2017 Draft: How do OHL Goalies Stack up Against NHL-Caliber Competition?

By: Mike Z

This article is being co-posted on NHLNumbers as well as on my own site, OriginalSixAnalytics.com. Find me @OrgSixAnalytics on twitter. All Junior stats used here were provided by @3Hayden2, the creator of www.prospect-stats.com. Please give him a follow and check out his great work. (Last, for those who do not know, I formerly wrote under the pen name of “Andrew Kerison”)

While both the NHL and Major Junior (WHL, OHL, QMJHL) can benefit from hockey analytics, the findings in each league are often quite different. The NHL is a league of parity, where the gap between a top player and an average one can be relatively thin. Junior, on the other hand, is the opposite. Top players like Mitch Marner, Matthew Tkachuk, and Brayden Point can often make an immediate impact at the NHL level, while many of their teenage competitors will never play an NHL game at all. The massive range of Junior talent is probably best illustrated by the fact that Connor McDavid was able to win the 2016-2017 NHL scoring race while still being young enough to play for his former Erie Otters team in last week’s Mastercard Memorial Cup!

This wide range of CHL talent has an outsized impact on player evaluation in one area in particular: goaltenders. The NHL draft already has a significant amount of randomness, and goalies are already likely the single hardest position to project NHL performance. Given the wide range of Junior shooting talent, quality of competition can have a disproportionate impact on certain goalies depending on their team’s particular schedule. If one goalie plays a substantial number of games against teams like the Erie Otters and London Knights, we shouldn’t expect him to have the same result as a peer with an easier schedule.

As such, in this article I will try to analyze these factors in an attempt to better evaluate this year’s goaltending prospects. Specifically, I will try to isolate how OHL goalies perform against the higher end of shooters at the Junior level.

NHL vs OHL: Shooting Talent Distribution

In the two tables below I have looked at the top 15 NHL and OHL shooters when ranked by their shooting %. All data is from 2016-17, forwards only, and I have restricted the samples to 5v5 to adjust for the impact of power play time. Both samples were drawn from players with >100 shots (NHL) and >70 shots (OHL) in order to remove outliers.

1. Top NHL

2. Top OHL

NHL data courtesy of puckalytics.com; OHL data from prospect-stats.com

As you can see, there is a substantial gap between the top 15 NHLers and OHLers. Rickard Rakell shot the lights out this year with a 19% NHL Sh%, and a full seven OHL shooters were at that level or higher. This disparity is highlighted even more from some general distribution statistics:

3. OHL v NHL Distrib Stats

NHL data courtesy of puckalytics.com; OHL data from prospect-stats.com

As you can see, this gap isn’t limited to just the top end of the spectrum. While the bottom quarter of shooters (25th percentile) aren’t too far apart between leagues, the gap grows at the median and then diverges significantly at the 75th percentile, where respective OHL and NHL shooters are almost 4.0% apart.

 OHL Shooting Talent – Team Level Gaps

The OHL is a league that has ‘juggernaut’ type teams, who will often dominate games except for when they are playing other top-ranked competition. Just look at the results from last week’s Memorial Cup, with two (Western Conference) OHL teams winning all of their games against the other league champions, sometimes by a wide margin, and eventually facing off in the final.

In order to determine performance against NHL-like shooters, in a perfect world we could isolate the shots coming from each individual skater against each goalie. Unfortunately, for this article (and based on what was most readily available), I have instead tried to isolate these dominant OHL teams, and considered that as a proxy for which players represent future “NHL-Caliber” shooting talent. Although this is a bit of an oversimplification, I think it will be an interesting analysis nonetheless. Even just looking at the NHL prospects on the Memorial Cup Finalist Erie Otters (Strome, Raddysh, DeBrincat, Cirelli, Foegle, Lodnia, etc.) – many of these ‘juggernaut’ teams are simply stacked.

