Introducing “KPO%”: Why Mitigating Shot Location Might Be the Next Important Layer of Measuring Defensive Value

 

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

 Although hockey analytics has come a long way, there is a still lot of room for improvement – particularly when evaluating the defensive contributions of skaters. Most analytics users are well aware of shot rate (CF%/relative CF%) and shot suppression (CA/60) stats by now – but after that, there aren’t many other easy-to-use defensive metrics. As a result, ‘single number’ stats like Wins Above Replacement from the (former) website War On Ice (WOI), often seem to undervalue defensive players.

In order to find another dimension for defensive evaluation, a logical area that many authors have thought to test is whether a skater can influence his goalie’s Sv% while he is on the ice. For those who haven’t been following, this is a hotly contested topic; but the short summary is that it is extremely hard to tell if players can actually influence On-Ice Sv%. Studies that show skaters can influence On-Ice Sv% tend to be inconclusive, at best – and most work has suggested that impacting On-Ice Sv% is largely driven by randomness.

Intuitively, many think it should be possible for a skater to impact Sv%, so the work continues. However, the most fundamental question that we are all asking is really, ‘What are the best tools we can use to measure a skater’s defensive contribution?’ So – let’s attack the problem at a slightly higher level.

Underlying Drivers of Sv% Impacts

Presumably, if skaters could impact on-ice Sv%, they would do so by reducing the ‘quality’ of the shots taken against his team – easier shots against, fewer goals. Even simpler than ‘quality’, if a skater can consistently mitigate the location of the shots against his team – e.g. ‘keeping pucks to the outside’ – we know that the decrease in Sh% as shots are taken further from the net should ease the burden on his goalie; regardless of whether the goalie actually stops those shots.

Fortunately, websites like Corsica and the former WOI having created quite rigorous Scoring Chance (SC) metrics that we can use to test this. With these, we can measure Scoring Chance mitigation two ways: through an overall rate stat (e.g. SCA/60), or as a proportion of all shot attempts against (as used by Scott Cullen, here). In Scott’s article he simply divides SC Against by Corsi Against, allowing us to see what portion of all shot attempts are Scoring Chances when a certain player is on the ice. Due to its straightforward nature, I’m sure many others have used/alluded to this figure in the past.

Although this stat is not at all complicated, in this article I will explore the idea that mitigating shot location/quality could actually be one of the next important layers in quantifying a player’s defensive contribution. Granted, some of the most complex, advanced models (e.g. xG from Corsica) already do go to great detail to factor in shot quality. Despite the value of those models, I hope to make the case that a statistic like Scott’s (On-Ice SCA/CA) can represent a simple, broadly usable metric to evaluate defensive contributions from skaters – a close second to things like CA/60 and CF%.

To make this argument, we need to know: (i) is this a metric that skaters can actually ‘influence’? To test that, we will have to see (ii) if past results are predictive of future results – e.g. do certain players perform the best/worst on this metric, year after year? Along the way, we should also figure out (iii) is it best to use Corsi Against, Fenwick Against or Shots Against as a denominator?

So – let’s dig in.

Defining Scoring Chances

First – let’s define what a ‘Scoring Chance’ (SC) is. @MannyElk has done a great job recently creating a Scoring Chance stat on his Corsica website, and all citations of ‘Scoring Chances’ here use his data and metric – so big ‘thank you’ to the hard work that he does. You can support Corsica here.

Manny goes into great detail on how he reached his metric here. In short, he built upon the War-On-Ice SC definition by putting shots into three danger ‘tiers’ (high, medium and low) – though Manny didn’t stick to the exact locations used by WOI. Instead, he focused on the likelihood of the shot to be a goal (based on a number of factors, like shot angle, rebounds, etc.), and worked backward into his ‘zones’ from there.

Below is Manny’s heat map of shot location by danger zone.

 corsica-heat-map

The next table is Manny’s summary of the Fenwick Shooting %, Shooting %, and percentage of all shots within each danger ‘tier’, or zone.

corsica-table

What is important about this table is the third column from the right. This ‘FSh%’ column summarizes just how dangerous each shot attempt is: low danger attempts have approximately 2% chance of going in, medium danger have a ~6% chance, and high danger (e.g. Scoring Chances) have ~16%  chance of becoming goals. Notably, the medium tier was deliberately set to be quite close to the league-wide ‘average’ shot attempt Sh%, of 6.79%.

Here is Manny’s definition of a Scoring Chance:

“Scoring chances may be defined as unblocked shots belonging to the High-Danger zone – that is, whose xG is equal to or exceeds 0.09. For convenience, one can approximate that one goal is scored for each 6 scoring chances”. [As compared  to 1 in ~16 medium danger chances, and 1 in ~50 low danger chances].

So to be clear – it isn’t quite as simple as ‘if a shot is in the mid-to-low slot, it is a Scoring Chance’ – which is closer to the WOI definition. However, as you can see from his heat map, the vast majority of SCs are originating from the dense yellow area in the mid-to-low slot – so we can consider SCs as largely coming from that location.

Mitigating Scoring Chances – Keeping Pucks Outside % (“KPO%”)

 Earlier I introduced Scott Cullen’s metric of (Scoring Chances / Corsi Against). Most players in the league come out in the 10-20% range of this number, meaning that 10-20% of their shot attempts against are ‘Scoring Chances’.

In order to make this metric somewhat more intuitive, I want to center it on the concept of ‘Keeping Pucks to the Outside” – a simple, easily understood concept that is core to defensive-zone play. As such, I will make two changes to the stat:

  • Instead of showing the % of shot attempts that ARE scoring chances – instead, I will show the metric as the % of attempts that were NOT scoring chances (simply by taking (1 – Scott’s metric).
  • As a result of this, I will give this stat a new name – “Keeping Pucks Outside %”, or KPO% – the percentage of shot attempts against that a skater prevents from being a Scoring Chance, or that he ‘keeps outside’.
    • (As a side note – I have deliberately tried to make this label clear and straightforward, for use by coaches or players who aren’t familiar with most analytics. For those who want a more formal name – you could also use ‘Scoring Chance Mitigation %’)

As a result, most players will instead be in the ~80-90% range – and should be aiming for as high of a % as possible.

KPO % – Repeatability

 Now, the most important question for this to be a relevant metric is – can skaters actually repeatedly ‘influence’ KPO%? To determine this, we will have to test past results against future results, to see how strong that relationship is.

To do so, I downloaded Corsica data for all Forwards and Defensemen who played from 2010-2016. 2010-2013 represents the ‘first half’ sample and 2013-2016 represents the ‘second half’, and players needed at least 1000 minutes in each. This resulted in a sample of 216 Forwards and 113 Defensemen, which I tested separately. Only 5v5 data was included.

The two charts below summarize the results.

defensemen-correlation

forward-corellation

As you can see, across both D and F there was a considerable relationship between past and future performance on the KPO% metric, at R^2 = 28.9% and 20.6%, respectively. This suggests that KPO% has solid predictive capability, supporting its use for player evaluation. Intuitively, it also makes sense that Defensemen would be able to more consistently influence this metric (shown in the higher R^2), as it is a larger part of their role.

It is worth noting that the charts above use SCA/CA to calculate KPO%, as CA had the strongest relationship tested. I also ran the results with SCA/Fenwick Against, and SCA/Shots Against, and the results are below:

rsq

On the defensive side, CA and FA are quite close, but after that there is a slight drop down to SA. I think it is also positive to see that Corsi Against has the strongest relationship, when we area including blocked shots in the denominator. Given that blocking a potential Scoring Chance is a meaningful way for a skater to add defensive value, it would be logical to include that in the calculation.

For the last few sections, I will quickly summarize how performance on KPO% tends to be distributed around the league, and if we can quantify how much ‘value’ it really contributes.

2015-2016 League-wide Performance

 In order for KPO% to have value, there needs to be a wide-enough distribution of results across the league in order for players to differentiate themselves. Below is a histogram of the distribution of defensemen on this metric, across the 2015-2016 season:

2015-2016-histogram

 (Note – I have omitted the forward chart as it follows the same general pattern)

Using the 2015-2016 season shows KPO% as following a relatively normal distribution, and with a reasonable variation of results, given the range of 8.7%. Given there is a moderate amount of variation across the league – how big of an impact does a change in KPO% have on expected goals against?

What is +/- 1% of KPO% actually worth?

Now – I want to try to understand how big of an impact the best players in the league can have on KPO% – two good examples from the sample were Mark-Edouard Vlasic and Roman Josi, scoring at 89.4% and 88.1%, respectively.

To answer this, I have done a very basic, ‘back of the envelope’ calculation for how many theoretical ‘goals’ a skater adds to his team over a season (at 5v5) if he were to have a KPO% of +1% or -1% from the league average.

goal-value So, to walk through the high level math here:

  • The average defenseman from the original sample had 997 5v5 Corsi Against over a season
  • 8% of those are HD SCA, on average – or 147.6 Scoring Chances
  • Increasing a skater’s KPO% by +1% above the league average results in 137.6 HD SCA per season, or a reduction of 10 SCA
  • With 6.2 SCA per goal, that is 1.6 goals prevented
  • However, given these shot attempts are being substituted by lower quality chances, we need to add-back the value of those chances:
    • Manny’s table showed LD and MD chances each make up ~40% of all shots – or roughly 50/50 split of all non-HD shot attempts
    • Thus, for each 10 HD SC mitigated, there will be 5 LD and 5 MD added back, or 0.4 goals ‘substituted’
  • Thus – the net goals prevented from a skater improving his KPO% by 1% is 1.21 goals per season

The one big caveat: the KPO% I am using is derived with Corsi Against, while Manny has only been able to calculate the Fenwick Sh% of his Scoring Chances. As such, the number of chances per goal stats (6.2, 16.0, 51.3) are proxies – and we should consider this calculation to be illustrative of the ‘directional’ impact, rather than actual.

