Protecting the Blue Line and Driving Break-outs: Repeatability and Impact of Exits & Entries Against

This article is being co-posted on NHLNumbers as well as on my own site, OriginalSixAnalytics.com. Find me @OrgSixAnalytics on twitter. If you are able, please provide some support to public data providers Corey Sznajder (@ShutDownLine), Corsica and Puckalytics

Every team in the NHL is constantly striving to find market inefficiencies to capitalize on towards the goal of creating value and ultimately winning games. Whether done by finding undervalued assets or developing a set of sustained competitive advantages – every team should always have these concepts in mind.

The analytics community has widely acknowledged we are not as good at evaluating defensemen as we are at evaluating forwards – in part due to having fewer effective metrics available. As a result, a number of people have spent time trying to expand our knowledge in this area – as any new development would be sure to present a strong market opportunity to capitalize on.

A particular focus has been on applying Eric Tulsky’s historical zone entry and exit analysis to defensive evaluation. Given controlled entries into the offensive zone (carry-ins) have been proven to generate more shots than uncontrolled entries (dump-ins) – intuitively, if a defender can prevent controlled zone entries against his team, that should reduce shot attempts against.

There has been a lot of great work done on this subject, including articles by the likes of Sean Tierney, Dom Luszczyszyn, JenLC and more. Dim Filipovic at Sportnet has written a number of times on the value of tracking the percentage of entries against that are controlled, as well as the number of controlled zone exits that a defender creates. This type of work has broken ground recently in evaluating defensemen. However, in an article late last season Dimitri summarizes the current gaps in the analysis as well:

“It’s important to retain perspective on all of these figures. At this point they’re much more descriptive than predictive, in the sense they’re telling us how a certain player did in the games he’s already played, but not necessarily helping us forecast how he’ll do in the future. There’s also the fact that most players only have [only] ~10 games worth of data to their name, which leaves plenty of room for inexplicable swings in either direction.”

So far, the best evidence to support the repeatability of these metrics is on the zone entries against side – where Eric Tulsky used a full season of tracked data for the Philadelphia Flyers’ defensemen to test this. His results are here. While this analysis definitely hints at a relationship, I suspect Eric himself would say a sample of only six defensemen should be seen only as a ‘first step’ that can built upon in the future.

As such, my goal in this article is to test, replicate and expand Tulsky’s work, as well as to hopefully broaden it to some uncharted territory. I also hope to encourage others to recognize the amazing data being made available by Corey Sznajder’s tracking efforts – which you can access here and here. Hopefully others will decide to support Corey by purchasing his data, and do further work to expand our knowledge in these areas as well.

Definitions

Let’s start with some important definitions:

  • Zone Entry Attempt Against (ZEA) – Any time an opponent is attempting to bring the puck into your defensive zone; it is registered against the player (e.g. defenseman) who is most closely defending the play across the blue-line
  • Controlled ZEA – When the opponent gains access to your defensive zone by successfully carrying-in or passing-in the puck, retaining possession
  • ‘Break up’, or ‘Failed Entry Against’ – When the defender is able to prevent the entry from gaining access to the zone, turning over possession
  • Zone Exit Attempt – Any time a skater is attempting to move the puck across his own blue line, whether carried, passed or dumped/cleared out
  • Zone Exit with Control – Any time a skater successfully carries the puck out or passes it out of his own zone, retaining possession

A simple way to think about preventing controlled ZEA’s is that it is the act of ‘Protecting the Blue-line’. Likewise, individual controlled zone exits represent players who are strong puck-movers, or who excel at ‘Driving Break-outs’.

Key Questions

With respect to preventing controlled ZEA, being able to break-up plays, and driving controlled zone exits (whether as a % of total, or per 60 minutes of play), my key questions are as follows:

  • Predictive – Are these repeatable skills? Is past performance a reliable indicator of future performance?
  • Relationship with Goals – Do these metrics drive improvement in Goals For Differential (GF%), by either increasing goals for or decreasing goals against?
    • If not, do these metrics at least help to drive the other inputs that translate into GF%? (I.e. xGF%, Shots, Corsi-For%, etc.)