The table below shows all OHL teams from the 2016-2017 season alongside their All Situation shooting % (all metrics in this article exclude empty net goals):

4. NHL Caliber Teams

OHL data from prospect-stats.com

As you can see above – I drew a (somewhat arbitrary) line at the 65th percentile to denote the ‘NHL Caliber’ shooting teams. An argument can be made to increase this cut off to maybe 11.0%, or 11.5%, but the tradeoff later on will be having enough teams included so there is still a useful sample for each goalie. Personally, I was surprised that this omitted some high-end, skilled teams like the Mississauga Steelheads, but they may have been held back due to a slow start to the 2016-2017 season.

As a whole, these teams shot at an average all situation Sh% of 11.7%, versus a league-wide average of 10.2%. For reference, the teams that were below the ~65th percentile shot on average at 9.3%, so a gap of ~2.4% between the included and excluded teams. From here on I will refer to these top teams as having ‘NHL-Caliber’ shooting talent.

Now – how do OHL goalies stack up when looking at shots faced from just these 8, very high-end shooting teams?

Unadjusted OHL Goaltender Performance (All Opponents)

The table below shows the top 20 OHL goalies when simply ranked by ‘raw’ All Situation Save% (list shortened for space). The table includes both all situation Sv% as well as 5v5, and it only includes goalies with over 500 total shots against. This chart also includes each goalies age, though I will caveat that the additional decimal place in some ages seem slightly off.

5. Top 20

OHL data from prospect-stats.com, goalies who are 18 years old and younger (e.g. never before eligible for the NHL draft) are highlighted in green

Looking at this table, you can see a wide range of OHL-level goaltending stats and ages. Highly touted prospects who are already signed to NHL contracts are older, coming out near the top – guys like Tyler Parsons (LDN – CGY) of USA 2017 World Junior fame, Dylan Wells (PBO – EDM), and OHL/CHL Goaltender of the Year Michael McNiven (OS – MTL)

The goalies that are eligible for the 2017 NHL draft are those born in 1999 and those after mid-September in 1998 (mostly 17 year olds). On this list that is only four individuals: Michael DiPietro (WSR), Matt Villalta (SSM), Stephen Dhillon (NIAG) and Luke Richardson (KIT). All four have reasonably strong results, especially at 5v5, where they have Sv% of 91.9%, 92.6%, 92.4% and 91.2%, respectively. It is worth noting that OHL rookie (16-year old) Jacob Ingham (MISS) has quite strong ‘raw’ results as well – but due to his age, he won’t be draft eligible until 2018.

Adjusted OHL Goaltender Performance (NHL Caliber Shooters)

As discussed earlier, not all OHL opponents are created equal – and depending on conference/division, goalies can face these opponents at quite different rates. In the table below, I have isolated the shots faced by these goalies strictly to those taken by the 8 teams identified earlier as shooting at an ‘NHL Caliber’ level. Further, due to sample size issues, goalies are included only if they have at least 200 shots against from these opponents in all situations. In general, some of these figures should be taken with a grain of salt, due to the smaller sample sizes resulting from this approach.

6.All Sit NHL v All

OHL data from prospect-stats.com. Note, if multiple goalies have the same rank, their Sv% is the same. ‘Appearances’ includes all games played (including going in a replacement), and is not equivalent to ‘starts’.

In the table above has two sections: how goalies’ All Situation Sv% ranks when isolating shots faced from NHL Caliber opponents, and also how they fared and ranked in Sv% against all OHL opponents. The table is sorted based on NHL Caliber opponents Sv%, and I have highlighted the 2017 draft-eligble goalies in green.

Starting at the top – Tyler Parsons (LDN) is clearly elite, and maintains his strong stats and #1 rank regardless of the opponent – I would expect him to be one of the most likely future NHL goaltenders in the OHL today. Michael DiPietro (WSR) is quite similar – but even more impressive is that he has performed at this level at the age of only 17 years old. Given his excellent results against opponents of all levels, and his starting role on the Memorial Cup Champion Windsor Spitfires – DiPietro definitely stands alone atop the ranks of this year’s draft eligible OHL goalies.