How big of an impact are 1.2 goals per 1%? With a range of 8.7% across the sample, 1.2 goals means the best player on KPO% is contributing ~10 goals prevented over the season more than the worst player. If we define ‘replacement level’ at approximately the bottom 20% of the league – then the top quartile of defenders in the league could add roughly 3.5-4 goals ‘above replacement’ on this metric. Given 6 goals are approximately equivalent to one win, the top 25% of the league is adding roughly 0.50-.66 of a win for their teams – which is not immaterial in a league where every little edge counts.

For the sake of clarity, I am not arguing that KPO% is ‘more important’ than Corsi Against/60 – e.g., if a skater gives up 300 additional CA over a season, that will more than offset a reduction of KPO% by 1%. Rather, I am arguing that these two elements are important to consider in conjunction with one another – as having a poor CA/60 can be somewhat mitigated by a strong KPO%, just as a great CA/60 can be off-set by a terrible KPO%.

Along those lines – if we were to add this to the WOI Goals Above Replacement (GAR) calculation, KPO% is looking like it could be a close 2nd to shot rate stats for the highest-value way to measure a skater’s defensive contributions. Given Forwards have ~5 areas where they add goal-value to WOI GAR – versus ~2 for D-men (CF/CA) – simply adding another element of defensive contribution will help to off-set the F/D value imbalance in today’s metrics.

 Top/Bottom KPO% Defensemen

Before concluding, I wanted to share the top 12 and bottom 12 KPO% performing defensemen from the 2015-2016 season, for reference. In the table below, I have also added the column ‘KPO% Above Average’ – this is simply expressing a player’s KPO% minus the league average score. Thus – the top players will be a positive figure, distance above average, and the bottom players will be a negative figure, distance below average.

top-12

bot-12

As you can see, there is an interesting set of defensemen in each category. The top defensemen have some well-renowned players like Vlasic and Josi, mentioned earlier, as well as some not-necessarily-analytically-loved players like Shea Weber and Roman Polak.

My own hypothesis for why we get this result is that there may be a connection between a defensemen’s play-style/skill set, and his resulting shot rate/KPO% stats, in a sometimes off-setting fashion. For example, Polak and Weber’s ‘stay at home’ style may help them lock down the front of the net defensively, while it causes them to struggle on the shot rate side of the equation. On the other hand, some of the league’s more dynamic defensemen (not listed, but Doughty and Klingberg both come out at roughly -2%  KPO% Below Average) may have quite strong Corsi stats, but their play-style causes them to give up higher quality chances against as a result. Granted – this is just a hypothesis – only more study and time will tell.

Conclusion

Despite having gone all the way from introducing KPO% to taking a high-level estimate of its goal—value, I definitely see this analysis as exploratory, rather than ‘complete’. Hopefully this article encourages some others to dig into the KPO% metric (or other, similar ones) – allowing us to continue to learn more about how to measure individual-skater defensive contribution outside of simply shot rate stats.

Some future areas to build on this analysis include adding the impact on and value in special team (e.g. PK) situations, creating more detailed versions of the stat (e.g. KPO% relative to teammates, usage adjustments), or to develop a more statistically rigorous calculation of its goal-value. Hopefully you have found this analysis to be interesting and thought provoking – or alternatively, that KPO% helps to decrease the number of On-ice Sv% debates in the world…

 

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The Financial Frontier: Defining the Characteristics of ‘Competitive’ Salary Cap Management

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

I recently wrote an article where I conducted a review of salary cap efficiency by team, across the NHL. In it, I argued that how efficiently a team manages its salary cap it just as important as how much they are able to spend on it. Having had some time to reflect after writing that piece, I wanted to dig a bit deeper into the subject and expand on my previous analysis.

In this article I will borrow from my prior work to try to determine the common salary cap management characteristics found in the leagues’ strongest teams. By the end I will introduce the ‘Financial Frontier’ – a concept that represents the threshold that only the ‘contenders’ in NHL are able to cross. Ultimately, my goal is to illustrate the components of financial management (within hockey operations) that are required for a team to be successful in this league.

Before getting to the frontier, let’s do a quick review of the components that make it up: (i) total cap dollars spent and (ii) salary cap efficiency.

Total Cap Dollars Spent

Prior to my initial piece on this subject, Dimitri Filipovic over at Sportsnet wrote an excellent article where he dug into the impact of total cap dollars spent on playoff success. In it, he successfully showed that the vast majority of cup or conference finals-winning teams spent at or above the league average against their salary cap.

To do so, he first showed a matrix that compares the playoff success of teams (y-axis) to total cap dollars spent (x-axis), from 2010-2011 to 2014-2015:

playoffspending

 

Source: Sportsnet

He also provided the following table, highlighting the seeming lack of success for teams that spend below the league average.

payroll

Source: Sportsnet

As you can see from the two graphics above, there are very few teams in the league that have won a conference final/the Stanley Cup while spending at or around the league average. Of the sample shown, the only teams to do so while being within +/- $5M of average were the 2010-11 & 2014-2015 Tampa Bay Lightning, the 2011-12 LA Kings, the 2011-12 New Jersey Devils, and the 2014-15 Anaheim Ducks (all found in the top-middle section of the first chart).

In the end, Dimitri’s analysis demonstrates that spending more (or at least having that option) is going to provide an advantage – which I think we all intuitively agree with. Now, let’s look at the cap-efficiency side of the equation.

Salary Cap Efficiency

 Following this analysis by Dimitri, I wrote an article of my own where I reviewed the salary cap ‘efficiency’ of all teams in the league in 2014-2015. To do, I used a methodology that I originally outlined here, where I compared the salary caps of the 2014-2015 Chicago Blackhawks and the 2014-2015 Toronto Maple Leafs.

In short, my prior methodology was based on a bottom-up analysis of all individual players on each team, their scores on the Goals Above Replacement (GAR) metric, and converting that metric to each player’s estimated ‘win value’ based on the free-agent market price of GAR in a given season. If you want to see the full detail behind the approach, I encourage you to check out the additional detail in those articles.

After applying that methodology, here was the final chart/conclusion I reached:

Chart 6 - Team Level Cap Eff - B

From the chart above, I drew a couple conclusions:

  • Cap efficiency won’t necessarily win a team a Stanley cup, demonstrated by:
    • The cup-winning Chicago Blackhawks sitting close to 0% (neutral efficiency)
    • Highly efficient teams like SJS and BOS still missing the playoffs altogether
  • However, having a poorly managed salary cap can definitely prevent a team from being successful, demonstrated by the poor performance of all teams in the red, shaded area

Although we can conclude from this work that both of spending power and spending efficiency are important factors to succeeding – we can’t necessarily make the conclusion that either one of these elements is more important. As a result, let’s get into the main point of this article, where will look at how these two figures interact with each other, and how we can use that to define the characteristics of ‘competitive’ salary cap management.

Introducing the ‘Financial Frontier’

 In order to demonstrate this point, I have mapped the same sample of teams as my prior analysis along these two dimensions (salary cap efficiency on the x-axis, total dollars spent on the y-axis). You can see the result in the chart below:

Financial Frontier

Personally, I think the insight within this chart is pretty neat. Examining it, some clear patterns emerge:

  • Naturally, teams want to be in the far top-right corner of this chart – where they are able to spend to the salary cap, and while doing so as efficiently as possible
  • As you can see – the further to the top right that teams get, the more likely they are to have made the playoffs, or be successful in them
    • Although anywhere in the top right quadrant is a good place to be, there is a clear cluster of (almost exclusively) playoff teams even further towards the corner
    • This cluster is concentrated around (a) spending roughly at the max salary cap, or (b) spending above/close to the league average on the cap, while having an efficiency at a positive 20% or greater
  • Based on this cluster, I have overlaid a line I labeled the ‘Financial Frontier’, representing the combination of total spend and cap efficiency that teams need in order to be competitive in the NHL

Overall – this analysis illustrates a trade-off that I think intuitively makes sense: spending more can make up for spending less efficiently; conversely, if you can’t spend to the limit, then you are forced to make up for it by being extremely efficient with your dollars. All that said, teams that don’t care about contending (say maybe, the pre-Shanahan Leafs?) should feel more than welcome to do whatever they like…

Final Thoughts

Looking at a few specific examples on this chart and comparing them to ‘commonly’ held views of each team can also help support the conclusions of this analysis. For example, the only teams who made the playoffs but were not at or past the Frontier were VAN, CGY and MTL. As most hockey fans are aware of – Vancouver and Calgary were largely considered fortunate to have made it into the playoffs at all, with many seeing Calgary as only making the second round by virtue of having played the Canucks in the first. Likewise for Montreal – as this past season’s results demonstrate – they were somewhat of an anomaly in 2014-2015, given the unbelievable value contributed by Carey Price being the major reason they were contending like they did.

