Myth-busters: Disproving the Idea that Stamkos “Left Millions on the Table” to Re-sign in Tampa

This article is being co-posted on Maple Leaf Hot Stove as well as on my own site, OriginalSixAnalytics.com. Find me @OrgSixAnalytics on twitter. Author’s note: for those who noticed – my apologies for the 4+ month gap between articles in this ‘Myth-busters’ series. This piece has been about 80% complete since September, though between my day job and hockey consulting work I have had limited time to write. While Stamkos’ contract negotiation is now very old news, the analysis herein remained meaningful, so I figured I would finish this up and publish regardless.

 As introduced in my article in September on the topic of the Leafs use of analytics, this piece will be the second of my ‘Mythbusters’ series. The point of this series was to write a short set of articles that use data and objective analysis to try to go against the grain on some of the narratives that came out over the summer of 2016.

In this article – my topic will be the (now ice-cold) Steven Stamkos free agency situation. Although I am posting this on Maple Leafs Hot Stove, this ‘myth’ is one that has been spread around the league at large. Rumors of Buffalo or Detroit offering Stamkos an AAV of $10-11M+ had people suggesting Steven left an annual $2M-3M+ ‘on the table’ to stay with Tampa. Stamkos was sometimes quoted as having “taken a big pay cut for the chance at a cup”.

Today, I will argue that – although those teams or Toronto may have given Stamkos ‘headline’ contract offers that are significantly above the $8.5M AAV he ultimately took in Tampa Bay – Steven actually maximized his own income by staying in Tampa. The most important factor here will be estimating the tax impact of Stamkos’ hypothetical contract offers – which we will get into shortly.

Most people give Steven credit for the facts that he is the franchise player in Tampa, he is loyal to his teammates, he is the team captain, and is on a team that has been in two consecutive conference finals – but not many give him credit for actually making just about as much money as possible as well.

So – putting aside Stamkos’ current injury and reflecting back to his time of signing – lets dig in.

Situation Recap

Last spring, I wrote two articles about Steven Stamkos: one estimated Stamkos’ market value, and the second was a long term analysis of the Leafs’ salary cap – to see if they had room for him in their ‘plan’. In these, I argued Stamkos’ was ‘worth’ anywhere from $9.5M to $10.5M of AAV for the maximum possible term, and that the Leafs should attempt to sign him for the $9.5M-$10M range.

As we all know, Stamkos ‘shocked’ everyone by re-signing with Tampa Bay days before reaching free agency (granted – he had had the chance to speak to other teams), at a contract value that had previously been released publicly – $8.5M over 8 years. But did he ‘leave money on the table’?

Adjusting Stamkos’ Contract for Tax Implications

It is easy to discount the impact of taxes – especially when it isn’t your money. Unfortunately, the fact of the matter is that no employed person in North American walks home with their ‘full’ salary in hand – the tax-man has to take his slice first. Depending on the region, someone who ‘makes’ $50K per year may take home $40K, and someone who makes $150K will keep maybe $115K – and the highest brackets are even more impactful when these numbers become millions.

Looking at fully-burdened income tax rates (including both federal and state/provincial), Ontario and Florida are at opposite ends of the spectrum – Ontario’s top tax bracket being ~53% and Florida’s being ~39% (source: http://gavingroup.ca/personal-income-tax-rates-in-nhl-cities/) . In order to illustrate this impact, the chart below shows what each team has to pay (on the y-axis) in order for Stamkos to receive a given amount of take-home pay (on the x-axis).

chart-1

This is bit of a simplified approach, so to clarify some of my assumptions:

  • Only applies the highest tax bracket (rather than each marginal bracket), which usually takes affect after a couple hundred thousand in income
  • Combines all levels of taxes (federal, state/provincial)
  • Excludes minor taxes/fees associated with playing away games in different cities/states
  • Ignores the impact of escrow – which would hit all teams equally as a percentage

What does this chart tell us? Quite simply – for Steven (or any player) to take home the exact same amount of money, Tampa Bay can pay him considerably less than other teams on an AAV basis, and still be ‘matching’ or exceeding the offers on a ‘actual take home pay’ basis. This amounts to a substantial financial competitive advantage for Tampa Bay (or the Florida Panthers), and a distinct disadvantage for high tax region teams like Toronto.

Now, given the public storyline was that Stamkos’ top two choices were Tampa or Toronto, let’s see how this would have made hypothetical offers to Stamkos from Toronto and Tampa Bay look on an ‘actual take home’ pay basis.

tml-v-tbl

As you can see – if TML had offered my proposed $9.5M to Stamkos, Steven would only get to keep ~$4.5M per year. At the $8.5M offer that he ultimately accepted from Tampa, the much superior tax rate allows Stamkos to take home pay of $5.2M, a gap of $700K per season!

For Toronto to have matched Tampa’s $8.5M AAV offer, it would have cost them $11.1M AAV per year on Stamkos, which was undoubtedly outside of the range that Lamoriello, Pridham & Co were willing to accept. Put differently – in order for Tampa to match a hypothetical $9.5M AAV offer from Toronto, they would have only needed to give Stamkos $7.3M.

The table below summarizes how this tallies up over the life of his contract, including the fact that Tampa can give him one more year than any other team:

to-v-tbl-table

And – just to be comprehensive here – thanks to TSN’s helpful after-tax take home pay calculator (http://www.tsn.ca/nhl/stamkos), anyone can see that even if Buffalo or Detroit offered Stamkos $11.0M AAV, they only come out at $40.2M and $41.7M in after tax total contract value, respectively – essentially matching the existing offer that Tampa Bay had already made.

 Conclusion

 Financially speaking, it should now be clear that Steven did not ‘leave money on the table’ to stay in Tampa – in fact, he made just about the maximum possible amount when compared to rumored offers from other teams. Further, I haven’t spent any time in this article looking at the wide range of other factors that could have caused Stamkos to want to re-sign in Tampa, including:

  1. Stanley Cup Competitiveness (near term & medium term)
  2. Steven’s Role & Contribution (as Captain)
  3. Total Financial Compensation
  4. ‘Legacy’ Potential
  5. Geography (proximity to friends and family)

Tampa certainly outperforms Toronto on (a), and (b), above, and we just showed he was actually maximizing his total after-tax compensation (c) by staying in Tampa as well. Sure – Toronto would be close to friends and family, and be an unbelievable ‘legacy’ (should he have helped the team become a contender) – but it is hard to knock a superstar athlete for prioritizing the team he captains, who is a regular Cup-contender, that has also given him the best financial offer. It should be no wonder to us as outsiders that Yzerman succeeded again in a long, drawn out negotiation where his offer was fair – and final.