What is interesting in this analysis really comes down to ‘movers’ – Goalies who are ranked higher when looking at just NHL Caliber opponents than when using all opponents. Troy Timpano (ER) and Matthew Mancina (MISS) both show well in this data – both moving up 6 ranks when looking at just their strongest opponents. Luke Opilka (KIT) and Joseph Raaymakers (SSM) are two of the biggest movers, jumping 12 and 15 spots respectively in the NHL-Caliber ranking. In a perfect world, you’d like to see consistently strong results against both categories, like star goalies McNiven and Parsons – but strong results against top opponents are good to see nonetheless.

Due to the low samples we need to be cautious with reading too much into this in general, but especially for certain goalies – e.g. Scott Smith (PBO), Matt Villalta (SSM), Aidan Hughes (SAR), and Luke Richardson (KIT). If anything, the small sample suggests these goalies were in the back-up role, potentially being sheltered for part of the season, or facing weaker opponents in general. However, that doesn’t mean strong results should be discounted altogether. As an example, Villata still ranks #11 against NHL Caliber shooters, and Hughes jumps up 13 spots on the NHL-Caliber ranking; so directionally, it is still a positive indicator to see in general.

7. 5v5 NHL v All

OHL data from prospect-stats.com

Last – the table above is the same approach but instead showing 5v5 Sv% rather than all situations. I wont go into too much detail on the findings, but generally you see similar names near the top or having made big moves. In general, many people in the hockey analytics community prefer 5v5 Sv% as it takes out discrepancies from playing down a player. However, with 5v5 we have to be even more mindful of the sample size. In this chart I have kept the same list of goalies from the All Situations chart (where the cut off was 200 Shots Against vs NHL Caliber shooters), but at 5v5 some drop below 200, ultimately representing only a handful of games.

 Conclusion

In the end, this analysis is really more of a starting point, rather than a comprehensive look at this year’s first-time draft eligible OHL goalies. In the future, better ways to look at this analysis would be to possibly create an OHL-level QoC-Adjusted Sv% metric, which incorporates both strong and weak shooting opponent data but adjusts the result depending on which team/player took each shot, or alternatively to create a xGSAA metric that factors in both opponent quality and shot location. Hayden’s website already features shot location and Sv% data that is definitely worth a read. It is also worth mentioning that this analysis was entirely focused on one league, the OHL, and it is not at all a comprehensive look at 2017 draft eligible goalies.

While it is rare that teams will use a 1st round pick on a goaltender due to the overall risks involved, I would argue that this analysis definitely helps to substantiate that Michael DiPietro is the real deal. While it may take years for him to develop and become NHL-ready, I expect that whoever ends up taking him will find themselves glad they did – with an extremely exciting goaltending prospect to monitor for the years to come.

 

 

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

 

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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.

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.

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

What Draft Round Can Tell Us About a Player’s Expected Long Term Performance and Development (Part 1)

This article is being posted here as well as in parallel as a guest post at Maple Leaf Hotstove. FYI, for those who read my last post – as promised – this article is a succinct summary of my draft analysis ‘report’ shared earlier. 

The hockey analytics community has looked at many aspects when projecting a player’s performance over their career: prior league, prior scoring rate, performance of players with similar characteristics, size, and date of birth – amongst others. One example of such work was an article from earlier this year by ‘moneypuck’ at NHLnumbers.com.

Moneypuck’s analysis derives its foundation from an excellent study by Michael Shuckers in 2011, where Shuckers was one of the first to create a standardized view of ‘draft pick value’. The quality of Schuckers’ analysis drove many other authors to do work that followed suit, building on his approach. However, in his paper, Michael chose to define ‘draft pick value’ entirely based on likelihood to play >200 games in the league. Although that is a reasonable metric – and few would argue that reaching 200 NHL games means a draft pick was NOT successful – there are limits to using only a single metric to define ‘success’.