“Protecting the Blue Line” – Zone Entry Defense

Let’s start with protecting the blue line: preventing controlled entries against, or breaking up entry attempts. To be clear, a lower percentage is better – the lower the proportion of controlled entries against a defenseman, the fewer goals and shots against you would expect. (In the interest of time, I have focused more so on ZEA, rather than break up %.)

Repeatability

To test repeatability I have done a ‘split-half’ test to see how well the 1st half of a season of data predicts the second half. All data is from Corey’s 2013-2014 tracking – the only full season of publicly-available tracked entry data. I have only included defensemen with over 150 entries-against in each half, for a total sample of N=148. (Note – the mid-season mark wasn’t technically the 41st game as Corey only captured zone entry defense for the final 60-65 games).

1-protecting-blue-line-repeatabilityAs you can see above, mitigating controlled entries against % is a highly repeatable skill with a relationship (R^2) of 0.37 between first half and second half results. I think this is a very exciting finding, as it shows the recent work of Dimitri, Sean and others is spot on – and that past performance should be predictive of future performance.

Relationship with goals

The next question is ‘does this improve GF%’? Intuitively I think we all expect it would, but it is still important to check. To evaluate this I tested the relationship (R^2) between controlled entry against (% and per-60) and a wide range of metrics: goal differential, goal and shot against rates, and ‘rel’ stats – i.e. stats that adjust goal and shot rates to be more focused on individual, rather than team-level, play.

2-zea-v-goals-table-incld-rel

As you can see above, there are some interesting results. First, many on-ice stats are significantly impacted by these metrics. In particular, ZEA with control (% and per 60) show a meaningful relationship with xGF%, CF% and CA/60, which are all at an R^2 around or above 20%.

However, it is interesting to note that when you focus on individual impact (i.e. rel stats), the relationship is considerably weaker than with unadjusted on-ice stats, which are more influenced by teammates. As such, while preventing controlled ZEA is repeatable, and does meaningfully impact goals and shot rates – there may also be team/system impacts that are difficult to separate out from the individual contribution.

Last, let’s ‘zoom in’ one on of the cells above: % Controlled Entry Against vs xGF%. the chart below illustrates the direct relationship between reducing controlled entries against and improving expected goals.

3-zea-v-goals

 “Driving Break-outs” – Zone Exits with Control

Now, let’s look at the zone exit side, or ‘Driving Break-outs’. Comparing the Exit data to ZEA, there are a couple nuances to mention: first, the data is slightly more limited in this area. Corey provides raw tracked data in 2013-2014 for all offensive zone entries and for all ZEA – which allowed me to separate it into split-half components. Unfortunately, I checked with Corey, and for the 2013-2014 zone exits the tracking was done offline and aggregated at the end. As such, there is no full season of raw data that we can use to do a repeatability test.

On the positive side, Corey has been providing the raw data for his new 2016-2017 tracking work which is well underway (maybe 10-15+ games tracked per team thus far). As such, by the end of the season, we should have another full year of data – and I will at that point confirm whether or not Controlled Zone Exits are a repeatable skill. My guess is that this will ultimately be shown to be the case, as simply from watching, certain defensemen seem to be natural puck-movers (Karlsson, Josi), and others seem to be less so (Polak).

Last, the 2013-2014 zone exit data lacked some key elements (which Corey has done well to include in the 2016-2017 work). The old data did not have a broad ‘failed’ zone exit category, nor did it show what Corey now calls ‘Transitional Plays’ – i.e. getting the puck out of the zone while surrendering possession via a clearing attempt. As such, we can’t say the proportion of the time an individual’s exit attempts were successful. However, we are still left with some very valuable data – individual Zone Exits/60 and Controlled Exits /60 – as defined above.

Relationship with goals

So – if we assume for now that Controlled Exits are a repeatable skill (and not simply randomness) – do they have a strong relationship with goal events?