Similarly, San Jose*, Dallas and LA’s strong positions at or past the Frontier make them look like outliers for having not made the playoffs. Lo and behold, all three were major contenders this year, all of whom had legitimate chances at making the Conference finals this year, if not the cup – and all doing so with relatively minor changes to their rosters.

*(Note: My salary cap data source may not properly account for all of San Jose’s total cap spend in 2014-2015, as it may be missing (at least) some cap-buyout dollars, at ~$5M for SJS. San Jose’s total spend was likely closer to high 60s. However, this error should not change the outcome of this analysis).

Conclusion

 In the end – I don’t think anyone will be too surprised by the fact that it is important both to have the flexibility to be able to spend to the cap, as well as to use those dollars as wisely as possible. Overall, it should be becoming clear there are a wide range of new financial dimensions that (I expect) teams have been placing increasing weight on when making decisions. For fans whose teams are performing poorly in the Frontier chart above, the silver lining is that many of those teams (Florida, Toronto, Buffalo) are well on their way to improving both their salary cap and roster situations towards becoming contenders once again. On the other hand, for fans of teams that look OK in the chart above, but who haven’t been aggressively managing their cap commitments (NY Rangers, Detroit) – you may want to brace yourselves for some less than pleasant years to come.

Valuing Stamkos: Estimating What Steven’s Next 7 Seasons are Worth

 This article is being co-posted on Maple Leafs Hotstove as well as on my own site, http://www.originalsixanalytics.com. Find me @OrgSixAnalytics on twitter.

 All season long, Toronto fans have been talking about Steven Stamkos. It is well known that Stamkos will be an unrestricted free agent (UFA) this July. This means that – you guessed it – Steven Stamkos could become a Leaf. Naturally, if it happens, many fans have presumed Stammer would be named captain, immediately become the face of the franchise, and lead the Leafs through its centennial season and beyond.

As a result, many have also asked – what is Steven Stamkos worth? Assuming he reaches UFA status, what will it cost the Leafs to sign him? Does he deserve the same type of money as Kopitar, Kane, and Toews? Back in January, Ryan Kennedy over at thehockeynews.com made some reasonable comparisons and suggested that he sees Stamkos as being worth roughly ~$9M in AAV. Full credit to Ryan – as that was earlier in the season and somewhat different situation – however, in this article I will argue that it is extremely unlikely that any team, Toronto included, will be able to sign Stamkos for an AAV of less than $10M, or for much less than the maximum term of 7/8 years.

Before getting into the approaches to valuation, let’s look at some of Stamkos’ key statistics to understand exactly how he adds value.

Stamkos’ Goals Above Replacement (GAR)

Stamkos GAR BARs

 (If this data doesn’t mean much to you – I recommend you check out my summary of the practical applications of Goals Above Replacement).

For individual player analysis, GAR (Goals Above Replacement) can be a useful starting point to illustrate the big picture. Looking at Stamkos’ GAR shows us:

  • He performs at an elite level, hitting 15-20+ GAR all but his rookie season, something done in fewer than ~6% of all player-seasons
  • Stamkos contributes value to his team almost entirely through his offensive play:
    • 95% of Stamkos’ career GAR comes from his offensive ability, approximately 2/3rd of which coming from his shooting percentage (15.2% at 5v5) and 1/3rd from his impact on shot-rates (Corsi For)
    • At times over his career he has been a slight defensive drag to his team – but ultimately he is a relatively neutral contributor in non-offensive areas
  • As noted in the chart, Stamkos’ 2012-2014 results may not be indicative of his capability as the seasons were impacted by the lockout and his broken leg, respectively

We likely didn’t need GAR to tell us any of the above, as many would know most of this from simply watching Steven. However, GAR will be important to keep in mind for when we estimate Stamkos’ value later on.

WARRIOR Chart

Next, I have copied Domenic Galamini’s WARRIOR chart of Stamkos, showing Steven’s relative performance within the league on a number of metrics. For those not familiar with WARRIOR charts, Dominic provides a great summary here.Stammer solo

Looking at this chart confirms the following:

  • Stammer is an elite scorer and a solid playmaker, scoring very high on Goals/60, Primary Points/60, RelCF60 and RelxGF60
  • Stamkos should not be considered a two-way forward: his defensive contribution is sub-par, as shown by his shot/goal suppression (CA60Rel/xGA60 Rel)
  • As a result, when valuing Stamkos, we should be comparing him to similar elite shooters (e.g. Ovechkin, Kane, Perry), as opposed to the leagues’ finest two-way forwards (e.g. Toews, Kopitar, Bergeron)

Given that elite shooters don’t always excel at driving 5v5 CF%, it will be important to keep in mind shooting percentage and power play results when comparing Stamkos to other players.

Before we get into valuing Stamkos, let’s quickly look at the qualitative side of the equation.

A Note on Intangible Factors

Although the hockey analytics community tends to discount ‘intangible’ factors, I personally consider them very meaningful. Consider: if you have two players with similar stats – would you pay more for the driven, hard-working, high-character guy, or the player who demonstrates none of these traits?

We all know Steven Stamkos is a strong leader. Last season he captained his team to game six of the Stanley Cup final. He also has an incredible work ethic; he is well known for his rigorous offseason training with Gary Roberts. While these strong intangibles are difficult to quantify, they still should play a meaningful role in justifying a premium valuation for Stamkos. But first – what aspects can we quantify?

Approaches to Financial Valuation

In the world of corporate finance – buying and selling companies – great effort is spent attempting to value the businesses being acquired. Generally speaking, there are two approaches:

  1. Relative (market based) valuation methods
  2. Intrinsic valuation methods

Let’s start with relative valuation methods.

Market Based Valuation: Comparable Company Player Analysis

For investors, ‘comparable company’ analysis is a key aspect any deal. The valuation multiples of companies across an industry (e.g. ‘Enterprise Value / Earnings’) set the baseline for determining what the price should be for a similar one. As many readers will know, the exact same approach is often applied to players: we can look at a set of comparable elite shooters to help derive a benchmark for Stamkos’ market value. Keep in mind – relative valuation methods like comparables tell us what others are willing to pay for an asset (the market), not necessarily the inherent or ‘true’ value of it.

Using Corsica’s Similarity score feature, focused specifically on Stamkos’ most important stats (e.g. G/60, P/60, Sh%), I have arrived at the following set of comparables. All players listed below had a similarity score of at least 85% with Steven. I have also tried to present this similarly to how it is done in finance:

Comp Table

(Note – All data shown is all situations, except where specifically noted).

As you can see, few players in the league score goals and contribute primary points at the same rate as Stamkos. Compared to this set as a whole, Stamkos outperforms the group average for all metrics listed, except for the Corsi stats and FirstA/60.

However, for this analysis to truly be relevant, we can only really compare Steven to players in the same free agency situation when signing, i.e. UFAs. As such, I have highlighted the four UFA players that I consider as having the most comparable performance statistics to Stamkos: Patrick Kane, Alexander Ovechkin, Evgeni Malkin, and Corey Perry. I have also shown their four-player average at the bottom of the table. As you can see, Stamkos is neck and neck with these players on every metric.

In terms of dollars, these four players averaged an AAV of $9.7M, and had an average term of the maximum 8 years. Considering that these players are also not historically known for their exceptional leadership qualities – which Stamkos has in spades – you can begin to see the case for Steven to be earning $10.0M-$10.5M+ per season.

Intrinsic Valuation Methods

In financial markets, the other approach to valuation is using ‘intrinsic’ methodologies – e.g., trying to derive what is a company is worth in an absolute sense. When valuing a company, this is based on the discounted ‘present value’ of a company’s future cash flows – how much money a company will generate cash for its new owner in the future.

My view is that the best ‘intrinsic’ player valuation method available based on public stats is using GAR, which I introduced at the beginning. The first approach I will use is valuing a player based on his historical GAR score, which I have shown previously. The second is a new approach, where I will show a high level attempt to forecast a player’s future GAR – and use that to derive what he should be paid. When we have confidence in a forecast for a player – (which I won’t necessarily say about my own Stamkos GAR forecast today) – forward-looking analysis will be the most ‘pure’ measure we can use in valuation.

Historical GAR

First I will plot Stamkos on the Fair Market Value curve, in order to see what he is worth based on what he has done historically. Typically I would encourage using a 3-year average GAR – however, due to the issues of the lock-out season and Stamkos’ injury, I will instead use his career average (ex-rookie season) GAR of 22.

Historical GARLooking at this chart shows:

  • Stamkos’ career Avg GAR/season of 22 values him at $10.8M in AAV
  • However, historical GAR will tend to over-estimate a player’s value, as it is backward-looking, and does not adjust for how players decline with age
  • As such, though this is a useful data point, we should take it with a grain of salt

Forward-Looking GAR: A High Level Estimate

To truly estimate a company’s, or players’, intrinsic value we need a forecast of how it/he is going to do in the future. Of course, like any prediction, this type of estimate is inherently flawed and almost never is how reality actually plays out. However, creating a forecast can help us to see some reasonable scenarios of how Steven might perform in the future. To do this, I will combine his past performance with the aging curve of a typical NHL forward.

