2017 Top 30 Habs Prospects: Ranking Methodology

It’s back.

This year marks a significant change in my player evaluation and writing. If you’ve been reading my work for a while, you’ve probably already noticed. I’ve become fascinated with statistics and applying them to junior hockey. I’ve tracked extensive data on the majority of prospects in the organization by hand, while racking up more viewings than ever before.

As in previous editions, I think it’s important to explain how I formulate these reports and rankings, other than saying “very slowly.” The truth is, I wasn’t a scout when I started these, and I sure ain’t one now. And I’m not a data analyst. So, of course, the logical process would be to combine these two together and hope that suddenly that I become better at both.

As the ranking advances, statistics will be increasingly important. Statistics will be utilized to convey a player’s style, what they need to improve, and describe their season. For prospects in unique developmental situations, I’ll create a comprehensive list of players who were in similar situations, and how their careers turned out.

Notes:

All the information is “in my opinion.“ The ranking likely has limited-to-no value. Think of the ranking as a way to catalogue players, notes, and data in a non-alphabetical way. The information within the profiles is far, far more valuable. I mentioned I’m not a scout right? If not, I’m not a scout. I appreciate all questions, comments, and criticism. You can reach me on Twitter (@MitchLBrown) or email (mitchbrown31@gmail.com). You can also tell me I suck. I don’t care. AHL/CHL/USHL Stats: Prospect-Stats.com CollegeHockeyInc.com Stats.SweHockey.se Stats.HockeyAnalysis.com

Series Navigation:

Top 30 Prospects: Ranking Methodology – Integrating Statistics into Analysis

Top 30 Prospects: #30 – #26 – Success Rates of Swedish Jr. and USHS Defenders

Top 30 Prospects: #25 – #21 – Success Rates of CHL Overage Forwards

Top 30 Prospects: #20 – #16 – Balancing Players with Opposing Skill Sets and Development

Top 30 Prospects: #15 – #11 – Weighing perceived NHL-readiness with NHL Upside

Top 30 Prospects: #10 Will Bitten – Why His Season Was Better Than You Think

Top 30 Prospects: #9 Victor Mete – How Undersized Defenders Can Excel Defensively

Top 30 Prospects: #8 Josh Brook – Improving Shooting Location Through Movement

Top 30 Prospects: #7 Michael McCarron – Do Big Players Really Take Longer to Develop?

Top 30 Prospects: #6 Charlie Lindgren – The Reverse-VH and When Skill Takes Over

Top 30 Prospects: #5 Charles Hudon – A Case Study on the Impact of Aging for NHL Chances

Top 30 Prospects: #4 Joni Ikonen – Multidimensionality in Attack

Top 30 Prospects: #3 Nikita Scherbak – Comparing Scherbak In and Out of Form

Top 30 Prospects: #2 Noah Juulsen – How to Excel at Defending the Blue Line

Top 30 Prospects: #1 Ryan Poehling – Full Breakdown, Importance of Little Details, and More

Right, so with the long-winded disclaimer for a series dedicated to nothing but claims, let’s begin with the interesting stuff.