4-zexits-v-goals-incl-rel

The chart above shows some very interesting findings. First, zone exits with control have a fairly meek impact on GF% and xGF% directly. This isn’t entirely surprising, as exiting your own zone with control can only get you so far towards scoring. However, there is a more relevant relationship between Controlled Exits/60 and CF% – with an R^2 of 15.5%. This shows that having a defender who is strong puck mover can definitely begin to ‘move the needle’ on winning the shot-differential battle.

Lastly, what I found most intriguing here were the Rel Stats – where the metric is adjusted for how a player’s team does with him on the ice versus off the ice (i.e. isolating individual contribution). While teammates can play a bigger role in helping a defensemen prevent controlled ZEA, Zone Exits are actually the opposite – both CF/60rel and CF%rel have a ~19% relationship (R^2) with Controlled Exits/60, stronger than CF% alone. This demonstrates that strong individual play from puck-moving defensemen can have a major CF% impact strictly from their ability to exit the zone with control.

Before wrapping up, let’s ‘zoom in’ again on the relationship between Controlled Exits/60 and CF%rel.

5-exits-60-v-cfrel

The chart above shows how a Defensemen’s individual controlled exits/60 have a meaningful impact on his ability to be a net-contributor to his team’s Corsi-For %.

Tying it all together – what these two sets of analysis tell us is that a great puck-moving defender can have a substantial impact on his team’s shot-attempts, as well as overall shot attempt differential, through his ability to Drive Break Outs. Further, a defenseman who excels at Protecting the Blue-line will have the greatest impact when surrounded by teammates and playing within a system that does so as well.  Naturally, the greatest value will be found in acquiring players who show both these skills , even if they don’t necessarily contribute significantly in terms of goals and points.

Conclusion & Practical Applications for Teams

As should now be clear, these types of new metrics for defensive evaluation are very interesting and important. They appear to be repeatable skills (at least for ZEA), and they do have a material impact on a team’s xGF% or at the very least, its CF%. While they are visible to anyone watching a defenseman closely – just like batting average or save % – only over dozens of games of tracking can we truly quantify and understand how well a player performs on each.

So – practically speaking – what should NHL or teams at other levels do about this? Gather the data! At a minimum, every team in the league should be tracking their own player’s performance on these metrics in order to better game plan, develop, and observe improvements or deficiencies over time. Further if any team ever took a systematic effort to track these on a league-wide basis (like – say – the Florida Panthers…), it would almost certainly present a clear competitive informational advantage that can help them build value over the long term

Such a team could have a greater understanding of the value of their own players, and also directly identify defenders around the league that may have a big impact on their team’s results, despite low goal or point totals. At this point, teams should be compelled to begin acting on this knowledge to build an ‘edge’. If not, eventually they may be doing so just to prevent themselves from falling too far behind.

 

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Does Every Team Need a Troy Terry? Quantifying the Value of a ‘Shootout Specialist’

This article is being co-posted on NHLNumbers as well as on my own site, OriginalSixAnalytics.com. Find me @OrgSixAnalytics on twitter.

 Troy Terry. Jonathan Toews. T.J. Oshie. These names are all recognizable in part because of a heroic shootout performance they each have put on for their country. Troy Terry was the most recent, scoring 3 goals in the USA World Junior semi-final win over Russia, and ultimately being the sole scorer in the championship game, winning Team USA a gold medal over Team Canada.

Due to shootouts being the subject of many conversations over the last couple weeks, I found myself wondering: for an NHL team, what exactly is it worth to have a shootout specialist in your line up? Should every team have one? Or is it a ‘nice to have’, merely a secondary consideration when evaluating players?

So – let’s see if we can figure it out.

