depth charts to clarify the picture.

depth chart.jpg

The graphic above shows the current depth charts (courtesy of Daily Faceoff) and each player’s rank among NHL forwards in even-strength primary points per 60 minutes. Here we confirm our belief that Winnipeg’s forward group is much deeper than Minnesota’s, as we can see that six Jets produced at a top-line rate compared to just three Wild players. To understand how the above results were achieved, we turn to heat maps.

Heat maps created and available on HockeyViz.com

Heat maps created and available on HockeyViz.com

The red areas indicate locations where a team shoots more frequently than league average, while blue is the inverse. In these maps we can see two teams who have a very different approach to generating offence. The Jets set up a triangle of attack, which results in a high volume of shots coming from the points and the mid-high slot. Being able to attack the slot with such regularity doubtlessly contributed to the success that the Jets experienced this season. The Wild, meanwhile, seem to play more on the perimeter with the goal of funneling pucks towards the crease. This explains why Minnesota produced more high-danger chances than the Jets despite generating less total scoring chances.

The offence matchup clearly favours Winnipeg. The Jets have the top-end firepower and the depth to roll scoring threats on every line. Throw in a dangerous power play, and the Jets are dangerous enough to make life miserable for anyone attempting to contain them.

Defensive Matchup

Winnipeg Jets:

Josh Morrissey – Jacob Trouba

Joe Morrow – Dustin Byfuglien

Ben Chiarot – Tyler Myers

Minnesota Wild:

Jonas Brodin – Matthew Dumba

Carson Soucy – Jared Spurgeon

Nick Seeler – Nate Prosser

The Winnipeg Jets allowed 216 goals in 2017/18, with 144 coming at even strength, while Minnesota allowed 229 goals (144 at 5v5). Winnipeg gave up an average of 31.9 shots per game, while Minnesota surrendered 31.3 on average. In terms of possession metrics, Winnipeg controlled 51.42% of shot attempts over the course of the 2017/18 season, good for 10th in the league, while Minnesota sits 29th with only 47.17% of shot attempts.

Comparing the top pairing defencemen for both teams using HERO charts:

http://ownthepuck.blogspot.ca/2017/05/hero-charts-player-evaluation-tool.html

http://ownthepuck.blogspot.ca/2017/05/hero-charts-player-evaluation-tool.html

The Minnesota Wild’s defence corps has taken a significant blow going into the postseason with the loss of number 1 defenseman Ryan Suter, who logged an average of 26:46 minutes of ice time per game before suffering a season-ending ankle injury on March 31. Veteran defender Jared Spurgeon remains a game-time decision due to an injured hamstring. The burden to cover these minutes will fall squarely on the shoulders of young defensemen Jonas Brodin and Matt Dumba, who will be counted on in key defensive situations. The Winnipeg Jets boast a tough lineup of physical defencemen, including Dustin Byfuglien and Tyler Myers, who will look to shut down the Wild’s top offensive lines. The Winnipeg Jets have the edge when it comes to top-tier defencemen, as well as much stronger depth on the blueline overall.

Finally, let’s compare the heat maps for both Winnipeg and Minnesota in their own defensive zones.

Heat maps created and available on HockeyViz.com

Heat maps created and available on HockeyViz.com

Taking a look at these maps, both teams are effectively limiting the number of scoring chances from high-danger scoring areas around the net (<25 feet) and in the slot. Minnesota’s heat map clearly indicates that the majority of chances are coming from the point (>40 feet out from the net) and down the right side, a potential weakness that Winnipeg’s quick wingers will look to exploit. Winnipeg’s defence is managing to limit almost all chances from high-scoring areas directly in front of their net, keeping the majority of shot attempts to the outside perimeter of the rink.

Goaltending Matchup:

We close our positional matchups by considering goaltending. Winnipeg will rely on Connor Hellebuyck, who broke out this year to post the winningest season ever by an American goalie. The young upstart will go toe to toe with Devan Dubnyk, the waiver-wire reclamation project that Minnesota has turned into a competent starter. Dubnyk has the qualitative advantage of playoff experience, but let’s see how the numbers stack up.

goalies.jpg

Unless otherwise specified, the above percentages reflect even-strength play. We see that Hellebuyck and Dubnyk performed similarly at even strength, as their save percentages for low, medium and high danger shots are all within a single percentage point. Where we see a difference, however, is on the special teams. While these stats are influenced by the quality of special team units, we see that Hellebuyck has significantly outperformed Dubnyk on both power plays and penalty kills. We also see that Hellebuyck saved about 2 goals more than expected given the quality of the shots being faced, whereas Dubnyk was over 7 goals in the hole on this metric.