 

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Myth-busters Series: Three Arguments Against the Idea that “The Leafs are Decreasing Their Focus on Analytics”

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

 As we move past Labour Day and the hockey world turns its attention to the upcoming World Cup and 2016-2017 season, there are a fresh set of narratives that have come to life this past summer – Stammer-geddon, Vesey-gate, and the 2016 draft, to name a few. In particular, Leafs Nation seems to have shifted its tone slightly: although most are still quite optimistic about the team’s future, some have also started to call into question the front office’s focus on analytics, it’s effectiveness at ‘salesmanship’, and more.

Reflecting on some of these storylines, there were a few that I thought might be interesting to test out with some objective, data-based analysis – and see just how accurate they really are. As a result – this article will be the first of a few I will call my ‘Mythbusters Series’. So – let’s get into it.

 Toronto’s 2016 Draft Picks

The first narrative I will focus on – and one of the biggest coming out of the summer – is the widely alluded-to ‘decreased emphasis on analytics’ coming out of Toronto’s front office. This storyline has come to life in part due to the picks made by Toronto in the 2016 draft, and in part due to (the term and AAV) of Matt Martin’s signing. Although a lot of ink has been spilled over the tradable, four-year contract of a 27 year old, representing 3.5% of the Leafs’ relatively flexible mid-term salary cap situation – today I will just be focusing on the 2016 draft.

We all know the story by now: in the 2016 draft, (i) Toronto picked a bunch of over-age players, many of whom were ‘off the board’ (e.g. unranked/not well known) (ii) Toronto seemed to prioritize height/size this year, and (iii) these two things combine to suggest that ‘analytics’ – and the implicit preference for small, speedy, skilled players – has departed from Toronto’s thought process.

Factually speaking, (i) and (ii) are quite accurate. Five of the Leafs’ picks were over-agers, and eight of their eleven picks were 6’2 or taller. However, what I will question today is the conclusion of (iii), and the idea that targeting size and over-age players suggests anything ‘anti-analytics’ about the Leafs’ front office.

In this article I will argue there is significant analytical support in favor of the type of player Toronto targeted (e.g. over-age and bigger players in general, rather than the specific individuals the Leafs picked). Further – if any team in any sport is truly trying to be on the ‘leading edge’ and develop innovative approaches to the game – that often might actually require doing things others see as questionable at the time.

Let’s dig into the three reasons why the Leafs’ older/bigger picks may be more supported by analytics than we all think:

  1. (Asset Management) Portfolio Theory

First off – let’s talk about size. Most in the analytics community tend to prioritize small, skilled players that can drive puck possession above anything else – and for good reason. However, I also think most can agree that there is some value to (the very different benefits brought by) large, physical players as well. Does ‘conventional thinking’ over-value size relative to other characteristics of players? Probably. But is there no value to having a physical presence on your team? Probably not.

That’s where the asset management concept of ‘Portfolio Theory’ comes in. In the financial world, diversity reigns supreme. “Don’t have all your eggs in one basket” sums it up. Put differently, any investor doesn’t want to be too concentrated in one stock, in equities, or bonds, or in any other asset class – lest they find themselves in a situation where that asset class is going to underperform.

After an excellent draft in 2015 and a strong prioritization of bringing fast, skilled players into the organization, the Leafs have arguably reached the point of diminishing marginal returns on that type of player – with an extremely deep pool of forwards in that mold. Portfolio theory suggests that their ‘return on investment’ of their next few 6-foot-plus players – who ideally have some speed and skill as well – will be much greater than picking an Alex DeBrincat-type player, even though guys like Nylander and DeBrincat are hugely valuable in an absolute sense.

The main point here: it should be safe to say that there is some logic to having a supporting cast of size to supplement Toronto’s already strong focus on speed and skill. Especially with a younger team, lacking ‘grinder’ type players – the tougher teams in the league would be silly not to make physicality a deliberate part of their game planning against Toronto this year. Compared to some of the observable alternatives (e.g. $6M AAV, 7 year signing of Milan Lucic…) – drafting some size seems like a solid idea.

Last – what are some of the other, innovative teams in the league with small, skilled line-ups saying on this topic? From Sportsnet:

In Crouse’s 6-foot-4, 212-pound frame, [John] Chayka [Arizona Coyotes GM] brings size to a club currently more focused on speed and skill in an effort to diversify the type of player the Coyotes are putting on the ice—the “portfolio theory,” he says.

        2. Over-Aged Players as a Market Inefficiency

Second – let’s talk about drafting over-aged players, or those who have ‘re-entered’ the NHL draft. Most of the critics of the Leaf’s 2016 draft found the decision to draft five re-entries as strange unexpected – and likely questionable. Even the analytically-minded crowd seems to see ‘less upside’ to over-agers, despite interesting analysis supporting targeting over-agers as a strategy.

Before we jump to that conclusion, I did a quick bit of analysis to compare the results of players drafted at 18, 19, and 20 years old. A few trains of thought that lead into this chart:

  • The top, top players will likely be identifiable when they are young, so it makes sense for most of the picks in the 1st and 2nd rounds to be focused on players in their first year of eligibility (e.g. 18 year olds) – you won’t be finding Olli Juolevi or Ivan Provorov in the 3rd round of the draft
  • For any draft pick – the theoretical goal should be to pick players that will be above replacement level, who can add significant value beyond just ‘filling a seat’
    • Replacement level can be defined in simple terms as top AHL players or free agents that can be signed for approximately a league-minimum contract

Thus – ‘success’ for a draft pick shouldn’t represent a player who just ‘makes’ the NHL, but is 4th line F or 3rd pair D. Those players are available essentially for free in the free agent/waiver market. Rather, success for a draft pick is someone who outperforms that replacement level of production.

All that said – what exactly is replacement level is very open to interpretation. We should be cautious about using strictly games played as the ‘success’ determinant – loads of 4th liners play 150+ NHL games without adding significant value to their teams, or earning more than single-digit minutes per night. For the purposes of the chart below, I have included only players with >200 NHL games played, and also included scoring rate data, across the ranges of >0.2 Pts/GM to >0.5 Pts/GM.

chart-1

 Note –This data is not adjusted for the rule change with respect to NCAA eligibility and declaring for the NHL draft – however, I believe the impact would be relatively minor.