Quantifying the Value of a ‘Shootout Specialist’

In order to answer this, I have looked at three areas:

  1. Simulated outcomes of two league average teams in the shootout
  2. Simulated outcomes of adding a ‘Shootout Specialist’ to a league average team
  3. Estimating the resulting win probability improvement, and quantifying it in terms of standings points/contract dollar value

In order to answer these questions, I have drawn data about the league average shootout (SO) Sh% and Sv% from the last three full seasons courtesy of Hockey-Reference (2013-14, 2014-15 and 2015-16), summarized below:

  • 31% SO Sh% (% of shot that are goals)
  • 69% SO Sv% (% of shots that are saved, or (1 – 31%))

In the next two sections, I will look at a hypothetical team and their Shootout Win Probability %. Let’s call the team in question ‘Team A’, and for now we will treat both Team A and their opponents as league average.

League Average versus League Average

Given shootouts, like wins, are zero-sum (i.e. same number of wins and losses), the average winning percentage on a league-wide basis will always be 50/50. As such, as a starting point, I have created the table below summarizing the round by round probabilities to reach that 50%.

lg-avg

As you would expect, the probability pretty cleanly comes out to 50% winning probability for Team A. If we define a ‘Round Win’ as when one team scores more goals in that round than their opponent, then we get the following distribution in each round:

  • 21% chance that Team A ‘wins’
  • 57% chance of a ‘draw’
  • 21% chance that the Opponent ‘wins’ the round

Now, naturally this is pretty straightforward, as the overall outcome will inevitably be 50/50. However, if we want to know what happens when we start changing players – and having these probabilities update dynamically – it gets a bit more complicated. To address part (b) – adding a Shootout Specialist to Team A, in the next section – I created a tool that simulates the outcome of a shootout, given a particular Sh% and Sv% for each team/goalie.

After running 1000+ simulations, the probabilities here represent the average outcomes, and I am happy to provide more detail on the actual calculations if anyone is interested.

Adding a ‘Shootout Specialist’ to Team A

Now, looking at ‘regular’ SO shooters – i.e. those who have 10 or more attempts in the sample – I will define a ‘Shootout Specialist’ as the players who are in the 90th percentile of these ‘regulars’. Here are all of the players who meet this level over the past three seasons:

list

I will come back to the specific individuals, but as you can see, this cohort of players will score on roughly 52% of their shootout attempts – an impressive number.

So let’s imagine that Team A decides to go out of its way to sign or trade for a Shootout Specialist – what happens if  we add him to our prior probability table?

specialist

With the simplifying assumption that a team will put their specialist in frequently (i.e. every shootout), you can see that a ‘Shootout Specialist’, or a top 10% shooter in the league will increase his team’s likelihood to win a shootout from 50% to 58%. This increase is a far cry from a guaranteed win, but also not an insignificant jump.

Now what exactly is that worth?

In order to convert this 8% improvement to standings points, and ultimately a contract dollar value, there are a couple more steps. Given 2016-2017 is one of the first seasons where the 3-on-3 is well-understood and teams have clearly formed strategies, let’s use the first half of the 2016-2017 NHL season as our proxy for a ‘typical’ year going forward. This half has shown roughly 4 games going to shootout per team so far – giving us ~8 or so for the year.

value

So assuming teams will typically be in ~8 shootouts over the year, and that a shootout specialist will increase their probability of winning by ~8% (50% to 58%), this illustrative scenario implies a shootout specialist would contribute an additional 0.68 shootout wins per season.

Given a shootout win is worth a single standings point, adding a SO specialist to a league average team is worth approximately 0.68 standings points; or about 1/3 of a win ‘win’ per year. Using @Behindthenet’s win value estimate of $2.80M per win (against the salary cap), this skill set is worth about $950K in contract value.

Going back to our first question, of ‘if every team needs a shootout specialist?’ – I think the answer here is likely ‘no’. On a big picture basis, NHL teams should have higher priorities than finding their own Troy Terry or T.J. Oshie – especially since teams shootouts are only relevant during the regular season. However, I would also say that SO capabilities are an important secondary consideration that teams and agents should still absolutely have in mind when negotiating player contracts.