Eligible Prospects

NAME POSITION SHOOTS HEIGHT WEIGHT BIRTHDATE DRAFT Addison, Jeremiah LW L 5'11" 183 1996-10-21 2015, 7, 207th Audette, Daniel C/LW L 5'9" 176 1996-05-06 2014, 5, 147th Bitten, Will RW/C R 5'10" 168 1998-07-10 2016, 3, 70th Bourque, Simon D L 6'0" 183 1997-01-12 2015, 6, 177th Brook, Josh D R 6'2" 185 1999-06-17 2017, 2, 56th de la Rose, Jacob C/LW L 6'2" 214 1995-05-20 2013, 2, 34th Eisenschmid, Markus C/RW R 6'0" 178 1995-01-22 2013 (FA) Evans, Jake C R 6'0" 185 1996-06-02 2014, 7, 207th Fleury, Cale D R 6'1" 201 1998-11-19 2017, 3, 87th Fucale, Zachary G L 6'2" 187 1995-05-08 2013, 2, 36th Grégoire, Jérémy RW R 6'0" 194 1995-09-05 2013, 6, 176th Hawkey, Hayden G L 6'2" 180 1995-03-01 2014, 6, 177th (2013 eligible) Henrikson, Arvid D R 6'6" 209 1998-02-23 2016, 7, 187th Hudon, Charles LW L 5'10" 194 1994-06-23 2012, 5, 122nd Ikonen, Joni C/RW R 5'11" 176 1999-04-14 2017, 2, 58th Juulsen, Noah D R 6'2" 185 1997-04-02 2015, 1, 26th Koberstein, Nikolas D R 6'2" 201 1996-01-19 2014, 5, 125th Lernout, Brett D R 6'4" 205 1995-09-24 2014, 3, 73rd Lindgren, Charlie G R 6'2" 190 1993-12-18 2012 (FA) McCarron, Michael C/RW R 6'6" 238 1995-03-07 2013, 1, 25th McNiven, Michael G L 6'1" 216 1997-07-09 2015 (FA) Mete, Victor D L 5'10" 165 1998-06-07 2016, 4, 100th Parisi, Tom D L 6'0" 194 1993-07-15 2011 (FA) Pezzetta, Michael C/LW L 6'1" 205 1998-03-13 2016, 6, 160th Poehling, Ryan C L 6'2" 185 1999-01-03 2017, 1, 25th Primeau, Cayden G L 6'3' 179 1999-08-11 2017, 7, 199th Scherbak, Nikita RW L 6'2" 190 1995-12-30 2014, 1, 26th Staum, Casey D L 6'1" 181 1998-01-08 2016, 5, 124th Tyszka, Jarret D L 6'2" 187 1999-03-15 2017, 5, 149th Vejdemo, Lukas LW/C/RW L 6'2" 194 1996-01-25 2015, 3, 87th (2014 eligible) Waked, Antoine LW L 6'1" 194 1996-05-17 2014 (FA) Walford, Scott D L 6'2" 190 1999-01-12 2017, 3, 68th

Just like the midseason ranking, Martin Reway is not considered due to health concerns.

Constructing the List

In my four years of writing this series I’ve learned a lot, changed my methodology countless times, and received plenty of feedback and criticism.

As I learned last year, explaining the underlying philosophy alone isn’t good enough. I also need to take the steps to explain the overarching philosophy, which coincides with how I perceive hockey. Everyone perceives the game differently. So, I’m not asking anyone to agree with the list–not in the least–but what I do want to ensure is that everyone understands my thinking.

This list requires balancing NHL upside and NHL readiness. While I argue that these can be two distinct entities (at least, to an extent), some may see it differently. Upside is how good of an NHLer the prospect could become. Readiness is how close to making the NHL the prospect is.

Take Greg Pateryn for example. I ranked him 15th in 2015, just as he was on the cusp of making the NHL. My thought process was that Pateryn is NHL-ready, but I hadn’t seen anything more than bottom-pairing upside, at best. I saw Pateryn as likely a replacement-level NHLer, which is a fine asset to get from a late-round draft pick, but not something of particular value. (Emphasis on replacement-level).

Instead, I prefer players with higher upside, even at the expense of an NHL-ready prospect if I deem their upside to be substantially lower. I admit it, it’s not the “sexy” way of making a list–there’s no immediate gratification from ranking an 18-year-old above a 23-year-old who makes the NHL the following season. I suppose I could rank the prospects by NHL-readiness, but then by number nine or 10 there’s going to a jumbled mess because projecting prospects is inherently unpredictable. Plus, NHL-readiness is also highly subjective, which will be discussed in Jacob de la Rose’s profile.

The list begins by ranking prospects in tiers. These tiers are almost exclusively established by upside. The ranking within the tiers is then generally decided by their NHL-readiness. Players in Tier 1 I perceive as having more upside than those in Tier 2, and so on. If there is any value in this ranking (which I greatly question), the tiers are where you’ll find it. The individual rankings are incredibly difficult and I change my mind constantly. So, I make a bunch of lists over the course of the season, and average them out. Ta-da!

I also need to point out this excellent tweet train from Corey Pronman, where he distinguishes between scouting and ranking prospects.

This section raises the question, what do I consider upside? Here’s the long-form answer with a handy table of contents.