I won’t go in huge detail into my methodology, as much of it is summarized in the fine print on the chart. In terms of what the chart tells us:

  • After the 1st and 2nd Rounds, players drafted at 19 years old (e.g. Draft year + 1) are roughly equally as likely to exceed replacement level as 18 year olds
  • Even more interesting – players drafted at 20 years old (e.g. Draft year +2) are significantly more likely to surpass replacement level than the other two ages, if defined as >200 NHL GP and anywhere between 0.2 to 0.5 career Pts/GM
  • (Note – to save time, I blended forwards and defensemen in this analysis – though the replacement level definition would of course be very different for each)

Put differently – if we choose >0.3 Pts/GM as our ‘replacement level’ threshold – in the time period sampled, only 55 players drafted after the 1st & 2nd rounds surpassed the ‘replacement level’ definition. Of these 55, 53% were drafted as 19 or 20 year olds (e.g. in their D+1 or D+2 years) – a huge portion of the players who ultimately were ‘NHL contributors’ to their teams.

FYI – I’m definitely not the first person to dig into this subject – here is a tweet from last June from recently promoted London Knights Assistant General Manager and Director of Analytics, Jake Goldberg:

jake

Some may disagree – but I would argue it’s probably a ‘good news story’ for Leafs fans that the rest of the league spent the last five draft rounds focused largely on first-year players who will make up 47% of the ‘above-replacement-level’ pool. Meanwhile, the Leafs spent the 2016 draft significantly prioritizing picking through that other 53%. If that is not taking advantage of market inefficiency, I don’t know what is.

  1. Draft Expected Value (DEV)

 Finally, just to round out the analytical view that is ‘pro’ overage players, let’s give credit to another pair who have done some great work on predicting prospect success: @Zac_Urback and @3Hayden2, and their Draft Expected Value model. I won’t go through every detail of their approach, as they have already summarized it very well in their posts: Introducing DEV, Explaining DEV, Limitations of DEV and – you guessed it – Draft Inefficiencies: Overage Prospects. These guys have rightly got a lot of attention since the draft, so I am happy to pile on.

In short, much like the ‘Prospect Cohort Success’ model created by @MoneyPuck_ (now of the Florida Panthers), DEV generates a list of the most comparable prospects to a particular one, based on age, league, adjusted scoring, and size – in particular, adjusting for whether they are in their Draft Year (D), before it (D-1), or 1 or two years after it (D+1, D+2). The model then converts that list to an expected NHL result, and then directly assigns a value to a prospect in terms of approximately when he should be picked, and his expected output.

You don’t need to look much further than Zac’s article on Overage Prospects to get an idea of if the Leafs are still putting their analytics team to good use. In it, Zac makes the following point:

 Looking forward to the 2016 NHL draft, I ran the DEV numbers for all draft eligible overage players. One player in particular that I want to discuss is Adam Brooks. Brooks is relatively undersized at 5’10, but in his 3rd year of draft eligibility DEV suggests he’s worth selecting with a pick from 28 – 33 overall. Brooks was valued as a pick from 55 – 82 last year, demonstrating two things: 30 NHL teams passed over a prospect worth selecting in the 3rd round with their late round picks last year, and Brooks has improved considerably since last year.

 Some of Brooks’ successful comparables include players like Claude Giroux, Derek Roy, Ondrej Palat, Patrick O’Sullivan, Martin Erat & Jordan Eberle. I suspect he will not be selected as high as DEV values him, but if he’s available in the mid-rounds, Brooks seems like the obvious candidate to draft if a team is looking for a value selection. Obviously Brooks is not a lock to be a successful NHL player, but DEV indicates that he’s just as likely to be an impact NHL player as any other player who is optimally selected in the top of the 2nd round.” 

 And wouldn’t you know it – in the fourth round at #92 overall – which team selected the small, skilled, but overage player, Adam Brooks? The Toronto Maple Leafs. Mark Hunter and Kyle Dubas seem to right back at it with their old tricks, trying to create value for their franchise. Well done, gentlemen.

Conclusion

 To wrap things up – I think it is safe to say that the analytics function in the Leafs’ organization seems to still be playing an important role – and doing well to convince their broader organization to make bold, un-loved picks based on statistics that suggest those moves will maximize value. If anything, the TML front office deserves a bit of credit for making their innovative decisions seem like they are not analytically-driven. The only downside of the approach (and to a small extent, articles like these) is that is that now the TML management team will need to continue searching for the next ‘new’ thing, if they want to keep their edge in 2017.

Does Stamkos Fit in the Shanaplan? A Long Term Analysis of the Leafs’ Salary Cap

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

“We have a five year plan that changes every day” – Lou Lamoriello

I recently wrote an article focused on estimating the value of Steven Stamkos’ production over the next seven years. In it, I concluded a ‘fair’ price for a player of his caliber would be $9M-$10M for the maximum term (7 years). Further, I suggested that – given Steven’s negotiation position resembles an auction – we should expect Stamkos to find a team willing to give him the high end of that range.

So, the next logical question posed by fellow Leafs fans:

“OK – Stamkos is worth a lot. But does it make sense for the Leafs to pay him that much?”

In this article I will walk through a detailed review of the Leafs’ cap over the next 7 years, and the strategic questions facing them. Looking at this analysis, my own conclusion is that Toronto should definitely attempt to sign Steven Stamkos – however, like in any negotiation, they should do so own their own terms, and only in a manner that fits within their broader salary cap strategy.

So – let’s get into it.

Approaching Long Term Salary Cap Analysis

Teams must consider a wide range of factors when planning their long term salary cap management. Besides ‘team-level’ factors, like on-ice strategy and their ‘competitive window’, there are a range of factors that must be evaluated for each individual player signed (on both a near term and long term basis):

Naturally, writing an article that goes in depth on all of these topics would take thousands of words – so I will have to narrow my focus somewhat. Today I will be focusing strictly on the financial side: how to analyze a team’s salary cap strategy and contract commitments over the long term, and what that tells us about the Leafs’ decision to pursue Steven Stamkos.

Before getting into the Leafs’ cap situation, I want to first talk about (i) the principles behind this type of analysis, and (ii) an example of a team who has managed their cap along these principles in the past.