For anyone who noticed the lesser known names on our list of specialists – like Brandon Pirri and Jacob Josefson – these guys might just be examples of ‘Moneyball’ contracts that New York (previously Florida) and New Jersey keep around in no small part for their shootout capabilities. Although both are RFAs, each team has them signed at low risk, 1-year contracts worth $1.1M for each player (Source: CapFriendly). Putting aside Josefson’s injury issues – assuming these guys contribute almost anything in the typical run of play – at #3 and #4 in the league in Shootout Sh% at 56%, they each are likely paying for their cap space in Shootout goals alone.

Other Considerations

Before closing, I should add a couple last qualifiers to my analysis, above:

  • For teams that are currently well below league average in shootouts, the ‘marginal’ value of a specialist is significantly greater than I have shown above, as their ‘baseline’ starting point is much lower
  • Further, any team who has focused on acquiring one or more Specialists should naturally be playing to their own strengths – potentially employing a more conservative strategy in 3-on-3 OT in order to drag extra games into shootouts (again – creating additional value beyond the average 8 games shown above)

Some areas for analysis that others may want to explore further include the value of other ways a team could go about increasing their shootout win %. What if a team had three specialists? Or what if they found a strong shootout goaltender? While each of these merit much further research, my short answers are that:

  • Adding a goalie will naturally have a dis-proportionately large effect, because he gets to face every shot against. However, given that teams have only one starting goalie (versus 12 forward roster slots), the shootout is less likely to be a goaltending priority
  • If a league average team were to have multiple Shootout Specialists, their win percentage in my analysis above would jump even further. My current estimate is that this would reach 66% with two specialists – meaning 1.3 shootout wins over a season, 1.3 standings point, or an aggregate $1.8M in salary cap value

Conclusion

In conclusion – while being a Shootout Specialist certainly increases the value a player adds to his team, I wouldn’t say it is a characteristic that every team ‘needs’ to have in their roster. That said, the New York Rangers and New Jersey Devils may have made some stealth, intelligent signings between Pirri and Josefson, who are some of the highest-value ‘depth’ Shootout Specialists a team could add. Further, when very high caliber players like T.J. Oshie (or Troy Terry down the road) head into their contract negotiations, I certainly would recommend both teams and agents do some work to understand exactly what they should be paying for this skill.

 

Developing Competitive Advantage: A Guiding Framework for Hockey Strategy

This article is being co-posted on Hockey Prospectus as well as on my own site, OriginalSixAnalytics.com. Find me @OrgSixAnalytics on twitter.

 I often write about concepts tied to finance valuation, forecasting, salary cap management, player transactions, and contract negotiations – all of which are extremely important in a salary-capped league. However, when driving towards every team’s ultimate goal – wins – maximizing value will only ever be one part of the solution. Many other factors also play integral roles: coaching, systems, team management, culture, work ethic, among many others. Although these factors are all essential to a team’s success, I believe there is a single, over-arching element that ties them all together: a team’s strategy.

Although this sounds straightforward, it is very far from it. In the simplest sense, a team’s ‘strategy’ represents how it intends to win, and what broad competitive advantages it can develop and maintain over time against other teams. To be clear, ‘strategy’ does not mean ‘systems’. Systems are extremely important and closely tied to strategy, but a team’s strategy is closer to its ‘identity’ – who it wants to be and how it wants to win – while its systems are the scenario-specific on-ice tactics it uses to help win games.

Naturally, having a clear and well understood strategy will directly impact all elements of running that team. A team’s strategy should be guiding its day-to-day (and year-to-year) priorities, how it invests its time and resources, and be the consistent thread connecting everything from its coach selection to its player evaluation and how practice time is spent. Unfortunately, this is much easier said than done.

 ‘Corporate Strategy’

Although everyone tends to have an intuitive grasp of the concept of ‘strategy’, many (especially corporations), frequently fail to pin down exactly what it is. Many corporations have ‘strategic visions’ such as ‘Becoming a $500M company’. While this phrase sounds glamorous, it actually is an objective, not a strategy (and dozens of books have been written about discerning the differences here). ‘Strategy’ relates to exactly how they plan to achieve that objective, and how they will be different and better than their competitors in order to be successful.