1.0 – Tools

2.0 – Style

2.1 – Pace

2.2 – “Creation” versus “Exploitation”

2.3 – Controlled Exits and Entries

2.4 – Shot Location

3.0 – Development

3.1 – Age

3.2 – League

4.0 – Execution

4.1 – CF% and GF%

4.2 – Production

5.0 – Integrating Statistics

6.0 – Conclusion

7.0 – Glossary

1.0 – Tools

While this is the shortest of the sections, it definitely composes the majority of the prospect profiles. There are a huge variety of eye test-based tools and skills that I consider. These tools are subjective, the product of what we perceive as being “good” or “bad.”

For example, let’s start with a classic skating debate. Player “X” gets from point A to point B quicker than most players, but has an awkward stride. One may argue that X is a good skater as a product of his speed, while another may argue he’s a bad skater due to his weak technique.

Of course, this example ignores some of the nuance, as skating is an incredibly complex mechanic. There are tons of factors to consider, such as acceleration, edge work, balance, pivots, strength, width of stride, length of stride, knee bend, speed, deceptiveness, etc. Some of these tools are very obvious, whereas others require active comparison to others on the ice, and other prospects. Just to give an idea of how much goes into examining a player’s skill set.

Before I list off the swath of factors I consider, check out Gus Katsaros’s Four S’s of Scouting, which is truly an incredible resource.

Tools (including, but not limited):

Skating (acceleration, edge work, agility, balance, stops/starts, pivots, lower-body strength, knee bend, stride length and width, deceptiveness, etc.)

Stickhandling (“quick” vs. “soft” hands, space creation, variety, puck protection, practicality, elusiveness, etc.)

Shooting (power, accuracy, consistency, release speed, release point, ability to shoot under pressure/off balance, etc.)

Passing (vision, distribution, ability to pass under pressure, saucer pass, backhand pass, through traffic, breakout pass, etc.)

Defensive (limiting defensive zone time, gap control, backchecking, forechecking, turnovers, takeaways, play support, etc.)

Possession (impact on possession, puck protection ability, support, etc.)

Smarts (the product of the above, also includes: decision-making, discipline, risk assessment, etc.)

Style (pace, “proactive” vs. “reactive”, decision-making, “creation” vs. “exploitation”, dynamism, utilization of teammates, zone exits/entries, shooting location, ability to create off the rush, etc.)

Development (age, league, quality of team, type of system/structure, etc.)

Execution (production, results)

Not all factors are considered equal, but I try to make as much into consideration, even if I don’t see huge value in it, like faceoffs or hitting (but that’s not to say I don’t enjoy them).

The last three–Style, Development, and Execution–are the three that will get mentioned time and time again in this series, particularly with the revamped ranking explanations. So, I think it’s important to take a look at certain elements of each.

2.0 – Style

2.1 – Pace

Pace isn’t merely about being able to skate fast, it’s about thinking fast. With the NHL being such a fast, quick game, it’s imperative that plays that read their situation and make a smart decision quickly. Often times decisions are made before they even have the puck. A lack of pace is often what those high-scoring juniors who never sniff the NHL have.

Locating pace is arguably a more scouting-based task than a statistical one. Even delving into the world of microstats, like controlled exits and entries, won’t necessarily tell you, at least at the junior level, the players who play with the pace.

2.2 – “Creation” versus “Exploitation”

This falls into a similar category as pace. It’s one of those elements that isn’t measurable at the junior level. This article will veer into statistics after this point, but I just want to make this distinction clear.

This specifically refers to the process of creating scoring chances, usually through creation of passing and shooting lanes (think: creating space). The blatantly obvious examples of lane creation are watching a forward use shiftiness to evade defenders to locate an open target, or a defender walking across the point to find a clear shooting lane. But it can also be subtle plays, like a calculated outside drive to draw a backchecker and forechecker in, leaving a teammate open, or a quick transition from forehand to backhand to get a shot off.

The process of creating scoring chances through the exploitation of space given is far more common among junior-level players. It’s the exploitation of poor gap control by getting a clean shot on goal or the exploitation of players reacting slowly to the play developing.