‘Asset-Liability Matching’

A major principle used by financial services companies is the concept of ‘matching’ assets to liabilities. In the case of banks, insurance carriers, and pensions – this means forecasting the future payments to their clients, and then building portfolios of assets (investments) to match when those liabilities will become due over time.

This line of thinking is extremely applicable for the strategic management of a salary cap. In the NHL the ‘assets’ are players – represented by a bottom-up forecast of each player’s individual production – and the ‘liabilities’ would be the length and term of each player’s contract. Production could be considered as either basic goals/points or as advanced stats like Goals Versus Threshold (GVT)/Goals Above Replacement (GAR).

The objective of this exercise is to closely match the value of your players (assets) to the cost you incur to secure them (liabilities) over time – while maximizing total value. In the interest of time, I will not be building a Stamkos-like production forecast for the entire Leafs’ roster. Instead, I will focus strictly on the liability side of the equation, and try to build up the estimated contract dollars and term for all of TML’s future ‘core’ players.

Before getting into the Leafs, let’s look at an example of an organization that has applied this approach beautifully in the past.

Salary Cap Management: Best Practices

Despite their recent 1st round exit, Stan Bowman’s Chicago Blackhawks represent one of the model franchises in today’s NHL (as I have previously written about). Borrowing a chart from that article, you can see that Bowman has been able to closely match each player’s proportional contract dollars and total production, when compared to the team as a whole.

Hawks - roster construction

As you can see, most of Chicago’s top 8-10 players in AAV (average annual value) closely match their top 8-10 players in terms of Goals Above Replacement – with many ‘outperforming’ their cost to some degree (e.g. Saad).

Looking at Chicago and others around the league, the best practice appears to be assembling a ‘core’ of players that can be secured for the long term. These core contributors typically represent ~60-80% of the team’s cap, while the remaining 20-40% can be dedicated to short term contracts, strong prospects on ELC deals, and cheap, role-playing replacement-level players for depth and filling specific needs.

Now – enough pre-amble – let’s talk about the Leafs:

TMLs’ Long Term Cap Strategy

 To analyze the Leafs’ salary cap, I will to look at:

  1. Current contract commitments
  2. Estimating future cap constraints
  3. Estimating future commitments to ‘core’ players

Ideally this analysis will both help us understand Shanahan, Lou, (and namely, Brandon Pridham’s) plan for the organization, as well as whether or not Stamkos fits within it.

To be clear: the upcoming analysis uses almost entirely estimates and will require many placeholders to complete it. As a result, this analysis as a whole should be considered ‘illustrative’.

TML’s Total Contract Commitments

 Given the NHL has a 50 contract limit per team, let’s start by looking at how many players the Leafs have committed to over the next seven seasons (all data here is courtesy of GeneralFanager.com).

Leafs Contracts

(Note: Although the league limit is 50 contracts per team, General Fanager caveats that six of the Leafs’ contracts in 2015-2016 are exempt: Zaitsev, Marner, Dermott, Kaskisuo, Nielsen, and Timashov)

Beyond the 2015-2016 season, Toronto has a solid amount of flexibility in terms of total contracts. However, the number of contracts is likely the smallest piece of the puzzle, and salary cap dollars will be the more important factor.

Historical/Future Salary Cap Growth

In order to have a meaningful projection of the Leafs cap, we first need an estimate of their future cap constraints. To gauge that, let’s first look at the growth of the salary cap over the last 10 years:

Historical Salary Cap

This chart shows the NHL salary cap can change dramatically over time – it rose from $39.0M in 2005-2006 to $71.4M this year – a CAGR (compound annual growth rate) of 7%.

However, we shouldn’t assume this rapid growth will continue – especially given the recent weakening of the Canadian dollar. Instead, I have projected the future annual salary cap growth rate to be roughly ~3% on average, shown in the chart below.

Projected Salary Cap

Given the NHLPA’s tendency to use its ‘inflator’ clause, along with the general currency inflation rates in Canada and the US being in the 2-3% range, I consider this 3% estimate to be a conservative assumption. Although the chart above will likely be wildly incorrect on a year-to-year basis, over the long term it will serve our purposes of providing a conservative estimate to be used going forward.

Leafs’ Existing Contract Commitments

 Now, let’s look at the contract dollars the Leafs have already committed against their salary cap:

Leafs Contracts - Projected

Much like the last chart, you can see the Leafs have some flexibility for the upcoming season, with roughly ~$16M of cap space available (assuming the 3% growth materializes). After 2016-2017, the Leafs have a huge amount of cap space available – in large part thanks to their trades of Phil Kessel and Dion Phaneuf over the last two seasons.

Although the upcoming sections will be focused on the 3+ year future for the Leafs, for anyone interested in a detailed review of the 2016-2017 and 2017-2018 Leafs rosters on a player-by-player basis – check out this great post by @yakovmironov from Bloggers’ Tribune.

TML’s Future ‘Core’ Players

When looking to the Leafs’ future, I will focus my efforts on the Leafs’ ‘core’ players and rough estimates of these players’ future contracts. As the Blackhawks’ strategy shows, the other ~40 contracts will likely change on a year-to-year basis, so it makes sense to focus strictly on the major assets. Although some other players may warrant inclusion, I think most fans would agree on the following guys:

  • Nazem Kadri
  • William Nylander
  • Mitch Marner
  • Auston Matthews (assuming they take him)
  • Jake Gardiner
  • Morgan Rielly
  • Nikita Zaitsev (assuming Zaitsev plays out as hyped; otherwise, consider him a top-3 D placeholder)

Of these seven guys, the Leafs recently locked up Rielly and Kadri until 2021-22 – six years each – and have three years left on Jake Gardiner’s contract. Of the players omitted, the strongest argument for inclusion would be JVR. Given Van Riemsdyk’s UFA deal will likely be significantly more expensive than his current one (at $4.25M AAV until 2017-2018), I have not included him in the ‘core’ group; however, I will re-visit JVR later on.

Let’s look as this group as a whole in terms of dollars that have been committed to them to date:

Core - Commitments

Notably, the Leafs’ don’t currently have an answer for their long term option in net. Maybe Reimer returns or maybe Kasimir Kaskisuo becomes the long term option – it is too soon to say. Regardless, having a #1 goalie is essential to any teams’ success, so I have included a placeholder for the Leaf’s future goalie in my analysis.