The most succinct description of a ‘true’ strategy I have heard, in a business sense, is answering the questions of ‘Where to play?’, and ‘How to win?’. Practically, this will segue into questions like: What industries/sub-industries should a company participate in? Should that company target the mass market, and win by using its large scale to provide the lowest cost product to consumers? Or should it target a specific ‘niche’, become a specialized provider, and sell a high-priced premium product to those who care about it most?

The most important decision coming out of this type of analysis for companies is how to invest their resources (people, dollars, etc.) across all of the activities they are involved in (product design, manufacturing, marketing, sales, R&D, etc.). Resources are inherently scarce, and different strategies will get greater value out of emphasizing and investing in different areas.

Applying Strategy to Hockey Operations

Now, as mentioned, in hockey, ‘strategy’ represents how a team intends to win, and how it can develop and maintain systemic advantages relative to the rest of the league. If a team is choosing to use a heavy, defensive shut-down style such as that of the LA Kings, they are going to select different players, coaches, and systems than if they were trying to use a fast, offensive ‘run & gun’ approach like the Dallas Stars. Once that strategy is determined, many other aspects of decision-making change – for example, I think most of us agree that Dean Lombardi and Jim Nill probably use different criteria to evaluate players, and how well those players fit into their team’s needs.

The most important aspect of effective strategy is making all hockey operations decisions work towards the exact same aim of fostering this on-ice identity. Just like in business, teams that try to be ‘good at everything’ will never be ‘the best’ at anything. Finally, because resources are finite, a true strategy is just as much about defining what a team is NOT going to focus on as it is about determining where it should be investing its time, energy and resources.

 One side note – every NHL team also happens to be a for-profit corporation of its own – so they should each also have a separate, profit-maximizing ‘corporate strategy’, focused on ticket sales, advertising revenue, etc. ‘Hockey operations strategy’ and ‘corporate strategy’ are closely connected – but not the same – and this article will be strictly focusing on the hockey side.

Now – a very important question for strategic analysis is what ‘levers’ are in our control? What factors can a team actually focus on in order to develop sustainable competitive advantages on the ice?

A Guiding Framework to Hockey Strategy

 In order to help think through the components of hockey strategy, I have created a basic framework that deconstructs wins into all of the major sub-components that create them. Naturally, winning starts with scoring more goals than your opponent, so goal differential % (goals for – goals against / total goals) was my starting point.

 Guiding Framework

 The framework above should be pretty objective. Teams win by scoring more goals than their opponents, with PP/ES being the main offensive game states and PK/ES being the main defensive ones. I have tried to further deconstruct each element into its sub-components, before getting to the final blue-shaded oval ‘levers’. For one example – a team could focus on driving goals-for by increasing its conversion rate (shooting %) or by increasing its volume (shot attempts/scoring chances). As I said – this isn’t rocket science.

A couple side notes:

  • Feel free to substitute the final ‘levers’ for whatever metric fits your own analysis; for example, some may prefer to further segment ES Goals-For into chances by Danger Zone, and Danger Zone conversion rates
  • I have listed that PP for and PK against ‘may or may not be influence-able at a team level’ – I personally think this could go either way (e.g. players like Nazem Kadri certainly can and the 2016 SJ Sharks seemed able to). As such, if anyone is aware of a study that shows penalty differential to be a repeatable talent at a team level, please send it my way

OK – we have a framework. How do teams turn these levers into a competitive advantage? Which strategies focus on which levers? To answer these questions, let’s take a look at some of the top teams from the 2016 Stanley Cup Playoffs as case studies.

Core Strategies Displayed in 2016 Playoff Teams

The table below is a summary of four major strategic approaches I have observed in the NHL that hopefully reflect some of the more common and successful strategies that teams use.

