NHL Draft Software: Final Rankings | Top200

[ProspectsFanatic]

Final Rankings:

Text version:

NAME

Rasmus Dahlin

Andrei Svechnikov

Jesperi Kotkaniemi

Olivier Wahlstrom

Filip Zadina

Noah Dobson

Quinn Hughes

Evan Bouchard

Adam Boqvist

Brady Tkachuk

Vitaly Kravtsov

Joseph Veleno

Joel Farabee

Ty Smith

Jonatan Berggren

Serron Noel

Martin Kaut

Mattias Samuelsson

K'Andre Miller

Alexander Alexeyev

Barrett Hayton

Ty Dellandrea

Bode Wilde

Rasmus Kupari

Isac Lundestrom

Filip Hallander

Grigori Denisenko

Ryan Merkley

Liam Foudy

Dominik Bokk

Jonny Tychonick

Bulat Shafigullin

Jared McIsaac

Jan Jenik

Akil Thomas

David Gustafsson

Niklas Nordgren

Jesse Ylonen

Jacob Olofsson

Jack McBain

Ryan Mcleod

Kirill Marchenko

Calen Addison

Kevin Bahl

Rasmus Sandin Albin Eriksson

Allan McShane

Jacob Bernard-Docker

Jake Wise

Jay O'Brien

Ivan Morozov

Nils Lundkvist

Stanislav Demin

Jachym Kondelik

Alec Regula

Jack Drury

Nicolas Beaudin

Martin Fehervary

Jett Woo

Carl Wassenius

Ty Emberson

Jakub Lauko

Benoit-Olivier Groulx

Danila Zhuravlyov

Cam Hillis

Ryan O'Reilly

Philipp Kurashev

Milos Roman

Xavier Bernard

Johnny Gruden

Libor Zabransky

Sampo Ranta

Adam Ginning

Alexander Khovanov

Aidan Dudas

Blade Jenkins

Riley Sutter

S. Der-Arguchintsev

Xavier Bouchard

Luka Burzan

Cole Fonstad

Blake Mclaughlin

Adam Mascherin

Filip Kral

Tyler Weiss

Olivier Rodrigue

Curtis Douglas

Sean Durzi

Nathan Dunkley

Ruslan Iskhakov

Linus Karlsson

Matthew Struthers

Dmitri Zavgorodny

Gabriel Fortier

Kyle Topping

Jacob Ragnarsson

Nando Eggenberger

Axel Andersson

Giovanni Vallati

Chase Wouters

Curtis Hall

Filip Johansson

Tyler Madden

Logan Hutsko

Lukas Dostal

Toni Utunen

Spencer Stasney

Anderson MacDonald

Pavel Gogolev

Scott Perunovich

Jakub Skarek

Marcus Westfalt

Alex Steeves

Riley Stotts

Alexis Gravel

Amir Miftakhov

Krystof Hrabik

Nikolai Kovalenko

Jordan Harris

Oscar Back

Yegor Sokolov

Patrick Khodorenko

Jacob Ingham

Justin Brazeau

Martin Pospisil

Brendan Budy

Vladislav Kotkov

Danila Galenyuk

Liam Kirk

Shawn Boudrias

Justus Annunen

Lukas Wernblom

Severi Lahtinen

Eric Florchuk

Karel Plasek

Kevin Mandolese

Michal Kvasnica

Linus Nyman

Connor Roberts

Kirill Nizhnikov

Matej Pekar

Vlasislav Yeryomenko

Jordan Koy

Samuel Fagemo

Joey Keane

Gavin Hain

Adam Samuelsson

Adam McCormick

Jacob Pivonka

Merrick Rippon

Veini Vehvilainen

Caleb Everett

Riley Damiani

Paul Cotter

Kody Clark

Jack Perbix

Jerry Turkulainen

Matthias Laferriere

Ryan Chyzowski

Einar Emanuelsson

Johan Sodergran

Nikita Rtishchev

Luke Henman

Declan Chisholm

Carter Robertson

Olof Lindbom

Jack Deboer

Jack Randl

Patrick Giles

Alexander Romanov

Justin Almeida

Mathias E. Petterson

Erik Middendorf

Austin Wong

Pavel Shen

Lenni Killinen

Michael Kesselring

Jaxon Nelson

Demid Mansurov

Simon Johansson

Nikita Rozhkov

Connor Corcoran

Micha Ivan

Wyatte Wylie

Joel Hofer

Santeri Salmela

Mikhail Shalagin

Marcus Karlberg

Nico Gross

David Tendeck

Divid Lilja

Sean Comrie

Carl Berglund

Zachary Bouthillier

Jackson Leppard

Dawson Barteaux

Max Golod

Mac Hollowell

Akira Schmid

Ondrej Buchtela

[TBODY] [/TBODY]

I named those my final rankings but I might make slight alterations until draft.

Remarks:

• Dahlin is decidedly ranked at #1, while Svechnikov is decidedly ranked at #2 with Svechnikov being a lot closer to 1 than 3.
• 3rd overall is a toss up for me, with the top11 all being very close. Small alterations in the algorithm or data entered (approximate playing time/quality of linemates) or scouting evaluations could have potentially placed either Wahlstrom, Zadina or Dobson instead of Kotkaniemi at #3 since they are all very close.

Data taken into account:

• Age, height, weight.
• Scouting evaluation; Skating speed, edge work, shooting, puck control, offensive IQ, competitiveness, physical play, defensive play. (Those evaluations are impacting the statistical evaluation, both sources of data are tested again each other in different ways.)