If there had to be a choice between the two to start a Game 7, Connor Hellebuyck would be a safe choice. Despite his inexperience, his exceptional season played a huge role in Winnipeg’s ascension to 2nd place in the NHL’s overall standings. He’s shown to be better than Dubnyk at stopping the puck, and for that reason, he gives his team a better chance to win.

In summary, the numbers indicate that Winnipeg has the advantage in terms of offense, defense, and goaltending. The Jets enter the playoffs on an absolute tear, having won 11 of their last 12 games. They are 3-1-0 vs. the Wild in their season series. We are predicting that the Winnipeg Jets will be victorious in their first-round series against the Minnesota Wild, likely in 5 or 6 games.  

What's a Corsi Anyway?: An Intro to Hockey Analytics by Scott Schiffner

By: Owen Kewell, Scott SchiffnerAdam Sigesmund (@Ziggy_14), Anthony Turgelis (@AnthonyTurgelis)

Advanced statistics is an area that has recently started to pick up steam and shift into the mainstream focus in hockey over the past decade. Many NHL teams now employ full-time analytics staff dedicated to breaking down the numbers behind the game. So, what makes analytics such a powerful tool? Aside from helping you dominate your next fantasy hockey pool, advanced statistics provide potent insights into what is really causing teams to win or lose.

Hockey is a sport that has long been misunderstood. Its gameplay is fundamentally volatile, spontaneous and difficult to follow. There are countless different factors that contribute to a team’s chances of scoring a goal or winning a game on a nightly basis. While many in Canada would beg to differ, ice hockey still firmly occupies last place in terms of revenue and fan support amongst the big four major North American sport leagues (NFL, MLB, NBA, & NHL). As such, hockey is on the whole overlooked and is often the last to implement certain changes that come about in professional sports. The idea of a set of advanced statistics that would offer better insights into the game arose as other major sports leagues, starting with Major League Baseball, began looking beyond superficial characteristics and searching for the underlying numbers influencing outcomes. Coaches, players, and fans alike have all been subjected over the years to an epidemic failure to truly understand what is happening out there on the ice. This is the motivation behind the hockey analytics movement: to use data analysis to enhance and develop our knowledge of ice hockey and inform decision-making for the benefit of all who wish to understand the sport better.

Another barrier to progress in the field of hockey analytics is the hesitance of the sport to embrace modern statistics. Most casual fans are familiar with basic stats such as goals, assists, PIM, and plus/minus. But do these stats really tell the full story? In fact, most of these are actually detrimental to the uninformed fan’s understanding of the game. For starters, there is usually no distinction between first and second assists in traditional stat-keeping. A player could have touched the puck thirty seconds earlier in the play or made an unbelievable pass to set up a goal, and either way it still counts as a single assist on the scoresheet. Looking only at goals and assists can be deceiving; we need more reliable, repeatable metrics to determine which players are most valuable to their teams. Advanced stats are all about looking beyond the surface and identifying what’s actually driving the play.

So, what are these so-called “advanced stats”? Let’s start with the basics.

PDO: PDO (it doesn't stand for anything) is defined as a team’s save percentage (usually 5v5) + shooting percentage, with an average score of 1. If you only learn one concept, it's this one. It is usually regarded as a measure of a team or player’s luck, and can be a useful indicator that a player is under/over performing and whether a regression to the mean (back towards 1.000) is likely. This will not happen in every situation, of course, but watch for teams that have astronomic PDOs to hit a reality check sooner rather than later. Team PDO stats can be found on corsica.hockey’s team stats page.

Without trying to scare anyone, the Toronto Maple Leafs currently boast the 4th highest PDO at 101.85. To help ease your mind a bit, the Tampa Bay Lightning who are considered the team to beat in the East have the highest PDO of 102.35, and there's a decent gap between second place. They could be currently playing at a higher level than they really are as well, time will tell. 

Corsi: You may have heard of terms like Corsi and/or Fenwick being thrown around before. These are core concepts that are fundamental to understanding what drives the play during a game. Basically, Corsi is an approximation of puck possession that measures the total shot attempts for your team, and against your team, and stats can be viewed for Corsi results when a specific player is on the ice.