The best players can do both. NHL scorers don’t just create space, they can exploit the space given to them. And the elite require little to no space at all to make plays (I’m looking at you, Crosby).

But the opposite is rarely true, those who are space exploiters don’t necessarily have the ability to create their own space with consistency.

But do “pace” and the creation really matter at the junior level? Yes, but skilled players usually score in junior, especially later in the four or five year career, regardless of this distinction. At the NHL level? Yes, I believe it’s important element that separates those who score often from those who score less often.

2.3 – Controlled Exits and Entries

If you’ve read my work before, I’m sure you’re familiar with my obsession with controlled exits and entries. For those unfamiliar, an exit refers to exiting the defensive zone, and an entrance to entering the offensive zone. Controlled refers to maintaining possession of the puck with a pass or carry, and uncontrolled refers to a dump out or in.

Essentially, tracking exits and entrances is a way to measure a player’s ability to breakout of their zone, transition the puck, and gain the offensive zone.

Tracking exits/entries first pops up in 2011 with Eric Tulsky’s work, and has since become a staple in the hockey analytics world, both in the public and NHL levels. Making plays with control has an undisputed effect on shot attempts, and subsequently goals. Garik16 found that controlled entries result in twice as many unblocked shot attempts than uncontrolled attempts, and this skill is repeatable from season-to-season.

For defenders, preventing attackers from gaining the offensive zone is of special importance, specifically preventing controlled entries. Zac Urback pointed out that the percentage of entry attempts against being with control predicts goals against. As a result, it’s imperative for a strong neutral zone defence. Tight, precise gap control as early as possible slows attackers down and allows backchecking forwards to pressure the carrier. Theoretically, this increases turnovers and uncontrolled entry attempts.

There isn’t statistical evidence that players who play with control more than their peers in junior will continue to do so in the NHL, but that’s not to say there isn’t great descriptive value in these numbers. However, it’s fair to assume that players who exit/enter with control more often than their peers in junior are more likely to do so in the NHL. After all, controlled exits and entries require a fair bit of skill, often a combination of pace, stickhandling ability, and vision.

2.4 – Shot Location

We hear it all the time, how teams need to “get to the net” and “get shots on goal.” There’s a fascination with players who get shots from the slot. Extensive studies have shown that there’s a link between the quality of the shot and likelihood for a goal. Ryan Stimson’s incredible Passing Project factors in the pass preceding the shot, and this is a repeatable and meaningful skill.

It has just been within the last two years that the OHL has started including the location of the shots on their website (as well as shots in general), so junior hockey data is stuck rather simple.

One of the key differences between the junior and NHL levels is shot location. Sportsnet’s Stephen Burtch showed that NHL teams shoot from the high-danger area 24% of the time, whereas juniors do 9% of the time. Additionally, goaltenders in junior stop less shots overall, but especially from the HD area.

The location of the shot does matter, but there’s plenty of volatility attached to the limited information we have at the junior. It’s important information, but also imperative to be mindful of the limitations.

3.0 – Development

3.1 – Age

Age is integral component of prospect evaluation. I’ve been accused of favouring younger players over older ones, to which I concede, “Yes, I do.” Here’s why:

First, an example. Player X and Player Y are of similar skill level, and produced similarly in junior. “X” is entering his fourth year of junior, while “Y” is entering his third of AHL. “X” hasn’t proven himself at the professional level, but “Y”‘s performance in the AHL is not indicative of an NHLer. In this instance, “Y” is physically closer to the NHL, but hasn’t shown NHL upside since his last year of junior. As a result, I (typically) prefer “X” because of the chance that he will become a better professional player. I’ll take a risk on the newer asset over the older one, because “Y” has given strong indications that he is not an NHLer, while “X” has yet to be determined.

Second, Money Puck found that NHL forwards peak between 22 and 26, while defenders around 24. To this in perspective, Alex Galchenyuk and Charles Hudon are 23. Michael McCarron is 22. If players are going to make the NHL, they typically show this early on. Which I’ll discuss more in the article dedicated to a certain high-scoring three-year AHL player.