How ‘successful’ a pick was, and the ensuing value of a draft pick, is highly sensitive to how we choose to define success. Is a pick successful after 40 games, 80, or 200? Are they successful after 30 career points, or 100? How about their points per game? Or their total career points? How would our definition of ‘success’ change on each of these metrics if they are a forward or a defenseman?

As you can imagine – this isn’t a simple thing to answer. Earlier in 2015, Stephen Burtch did some interesting work down this path, where he combined expected GP with expected Pts/Gm to create a new draft pick value figure – which was a big step in the right direction. However, even Pts/Gm has its gaps, given that it only considers players still in the league. As time goes on, the least successful players will leave the league sooner, increasing the average Pts/Gm of those remaining. In a perfect world, we would want a metric that has already been adjusted for a player’s likelihood to succeed in the league, rather than one based on his success if he can stay in the league. (Though – to be fair – Burtch does seek to address this point through multiplying probability of reaching 200 games by expected Pts/Gm).

In order to address these points, I have taken a very detailed look at long term player performance and development based on draft round, incorporating a wide range of metrics into my analysis. Specifically, I have reviewed the five years of players drafted from 2000-2004, as well as the ensuing 9-13 years of NHL season data.

Arguably the biggest factor in whether or not analysis is put into practice is if a team’s front office and coaching staff truly understand it, and believe the results of the analysis enough to buy-in to it – which will often come down to the method by which that analysis is communicated. As such, I have tried to simplify the statistical methodology involved in this work, and display the output visually in a way that is easily understood and hopefully very accessible to stats and non-stats folks alike.

(As a note – this article focuses strictly on metrics related to player performance and development. However, a natural follow on to this is then connecting that information to draft pick value, as mentioned, and after that, how successful teams have been in drafting – both of which are covered in my full report, originally posted here). 

What I Hope To Answer

The objective of this analysis is to investigate ‘typical’ player performance and development trajectory after being drafted in a given round, in order to answer the following three questions:

  • If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?
  • How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round?
  • Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?

So – let’s get into it.

Analysis of Long Term Player Performance and Development by Draft Round

I have split out the upcoming sections of analysis by each type of metric used. I will then revisit the three questions above directly in the final section on conclusions.

Games Played Thresholds

As Michael Shuckers showed very clearly – players drafted in the first 2-3 rounds are much more likely to appear in the NHL; however, the likelihood of a playing one or more full seasons diminishes substantially after the first round

Games Played Pic 1

Games Played Pic 2

In terms of player development, this data suggests that:

  • If a 1st round pick hasn’t played a game by their fourth potential NHL season, they likely will never appear in the NHL
  • 20-30% of successful 2nd and 3rd round players only begin to meaningfully play for their franchise between 5-7 years after being drafted (e.g. the pink shaded area on the ’80 games played’ chart)
  • The gap between the top 10 overall and the rest of the first round is actually relatively small when looking at the likelihood to pass the 150 game threshold (especially in comparison to metrics later in the article)
  • And, as we know – all other rounds after the first three appear to have close to equal likelihoods of producing long term NHL players

Points / GM Data

Forwards taken in the top 10 overall show an unbelievable ability to outperform in P/GM over their careers (which Stephen Burtch has shown is even more distributed within the top 10¸ where the top 1-3 picks overall are meaningfully better than picks 4-10)

Pts per Game - F

  • Interestingly, 2nd and 3rd round forwards tend to increase their per-game output over time, largely converging with players drafted 11th-30th overall
  • However, given this metric is an average of those still playing, there will be a survivorship bias that partially drives this effect
    • E.g. Low producers will leave the league more quickly, increasing the average for those remaining – as shown by the fact that 30% of the players shown are from Rd 1 in season ‘6’, this increases to 40% by season ‘10’
    • This data can more reasonably be said to tell us that, in order to stay in the NHL over the long term, a forward must achieve a minimum of roughly 0.20 points per game