A shot attempt is defined as any time the puck is directed at the goal, including shots on net, missed shots, and blocked shots. Anything above 50% possession is generally seen as being positive as you are generating more shot attempts than you are allowing.

Corsi stats are typically kept in the following ways: Corsi For (CF), Corsi Against (CA), +/-, and CF%. An example of how CF% can be useful is when evaluating offensive defensemen. Sometimes, these players are overvalued because of their noticeable offensive production, while failing to consider that their shaky defensive game offsets the offensive value they provide. 

Fenwick: Fenwick is similar to Corsi, but excludes shot attempts that are blocked. Of course, with both of these stats, one should also take into account that a player’s possession score is influenced by both their linemates as well as the quality of competition (QoC). These stats can always be adjusted to reflect different game scenarios, like whether the team was up or down by a goal at the time, etc.

Measuring puck possession in hockey makes sense, because the team that has the puck on their stick more often controls the play. Granted, Corsi/Fenwick are far from perfect, and the team with the better possession metrics doesn’t always come out ahead. But at the very least, including all shot attempts offers a much larger sample size of data than traditional stats, and provides a solid foundation for further analysis.

Zone Starts (ZS%): this measures the proportion of the time that a player starts a shift in each area of the ice (offensive zone vs. defensive zone). A ZS% of greater than 50% tells us that the player is deployed in offensive situations more frequently than defensive situations. This is important because it gives us insight into a player’s usage, or in what scenarios he is normally deployed by his team’s coach. It also provides context for interpreting a player’s Corsi/Fenwick. Players who are more skilled offensively will tend to have a higher ZS% because they give the team a better chance to take advantage of the offensive zone faceoff and generate scoring opportunities. At the very least, ZS% can be used to get a glimpse at how a coach favors a player’s skillset.

Intro on 5v5 Isolated Stats and Repeatability

Often times, you will see those who do work with hockey analytics cite a player's stats solely while they are at even strength, or 5v5. Why? There's a few reasons.

First, 5v5 obviously takes up most of the hockey game. If a player is valuable to his team at 5v5, he will be valuable to a team for more time throughout the game, and this should be seen as a large positive. A player's power play contributions are certainly valuable to a team, but often over-valued. Next, the game is played very differently at different states. It would be wildly unfair to penalty killers to have their penalty kill stats included in their overall line, as more goals against are scored on the penalty kill, even for the best penalty killers. Separating these statistics helps provide a more complete picture into the player's skillset and value that they have contributed to their team. Finally, 5v5 stats are generally regarded as the most repeatable, partially due to the larger sample. While players' PP and PK stats can highly vary by year, 5v5 stats typically remain relatively stable (read more at PPP here if you like).

In addition, primary points (goals and first assists) have been regarded as relatively repeatable stats, so be on the lookout for player's that have many secondary assists to possibly have their point totals regress in the future (read more on this here).

Intro to Comparison Tools

One of the areas that has most benefited from hockey analytics is the domain of player comparison. One of the best and most intuitive tools is the HERO chart, as pioneered by Domenic Galamini Jr (@MiminoHero). The HERO chart is a quick comparison of how players stack up across ice time, goal scoring, primary assists, shot generation and shot suppression. At a single glance, we can get a sense of the strengths and contributions of different players. Here we compare Sidney Crosby to Connor McDavid:

hero.png

We can see that Crosby is better at goal-scoring and shot generation, while McDavid is better at primary assists and shot suppression.

To compare any two players of your choice, or to compare a player to a positional archetype like First-Line Centre or Second-Pair Defender, you can use Galamini’s website: http://ownthepuck.blogspot.ca/. These comparisons can be used to enhance understanding of a player’s skill set, inform debates, and evaluate moves made by NHL teams, among other uses.

All-3-Zone Data Visualizations:

While a HERO chart is an all-encompassing snapshot of a players contributions on the ice, the All-Three-Zones visuals are concerned with more specific aspects of the game. CJ Turtoro (@CJTDevil) created two sets of visuals using data from Corey Sznajder’s (@ShutdownLine) massive tracking project.

You can find both sets of visuals at the links below:

  1. https://public.tableau.com/profile/christopher.turtoro#!/vizhome/ZoneTransitionsper60/5v5Entries

  2. https://public.tableau.com/profile/christopher.turtoro#!/vizhome/2-yearA3ZPlayerComps/ComparisonDashboard

In the first set of visuals, you will find 4 leaderboards. Players are ranked in the 5v5 stats listed below.