• Last 3 seasons stats in all leagues played; League difficulty, ice time, quality of teammates, organizational depth, (regular+playoff) GP, G, A, PTS and if available A1, A2, Sh%, relative +/-. Tournament play: WJC18, WJC20, Hlinka.
• The statistics are used as an indicator of performance, but where the player stands in terms of organizational depth is also weighted, those two are weighted differently according to the algorithm depending on the statistical relevancy of each player.
• On rare occasions some players are given positive or negative bonuses if something of relevancy hasn't been taken into account by the software, example; injury prone, attitude issue, extra international play.

Last year (comparison with this year rankings is imperfect; algorithm revamped):
Software evaluating draft eligible player

Previous drafting results (drafting as MTL for the last 5 years):
http://hfboards.mandatory.com/threa...-can-now-start-comparing-the-results.2430975/

Previous 2018 draft rankings:
http://hfboards.mandatory.com/threa...0-end-of-the-regular-season-rankings.2471469/
If you have questions about the functionality of the software (such as why I use scouting evaluations (post #51 in that thread)) it probably has been answered in that thread.

Why use software to evaluate prospects?

"I believe that this software can be a useful tool to support the evaluation of prospects, in fact, I would even go as far as to say that it has the potential to give better results than the standard way of evaluating players. When I started doing the exercise of creating my own rankings of draft eligible players I quickly realize how complex of a mental process it is considering all the variables of evaluation that have to be taken into account when evaluating a player (speed, skill, shot, defensive game, compete level, age, offensive production, quality of teammates, league difficulty, ice time, height, weight, tournament play, etc, etc, while assessing to each criteria their degree of importance), inevitably some criteria of evaluation become overvalued or undervalued. Once you start comparing two players the mental process increase in complexity, recalling every relevant information about each player, gaging each gap between the players and assessing which accumulation of gaps outweigh the other, the increase in complexity inevitably creates more room for misjudgment. At that point, if two scouts are arguing about two prospects a fight of will must take over to a certain extent since the analysis of the objective subtleties that differentiate the players can't be addressed in his totality. As a matter of fact, we face ourselves against the limitation of our brain even when isolating simple variables easy to grasp, even the ones who are already objectively defined for us can lead to misevaluation, let’s take the age of player for example, the brain can't associate a precise value for each day of the year, instead, for the sake of simplification, the brain will tend to reduce the number of values by regrouping to a single value weeks or entire month or possibly closer months with one another. This can be seen as a marginal difference that might not have a considerable impact, but those marginal differences can be found everywhere, they add up and their sum can make noticeable differences, even more so if you have a bias towards a specific player your brain will unconsciously selectively favored the regrouping of values in a manner that will confirm your bias opinion. But most importantly, this software eases the process of evaluation by isolating each variable of evaluation in order to come to a final value, the brain performs much better at assessing the most precise value possible to one single variable at the time than taking everything into account at once in order to come to a final value. For those reasons, I believe that if the exercise of assessing the most exact value possible to each isolated variable is done properly with the help of a software it will give results of greater accuracy."