Defensemen naturally display a much more narrow distribution of results, accounting for the fact that a ‘strong’ defenseman will not always play a significant point-scoring role

 Pts per Game - D

  • P/GM data for defensemen is not terribly insightful, but I have included it in order to provide the data for those interested
  • One note – If you look closely, you can see surprisingly strong (and erratic) performance of Round 5 defensemen – starting very weak (no points registered in season ‘2’), but then ultimately being among the highest points per game in seasons ‘5’ through ‘10’
    • This particular point is driven by a small sample size issue: 49 D were drafted in the 5th round, but only a handful played many games – three of whom happen to be John-Michael Liles, Kevin Bieksa, and James Wisniewski

Points Scored Thresholds

A player’s likelihood to surpass the 30-point threshold tends to resemble their likelihood to pass ~150 career games played…

Pts Threshold Pic 1

… However, players drafted in the third round fall behind in terms of likelihood to pass the 100-point career threshold

Pts Threshold Pic 2

  • Where earlier charts show strong similarities between the long term potential of 2nd and 3rd round players, the ability of those taken in the 2nd round to break the 100-point career threshold is a clear differentiator between the two
  • Based on this, teams may do well to target top scorers in rounds 1 and 2, before moving to defensemen, shut down forwards and goalies in the third round and onwards
  • Again, top 10 overall picks differentiate themselves here as well, with over 70% passing 100 career points

Cumulative Career Points

The ideal metric to compare performance by round must be adjusted for players with limited NHL careers – which brings me to Lifetime Production, or Cumulative Career Points Scored

Lifetime Production - F

Lifetime Production - D

(Note – Forwards and Defensemen are shown on different scales)

 

 

  • Here, 1st round picks wildly outperform all others, showing that the combined skill and typical longevity of even a mid-to-late 1st round player (11th-30th) will equate to an average 159 points over 10 seasons for forwards, and 105 points over the same timeframe for defensemen
  • This compares to the significantly lower 68 average career points for second round forwards, and 44 average career points for second round defense
  • Notably, third round forwards also re-assert their value here, showing that – although they will only typically produce a total of 36 points over 10 seasons – they still will consistently outperform rounds 4-9 in career points

Drawing Some Conclusions

Having now walked through each chart and its meaning, I want to summarize my findings from above. To do so, let’s revisit the original list of three questions:

If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?

  • First round players typically make their initial NHL appearance within 1-2 years, and will almost always have played their first full season (~80 games) by their fourth year after being drafted
  • 2nd and 3rd round players take much longer to develop, and many only play a full season by their 5th-7th years after being drafted
  • Players who haven’t played by these general timelines become highly unlikely to ever make serious NHL contributions (>1 season played)

How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round?

  • Most players drafted outside the first round never make the league at all (2nd round players have a 60% likelihood of playing one game, and a 35% likelihood of playing a full season; for 3rd round players, closer to 40% play one game, and only 28% play a full season)
  • Based on their combined likelihood to play 2+ NHL seasons, score 30+ NHL points, and reach 0.4-0.5 or more pts/gm, 1st, 2nd and 3rd round players are the only players with a meaningfully higher likelihood in succeeding in the league
  • However, based on the likelihood to score >100 NHL points, 1st and 2nd round players are able to separate themselves from the 3rd round as well

Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?