  • 5v5 Entries -- How often players enter the offensive zone by making a clean pass to a teammate (Entry passes/60) or by carrying the puck across the blue line themselves (Carry-ins/60).

Other notes: The best way to enter the zone is to enter with possession of the puck (Entry passes + Carry-ins, as discussed above). These types of entries are called Possession Entries. Although other types of attempts are included in the leaderboard as well, players are automatically sorted by Possession Entries/60 because these alternative attempts are less than ideal. If you decide to change this, use the “Sort By (Entries)” filter to rank the players in other ways.

  • 5v5 Exits -- This is the same as 5v5 entries, except at the blue line separating the defensive zone from the neutral zone. Players are ranked based on how often they transition the puck from the defensive zone into the neutral zone either by carrying it (Carries/60) or by passing it to a teammate (Exit Passes/60).

Other notes: Like 5v5 entries, the best ways to exit the defensive zone are classified as Possession Exits. This is why players are automatically sorted by Possession Exits/60. Again, the “Sort By (Exits)” filter will let you change how the leaderboard is sorted.

  • 5v5 Entries per Target (5v5 Entry Def %) -- This stat measures defence at the blue line. It answers the question: When a defender is in proximity to an attempted zone entry, how often does he stop the attempt?

Other Notes: It is important to note that a “defender” is any player on the team playing defence (i.e. the team without the puck). Forwards are included in this definition of defender, but the best way to use this leaderboard is to judge defensemen only. This is why forwards are automatically filtered out of the leaderboard, but you can always change this using the filter if you wish.

  • 5v5 Shots and Passes -- Players are ranked based on how often they contribute to shots. Players contribute to shots by being the shooter or by making one of three passes immediately before the shot in the same way they earn points by scoring a goal or by making one of two passes immediately before the shot was taken.

If you want a closer look at certain groups of players, the filters allow you to look at players who play certain positions (forwards/defencemen) and players who play on certain teams. In the screenshot below, for example, I filtered the 5v5 Entries leaderboard to see what it looks like for forwards on the Oilers:

entries:60.png

You can use these leaderboards to judge offence (5v5 entries, 5v5 shot contributions), and defence (5v5 exits, 5v5 Entry Def %). Ultimately, these four leaderboards will help you identify the best and worst players in these areas.

In order to focus on one or two players, you should use the second set of visuals: The A3Z Player Comparison Tool. While HERO charts allow for player comparisons in stats collected by the NHL, this visualization was designed to help you judge players based on their performance in several stats from the tracking project. Instead of standard deviations, however, the measurement of choice in this comparison tool is percentiles. So keep in mind that “100” means the result is better than 100% of the other results. You can view a players results in two 1-year windows and one 2-year window, covering the 2016-17 season and the 2017-18 season. Here’s a two-year snapshot of how Erik Karlsson and Sidney Crosby rank in some of these key stats:

a3z.png

You probably noticed that the stats for forwards and defencemen are slightly different. The only difference is that defencemen have three extra categories, which measure their ability (or lack thereof) to defend their own blue line (i.e. their 5v5 Entries per Target, as discussed in the previous section). You may have also noticed some useful information hidden beneath each players name, including the numbers of games and minutes that have been tracked for the player. Although the numbers in the screenshot above are from two seasons, another thing to keep in mind is that you can also compare a players development over two seasons by looking at their stats in one-year windows. To see what I mean, take a look at Nikita Zaitsev’s numbers in two consecutive seasons:

zaitsev.png

Visualizing the dramatic fall of Nikita Zaitsev in this way is an excellent starting point for further analysis. Likewise, you can also compare two different players in the same season or over two seasons. This is, after all, a Player Comparison Tool. Other common uses for both sets of A3Z visualizations are to identify strengths and weaknesses of certain players, to evaluate potential acquisitions, to design the optimal lineup for your favourite team, and many more.

Of course, there are countless other useful terms and concepts to consider in analytics, like relative stats, shot quality, and expected goals (xG), which we’ll be touching upon more in-depth in future articles. If you’re interested in advanced stats and would like to learn more, we’ll be putting out more content on exciting topics in hockey analytics over the coming months, so stay tuned.


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