"The human brain has limitation in regards to his precision (exact value attributed to a prospect playing in a certain league at a certain point in time considering his specific date of birth), consistency in evaluation (I give the exact same value to that player as for each other player in that same context, if it is a slightly different context the value is slightly altered proportionally) and processing power (quantity of variables factored in to precision at the same time, looking at all that data at once), those are three major limitations which software does not have, I believe that there is potential there to be exploited in the scouting hockey world."

Forwards entered in the software (scoring higher than 43):
- DY: Draft year. DY-1: 1 year before draft. DY-2: 2 years before draft.
- The first section (with DY, DY-1, DY-2) are the evaluations of each prospect season performance calculated by the software based on the data entered.
- The second section shows the percentage taken into account in the final score, it varies depending on the games/tournaments played for each season.
- The third section shows the player's style of play which is automatically generated based on the scouting evaluation I entered for the player.

Defensemen entered in the software (scoring higher than 43):
- DY: Draft year. DY-1: 1 year before draft. DY-2: 2 years before draft.
- The first section (with DY, DY-1, DY-2) are the evaluations of each prospect season performance calculated by the software based on the data entered.
= The second section shows the percentage taken into account in the final score, it varies depending on the games/tournaments played for each season.

Best re-eligible players/overagers:

1. (F) Adam Mascherin, OHL +2
2. (D) Sean Durzi, OHL +1
3. (F) Logan Hutsko, NCAA +1
4. (D) Scott Perunovich, NCAA +2
5. (F) Patrick Khodorenko, NCAA +1
6. (F) Justin Brazeau, OHL +2
7. (F) Shawn Boudrias, QMJHL +1
8. (F) Severi Lahtinen, Mestis +1
9. (F) Linus Nyman, OHL +1
10. (D) Vladislav Yeryomenko, WHL +1
11. (D) Joey Keane, OHL +1
12. (G) Veini Vehvilainen, SM-Liiga +3
13. (F) Jerry Turkulainen, SM-Liiga +1
14. (F) Einar Emanuelsson, SHL +3
15. (F) Justin Almeida, WHL +1
16. (F) Pavel Shen, KHL +1
17. (D) Simon Johansson, SuperElit +1
18. (F) Mikhail Shalagin, MHL +1

Best draft eligible goaltenders:

1. (G) Olivier Rodrigue, QMJHL
2. (G) Lukas Dostal, Czech2
3. (G) Jakub Skarek, Czech2
4. (G) Alexis Gravel, QMJHL
5. (G) Amir Miftakhov, MHL
6. (G) Jacob Ingham, OHL
7. (G) Justus Annunen, Jr.A Liiga
8. (G) Kevin Mandolese, QJMHL
9. (G) Jordan Koy, OHL
10. (G) Veini Vehviläinen, Liiga +3
11. (G) Olof Lindbom, SuperElit
12. (G) Joel Hofer, WHL
13. (G) David Tendeck, WHL
14. (G) Zachary Bouthillier, QMJHL
15. (G) Akira Schmid, Elite Jr. A