Forecasting the Leafs’ Next Seven Years of Salary Cap

Although the Leafs’ core players don’t have huge contracts yet – those deals will eventually come. The issue Stamkos presents is 3-5 years down the road, when many of these young stars need new, big-dollar deals. In order to understand how much space the Leafs could offer to Stamkos while not sacrificing their top young talent, I have estimated the AAV of each core player’s next contract. To do so, I simply picked a relatively comparable player for each and used the comp’s AAV as a proxy (see footnotes for specifics).

The next two charts summarize a high-level view of the Leafs’ future cap situation, both in absolute dollars and as a percentage. I have included Steven Stamkos for the sake of illustration.

Leafs core projected - aav

Leafs core projected - cap percentage

Looking at the charts above, I think there are reasonable grounds to argue the Leafs should pursue Steven Stamkos, given:

  • The Leafs project to have ~$25M in cap space remaining in their tightest future year (2019-2020), representing 30.4% for non-core players, almost exactly the amount of the 2014-2015 Blackhawks
  • Assuming long term salary cap inflation, this number continues to slowly decline thereafter

Based on the above, I think it is safe to conclude that there is space for Stamkos within the Leafs’ future salary cap space. Naturally, we could debate the appropriate number to project for each player, but treating the ‘core’ as a whole, I think the outcome will be largely the same. Some fans may argue they would rather have 3 or 4 more of the current Marlies for 4-7 years than have Stamkos; however, I personally subscribe to the view that NHL teams require ‘elite’ talent (15-20+ GAR) to win a Stanley Cup – and not just a lot of ‘good’ talent.

One last note: including JVR at $6M per year starting in 2018-2019 would put the Leafs at 77% of the cap allocated to a core of 10 players (in 2019-2020). I think this is the absolute high-end of the range to give to a core group. That said, it would be hard to argue with the strength of that set of top 6 forwards and top 3 defensemen, all ideally supplemented by a goalie that deserves the $6M per year being allocated to his position.

Before wrapping up, I want to touch on two final areas: mitigating ‘over-payment’ risk, and TML’s negotiation strategy.

Minimizing Over-payment Risk

Many fans are concerned that signing Stamkos’ could create a cap drag in the last 3-4 years of his deal. Generally, I agree that this is a reasonable concern – but as I mentioned earlier, I believe the Leafs should attempt to sign Stamkos, but only do so on terms that fit their own strategy and needs.

The Leafs can limit their over-payment risk by refusing to give Stamkos a full No Move Clause (NMC) or No Trade Clause (NTC) – instead, they should only offer deals with a modified NTC (allowing Steven to list ‘X’ teams he will accept a trade to). A modified NTC is a strong hedge against the potential downside of the Leafs overpaying Stamkos, as even if he declines, they likely will be able to offload him for some combination of picks, prospects and retained salary. There happens to be a clear precedent for this clause in Stamkos’ UFA comparables  with 50% of them (Ovechkin, Nash and Kessel) having agreed to modified NTCs. Last, the Leafs would also be wise to front-load Steven’s cash compensation (salary), to increase his outer-year appeal to ‘budget’ teams that are cash-poor but have ample cap space.

Side note – @yakovmironov also included a great table in his article that summarizes the production of many 33-year old NHL players (Stamkos’ age in the final year of his contract) –it isn’t as bad as you might think.

Approaching the Stamkos Negotiation

Based on all of the information above, I think the Leafs should give Stamkos an ‘opening offer’ of $9.5M for 7 years, including a modified NTC for the duration of the contract. Depending how the negotiations proceed, I would recommend Toronto be willing to increase this offer up to $10M per year, and to reduce their NTC to only the final 3-4 years of his term. I personally suspect that if Stamkos has interest in coming to Toronto in general, that he would be willing to accept somewhere in this range. However – as in any negotiation – it is essential Toronto knows their maximum offer, and that they are willing to walk away from the deal if Stamkos pushes for anything beyond it.

Conclusion

Overall, I think the Leafs would do very well to try to sign Steven Stamkos this July. Stamkos is an elite shooter, a former first-overall pick, and a potential captain who could lead Toronto through a key stage of their rebuild. Projecting forward the Leafs’ core talent shows there should be a reasonable amount of room in the Leafs’ cap space for him, while keeping roughly ~30% of their salary cap to be allocated outside the top ~10 players on the team. Finally, by only offering Stamkos a contract that includes some version of a modified NTC – and otherwise walking away – the Leafs can carefully mitigate their biggest risk in this contract while also signing a hugely valuable asset at the same time.

In the end – I suspect the Leafs’ front office has a very clear view of their current long term roster construction plans, approach to salary cap management and their strategy for negotiating with Stamkos and his agent. As a result, all that is left for us fans to do is to sit back and count down the days until July 1st.

 

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.

 

 

 

How to Value Draft Picks vs. Active Players

What is a Draft Pick Worth in a Trade for an Active Player?

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.

Many, many writers have touched on the concept of Draft Pick Value, myself included. Those who find it interesting are happy to talk about it for days, and those who don’t tend to steer clear pretty quickly. The one downfall of the work done to date is that almost all of it has focused on what draft picks are worth when traded for – you guessed it – other draft picks. In the spirit of the (now passed) trade deadline, I want to take a quick look at answering the following question:

How can we reasonably compare the value of a draft pick to the value of an active player?

To answer this, I will (i) introduce the concept of ‘absolute’ draft pick value, and (ii) go through an example, looking at the Leafs’ recent trade of Roman Polak and Nick Spaling to San Jose. My goal is not to conclude who ‘won’ the deal (I think that has already been decided), but rather to apply the concepts to a concrete example and give this analysis a bit more of a practical implication.

I want to make one caveat clear before comparing pick value and player value: This — and any other type of ‘valuation’ analysis — will always be an inexact approach and will not tell us the full picture. Teams don’t make decisions on deadline day because of abstract math; they make trades because they want to win, they have a specific spot to fill, and they believe that particular trade is the best way to fill it. The market dynamics of the trade deadline also have a huge impact in how trades go. This year, demand exceeded supply for defensemen, driving up the price of players like Roman Polak. Similarly, no one was really looking for a rental goalie, so James Reimer fetched far less than he is probably worth. As such, keep in mind this type of analysis should always be considered in conjunction with a wide range of other quantitative and qualitative factors.