Fortunately for us, part 2 of Moneypuck’s Building a Contender Series has an excellent chart that summarizes the league-average GAR aging curve for forwards (is anyone tired of me referring to this series, yet?):

Moneypuck GAR Aging curve

(Source: Moneypuck)

In order to connect this to Stamkos’ expected performance, I will use a rather blunt methodology of simply applying the same absolute value of decline in GAR score showed here to ‘extend’ forward Stevens’ historical performance. The most accurate way to do this would likely be to base Stamkos’ long term decline on how his comparables declined at the same age. However, for the purpose of brevity, I will use this simplified approach to help illustrate the methodology.

Stamkos GAR Forecast

In the chart above I did the following:

  • Extended the ‘historical’ performance by one year in 2015-2016, assuming his current season is comparable to 2014-2015
  • Starting at Stamkos’ current age of 26, I applied the absolute decline from Moneypuck’s chart to Stamkos in five and three year chunks
    • (As you can see, the projection begins to decline more quickly after 2020-2021F)
  • Last, I adjusted this ‘base’ case by +20% and -20% to create illustrative upside and downside cases

Although this is a very high level estimate, I think this gives an interesting, basic idea of what we could expect from Stamkos over the next eight seasons. What does this equate to in terms of dollars? The base case results in an average GAR per season of 18.3, equating to $9.1M per year in AAV. The downside and upside cases each average GARs of 14.7 and 22.0, respectively, coming out at AAV values of $7.4M and $10.8M.

Sizing it all Up

So – lets stack these all up next to each other:

Aggregate

Naturally, looking at a wide range of methods gives a wide range of potential values. However, all three of these approaches can be used to justify a valuation for Stamkos at $10.0M+ of AAV. There is also every precedent for him to get the full 8-year maximum term, or 7 years with the Leafs (in the case of a UFA signing with a new team). We can all be sure Stamkos’ agent is well aware of all of these approaches and will be pushing for the high end, trying to secure as much as possible for his client.

Practicalities of TML’s Negotiation Position

Much like in valuing and buying/selling companies – you can do as many fancy calculations as you want – but something is ultimately worth what someone is willing to pay for it. Given that Stamkos is a UFA, he is essentially able to auction his contract to the highest bidder – putting all of the power in his hands. This power dynamic, combined with the prior valuation approaches, gives me strong reason to believe Stamkos deserves $10M+ – and teams should be willing to pay it, if they have the cap space available.

I’m sure some readers will point out that Stamkos’ basic counting stats (goals/assists/points per game) have declined in the past two seasons, which may be a red flag for the future. Travis Yost and Stephen Burtch both addressed this somewhat, arguing that his quality of teammate and usage are both factors that may be impacting his performance. However, in terms of the value he commands, the strength of his negotiation position will ultimately rule the day. If teams want him, it will take big dollars to get him – either they will be willing to pay for an elite player, or they will not – and I suspect there will be at least one team left standing at a double-digit AAV.

Conclusion

In the end, I know Leafs fans will continue to argue that Stamkos wants to come home, and that maybe he will take a hometown discount to join the Leafs. Although this is possible, in reality the Leafs should not be expecting to walk into a bargain contract with Stamkos. He is an elite scorer, a strong leader, he deserves a top dollar contract, and I strongly believe there will be a team willing to step up and give him the $10M+ per season he commands. Stamkos is entirely worth that amount, and he is an excellent candidate to lead this young Leafs’ team through their centennial season, and six more after that. All us fans can do now is sit and wait.

Your move, Lou.

 

This article is presented by OAK Coasters, where you can buy hand made, One of A Kind (OAK) coasters that make the perfect housewarming gift. Check them out at OAKCoasters.com.

 

 

 

The Importance of Financial Analysis: A League-wide Review of Salary Cap Efficiency

Dimitri Filipovic recently wrote a great article summarizing that – despite the NHL’s rhetoric of ‘parity’ – it is a still a league where the teams’ with the greatest financial resources tend to be the most successful. After reading his piece, I wanted to write a short article building on this concept, where I will show that although having more resources is an advantage, how efficiently a team manages its salary cap space is just as important as how much they spend.

In this piece I will apply the same methodology I have used previously, where I showed how the Goals Above Replacement (GAR) metric can be used to quantify both a player’s and their team’s salary cap efficiency. I will apply the same methodology in order to do a league-wide review of salary cap efficiency in 2014-2015. Doing so, I will argue that (i) salary cap efficiency is an integral lens for all teams to look through when building their roster, and (ii) although an efficient use of salary cap alone will not win a team the Stanley cup – ignoring this type of financial analysis can quickly push them out of Cup or playoff contention entirely.

Determining Team-level Salary Cap Efficiency

Salary Cap Efficiency is defined as how effectively a team uses its salary cap space, based on a bottom-up analysis of each individual player.  To calculate it, I first derive the fair market value of all players as a function of their GAR (FMV = $575K + ($467K * GAR)). Then, I compare that to the Average Annual Value (AAV) of each player’s contract – his cap hit – in order to see if he is being over or under-paid. By aggregating this analysis for an entire team, you can see (a) which player contracts on each team are the most cost-efficient, and (b) approximately how well that team manages its salary cap as a whole.

As a quick refresher, here are two previous charts that summarize the cap efficiency of the 2014-2015 Chicago Blackhawks. The first chart shows all members of the team in relation to the Fair Market Value equation that I just described. The second shows the amount of value created or lost on each individual’s contract.

Hawks - Arbitrage line

Hawks - by player calc

You can see from this that – as the Stanley Cup winning team – most of Chicago’s players are quite close to the FMV line, with only a few exceptions (Seabrook, Toews, Saad). Also, to be clear, we should never expect a team to price/negotiate any given player’s contract ‘perfectly’ – there will always be far too many changing variables in that player’s performance in the years after signing. However, a team can be most competitive by largely signing players with positive GAR, and by generally paying them within a reasonable range of their FMV – or much less, as enabled by their ELC/RFA deals.

In the second chart, I also want to draw your attention to the ‘Net Value Created/(Overpaid)’ figure shown under the legend, of -$6.3M. This figure is the sum of all individual player amounts displayed on the chart. Keep it in mind, as it provides the core of my analysis in the next section.

 (Side note: this Net Value Created figure for the Blackhawks specifically will not tie perfectly to my analysis in the next section. The next section is based on team 2014-2015 GAR scores, where the chart above is based on 3-year average GAR).

Application Across the League

Now, I will apply this type of analysis across the league in order to show which teams had the most cap-efficient roster in 2014-2015, and how that may have impacted their results in the standings. Given this is a GAR-based analysis I will start by showing the total Goals Above Replacement of all teams in the league.

Chart 1 - GAR by team

Looking at this, a few things are worth pointing out:

  • The vast majority of playoff teams are concentrated on the left hand side of this graph, and likewise the non-playoff teams are on the right
  • There are obviously exceptions where teams likely should have made the playoffs, but didn’t (e.g. BOS, LAK), or teams that did make the playoffs and maybe shouldn’t have (CGY, VAN)
  • Although this is a sample of only one season, I think it supports the validity of GAR as having a strong connection with a team’s success – despite only being available retroactively

In order to keep this brief I will fast forward a few steps (e.g. I will not show the calculations for getting the FMV of each team’s roster, and subtracting their total salary cap hit from that). Instead, here is the result, which is the Net Value Created/Overpaid for each team over the season, in absolute dollars.

Chart 2 - Net Value Created by team

Although this is a useful way to look at how well a team spent, it is also impacted by their total salary cap dollars. Given Dimitri’s point about the varying spending power across teams, it is important to look at this on a basis that is directly comparable across teams. As such, I have calculated each team’s ‘Salary Cap Efficiency %’ by simply dividing their Net Value Created by their total cap hit. The larger the percentage, the more efficiently a team has managed its cap.

The chart below shows the relationship between where a team finished in the playoff race along with its Cap Efficiency %. To be clear – a higher percentage (top of the chart) is a more efficient use of cap space, and a lower percentage is a worse use of cap space.

Chart 3 - Team Level Cap Eff - A

Here are some observations from this chart:

  • Most playoff teams are above 0% on cap efficiency – e.g. successful playoff teams are not wasting money by overpaying for players
  • Notably, Chicago was barely above 0%, having won the cup, and both Nashville and Winnipeg were in the ~30% range, while both being eliminated in the first round – showing that maximizing salary cap efficiency clearly won’t win you the Stanley cup on it is own

As Filipovic’s article made clear, greater financial resources will always help a team get the best talent, despite their efficiency or inefficiency. This chart does not highlight which teams spent the most overall, and naturally if two teams are getting the same ‘bang for their buck’ (spending at equal efficiency), the team that spends more will of course win out.

Despite this, a team can very easily ruin its own chance of being a contender by failing to look at its roster through a properly quantified salary cap efficiency lens. Below is another version of the same chart, where I have highlighted the large and unfortunate set of teams whose inefficient use of cap space has very likely impacted their ability to be a contender.

Chart 4 - Team Level Cap Eff - B

Fortunately for the teams in the bottom left quadrant, I think they are all well aware of their situations, and doing everything in their power to undo the current mess they find themselves in (with maybe one exception).