Other notable prospects (excluding goaltender):
(D) Nico Gross, OHL
(F) David Lilja, Allsvenskan
(D) Sean Comrie, AJHL
(F) Carl Berglund, SuperElit
(F) Jackson Leppard, WHL
(D) Dawson Barteaux, WHL
(F) Max Golod, OHL
(D) Mac Hollowell, OHL +1
(D) Ondrej Buchtela, Czech2
(D) Dennis Busby, OHL
(F) Krisitan Reichel, WHL +1
(D) Marc Del Gaizo, USHL
(D) Thomas Gregoire, QMJHL +2
(F) Damien Giroux, OHL
(F) Jake Goldowski, OHL
(D) Jack St. Ivany, USHL +1
(D) Christian Lindberg, Allsvenkan
(F) Angus Crookshank, BCHL
(F) Ryan Roth, OHL
(F) Yegor Sharangovich, KHL +2
(F) Ivan Muranov, MHL
(D) Tyler Tucker, OHL
(F) David Levin, OHL
(F) Alexei Polodyan, VHL +2
(F) Kristian Tanus, JrA-Liiga
(F) Samuel Bucek, USHL +1
(F) German Grachyov, MHL
(F) Trey Fix-Wolansky, WHL +1
(F) Alex Gritz, OHL
(F) Adam Liska, OHL
(F) Luke Burghardt, OHL +2
(D) Zack Malik, OHL
(D) Adam Thilander, OHL +1
(D) Peter Stratis, OHL
(D) Jacob Semik, USHL
(F) Zach Solow, NCAA +1
(D) Artyom Minulin, WHL +1
(F) Aleksi Halme, Mestis
(D) Owen Lalonde, OHL
(F) Maxime Grondin, OHL
(F) Adam Gajarsky, Czech2
(F) Razat Timirov, MHL +1
(F) Oliver Okuliar, Slovakia
(F) Demetrios Koumontzis, USHS
(F) Mitchell Hoelscher, OHL
(F) Connor Dewar, WHL +1
(D) Christian Krygier, USHL
(F) Jeremy Mckenna, QMJHL +1
(F) Albert Michnac, OHL +1
(F) Tristen Nielsen, WHL
(F) Brandon Saigeon, OHL +2
(D) Dillon Plouffe, WHL +1
(F) William Moskal, OHL
(D) Dennis Busby, OHL
(D) Cole Krygier, USHL
(F) Joel Teasdale, QJMHL +1
(F) Hunter Holmes, OHL
(D) Radim Salda, QMJHL +1
(F) Carson Focht, WHL
(F) Cole Reinhardt, WHL
(F) Eli Zummack, WHL
(F) Thomas Ethier, QMJHL
(D) Spencer Meier, USHL +1
(D) Alex Kannok-Leipert, WHL
(D) Tommy Miller, NCAA +1
(F) Carl Jakobsson, SuperElit
(D) Benjamin Gleason, OHL +2
(D) William Worge Kreu, SuperElit
(F) Yegor Zudilov, WHL
(F) Samuel Bitten, OHL
(F) Ryan Savage, USHL
(F) Conner Bruggen-Cate, WHL
(F) Kevin Hancock, OHL +2
(F) Tyler Popowich, WHL
(F) Ivan Kosorenkov, QMJHL +2
(D) Martin Bodak, WHL +1
(F) Colin Schmidt, USHS
(F) Alexander Zhabreyev, MHL
(F) Leif Mattson, WHL +1
(D) Peetro Seppala, JrA-Liiga
(F) Jan Kalus, Czech2
(F) Quinn Yule, OHL
(F) Riley Hughes, USHS

Click to read more...

 

[Henkka]

Finally, thanks.

Now I understand those high Kotkaniemi ranks.

 

[Mrb1p]

Why doesnt the defenseman ranking have the primary/secondary variable ?