Third, ESPN’s Corey Pronman took a fascinating look at the effect of the “late birthdate” (Players born after September 15 to December 31 of a given year have their eligibility pushed a year back to ensure that they are 18 when playing in the NHL. As a result, they are oldest players in their respective draft class). Pronman discovered that after the top of the draft, the late birthdate players average 0.10 P/GP less in the NHL than their younger counterparts. He also found that these younger players were generally undervalued in the draft.

Finally, given junior hockey’s age disparity at prime developmental times (players range from 16 to 20), it’s important to consider age as being older gives distinct advantages in strength, development time, and maturity. Therefore, it’s important to adjust scoring for age. Rhys Jessop pioneered adjusting scoring for age, and it has since become a staple in prospect projection models like Player Cohort Success model, Draft Expected Value, and the Prospect Graduation Probably System, and formed the ‘A’ of SEAL-Adjusted Scoring.

tl;dr: Players peak at a young age, late birthdate players generally produce less in the NHL, and age-adjusted scoring is an integral part in models that can predict NHL success.

3.2 – League

Not every league is created equal. The three CHL leagues dominate the draft and the production of NHLers. Prospects can be drafted from leagues where they play against other junior-aged players (CHL, Jr. A Leagues, SuperElit, Jr. A SM-Liiga, etc.), NCAA leagues where plays can range from 17 to 25, or professional leagues. So, it’s important to consider age in all scenarios.

Balancing players performances in their respective leagues is incredibly difficult, particularly if it’s comparing an OHL player to an SHL player. With the SHL prospect, production remains important, but there’s certainly a greater amount of context needed to evaluate this situation (League scoring, physical development, usage, size of rink, etc.).

There’s also the problem of familiarity bias, and the problem of players certain leagues having lower success rates. So, the big question is, how does one account for this?

There’s more to evaluating prospects than simply watching that prospects. It’s about comparisons to their peers, which requires nuanced knowledge of the league. So, for full disclosure, these are leagues I watched the most of this past season: OHL, WHL, NCAA, J20 SuperElit, and SHL.

4.0 – Execution

4.1 – CF% & GF%

Corsi and Goals For are likely the two most visible statistics in today’s stats debates. Corsi is the measure of shot attempts, including missed, blocked, and on goal. Goals For is simply the measure the 5v5 goals that the player was on the ice for.

I won’t delve too much into the value of CF% and GF%, as they’ve been discussed to death. While both are predictive stats in the NHL, that may not be the case at other levels. Particularly when predicting prospects to the NHL. But just as I’ll discuss in the following section on production, the better the player is relative to their peers, the better chance they have at making the NHL.

For both CF% and GF%, I’ll focus on the relative numbers. That is, their GF% compared the their team’s GF% when that player is not on the ice. This can help adjust for weaker teams, and give a better indictor for who is driving the play forward.

4.2 – Production

Production is extremely important for turning prospects in the NHL players. In fact, I’d argue there is not a better indicator of NHL success than production for junior prospects. While it is true that there’s an extensive list of high-scoring junior players failing to make the NHL, the list of low-scoring junior players making the NHL is extremely small.

There have been countless articles and studies conducted on the link between junior scoring and NHL success. Rhys Jessop in 2013 and 2014, Money Puck and Josh Weissboch’s PCS Model, the brilliant Sham Sharron, etc. Even yours truly, with a rudimentary study on excel, looked at this important predictor of NHL success.

So, I think it’s imperative that players score. And it’s also important how they score.

Even-strength production and primary point production are both important factors for projecting prospects. All of the models I’ve cited adjust for situational and primary production, as primary production (Goals + Primary Assists) is the best predictor of NHL success. Meanwhile, secondary assists have a substantial year-to-year variance.

For defencemen, scoring is still a strong indicator of NHL success, as shown by Rhys Jessop. However, it’s fair to concede that defenders are more reliant on secondary assists and powerplay to grab their points given the nature of the position.

In this series, production will also be examined in other ways. For example, I include Team Involvement Percentage (TM INV%) in all the profiles. TM INV% measures the percentage of the player’s team goals that the player had a point on. I adjust this for games that the player was not in the lineup. At this point, it’s merely a descriptive stat.

5.0 – Integrating Statistics

Integrating statistics into player analyses has become a staple in my work this year, whether that be full player breakdowns like Mikhail Sergachev or Simon Bourque, or analyzing one particular element like Will Bitten’s playmaking or Victor Mete’s ability to activate off the point.