Conclusion

To wrap up, I think it should be clear that any team not conducting salary cap efficiency (or similar) analysis when contemplating its long-term roster construction is taking a significant risk. Being the most cap-efficient team may not be able to win you a Stanley Cup on its own (which Boston and San Jose learned the hard way in 2014-2015) – but having a very poorly managed cap is uniquely capable of taking your team out of contention. All that being said, we of course should keep in mind that cap efficiency is only one piece of the puzzle – to use dollars efficiently a team first has to acquire players worth spending money on.

 

Negotiation Leverage and the Massive Hidden Value in ELC/RFA Deals

This article is being co-posted on Maple Leafs Hot Stove as well as on my own site, http://www.originalsixanalytics.com. Find me @OrgSixAnalytics on twitter.

Recently I completed a three-part series on the Wins Above Replacement (WAR) and Goals Above Replacement (GAR) metrics. In it, I summarized how the GAR metric can be used for player evaluation, how it can be used to calculate player value, and how GAR can be used to derive an entire team’s salary cap ‘efficiency’. I’d like to add one last bonus article to the series to touch something I have often mentioned: the huge importance of Entry Level Contracts (ELCs) and Restricted Free Agency (RFA) years in a salary capped league.

In this piece, I will touch on (i) the impact of negotiation leverage on contract discussions, (ii) how to quantify the value of ELC and RFA deals to teams, and (iii) a short review of these things in practice, by looking at Steve Yzerman’s impeccable handling of Jonathan Drouin’s trade request situation. By the end, I will hopefully demonstrate the immense amount of value for teams that ELC/RFA contracts can create – which further establishes the importance of building through the draft, as well as generally acquiring players at the very early stages of their careers.

 

Market Dynamics and Negotiation Leverage

How negotiation leverage impacts transactions (e.g. trades, contract signings) is something I think most people understand intuitively. Whether inside or outside professional sports, in any transaction the buyer wants to minimize the price paid for an asset, and the seller wants to maximize it. The way market dynamics play into this is driven by the options available to both parties. If a seller has a scarce asset, and can get 5-10+ interested potential buyers (e.g. resembling an auction), the seller is in a position of strength, and will likely get an excellent return. This can even hold true with as few as two or three serious buyers. However, if a buyer is somehow able to get into an exclusive, one-on-one negotiation with a seller, that buyer instead holds significant leverage, especially if the seller is ‘forced’ to get rid of the asset.

Although this is true in every-day scenarios (e.g. to get a good price, it is better to look at three retail stores when buying a new TV, not just one), a great example of the market dynamics side is buying a house. Toronto’s housing market is currently quite ‘hot’ – with 5+ buyers bidding on most houses – as a result, almost every single one trades for a premium to its asking price. Like I said –intuitively, I think we all already know these fundamental concepts.

Now, in the case of hockey, there are two primary situations where negotiation leverage becomes important:

  1. Player-Team negotiations
  2. Team-Team negotiations

For what it’s worth, NHLPA-NHL negotiations also take place, essentially being Player-Team talks at the level of the entire league.

In this article I will be focusing on team-player negotiations, in particular on the substantial impact that ELC/RFA constructs have on them. However, for those interested, my last post touched on the team-to-team side briefly, discussing how the strong trade deadline demand for defensemen and weak demand for goalies impacted the Leafs’ yield on Roman Polak and James Reimer, respectively.

How do Entry Level Contracts (ELCs) and Restricted Free Agents (RFA) Work?

Before quantifying the value of the ELC/RFA concepts, I will give a brief summary of how they work, simplifying it into three categories: (a) ‘ELC’ deals, (b) ‘RFA’ deals, or (c) ‘UFA’ deals (Unrestricted Free Agent) – driven by age at signing and years played in the league. However, keep in mind this is a high level summary, as these concepts fill a number of sections in the Collective Bargaining Agreement (CBA), and free agents are actually divided into six distinct categories. For those who want a very detailed walk through of these concepts and the rest CBA, I recommend you check out @JJfromKansas’ great 2013 series Getting to know the CBA’.

 

At a high level, Entry Level Contracts:

  • Apply to players in their first three seasons in the league, with seasons defined different depending on the player’s age
  • Also typically expire when a player turns 25, if that player has not yet reached three seasons
  • Limit players’ base salary to a maximum of $975K, or a cap hit of $3.8M after including possible performance bonuses (regardless of if those bonuses are paid)
  • Cannot include no-trade/no-movement clauses, and must be two-way contracts

Following a players’ ELC, they enter Restricted Free Agency. RFA players:

  • Will be an RFA either until they turn 27, or until they have played 7 professional seasons
  • Are only able to negotiate with the team that holds their exclusive rights
    • Other teams can extend RFAs an ‘offer sheet’, but generally (i) the current team can match it, or (ii) the current team will be compensated by the ‘poaching’ team with draft picks
    • Compensation ranges from a third round pick for players earning $1.1M-$1.6M per year to four first round picks for players making $8.4M+ per year
  • Also prohibit no-trade/no-move clauses during RFA years
  • Last, as long as teams extend RFAs a ‘qualifying’ offer, players have limited leverage in the negotiation, with the exception of invoking (or threatening to invoke) arbitration

Unrestricted Free Agents (UFAs) are just that – the first time a player can ‘test’ his value on the open market, unencumbered by the CBA. For what it is worth, I currently have no strong view on if this system is fair or not – as of right now, these are the rules of the game, so they are what they are.

ELCs/RFA deals are specifically designed to give teams negotiation leverage over players early in their careers – with ELCs putting a direct cap on that player’s salary, and RFA’s implicitly stifling their compensation by forcing them into one-on-one negotiations.

To be clear, just based on the ever-increasing value of draft picks in trades, all GMs already know that these ELC/RFA years are hugely valuable – this is not an innovative or original concept. However, what is less clear is whether many NHL teams understand exactly how much these rights can ultimately be worth to them.

As such, I now want to attempt to answer:

What Are a Players’ Exclusive Negotiating Rights Worth to a Team?

We won’t be able to get an ‘exact’ answer to this question, because it is a very theoretical concept and is entirely dependent on which actual players is being discussed. As a result, I will instead use an illustrative example that shows – for a particular set of players – how much value they contributed to their respective teams over each of their ELC/RFA/initial UFA years in comparison to how much they were paid in that time. The method I will use to determine what a player is ‘worth’ is the exact same as I used in my previous articles, so I encourage you to check those out if the math is unclear.

To start, I selected a sample of elite-level players, to illustrate the extreme end of the spectrum in terms of contribution to their teams. I selected 10 forwards from across the league who have (almost all) reached the UFA portion of their contracts. Here is the sample I looked at:

Table

First, I stacked up all of these players next to each other in terms of their annual salary cap hits (Average Annual Values, or AAV), with data from General Fanager.  Here is the group’s average AAV per player over their first 10 NHL seasons:1 - AAV

(Note: Although players become UFAs after seven years played, all of the players listed were signed to at least a five-year deal when their ELC expired. As a result, teams earned the savings on these players’ contracts up until their 8th NHL seasons, and I have treated their 8th season as a year locked into an ‘RFA’ contract)

Just from this you can already see the huge impact that ELC and RFA years have on a players’ compensation – these guys are the equivalent of slave labour in their first three seasons, when compared to their contributions/value.

Next, in order to derive what these players are worth to their respective teams, I calculated their average annual GAR contributions, as well as what those GAR values should be worth based on Hawerchuck and Eric T’s relationship between WAR/GAR and contract dollars (e.g. AAV FMV =$575K + ($467K * GAR)).2- GAR3 - FMV(Note: For the players who had not yet reached 8-10 seasons of data, I estimated the last couple years of GAR data based on that player’s 3-yr Avg GAR, and the player’s UFA AAV was treated as equal to their FMV – this was only the case for one or two players)

Last – I calculated the amount teams’ saved, on average, in each year of these players’ contracts. In short – this was done by subtracting the average actual AAV (e.g. Chart #1) from the value contributed / FMV of these players to their teams (e.g. Chart #3, above). Here is the result, on both an individual year and a cumulative basis:

4 - Value Created5 - Cumulative Value

Now – I think this tells us a very interesting result: for the contribution made by this type of elite, franchise player, the combination of ELC and RFA years equate to almost $27M in salary cap dollars saved by their teams. Further, the vast majority of this is driven by ELC years – players get only slightly less than FMV in their 4 or 5 RFA seasons. I don’t know about you, but I was a bit blown away by the huge amount of value this can create for teams.

And again – to be clear – because these are 10 of the highest-performing players in the league, this illustrative analysis shows essentially the maximum that ELC/RFA years could be worth, not the league average, nor what ELCs/RFAs will even ‘usually’ be worth. The other end of the spectrum – replacement level players who make the league minimum salary ($575K per year) – there is little to no incremental value in these years, as those players are not commanding premium prices in the first place.

Negotiation Leverage in Practice: Jonathan Drouin vs Tampa Bay Lightning

Finally, I want to bring this all together in a specific example, where Steve Yzerman (GM of Tampa Bay Lightning) demonstrated a clear understanding of his own negotiating position, as well as of the value of ELC/RFA deals in his recent interactions with Jonathan Drouin. For those who don’t know, Drouin is a former 3rd overall pick, still on his entry level deal, who was apparently having issues with Jon Cooper, Tampa’s head coach. As a result, Drouin privately requested a trade at the beginning of the season, and went public with his request in early 2016. In January, Drouin was then sent to Tampa’s AHL affiliate, the Syracuse Crunch, where he eventually chose not report to play, and was subsequently suspended by his team. As the trade deadline approached, Drouin was the subject of many trade discussions – with other teams in the league likely trying to take advantage of Yzerman’s seemingly ‘forced seller’ position, presumably giving him slightly ‘low-ball’ offers  for the young star.