Core strategies

As the chart above shows, in a hockey context, the ‘where to play’ and ‘how to win’ questions fall into four major ‘strategic approaches’ existing in the league today. Two of the approaches are singularly offensively or defensively-focused, and two strategies represent more balanced styles of play.

Before getting into the detail, some caveats:

  • This is merely one way to try to categorize potential strategies – I don’t think of this list an exhaustive nor necessarily the ‘best’ way
  • There are likely other effective strategies employed within the league today – for the sake of simplicity I have focused on teams from the 2016 playoffs
  • Some of the teams not listed may either (i) not have a clearly articulated strategy (ii) be executing their strategy ineffectively, or (iii) have chosen a strategy that doesn’t fit well with their player/system/coach choices, (or (iv) I simply haven’t watched enough of their games to know)
  • Please feel free to comment/tweet if you have some ideas for other categories, or additional teams that fit into each bucket

Despite these points of clarification, I think it is safe to say that the 11 teams listed here do have a clear vision of how they want to compete. These teams know how they are trying to win, and when facing an opponent that is trying to be ‘all things to all to people’, I would argue the teams listed have a distinct advantage against any of their less-focused opponents.

In order to understand exactly how these core strategies are used, in the next section I will walk through the fundamental aspects of all four. To do so, I will use the original framework to illustrate the similarities, differences, strengths and weaknesses of each.

 Defensive Shut-Down Strategy (DSD)

 Below I have tweaked my ‘Guiding Framework for Hockey Strategy’ in order to highlight the (typical) strengths and weaknesses of a team employing a DSD Strategy.

Defensive Shut down

 Looking at the chart above:

  • As mentioned, the DSD strategy focuses on using heavy-style players with a very strong fore-check and frequent dump-ins, all in an effort to pin the other team in their own zone, and to prevent any counter attack
  • As you would expect – defensively-minded strategies have the majority of their green-shaded strengths on the bottom half of the framework: suppressing goals/shots
    • LA and ANA epitomize this, finishing 2016 at #1 and #4 in CA/60 in the league
  • The chart above suggests this archetype would be strong in both reducing shot attempts against as well as shot quality against (e.g. improving Sv%, or increasing the proportion of Low Danger shots against); however, some teams may emphasize one of these levers over the other

One thing to keep in mind looking at these charts – I have applied the strengths of the strategy in a general sense, and made the assumption they would apply to both even strength and special team play. In reality, many teams will not necessarily be equally strong at employing their strategy in both ES and special teams situations.

Defensively Responsible, Balanced Strategy (DRB)

 I have omitted the chart for this category, as it looks very similar to the one above, and it can tend to be highly dependent on the specific team. As you would expect however, this strategy is used by teams that take a balanced approach but that still are ‘defense-first’. Teams with a DRB Strategy have very strong defensive cores and play a balanced game, with excellent shot suppression, puck possession, and controlled zone exits. If you think about the defensive ‘core’ embodied on each of the three teams listed (St Louis, San Jose and Nashville), you come across some well recognized and very successful defenders:

  • Alex Pietrangelo
  • Kevin Shattenkirk
  • Jay Bouwmeester
  • Colton Parayko
  • Brent Burns
  • Marc-Edouard Vlasic
  • Paul Martin
  • K. Subban
  • Roman Josi
  • Ryan Ellis
  • Shea Weber (formerly)
  • Seth Jones (formerly)

Hopefully this list serves as an example of the type of defensive strength and depth required to succeed with this strategy. That said, there are definite overlaps between DRB and the second ‘balanced’ strategy, below.

Offensive Play-Driving Strategy (OPD)

Next is one of the strategies most loved by the analytics crowd, emphasizing speed, skill, puck possession and dynamic offense – while often sacrificing or de-emphasizing size. See below for the core strengths of the Offensive Play-Driving Strategy.