 

[ProspectsFanatic]

Why doesnt the defenseman ranking have the primary/secondary variable ?

Didn't had time to program it (it is automatically generated), will do it for next year.

 

[Hoquei]

Dahlin and Svech with a big gap for the rest.

I really appreciate your ranking, it should take up a lot of your time.

 

[NickH8]

Thank you for putting time into this! It'll be interesting to see how this experiment pans out.

 

[Nedved]

excellent job!

 

[Michael Brand Eggs]

Some under the hood type questions: what do you use to generate your predictions? Did you write an application, or is this a model you work with in Python or R? I notice you've been doing this for a couple years now. Have you noticed any degradation in your model as the years have gone by, maybe because of shifting player preferences?

How did you go about choosing your non-obvious features/independent variables?

 

[37 others]

You've done redrafts using this this before, right? We're the redrafts done using the players stats at that time? And were the redraft picks based solely on the rankings from the program?

 

[TylerJCampbell]

Terrific work! Really appreciated you laying out how you go about getting to the conclusions by including the human element, not just blindly going with what the analytics say as some do. I guess you believe in what you believe in, but the way you seem to go about it seems like it would yield much better results.

 

[ProspectsFanatic]

Some under the hood type questions: what do you use to generate your predictions? Did you write an application, or is this a model you work with in Python or R? I notice you've been doing this for a couple years now. Have you noticed any degradation in your model as the years have gone by, maybe because of shifting player preferences?

How did you go about choosing your non-obvious features/independent variables?

Everything you see right now has been done in excel, the algorithm is probably 3 pages long of purely formulas, I want to transfer it to software now (I have no experience with those coding languages, I just know maths, I will need help for this). It is only the 2nd year I have been using this to evaluate prospects, so no way to notice degradation yet, but that is a valid point which I will need to keep a close eye on (for example smaller defensemen project a lot better than a few years ago). But the reality is that I am already constantly tweaking the algorithm; each time I enter a player I am asking myself if the results make sense, if not I try to think if I can see patterns with players on similar situations, if particular inputs indeed seem to lead to inaccurate evaluations I try to find ways to fix that by either altering the current formulas and/or adding new ones.

About choosing the independent variables; my goal is to mimic the way your brain would naturally evaluate prospects, but with further precision with superior computational processing power, if I am naturally inclined to take into account independent variables when evaluating prospects I try to add them to the software in the same proportional level of importance. Again, each time I enter a player I assess if the independent variables I am using achieve their desired results in improving the evaluation of players.

 

[ProspectsFanatic]

You've done redrafts using this this before, right? We're the redrafts done using the players stats at that time? And were the redraft picks based solely on the rankings from the program?

Haven't done it yet, that's something I would like to do in the future.

 

[Sens of Anarchy]

Everything you see right now has been done in excel, the algorithm is probably 3 pages long of purely formulas, I want to transfer it to software now (I have no experience with those coding languages, I just know maths, I will need help for this). It is only the 2nd year I have been using this to evaluate prospects, so no way to notice degradation yet, but that is a valid point which I will need to keep a close eye on (for example smaller defensemen project a lot better than a few years ago). But the reality is that I am already constantly tweaking the algorithm; each time I enter a player I am asking myself if the results make sense, if not I try to think if I can see patterns with players on similar situations, if particular inputs indeed seem to lead to inaccurate evaluations I try to find ways to fix that by either altering the current formulas and/or adding new ones.

About choosing the independent variables; my goal is to mimic the way your brain would naturally evaluate prospects, but with further precision with superior computational processing power, if I am naturally inclined to take into account independent variables when evaluating prospects I try to add them to the software in the same proportional level of importance. Again, each time I enter a player I assess if the independent variables I am using achieve their desired results in improving the evaluation of players.

so its all table driven with no hard coded factors or multipliers ? It should not be that hard to code something even with something like access as a database.
That must be one ugly formula to maintain in excel

 

[ProspectsFanatic]

so its all table driven with no hard coded factors or multipliers ? It should not be that hard to code something even with something like access as a database.
That must be one ugly formula to maintain in excel

Yeah I only concentrated on finishing the rankings for this year, excel is inefficient in many ways, next step ideally is to program it on software, which I would like it to be accessible for anyone to use. I don't have the extra time to work on this as of now.