  • The top 10 overall picks are significantly more capable than all others, even versus their first round peers
  • Over 70% of top 10 overall picks pass 100 career points, typically after ~6 seasons, versus 50% of those picked 11th-30th, who often take 9-10 years or more
  • Only forwards taken in the top 10 overall can truly be expected to score 0.6-0.7 pts/gm or more over their careers (although there are many examples of players who perform at this level of production that were taken outside the top 10, such as Ryan Getzlaf and Corey Perry)
  • In a hypothetical trade for active players, a ‘typical’ top-10 overall pick should be treated as likely reaching >350 career points as a F, or >170 as a D – thus, one-for-one, a team should be expecting to get a true star player in return if they are giving up a potential top 10 overall pick

In the end…

The long term performance expected of a player based on their draft round is something that is highly relevant to teams throughout their decisions in trades, on draft day, and in supporting a player’s development over his career. Hopefully you have found this analysis to be interesting, and found that the work was also able to build upon what is already out there by expanding the range of metrics that we look at. As mentioned, the PDF I linked to above also begins to apply this to both a revised (and straightforward) metric of draft pick value, as well as to answer the question of ‘Which teams were the most successful?’ in the draft years studied. Keep an eye out for ‘Part 2’ of this article – where I apply the data above to the trades done by the Leafs on Draft Day last summer, in order to see if Hunter, Dubas and friends were winners or losers in their exciting deals…

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

 

 

 

What is an NHL Draft Pick Really Worth?

A Detailed Analysis of Player Performance and Development by Draft Round

It is hard to describe drafting as anything less than essential to the success of a professional sports franchise. The teams that are able to plant the seeds for long term success on draft day each year will have a clear advantage over the five to ten years that follow – if not sooner. We all know that every NHL franchise has an expert scouting team, and many likely use metrics like league equivalencies to see what they are getting from a pick – but how many teams know what to expect from a player’s long term performance based on the round they drafted him? Further, how would that knowledge impact how a team assigns ‘value’ to its future picks in trades?

In order to answer these, and other questions, I have dug into some of the data available, and summarized into a report that you can find hereWarning – it is long. In this ‘report’, I try to answer the two questions above, as well as a more detailed list of questions that you can find at the bottom of the page.  Quickly, thanks to Hockey-Reference.com for the draft year data, and HockeyAbstract.com for the historical NHL season data.

As the first piece of work I am releasing to the NHL analytics world, the most basic reason that I have focused on draft analysis is because I personally am very curious about it. Like many fans, I often find myself looking at the exciting picks selected each year, but previously I had not quantitatively understood what to expect from those players in the NHL – specifically on a long term, year by year basis, generalized to the round they were drafted in. Before starting to analyze some of the data available, I also didn’t have a full appreciation of how a team should be treating the long term potential value behind those picks versus what else is out there (though some great work has been done on that in the past, by Stephen Burtch and Michael Shuckers, amongst others).

Lastly, I also think draft analysis has a number of strong parallels to the analysis done in value investing (aka the purpose of this blog). When an investor is evaluating a potential company to buy, the major focus is on its growth and earnings potential over a very long term time horizon, e.g. 4-7 years, or more. Much like ‘investing’ in the potential, growth and long term development of a player, the same type of thought process needs to be followed when buying a company. Investors operate with scarce resources and other constraints (e.g. contract limits, salary caps), they must have a clear understanding of their own strategy and needs (e.g. immediate cup contender vs. rebuild scenario), they have to undergo prioritization by evaluating the alternatives against a set of criteria (e.g. prioritizing positions, ranking players in each), and ultimately they have to make a decision on how to move forward – a decision that they will often have to live with for half a decade or more (in the case of private companies).

In investing, as in drafting to help build an NHL organization – the more detailed, understandable, and accurate quantitative information you have to support your decisions, the more likely you are to be successful. Thus, as the NHL enters the months before the trade deadline, I hope the attached analysis can give the online community some food for thought as to who the winners and losers are of trades to come. Please let me know any comments, questions, feedback or areas for further analysis at @OrgSixAnalytics or OriginalSixAnalytics@gmail.com.

 

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

 

For reference, the analysis seeks to answer the following questions:

  • If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?
  • How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round? Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?
  • 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?
  • Which teams were the most effective at drafting in the period sampled?