(Relative) Draft Pick Value

As mentioned, previous work has largely focused on relative draft pick value – that is, what a pick is worth in a deal for other picks. These draft pick value charts often end up quantifying picks/future players in terms of some currency or ’unit’ that is hard to assign meaning to out of context. Relative draft pick value is most useful on the day of the draft, when many pick-for-pick trades are made and we know the exact selection number a pick relates to (as opposed to only knowing the round). However, in order to compare between draft picks and players, we need to compare players on the same metric — one that addresses the concept of absolute draft pick value.

Absolute Draft Pick Value

In his recent piece, Stephen Burtch took another big step forward in this area by laying out a number of simple and clear metrics in a concise table to illustrate some proxies for draft pick value. Here is his table:

burtch

I am a big fan of this table. Not only does Burtch introduce an absolute value metric for players (Expected Pts/GP), but he shows how long it takes for those players to become real contributors (Seasons until 150GP). He also connects it all to the Goals Above Replacement metric – another thing I am a big fan of. Later on in his article, Burtch also essentially splits teams into ‘buyers and sellers’ – a very useful lens through which to view the market dynamics on deadline day (e.g. availability or scarcity of particular assets, driving demand and price).

Although this chart is a great start for absolute pick value, I think we can go one step further. Having analyzed Expected Pts/GP for draft picks myself, there are two things I want to point out. The first, which we’re all aware of, is that it differs significantly by forwards and defensemen (Burtch no doubt recognizes this; he just wasn’t showing that level of detail in his table). Secondly, it has a survivorship bias – that is, over time, only the strongest players remain in the league and continue scoring points, thus driving up the averages over time. Thus, Pts/GP does not appropriately account for the probability that a player stays in the league at all.

Player Lifetime Production

While I recommend using as many value-metrics as possible to establish a well-rounded view, one of my own favourites for absolute pick value is Lifetime Production, calculated as the expected average cumulative points per player (e.g. total career points). This metric implicitly adjusts for players who never make it into the league at all, as the denominator in the equation is total players drafted rather than total games played. See below for two of my previous charts, showing this metric for both forwards and defensemen.

Lifetime Production - F

Lifetime Production - D

(Note: The chart sample is all players drafted between 2000 and 2004, and their subsequent playing histories over each of their first ten seasons in the league).

Although this data doesn’t separate the top three picks overall, who deserve their own echelon, you can still see some clear results:

  • Over their first ten seasons in the league, a top-10 overall pick should be considered to be worth ~170 total points if a defenseman is selected, or ~350 points if a forward is selected.
  • Depending on how soon that player really begins to contribute, (e.g. many players only are NHL regulars for 5-8 of their first ten seasons), top-10 overall defensemen come out as ~20-30 point per season players, and top-10 forwards come out as ~40-60 point per season players.

Now, this metric is also not perfect. It works well when used far in advance of the draft, when it is unclear what overall selection number a pick relates to. However, it can be a pretty high level approach when a team knows it is holding the 33rd overall pick, for example, which could be treated much the same as a late first round pick. Many will also rightfully point out that these are averages with huge distributions in results. In all rounds, there will be many players who will have 500-600+ points over these 10 seasons, and many who will have less than 10. As a result, these averages/expected values will only ever be one piece of the puzzle.

Case Study: TML trade Polak/Spaling for two second round picks

Lastly, I will try to illustrate this concept a little more clearly by looking at the Leafs’ recent trade of Polak and Spaling to San Jose in return for two second round picks. Here are the assets that changed hands in the deal:

Pic 1

Now, Raffi Torres was more of a cap offload by San Jose, who the Leafs have let remain at San Jose’s AHL affiliate (he’s also not playing for the rest of the season). As a result, let’s exclude him from the comparison. For simplicity’s sake, to stack up the remainder of the trade I have assumed TML uses the two picks to select one forward and one defenseman. Further, based on both Burtch’s and my charts above, second round picks only really begin to meaningfully contribute around their fifth season after being drafted. As such, you should consider the ‘lifetime production’ for these picks to be over approximately five or six active NHL seasons.

The table below summarizes the career points we can expect from these picks versus what could be expected from Roman Polak / Nick Spaling over each of their next five seasons.

Lifetime total

In order to try to show this on an apples-to-apples basis, I have assigned Polak/Spaling the value of their cumulative points over their last five seasons. Now, this is not exactly scientific, and should not be treated as a ‘trade-defining’ result. However, the chart does show an interesting finding: based on what can reasonably be expected from these picks over ~10 years after being drafted (which includes adjusting for their likelihood to succeed in the league at all), they will not necessarily be as productive as Polak/Spaling will be over each of their next five seasons, in the aggregate.

However, that is not the full story of course. Polak and Spaling are shown here in a somewhat generous view of what you could expect out of them for the next ~5 seasons. It does not discount their performance at all for declining with age, nor does it consider their moderate cap hits, as 27 and 29 year old players. Given that they are both Unrestricted Free Agents (UFAs) at the end of this year, San Jose may only actually realize the value of ‘one’ of the next five seasons from these two players. The table below adjusts this data to be shown on a per-season basis, rather than in aggregate, simply by dividing the last chart by five expected seasons:

Per season total

Looked at differently, although San Jose got a total of 32 ‘Pts/Season’ worth of production, the Sharks only have certainty that they acquired the tail end of the 2015-16 season (e.g. ~20 games) before these two players could walk away and sign with any team in the league.

At the same time, Toronto ‘only’ acquired 23 Pts/Season, but this will be spread out over five productive years. These will also be the prime years of those players’ careers, where their value is the highest, due to being a low cap hit while on Entry-level/UFA deals; Toronto has the players’ exclusive rights while they develop. Last, as has been touched on in the past, ideally Mark Hunter and company can actually increase the probability of turning these picks into higher-calibre players than average, given his and his team’s strong network and scouting capabilities.

A Note on Time Value

One final note before concluding: It is also worth pointing out that, given these picks are for 2017 and 2018, they are inherently less valuable than a pick for this upcoming draft, and I suspect that is a key reason that San Jose was willing to make this trade. Even rebuilding teams want to rebuild now, not 2-3 years from now.

To illustrate this concept, ask yourself if a second round draft pick in 2022 is worth the same as one in 2016? Standing here in 2016, it is not. The same logic applies to 2017 and 2018 picks, although to a lesser extent. Fortunately for Toronto, the Leafs have so many picks in 2016 that arguably they were looking for picks in later years in the first place. However, this willingness to accept a later date (plus the lack of supply of rental defensemen this year) likely helped the Leafs increase their yield in this trade significantly. For anyone interested, this ‘time value’ concept is directly borrowed from the world of corporate finance, where ‘discounting future cash flows’ (e.g. player production) is the foundation of assigning a value to a business.