 

[StatisticsAddict99]

These our my favourite rankings, no question. People deathly underrated Kotkaniemi, IMO he looks allot like Datsyuk and is deserving of landing No.3. Glad to see he’s being noticed this high by actual data.

 

[tarheelhockey]

This is amazing work. Well done.

 

[atrud66]

If you have the data, I'd be interested in trying to create a machine learning model and see how that performs

 

[Michael Brand Eggs]

If you have the data, I'd be interested in trying to create a machine learning model and see how that performs

I would love to see a true ML-oriented take on the draft. I don't know how long we've had this complex CHL of data, but we should be getting to the point where you could have a pretty robust training set with it.

 

[atrud66]

I would love to see a true ML-oriented take on the draft. I don't know how long we've had this complex CHL of data, but we should be getting to the point where you could have a pretty robust training set with it.

The challenge would be developing some sort of score that would be the output of the model. The data that ProspectsFanatic is using are great input features but the model would have to output some sort of score that could be used to rank the prospects

 

[ProspectsFanatic]

These our my favourite rankings, no question. People deathly underrated Kotkaniemi, IMO he looks allot like Datsyuk and is deserving of landing No.3. Glad to see he’s being noticed this high by actual data.

The best way to illustrate why Kotka landed there I believe, in comparison with the most conventional 3rd overall in Zadina, if you are to compare both players statistics you are better off comparing Kotka current season with Zadina previous season, making Kotka 4.75 months younger instead of 7.25 months older than Zadina. What Kotka did this year compare to Zadina last year in a men's league isn't somewhat close, Zadina had 25-1-1-2 in the Czech league (which is the easier league so normally should have somewhat mitigated the age difference). Obviously, you can't negate what Zadina accomplished this year, especially in the WJC20, but that can still give you an idea about why the software puts Kotka in front of Zadina. You can also add to this that Kotka is already the faster skater and is taller than Zadina.

 

[ProspectsFanatic]

If you have the data, I'd be interested in trying to create a machine learning model and see how that performs

Interesting, could you further elaborate on what you have in mind? (can PM me if you want)

 

[Peeps]

Thanks this is an awesome tool!

 

[TheGoldenJet]

Final Rankings:

I named those my final rankings but I might make slight alterations until draft.

Remarks:
- Dahlin is decidedly ranked at #1, while Svechnikov is decidedly ranked at #2 with Svechnikov being a lot closer to 1 than 3.
- 3rd overall is a toss up for me, with the top11 all being very close. Small alterations in the algorithm or data entered (approximate playing time/quality of linemates) or scouting evaluations could have potentially placed either Wahlstrom, Zadina or Dobson instead of Kotkaniemi at #3 since they are all very close.

Data taken into account:
- Age, height, weight.
- Scouting evaluation; Skating speed, edge work, shooting, puck control, offensive IQ, competitiveness, physical play, defensive play. (Those evaluations are impacting the statistical evaluation, both sources of data are tested again each other in different ways.)
- Last 3 seasons stats in all leagues played; League difficulty, ice time, quality of teammates, organizational depth, (regular+playoff) GP, G, A, PTS and if available A1, A2, Sh%, relative +/-. Tournament play: WJC18, WJC20, Hlinka.
(The statistics are used as an indicator of performance, but where the player stands in terms of organizational depth is also weighted, those two are weighted differently according to the algorithm depending on the context of each player, for example, independently of statistics being 2nd line SHL on draft eligible year mean something and can be measured as a value.)
* On rare occasions some players are given positive or negative bonuses if something of relevancy hasn't been taken into account by the software, example; injury prone, attitude issue, extra international play.