Offensive Play Driving

Looking at the chart above:

  • This is one of the strongest archetypes for driving ‘possession’ (shot-rate) stats such as corsi, at both ends of the ice
    • Three of the most successful teams in the last half decade seem to have employed this strategy: Chicago, Tampa Bay, and Pittsburgh
  • These teams excel at all aspects of puck possession, including controlled zone exits and entries, and use their pace and skill to retain and recover pucks in all three zones
  • CHI/TBL/PIT also have among the fastest and most skilled players in the league – with multiple top 3 overall picks in each lineup – signifying the type of high-end offensive and two way talent that OPD requires to succeed
  • While there is not much red in the chart above, I would stop short of saying teams using this strategy ‘don’t have any weaknesses’ – however, it does tend to be one of the most balanced and well-rounded styles, and its weaknesses will largely be team-specific

Earlier I mentioned that teams won’t necessarily fit perfectly into these boxes, and I think the Stanley Cup winning 2016 Pittsburgh Penguins are a great example of this. Although I would argue the 2016 Pens do fit in the Offensive Play-Driving Strategy, Pittsburgh also took a distinctly physical approach to it – emphasizing an extremely aggressive, hard-hitting fore-check. This physicality factor, in conjunction with their incredible speed, was a strong differentiator in the Pens’ cup run that made it very difficult for others to compete.

Offensive Run & Gun Strategy (ORG)

 Last, below is the summary table for the Offensive Run & Gun Strategy:

Offensive Run & Gun

Finally, with respect to the Offensive Run & Gun Strategy:

  • Although out of favour for the analytics community, this is definitely a strategy teams have employed for some time
    • The 2016 Capitals and Stars are arguably two of the most successful versions of this approach
  • Teams employing this strategy are characterized by their high-risk, high-reward approach, playing a very up-tempo and highly skilled offensive style
  • Although the Run & Gun Strategy de-emphasizes defensive structure and shot suppression, teams employing it often make up for this in shot quality, demonstrating some of the highest shooting %’s around the league
  • When compared to the Offensive Play-Driving Strategy, Run & Gun teams differentiate with their back-and-forth style of attack, outplaying teams on the transition, and by capitalizing on increased odd-man rushes throughout each game

Last, for the ORG strategy to work, often it requires an ‘elite’ level goaltender to shore up the defensive side of the equation for his team. This was demonstrated in 2016 both by Holtby’s consistency helping Washington be a legitimate contender and how the lack of consistent net-minding in Dallas was seen as the biggest barrier from them being able to do the same.

Final Thoughts: Strategy & Player Evaluation

Although this is already 2000+ words, there is of course much more to be said on the topic of strategy. Deciding on a strategy is simply the first step in a very long, difficult process of managing a team towards it, and implementing it effectively. A team’s strategy will impact coach selection, practice time usage, game planning, the decision of investing in analytics (or not), and all other aspects of its operations. Above all, it should be one of the most significant factors driving a team’s roster construction decisions, and the characteristics of players they are prioritizing.

A great example of this was last years win-win trade between PIT and ANA, swapping David Perron for Carl Hagelin. Perron was an under-utilized asset in Pittsburgh’s fast system, and Hagelin didn’t have the size that ANA wanted to play their heavy, slower style. After the trade, both players clearly hit their stride, substantially increasing each of their production rates on their new teams. I think this tells us an incredibly important and under-appreciated point – that player evaluation is never an ‘absolute’ exercise – rather, each player’s value will be in part based on his innate capability, and also in large part based on how well he fits in and contributes to the strategy, identity and system of his team.

Conclusion

To wrap up, I will point out that I personally haven’t commented or concluded that any of these strategies are the ‘best’. Just like in business, no strategy will ever be perfect, but there will always be one that is the best fit for each team based on its current roster, resources, strengths and weaknesses, and financial situation. I believe that one of the most significant factors in a team’s success is how cohesively and clearly the strategy, capabilities, and end-to-end decisions made across a team’s hockey operations fit together – and how disciplined they can be in maintaining their approach over 5+ year horizons. The only thing worse than not having a clear strategic direction is to attempt to pick one, second-guess it, and then continually turn an organization upside down every 1-2 years.