Conclusion

To wrap up, I have hopefully heavily caveated that this analysis should not be considered scientific, the best, or even the only way to compare the value of draft picks to active players. What this hopefully does provide us, though, are some useful heuristics (rules of thumb) to keep in mind for future deals, which can be combined with all of the other methods available at our disposal to evaluate transactions. As a result, the next time you see a team toss around two 1st round picks (or equivalent players) for a long-term Phil Kessel-type player, or an short-term rental of an Andrew Ladd-type player, hopefully you walk away thinking about just how many hundreds of future points they are giving up down the road.

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.

2015 Draft Day: How Hunter and Dubas May Have Out-Played the League (Part 2)

This article is being posted here as well as in parallel as a guest post at Maple Leaf Hotstove.

In my last post I shared my analysis on long term player performance and development based on draft round. As a follow-on to that article, I’d like to do two things: first, I’ll convert my last analysis into a relatively straightforward and ‘usable’ metric for draft pick value. Then, I’ll apply this metric to two short case studies in order to illustrate who won each of the Leafs draft day trades this past summer (hint: it wasn’t Ron Hextall or Jarmo Kekalainen…).

Draft Pick Value

The third and final objective from my original report posed the following question:

  • How much more valuable is a pick in the first round versus the other rounds? All things being equal, what should a pick from each round be worth in a trade?

Building on the analysis done by others mentioned in my last post, the chart below summarizes how I have approached ‘converting’ long term performance data into a relative draft pick value metric. To be clear: I am not proposing the values shown in this chart, rather, I hope to use this chart to illustrate the methodology I have applied across a number of metrics.

Games played data is one metric that can inform relative pick value by draft round

Pick Value Demo Chart 

  • The chart above shows how likely a player from each round is to play ~2+ seasons in his career, and by when he should be expected to do so
  • The chart then calculates how much more likely a player from each round is to pass 150 GP than the bottom cohort of rounds (e.g. average of Rounds 4-9) – shown as a multiple of those rounds
  • Thus, if we define a pick in rounds 4-9 as the ‘base unit’ (e.g. ‘1.0 units’), using the >150 GP threshold shows a third round pick to be worth 1.8 units, a second round pick being worth 2.4, an 11th-30th overall pick being worth 6.0, and a top 10 overall pick being worth 7.6

Applying this approach to multiple metrics will give us a more robust view of relative pick valueRelative Pick Value Chart

  • The table above shows the ‘Draft Value Units’ (working name) of a pick in each round across three metrics: >30 Pts, >100 Pts, and Avg Career Pts. In essence, ‘Draft Value Units’ are comparable to a currency with which teams can value and exchange draft picks
  • As mentioned – each round is shown as its multiple of the lowest group (Rounds 4-9) – and most of my attention going forward will be on the far right, highlighted column; also, all of the values of this chart are derived from the data shown in my last article
  • As Michael Schuckers and Stephen Burtch previously showed, this data suggests teams should use caution when trading their first and second round picks, as they can be worth many times more valuable than the other rounds
  • It is worth noting that Lifetime Production data can also shed light on ‘absolute’ pick value; e.g. in a trade for active players, a pick in the top 10 overall should be treated as if it has a career lifetime value of ~350+ points as a forward, or ~170+ points as a defensemen – something directly comparable to ‘remaining’ production in an active NHLer

Part of the goal of this exercise was to create a pick valuation methodology that is highly simple, and usable by many, regardless of their level of analytical sophistication. As someone very familiar with the world of corporate finance and valuations, I can tell you first hand that – despite financial firms using the ‘fanciest’, most complex valuation models you could imagine – the most effective of these models will often reduce complexity, rather than create it. All investors and bankers also know that valuation analysis is a ‘blunt’ tool, and it will never give you an exact, ‘true’, intrinsic value for a corporation or a stock (i.e. picture using an axe to carve a statue). I think the same thought process applies to this draft pick value methodology – it is directional, rather than exact – but hopefully it also intuitive to understand and apply. My general philosophy is that decision-makers do best when considering metrics that are reflective of the big picture, while simultaneously weighing those against the typical qualitative information they bring to the table, such as team needs, player skill, size, character, etc.

Now – let’s get into the deals.

Draft Day 2015 Deal #1 – Dubas and Hunter v. Ron Hextall of the Philadelphia Flyers

After seemingly endless conversations and hustling around the draft floor, Dubas and Hunter’s first trade of the day was with Philadelphia:

 TOR PHL Trade Chart

Now, based on the far right column in my ‘Draft Value Unit’ table above, we would think to assign the following values to these picks:

TOR PHL - 'Wrong' Pick Chart

Huge win for the Leafs, right?

Not necessarily. As we all know, and as Schuckers and Burtch’s analyses clearly demonstrate – all picks in each round are not created equal. The 11th overall pick is not equivalent to the 30th, even though the table above would be treating them as having the same value. Because of this, the Draft Value Units shown above would be most appropriate to use when a trade is done well in advance of a draft, and it is not known exactly which overall draft number a given pick will relate to. In order to make this metric meaningful to trading ‘known’ pick numbers, we will have to do some adjusting.

Applying ‘Draft Value Units’ directly to draft pick numbers will show some counter-intuitive results

Draft Value Units - Step Function

Instead, for individual pick numbers, we need to ‘fill in the gaps’ with a new curve, equivalent to the equation shown below

 DVU - Curve

  • Once we do know what pick numbers each team will have, we need to adjust the value of each pick appropriately
  • To do this, I have derived the solid, light blue line in the chart above, which best fits the original Draft Pick Values shown
  • This line shows what the appropriate Draft ‘Value’ is for a pick once we know the exact pick number that it relates to
  • The line was derived by looking at the (x,y) coordinates of each pick number and its respective Draft Value Units, after assigning the values in the first table shown to the mid-point of each round (e.g. 5th overall pick being worth ~11.1 DVUs, 20th overall pick being worth ~7.0, etc.)