Last year (comparison with this year rankings is imperfect; algorithm revamped):
Software evaluating draft eligible player

Previous drafting results (drafting as MTL for the last 5 years):
http://hfboards.mandatory.com/threa...-can-now-start-comparing-the-results.2430975/

Previous 2018 draft rankings:
http://hfboards.mandatory.com/threa...0-end-of-the-regular-season-rankings.2471469/
If you have questions about the functionality of the software (such as why I use scouting evaluations (post #51 in that thread)) it probably has been answered in that thread.

Why use software to evaluate prospects?


Forwards entered in the software (scoring higher than 43):
DY: Draft year. DY-1: 1 year before draft. DY-2: 2 years before draft.
The first section (with DY, DY-1, DY-2) are the evaluations of each prospect season performance calculated by the software based on the data entered.
The second section shows the percentage taken into account in the final score, it varies depending on the games/tournaments played for each season.
The third section shows the player's style of play which is automatically attributed based on the scouting evaluation I entered for the player.

Defensemen entered in the software (scoring higher than 43):
DY: Draft year. DY-1: 1 year before draft. DY-2: 2 years before draft.
The first section (with DY, DY-1, DY-2) are the evaluations of each prospect season performance calculated by the software based on the data entered.
The second section shows the percentage taken into account in the final score, it varies depending on the games/tournaments played for each season.

Best re-eligible players/overagers:

1. (F) Adam Mascherin, OHL +2
2. (D) Sean Durzi, OHL +1
3. (F) Logan Hutsko, NCAA +1
4. (D) Scott Perunovich, NCAA +2
5. (F) Patrick Khodorenko, NCAA +1
6. (F) Justin Brazeau, OHL +2
7. (F) Shawn Boudrias, QMJHL +1
8. (F) Severi Lahtinen, Mestis +1
9. (F) Linus Nyman, OHL +1
10. (D) Vladislav Yeryomenko, WHL +1
11. (D) Joey Keane, OHL +1
12. (G) Veini Vehvilainen, SM-Liiga +3
13. (F) Jerry Turkulainen, SM-Liiga +1
14. (F) Einar Emanuelsson, SHL +3
15. (F) Justin Almeida, WHL +1
16. (F) Pavel Shen, KHL +1
17. (D) Simon Johansson, SuperElit +1
18. (F) Mikhail Shalagin, MHL +1

Best draft eligible goaltenders:

1. (G) Olivier Rodrigue, QMJHL
2. (G) Lukas Dostal, Czech2
3. (G) Jakub Skarek, Czech2
4. (G) Alexis Gravel, QMJHL
5. (G) Amir Miftakhov, MHL
6. (G) Jacob Ingham, OHL
7. (G) Justus Annunen, Jr.A Liiga
8. (G) Kevin Mandolese, QJMHL
9. (G) Jordan Koy, OHL
10. (G) Veini Vehviläinen, Liiga +3
11. (G) Olof Lindbom, SuperElit
12. (G) Joel Hofer, WHL
13. (G) David Tendeck, WHL
14. (G) Zachary Bouthillier, QMJHL
15. (G) Akira Schmid, Elite Jr. A