However, it is now clear that Yzerman fully understood his negotiating position and level of control over the situation as it was unfolding. Further, he seems to have had the foresight to avoid setting a precedent that if a player (with zero leverage) were to complain enough, that the team would ultimately cave. Based on the analysis above, I would guess that Yzerman knew very well that he had up to $27M in salary cap efficiency he was going to lose if he dealt Drouin for less than his fair value – so his ask from other teams was rightfully high. As a result, no team stepped up with a good enough offer, and Tampa Bay kept Drouin at the deadline – fully understanding the risks.

Now, with the recent news this past week, it appears the situation has come full circle. Drouin has apparently realized that Yzerman is no push-over, and if he ever wants to play hockey in the NHL again he will have to do so as the Tampa Bay Lightning see fit. Lo and behold, earlier this week Drouin approached Yzerman about being willing to play for the Syracuse Crunch, has reported to the team, and apparently even had conversations about Drouin possibly coming back into the fold for Tampa’s playoff run. Although we knew Yzerman’s trade ask was high, many observers likely thought that the pressure on Yzerman was so great that he would be ‘forced’ to off-load Drouin at below his fair value. Instead, Stevie Y recognized his leverage in the situation, stood his ground, and either will reclaim one of his top prospects, or at least be able to trade him for full value in the future.

Well played, Steve.

PS – Here was my take on the situation back in January, shortly after it was announced that Drouin had refused to play for Tampa’s AHL team, in an effort to force Yzerman’s hand to trade him:

Tweet1

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Conclusion

Hopefully this provided an interesting look at how negotiation leverage impacts contract situations, the huge value of ELC and RFA deals to teams, and also a useful example of this playing out in reality. In the end, it should now be clear that there is a huge amount of hidden value in getting a player at the front end of his ELC/RFA years – up to $27M, in fact. Last, I think it is clear that Steve Yzerman has successfully sent a message to his players, and potentially the league, that they are a team with a clear understanding of these two concepts, they are steadfast negotiators, and especially when they find themselves in a position of strength – Tampa Bay is not an organization to that will be pushed around.

 

The Model Franchise: GAR, Roster Construction, and Maximizing Team-Level Cap Efficiency (Part 3)

 (OSA’s WAR “Explainer” Part 3)

This article is being co-posted on Maple Leafs Hot Stove as well as on my own site, http://www.originalsixanalytics.com. Find me @OrgSixAnalytics on twitter.

Thus far in my Wins Above Replacement (WAR) ‘Explainer’ series I have covered:

  • Goals Above Replacement (GAR) & Player Evaluation, and
  • Using GAR to Quantify Player Value & Salary Cap Efficiency

Now, for the final post in the series, I’d like to show how GAR can be applied to team level decisions. First, I will show some analysis done by Moneypuck to demonstrate the relationship between GAR and standings points. Second, I will look at the 2014-2015 Chicago Blackhawks and Toronto Maple Leafs to show (i) a textbook example of using GAR to guide a team’s roster construction and salary cap management and (ii) what happens when a team ignores it altogether. Last, using the 2000-2015 Chicago Blackhawks as the best example of a modern ‘Model Franchise’, I will show how Brendan Shanahan’s Leafs’ organization seems to be borrowing a few pages from the Blackhawks’ playbook of the last decade.

Why GAR is Important for Roster Construction

Last summer, Moneypuck did some excellent analysis where he demonstrated the very strong relationship between a team’s total GAR score and its points in the standings – even stronger than Corsi. I borrowed the chart below from Moneypuck’s analysis, showing how a team’s GAR score for a season on the x-axis can be a driver of its total points, on the y-axis.

Moneypuck image

Source: http://canucksarmy.com/2015/8/17/how-to-build-a-contender-part-1-war-what-is-it-good-for

Here is a summary of his findings:

  • Based on the R2 above, GAR has the ability to predict roughly 72% of how a team will end up in the standings (retroactively)
    • This compares to ~38% predicted by 5v5 Corsi%
  • Using this equation, a team with a total GAR of zero – the same as a hypothetical ‘replacement level’ team – would score roughly 76 points in the standings
  • Adding players above/below replacement level to a team would conceptually ‘move’ that team’s expectations up or down the curve shown, based on that players’ GAR

Moneypuck then split up all conference finalist teams since 2009 by GAR score, and had some pretty clear findings:

Moneypuck chart

Note: All GAR data original ly comes from WAR-on-ice.com, and the contract information from charts later on comes from Rob Vollman’s 2014-2015 comprehensive stats database.

The chart above shows that, although Cinderella stories do take place, 80% of conference finalist teams have total GAR scores of 107 or more. This analysis can almost be said to define the ‘goal posts’ of how GAR can be used for roster construction.

Based on this, NHL GM’s could reasonably set a target of 107 GAR for their teams. In years where a team is forecasting close to 107 GAR, the GM should consider trading for those last 1-2 key pieces to make a run. If the team is well off of 107, the GM can instead use it to guide his long term plan by answering (i) how he can acquire a core group of players to reach 107 GAR, and (ii) once acquired, how can he best divide his cap space between those players in order to keep them?

Now that the goal posts are established, I will look at team-level cap efficiency and roster construction of our two example teams: the 2014-2015 Blackhawks and Leafs, based on their season-end rosters.

Team-Level Salary Cap Efficiency

First, I will revisit the Cap Efficiency Curve from my last post, but for a whole team at once, rather than for just a single player. I encourage those who haven’t read my last two articles to go check them out, as it will provide the necessary context for the upcoming analysis.

Hawks - Arbitrage line

Looking at the above, you can observe the following:

  • Almost all Chicago players are on or to the right of the ‘zero GAR’ line – that is, almost all have contributed more than replacement level
  • Relative to the Fair Market Value (FMV) line, Chicago has players both on value-creating and over-paying contracts
    • However, most players don’t stray too far from their FMVs; generally the team slants upward and to the right, with the highest pay going to the greatest contributors
  • The most notable exceptions to this pattern are:
    • Brent Seabrook, who has weaker shot-rate contributions than you would expect, a major driver of GAR
    • Jonathan Toews, who was in his last RFA year in 2014-2015 (which also explains the 2016 bump to 10.5M, shown in my last article)
    • Brandon Saad, who was finishing his ELC in 2014-2015, was understandably traded to Columbus once he came due for a raise in the offseason; the Blue Jackets promptly signed him for 6 years at $6M per year

Now – let’s compare this to the 2014-2015 Leafs:

TML - Arbitrage line

Here, you can make largely the opposite observations:

  • Many Leafs players are on the wrong side of the zero GAR line, putting them below replacement level over this period
  • There are very few examples of players in the ‘green’ area of the chart, with only Kadri, Bernier and Panik having value-creating contracts
  • For what it is worth, this under-sells some players: e.g. Morgan Reilly is being dragged down here by his rookie and sophomore seasons, a time when few players will score well on GAR

Value Creation / Overpayment by Individual Player

We can also look at this output as the actual dollar value created (or overpaid) for each individual player; similar to what I did for Toews, Phaneuf, Parenteau and Boyes previously. I calculate this by subtracting each player’s contracted AAV from the AAV of the FMV line, at the same GAR score. Green bars represent value being created for the team, while red bars represent value lost/overpaid to the player.

Hawks - by player calc

The Hawks’ results here are consistent with the earlier chart, where Saad, Toews and Seabrook were the most extreme examples in an otherwise balanced group. This chart also shows:

  • The Hawk’s 3-year Avg Team GAR was 105.6 – just what you would expect of a conference finalist/Stanley-Cup winning team
  • The team’s net total value overpaid was -$6.3M
    • This represents the approximate year-end cap hit of the Blackhawks at ~$69.9M(1), minus the total FMV of their players at $63.6M

(1)- Slightly off of year-end total due to timing

Although the Blackhawks slightly ‘overpaid’ their players according to this analysis, more broadly I think the Hawks were generally quite close to paying players the appropriate amount across the board.

However, what this result tells me is that a big part of effectively managing a roster will come down to simply not overpaying players. It is extremely hard to find a player that can be signed for less than he is worth, largely happening only when the player is drafted and held for all of his ELC/RFA years. As a result, the simplest way a team can effectively manage its salary cap is to be disciplined in contract negotiations, and avoid giving large contracts to high risk or potentially declining players.

Speaking of overpaying players…

TML - by player calc

This chart shouldn’t need much explaining. Nazem Kadri is the sole shining light of the Leafs’ from last season, and Phaneuf was the largest contract drag they (previously) had on the books. (Note – This was supposed to be based on season end roster but somehow guys like Holzer snuck in there).

Applying GAR Directly to Roster Construction

Last, I will look at how teams like the Blackhawks allocate cap space when constructing their rosters. Specifically, I will compare the percentage of the cap that each player receives in pay, as well as the percentage of the team’s GAR that each player contributes. Any team that is applying this type of thinking to its roster construction would ideally attempt to match these two percentages closely, so as not to ‘waste’ cap space on non-contributing players.