In order to test validity, we can compare this curve/equation to those derived by Shuckers and Burtch. The similarities between the three draft value methodologies help to support the accuracy of the findings of each. Note – the one downfall of this curve is that, in order for it to appropriately reflect the value of the first 100 picks, the DVU’s hit zero around pick 100. As a result, my advice for those trying to use this to value picks from the 4th Round and onward is to treat each pick as having a Draft Value of 1.0 units, rather than zero (e.g. revert back to the dotted line).

Now – back to Leafs v. Philadelphia.

Plotting the three Leafs/Flyers picks traded on our curve shows the value of each individually

 DVU Curve - TOR PHL

  • The chart above shows that the theoretical value of the 24th overall pick is 6.0 DVUs, with the 29th and 61st overall being worth 5.2, and 2.3, respectively
  • As I did here, in order to use this on any other trade, all you have to do is find each pick number on the x-axis, trace it to the curve, and then trace that point on the curve back to the y-axis – giving you the Draft Value Units of that pick
    • The end of this article also has a table showing Draft Value Units for each pick number, e.g. the coordinates that make up this line

On an expected value basis, the Leafs won the trade with Philly by 25%

TOR PHL - Stacked Bar

  • Based on the values assigned above, the Leafs were the clear winners of this trade
  • As a reminder – these Draft Value Units originate based on an average of the probabilities for each player drafted to do three things: 1) Exceed 30 career points, 2) Exceed 100 career points, and 3) Maximize their lifetime (point) production

One last important thing to point out – as I’m sure many Flyers fans and general non-stats folks will want to discuss – the Flyers executed this trade because they badly wanted to pick Travis Konecny, of recent Canadian World Junior ‘fame’. They saw him as materially better than their next choice (if they had picked 29th) – Nick Merkley. This piece isn’t about scouting or individual player evaluation, which are of course important factors to consider – generally speaking, every team should be drafting with a broad strategy in mind, that drives towards filling its specific needs (which I’m sure justified this trade from the Flyers’ perspective). The point of this analysis is to say that – on a long term, expected value/probability basis – a team will do better to be on the Leafs’ side of this trade. Even if 10 years from now Konecny is the next John Tavares, and everyone thinks Ron Hextall is a genius – I think the Leafs were on the right side of this trade based on what was known on draft day.

(As a side note: this article by Travis Hughes gives a bit of background about Philadelphia’s rationale for being so eager to trade up for Travis Konecny).

So far, Leafs 1, League 0

Draft Day 2015 Deal #2 – Dubas and Hunter v. Jarmo Kekalainen of the Columbus Blue Jackets

 It didn’t take long for Dubas and Hunter to turn around and offload their newly acquired 29th overall pick either – employing a highly similar strategy in their trade with Columbus:

TOR CLB Trade Chart

Now if we apply the same analysis to this deal:

Looking at the value of each pick individually…

DVU Curve - TOR CLB

… It is clear the Leafs ‘won’ this deal too – by 23%

TOR COL - Stacked Bar

You don’t need me to tell you too much more, as the same analytical framework shows the Leafs fared similarly well in their trade with Columbus as they did with Philadelphia.

Before I wrap up, just for fun let’s look at the two deals as whole:

TOR Aggregate Trade Chart

The Leafs’ two trades during the 2015 draft created an incremental two players, 2.7 draft value units, or otherwise a 43% increase in relative value

TOR Aggregate Stacked Bar

By the end of the draft, the Leafs had used these three picks to select Travis Dermott, Jeremy Bracco, and Martins Dzierkals, based on the scouting expertise employed by Hunter and his team. It also helped fill some of the team’s draft objectives early on, enabling them to wait it out for sleeper picks like Dmytro Timashov in the fifth round – a player who turned many heads at the recent World Juniors tournament. I wouldn’t (yet) go as far as to say that Hunter has a legitimate ‘competitive advantage’ over other teams in player scouting and evaluation until more time has passed. However, Dermott’s OHL performance this year and recent World Junior selection also suggest that getting him at 34th was a also bit of a steal in its own right. Regardless, it should be clear that on this fateful day last June, Kyle Dubas and Mark Hunter made some excellent decisions, which created a ton of value and long term potential for the Toronto Maple Leafs club.

Also – I won’t go into it in depth here – but how did the Leafs acquire that original 24th overall pick? The Leafs got it by trading two ‘rental’ players (Franson/Santorelli), who were both about to become UFA’s, in return for that 1st Round pick, Brandon Leipsic (another solid prospect), and Olli Jokinen’s cap space. Keep an eye out for the Leaf’s front office to hopefully make a couple similar deals approaching the trade deadline this year, and repeat their excellent 2015 performance next June.

Leafs 2, League 0

Conclusion

When considering teams that have been successful in the NHL draft, the teams that come to mind intuitively support the findings of this analysis. The Chicago Blackhawks are a great example where their distinct strategy has been ‘quantity over quality’: rather than trying to pick ‘better’, as has been shown to be very difficult to do, Chicago has simply focused on using transactions like these to draft as many players as possible. Chicago’s massive, league leading number of picks from 2000-2004 show that the Hawks certainly planted their seeds – and in case anyone has been paying attention, they have been doing ‘OK’ in the last 5-10 years. Another great example of this strategy is Bill Belichick and the New England Patriots – who seem to have done alright in the last 10 or so years as well…

In the end – as one of the small but growing number of patient, excited Leafs fans out there, I will make our collective opinion clear: the current front office knows what they are doing – and we are behind them.

Appendix Table

About the author, and this site

Original Six Analytics is (yet another) blog focused on exploring the world of advanced statistics within the NHL and other hockey leagues. My own background is in the field of private equity investing, and before that as a management consultant – both fields heavily focused on summarizing quantitative analysis into easily understood output in order to support decision making. As such, I hope to specifically use this blog to apply the concepts of value investing (and to an extent, corporate strategy) to the world of hockey analysis.

Lastly – as the image of Morgan Rielly and Leo Komarov may suggest – I am a lifelong Leaf fan, with a great deal of patience and support behind the approach of the current front office. The name of ‘Original Six’ Analytics refers to what seems to be the vision of the current Leafs organization – ‘returning an original six franchise to its rightful place in the league’. Which I think we can all get behind.

My inaugural post (with actual analytics content) will be coming soon, on the topic of the value of draft picks, based on typical player performance and development from a given draft round. Please feel free to reach out at any time, with questions, comments, feedback or any requests for a future type of analysis:

  • @OrgSixAnalytics on Twitter
  • OriginalSixAnalytics@gmail.com