I’ve noticed myself becoming increasingly influenced by statistics, both outputs statistics like CF% and GF%, as well as microstats.

Statistics and “scouting” have a symbiotic relationship of sorts, where both sides answer questions asked by the other. When I notice something peculiar, like a player having an unexpectedly high controlled exit rate, I adjust my watching and analysis to focus on that element. Alternatively, I have taken more in-depth statistical approaches by breaking down controlled exits into pass exits, passed-to exits, carry outs, and noting the amount of forechecking pressure.

There have been countless examples of statistics informing my reports, such as Noah Juulsen’s insane ability to defend the blue line or how Mete has recognized his shot-based weaknesses and counteracted them by shooting from closer. But perhaps the most interesting case is Brett Lernout.

I saw Lernout as a player who struggled with controlled exits. I noticed poor outlet passes, an inability to retrieve the puck off the wall in stride, and turnovers by skating himself directly into pressure. What I didn’t notice was how effective he actually is until I got past these egregious mistakes. After tracking zone exit data, it turned out not a single IceCaps defender was more effective at controlled exits (particularly pass exits)–not even Mark Barberio or Zach Redmond. And to top it off, he was highly successful at evading pressure to send an outlet pass. My player report on Lernout no longer reads that he struggles with exits, but rather he excels at it. Simply put, tracking the data enabled me to accurately communicate Lernout’s skills.

One of the most fascinating aspects is that statistics are generally in line what I look for in prospects. When I’m looking at defence, I want to look specifically at their gap control. I can track zone entry prevention events, which can answer questions about their gap control. My notes and video then allow me to fill in the blanks: The player is great at preventing zone entries because of a tight gap control, excellent four-way ability, and aggressive and strong with his stick, and so on.

Another area where statistics help is measuring performance, particularly measuring performance relative to their teammates. Generally, I’m able to watch a game and understand who is performing well and who is not. But statistics give exact numbers. It makes performances measurable, and is easier information to store for access in the future. Additionally, it aids in the elimination of bias (of course, statistics are unbiased, the presentation of them is not). Charts and graphs are nice to look at, too.

6.0 – Conclusion

I want to reiterate: The rankings aren’t the most important part, and to take my borderline obsession with downplaying this, my ranking explanations will focus less on the players below them, and more on their individual development case and the tier that they rank in. You’ll find more value in the profiles and explanations than the ranking itself.

This edition also marks a change from the previous structure of the articles. Every prospect within the top-10 will have an individual article, with an overarching theme. For example, these articles will focus on the importance of defensive efficiency, on AHL development curves, the importance of the “little details,” or how turnovers and poor offensive plays are not just a product of the individual committing them.

So, with that in mind and my current obligations (I’m working an irregular job in the Yukon!), the release schedule of these articles might be sporadic. For that I apologize, but I’ll do my best to keep everyone in the loop.

If you made it this far, thanks for reading!

7.0 – Glossary

A1: Primary assists

A1/GP: Primary assists per game played

Corsi: A measure of shot attempts, including missed, blocked, and on goal.

CF%: Corsi for percentage. The percentage of all corsi evens that player is on the ice for that are goals for

CF.Rel%: Relative Corsi for percentage. The player’s CF% subtracted by the CF% of the player’s team when the player is not on the ice

DZ: Defensive zone

G: Goals

G/GP: Goals per game played

GA: Goals against

GD: Goal differential (GD = GF – GA)

GF: Goals for

GF%: Goals for percentage. The percentage of all goals that a player is on the ice for that are goals for

GF.Rel%: Relative goals for percentage. The player’s GF% subtracted by the GF% of the player’s team when the player is not on the ice

NZ: Neutral zone

OZ: Offensive zone

P: Points

P/GP: Points per game

P1: Primary points (G + 1A)

P1/GP: Primary points per game played

SOG: Shots on goal

SOG/GP: Shots on goal per game played

SH%: Shooting percentage. The percent of shots that are goals

SV%: Save percentage. The percent of shots against that saved

TM INV%: Team Involvement percentage. The percent of team goals that the player had a point on.