Not surprisingly, that is exactly what we see from the Blackhawks:

Hawks - roster construction

  • The chart above shows a very interesting, and potentially deliberate matching of a player’s GAR contribution and his portion of the cap earned
  • Many of the Blackhawks’ largest GAR contributors have slight greater GAR percentages than cap percentages, again suggesting the Hawks are getting good returns on their dollars
  • Last, this chart helps to show that the Blackhawks have constructed their roster around a ‘core’ set of 7-8 players that drive their results:
    • This core consists largely of the team’s top 4 forwards, top 3 defensemen, and starting goalie (Toews, Kane, Hossa, Sharp, Keith, Seabrook, Hjalmarsson, and Crawford)
    • These players collectively earn 64% of the salary cap, and contribute 67% of the team’s GAR
    • Interestingly, this directly matches the typical conference finalist team having ~8 or so 10+ GAR players, shown in Moneypuck’s analysis cited in my first article

This chart is relatively clean and easy to read, in part due to how well the Hawk’s connect their cap hits to player’s GAR. The Leafs, unfortunately, had a lineup that was mixed between players with positive and negative GAR scores – making this type of analysis less clear and intuitive. To make up for this, I have split the Leafs’ team GAR chart into two sub-charts, one for each of the team’s positive GAR players and its negative GAR players, with each group separately totaling to 100%. Note: as a result, the percentages of the positive and negative bars are not directly comparable to each other.

TML - roster construction

A few comments:

  • Almost 60% of the Leafs cap space was going to players who were contributing zero or negative GAR value to the team
    • 15% of this was also driven by their surprisingly high, non-contributing $10.5M of bought out and retained cap space
  • As mentioned, the Leafs’ net GAR score is 19.5, or the difference between positive GAR players of 43.9 and the negative players of -24.4
  • The Leafs ‘core’ players were simply not of the same caliber or ability to drive a team’s results as the core on the Hawks

In the end, it is clear this type of analysis was not driving the roster construction decisions of the legacy TML front offices. Instead, the lack of it helped to dig the giant salary cap hole that Shanahan inherited.

Building ‘The Model Franchise’ In Toronto

Although the Leafs entered 2015-2016 in a difficult position, the last 8 to 10 months have given fans ongoing reasons to be optimistic about the future. As such, I will close out by touching on five major parallels between the 2000-2015 Chicago Blackhawks organization, and what Brendan Shanahan has begun to do to fulfill his vision of “returning an original six franchise to its rightful place in the league”.

  1. Front Office & Coaching

Under GM Stan Bowman, head coach Joel Quenneville, and with a senior advisor of the winningest coach in NHL history, Scotty Bowman, the Chicago Blackhawks easily have one of the best front offices in the league. Over the last two years, Shanahan has done an unbelievable job putting together a team of arguably the same caliber: between Lou Lamoriello, Mike Babcock, Mark Hunter, and Kyle Dubas, the Leafs’ have an equally all-star leadership team. It is also worth noting the similarity between Babock’s and Quenneville’s system-driven styles, which are both centered on driving puck possession.

  1. Building Through the Draft: Quantity First

Between 2000 and 2004, the Chicago Blackhawks had the highest number of picks of any team in the league at 64, versus the league average in that period of 48, and the next highest of 58. This allowed them to pick up many core pieces they still have, long before Toews & Kane arrived (e.g. Keith (2nd round), Crawford (2nd round), or more recently, Saad (2nd round). As I discussed in a previous article, the Leafs are employing a similar strategy, both by maximizing the quantity of their picks, and also by hopefully leveraging Mark Hunter’s strong scouting organization and network.

Separately, to the concept of ‘building a core 7-8 players’, many fans have enjoyed speculating that the Leafs’ major recent draft picks of Nylander, Marner, Reilly, Kadri, (as well as Gardiner, who they traded for) etc., will make up that group going forward. Only time will tell.

  1. Investing in Player Development

Both teams focus on managing their organizations holistically, by working closely with all of NHL, AHL, and often ECHL rosters. Like Babock’s former Red wings, both teams also push players to develop in the minors, with even two-time Norris Trophy winner Duncan Keith having spent two years in the AHL. The Leafs also lean on the Marlies to help young players learn the team’s system, and help the entire organization focus on every player’s development at both levels. Finally, building a large pipeline of young talent through the draft also allows both the Hawks and Leafs to hold those players on very beneficial contract terms for approximately seven seasons while players play through their ELC/RFA years.

  1. Global Scouting & Free Agents

Finding elite talent is a very difficult task, and the most successful organizations leave no stone unturned. The way Chicago’s was able to pick up a first-line player like Artemi Panarin as a free agent signing (also currently on an ELC deal) is the NHL-equivalent of found money. Although Nikita Zaitsev will not necessarily be of the caliber of Panarin, if the media is right that Zaitsev plans to sign with the Leafs at seasons’ end, he will no doubt be a major player to land. The potential to pick up a developed, 24 year old potential top four defensemen provides even more strong evidence in support of investing in scouting around the globe.

  1. Strategic Cap Management & Roster Construction

Last – although the analysis above shows Shanahan and company have inherited a very unfortunate roster situation, they are clearly doing the right things to slowly off-load anchor contracts, sign value-creating free agents, and offload pending UFA contracts for future assets in picks and prospects. I think it is safe to say that two or three years from now, the Leafs’ roster and salary cap situation will look a lot more like that of the 2014-2015 Chicago Blackhawks’ than it resembles the Toronto Maple Leafs team that Shanahan inherited.

Conclusion

With that, I will wrap up my ‘WAR Explainer’ Series – so thank you to those who have made it through all three parts. In it, I have covered (i) GAR & Player Evaluation, (ii) Player Value and Contract Efficiency, and (iii) GAR and Roster construction/Team-Level Cap Efficiency. Hopefully the series has also provided an interesting view into how the current Leafs’ organization is implementing these principles in their long term rebuild, and helped us all build our patience a little longer. Maybe, just maybe, 5-10 years from now fans will be looking back at the Shanahan-Era Toronto Maple Leafs as the modern NHL’s next Model Franchise, to be emulated for years to come.

Using GAR to Quantify a Player’s Value and Salary-Cap Efficiency (Part 2)

(OSA’s WAR “Explainer” Part 2) 

This article is being co-posted on Maple Leafs Hot Stove as well as on my own site, http://www.originalsixanalytics.com. Find me @OrgSixAnalytics on twitter.

In my last article I walked through what the WAR/GAR metric is, and the practical applications, and limitations, of using it to evaluate individual players. In this post I would like to build on that work to (i) show how to use GAR to quantify what a player is worth in dollars, (ii) introduce the concept of ‘contract arbitrage’, and (iii) use that concept to review the cap efficiency of Jonathan Toews, Dion Phaneuf, P.A. Parenteau and Brad Boyes.

GAR and Quantifying Player ‘Value’

In a salary-capped league, NHL franchises operate under a series of constraints:

  • Maximum of 50 contracts per team
  • $71.4M salary cap in 2015-2016
  • $52.8M salary minimum
  • Minimum NHL-level salary of $575K
  • Maximum NHL-level salary of $14.3M (20% of the cap)
  • Maximum Entry Level Contract (ELC) base salary of $925K, or $3.78M after performance bonuses

As you can see, the Collective Bargaining Agreement (CBA) defines the limits that teams must optimize within. When faced with this, the concept of opportunity cost becomes extremely important. Opportunity cost is the implied cost of a decision by not choosing the next-best alternative available at the time. Think of signing a 35+ year-old to a five-year, $35M deal. Even if that player is a strong contributor, the team’s opportunity cost (five years of losing ~10% of their cap) will often make these deals unjustifiable, especially as that player’s performance declines over time.

Having looked at GAR and player evaluation, I now want to incorporate contract dollars to show how to use GAR to quantify player ‘value’. To do this, I will be focusing on salary cap impact (rather than annual salary paid), as that is what matters to teams when making contract decisions – at least those that aren’t more constrained by their own finances than the cap (e.g. ‘budget’ teams). As you may know, salary cap hit is calculated by the league as the average annual value (AAV) of a player’s contract.

The central approach I will be using to value players is based on some very helpful past analysis by Eric T and Hawerchuk. Amongst many other things, their work showed us the following:

  • 1 win (WAR) = ~6 goals (GAR)
  • Based on the free agent market and the current cap, every 1 WAR a player contributes is worth $2.8M in contract value
  • After adding in the baseline salary ($575K minimum contract, AKA a zero-GAR player): the market ‘price’ of a 1 WAR or 6 GAR player is approximately $3.4M in player salary per year

Estimating Player Value: Some Initial Examples

One way to connect our $2.8M cap-dollars per win to players is to simply insert it into the table I shared in my last post. Doing so will give a range of estimated player ‘value’, shown in terms of contract dollars against the cap.

Expected Contract Dollar by WAR Range

Hopefully this table helps demonstrate an initial idea of what these players are ‘worth’, based on their GAR scores. However, much like my previous draft-pick-value analysis, what works well for a range is not necessarily as applicable on an individual basis. Instead, showing this on a curve can help us assign players a more precise estimate of the value they contribute, and deserve.

The Cap Efficiency Curve

To demonstrate this, I have created the chart below, which I will call the ‘Cap Efficiency Curve’. This curve illustrates the linear relationship between a player’s cost (in contract AAV) and the GAR/WAR a team should expect from an individual with that level of compensation:

Player Value vs Cost Curve

The relationship shown above is relatively straightforward, directly derived from our earlier concepts – that each 1 WAR/6 GAR is worth $2.8M in AAV, above the player’s minimum salary. Hopefully this visualization can help turn the general relationship into an intuitive, usable tool for a team. For example, this curve allows us to:

  1. Evaluate if a player is outperforming/underperforming expectations in his existing contract
  2. Define the ‘fair’ market price for a player’s future contract, based on what he has been able to do historically, and ideally, based on what we can project he will do in the future

One note: as you can see, the equation above only holds between the minimum and maximum player salary levels, as a player currently cannot be paid less than $575K or more than $14.3M per season.

Now that we have a good understanding of what a player is worth – how can a team leverage this relationship to ensure they use their cap space as efficiently as possible?


‘Contract Arbitrage’: How to Take Advantage of Market Inefficiencies

The word ‘arbitrage’ can have multiple meanings, depending on whether it is being used in a very technical, financial sense, or a more general one. More generally, people use arbitrage to mean buying something for less than it is worth. Like a typical ‘value’ investor, this is done by conducting detailed research to figure out an asset’s ‘true’ value, before searching for opportunities to acquire it at a very good price. After purchase, value investors typically hold assets (companies) for a very long time, often continuing to invest their dollars, time and expertise in order to maximize future growth, profits, and investment returns.

Applying this value investing arbitrage to the world of NHL contracts brings me to the idea of ‘Contract Arbitrage’. I will define ‘Contract Arbitrage’ as any situation where a team receives more value from a player’s contract than it costs them. Specifically, that would mean earning more in WAR/GAR than the team gave up in equivalent cap-space, making contract arbitrage a measure of cap-efficiency as much as it is ‘financial’ value. While GMs are most obviously focused on acquiring talented hockey players, a key component of the job of a GM is to maximize his team’s wins per cap dollar spent.

Let’s go back to the Cap Efficiency Curve to illustrate this concept:

Value vs Cost Curve - ShadedLooking at the above:

  • Fair market value (FMV) for a player would be any contract value along the curve shown
  • Overpaying for a player would be a contract that falls into the area above the curve (shaded red)
  • Creating value (e.g. signing a player with the potential for contract arbitrage) would be any contract that falls into the green area, below the curve
    • Simply put, the green area represents any time a team pays a player less than the goals/wins he contributed to the team would justify

For simplicity, I will focus my upcoming examples on past performance, in order to illustrate these concepts. In the truest sense, teams should be using this concept on a forward-looking basis. For example, if a team can reasonably forecast a 20-22 year old to reach 10, 15, or 20+ GAR over the next 5+ years, they should be inclined to ‘lock in’ his contract now – ideally within the green area of the chart above. Keep an eye on Aleksander Barkov’s GAR performance over the next few years, as he may grow into an excellent example of such a contract.

Now – let’s get into those examples:

Our Original GAR Case Study: Jonathan Toews

I will start with Jonathan Toews in order to connect this analysis back to the player evaluation case study from my last post. Toews’ 3-year average GAR is 20.3, and his current AAV is $10.5M on a contract with seven years remaining. Plotting Toews’ GAR together with his contract dollars on the curve below will help us see if he is currently being over, or underpaid. The curve will also allow us to see how much over/underpaid Toews is – measured by the distance between his (x,y) coordinates and the FMV line.

Contract Eval - Toews

  • As shown above, plotting Toews’ onto the Cap Efficiency Curve shows he is ‘worth’ ~$10.1M (e.g. where his ~20 GAR hits the curve)
  • Compared to the $10.5M AAV he currently takes of the Blackhawks’ cap, this would show Toews’ to be paid quite close to his fair value, receiving a ‘premium’ of only $400K

Although Toews’ performance has started to show slight declines, I would argue that Toews’ contract appropriately reflects his FMV. Teams will never be able to predict exactly how a player will perform, but the Blackhawks have come pretty close here. Further, this calculation doesn’t attribute any value to qualitative factors, such as the incredible leadership, work ethic, and experience that Toews brings to his team. In my view, these factors will more than offset the $400k premium that the Blackhawks are paying.

Now let’s look at an example at the opposite end of the spectrum…

Former Toronto Maple Leaf: Dion Phaneuf

First, I want to go on the record and say that I had written almost this entire article and analysis of Dion prior to the recent announcement of his trade to the Senators. As a result of that announcement, I now get the benefit of no longer explaining to the world just how bad Dion’s contract is for the Leafs, and how hard it will be for them to offload it. So that is nice.

Second, I will deliberately avoid getting too far into reviewing the trade directly, as there are many other good examples of people who have done so already. My big picture ‘take away’ is that I was very impressed by the Leafs’ ability to source and negotiate a deal that rids them of his contract, with zero salary retained. I also will say that, from the seat of a Leafs’ fan – i.e. supporting a team that has always been overflowing with cash, and is only constrained by the cap – it can be hard to appreciate this deal from the Senators’ perspective.

However, I think the swap has more positives for the Senators than most think. In this trade, the Sens found a creative way to convert very unproductive cap space (injured/inactive players) into a contributing asset (Dion) with a similar cap hit, only for a longer term. Further, James Mirtle’s insightful tweet summarized that the Sens’ actual cash out the door for next season went down by $4.2M after this deal. For a budget team, that cash compensation change is arguably just as valuable as offloading an additional $4.2M in salary cap – all while picking up a solid, top 4 defensemen.

Now – let’s take a look at Dion’s contract.

Contract Eval - Phaneuf

  • Plotting Dion’s $7M AAV and his 3-year average GAR of -3.4 (2012-2013 to 2014-2015) paints a dismal picture
  • Relative to the FMV line, Dion is being paid $6.5M per year more than he contributes to his team’s goal differential
  • The most pessimistic way of looking at this (which I’m sure will make most Leafs’ fans glow), is to directly multiply the $6.5M loss by the five remaining seasons on his deal: giving a total maximum overpayment/loss of value of $32.5M

However, much like we couldn’t reasonably interpret the first graph as simply ‘Toews is overpaid’, I think we need to caveat this analysis for Dion as well – even if that only results in being nice to Sens fans. To qualify this analysis of Phaneuf’s contract:

  1. First, as shown in my last post, GAR doesn’t necessarily include every aspect of how defensemen contribute to their teams – thus, it may understate the value of Dion, or any other D-man
  2. Second, this data also only includes up to 2014-2015; the eye-test alone makes it clear that Phaneuf has stepped up in the current 2015-2016 season. The change in Dion’s usage and minutes under the Babcock regime have likely bumped up his recent GAR considerably – and to Ottawa’s credit, they were buying into Dion’s play this year, not his play over the three years before this one
  3. Last – I legitimately believe Lamoriello and Babcock’s comments that Dion is an excellent leader, person, and guy to have in the Leafs’ dressing room. I think this was providing a lot more value to the Leafs than us number-crunchers tend to give credit for – and it will be missed

Going forward, the same analysis above can be applied to see if Dion is living up to his contract any better in the future than he has historically. By tracing his $7M over to the FMV line, we can see that a $7M AAV player ought to be in the 13-14 GAR range each year. Thus, if Dion’s GAR for 2015-2016 and onward come out anywhere north of 10, Ottawa will be looking a lot better than we all are currently giving them credit for.

For one last example, let’s look at the free agent signings done this past summer by the Leafs’ current front office:

Brad Boyes and P.A Parenteau

Brad Boyes and P.A. Parenteau are consummate examples of the strong decision-making process and asset management analysis that Shanahan, Lou, Dubas (and likely Brandon Pridham) may be implementing. Boyes and Parenteau each have 3-year average GARs of 5.1, and 5.2, respectively – almost contributing 1 WAR each to their prior teams. On the cost side, Boyes was picked up for an AAV of only $700K and Parenteau for an AAV of $1.5M. Here are the charts to evaluate their respective contracts:

Contract Eval - Boyes

Contract Eval - ParenteauAs the charts above show, given the fact that a ~5 GAR player is typically worth $2.9M, as long as Parenteau and Boyes perform in line with their recent history, the Leafs will have immediately created a combined $3.6M in salary cap value when they signed these two players. Further, being more than halfway into the season, the value that Pierre-Alexandre has brought on the ice thus far speaks for itself. Finally, none of this analysis even factors in the potential ‘exit’ value that Lamoriello & Co. could pick up by offloading P.A. or Brad for picks or prospects at the deadline, which is hopefully made easier by their very minor cap requirements.

Conclusion

To wrap up, the great work done by the likes of Hawerchuk and Eric Tulsky has provided us with the perfect framework to dig deeper into using GAR to quantify player value. Hopefully this article has been helpful to walk through those concepts, illustrate what this relationship looks like visually, and to show how the Cap Efficiency Curve can be a useful tool for analysis of player contracts and salary negotiations.

Building on these concepts, within the constraints of the CBA, the most legitimate, ‘fair’, and repeatable way for a team to maximize their cap efficiency is to either focus on acquiring young players in the draft, or by trying to trade for prospects early into their tenures as NHLers. As such, the upcoming third part of this series will focus on how teams can take advantage of Entry Level Contracts and Restricted Free Agency to consistently generate contract arbitrage opportunities for themselves, and maximize their wins per cap dollar spent.