Other notable prospects (excluding goaltender):
(D) Nico Gross, OHL
(F) David Lilja, Allsvenskan
(D) Sean Comrie, AJHL
(F) Carl Berglund, SuperElit
(F) Jackson Leppard, WHL
(D) Dawson Barteaux, WHL
(F) Max Golod, OHL
(D) Mac Hollowell, OHL +1
(D) Ondrej Buchtela, Czech2
(D) Dennis Busby, OHL
(D) Marc Del Gaizo, USHL
(D) Thomas Gregoire, QMJHL +2
(F) Damien Giroux, OHL
(F) Jake Goldowski, OHL
(D) Jack St. Ivany, USHL +1
(D) Christian Lindberg, Allsvenkan
(F) Angus Crookshank, BCHL
(F) Ryan Roth, OHL
(F) Yegor Sharangovich, KHL +2
(F) Ivan Muranov, MHL
(D) Tyler Tucker, OHL
(F) David Levin, OHL
(F) Kristian Tanus, JrA-Liiga
(F) Samuel Bucek, USHL +1
(F) German Grachyov, MHL
(F) Trey Fix-Wolansky, WHL +1
(F) Alex Gritz, OHL
(F) Adam Liska, OHL
(F) Luke Burghardt, OHL +2
(D) Zack Malik, OHL
(D) Adam Thilander, OHL +1
(D) Peter Stratis, OHL
(D) Jacob Semik, USHL
(F) Zach Solow, NCAA +1
(D) Artyom Minulin, WHL +1
(F) Aleksi Halme, Mestis
(D) Owen Lalonde, OHL
(F) Maxime Grondin, OHL
(F) Adam Gajarsky, Czech2
(F) Razat Timirov, MHL +1
(F) Oliver Okuliar, Slovakia
(F) Demetrios Koumontzis, USHS
(F) Mitchell Hoelscher, OHL
(F) Connor Dewar, WHL +1
(D) Christian Krygier, USHL
(F) Jeremy Mckenna, QMJHL +1
(F) Albert Michnac, OHL +1
(F) Tristen Nielsen, WHL
(F) Brandon Saigeon, OHL +2
(D) Dillon Plouffe, WHL +1
(F) William Moskal, OHL
(D) Dennis Busby, OHL
(D) Cole Krygier, USHL
(F) Joel Teasdale, QJMHL +1
(F) Hunter Holmes, OHL
(D) Radim Salda, QMJHL +1
(F) Carson Focht, WHL
(F) Cole Reinhardt, WHL
(F) Eli Zummack, WHL
(F) Thomas Ethier, QMJHL
(D) Spencer Meier, USHL +1
(D) Alex Kannok-Leipert, WHL
(D) Tommy Miller, NCAA +1
(F) Carl Jakobsson, SuperElit
(D) Benjamin Gleason, OHL +2
(D) William Worge Kreu, SuperElit
(F) Yegor Zudilov, WHL
(F) Samuel Bitten, OHL
(F) Ryan Savage, USHL
(F) Conner Bruggen-Cate, WHL
(F) Kevin Hancock, OHL +2
(F) Tyler Popowich, WHL
(F) Ivan Kosorenkov, QMJHL +2
(D) Martin Bodak, WHL +1
(F) Colin Schmidt, USHS
(F) Alexander Zhabreyev, MHL
(F) Leif Mattson, WHL +1
(D) Peetro Seppala, JrA-Liiga
(F) Jan Kalus, Czech2
(D) Quinn Yule, OHL
(F) Riley Hughes, USHS

Well done, thank you for the effort.

You should be proud of your 2017 rankings as well, your top 4 F and your top 3 D from 2017 are the same as mine, and the same as the consensus in the redraft thread. Excellent work!

 

[Maverick41]

Yeah I only concentrated on finishing the rankings for this year, excel is inefficient in many ways, next step ideally is to program it on software, which I would like it to be accessible for anyone to use. I don't have the extra time to work on this as of now.

If you ever manage to turn this into software, I'll definitely buy it.

I hoped to do something in this vain just for German junior players a few years back, but I quickly realised three things:
1) My highschool level math skills are not nearly sufficient to really get into this.
2) Excel can be extremely frustrating to work with.
3) There is just not enough reliable data for German junior hockey out there.

So kudos for getting it this far, I am extremely impressed with your work.

 

[TheGoldenJet]

Didn't had time to program it (it is automatically generated), will do it for next year.

For the overagers, did you enter Marcus Sylvegård in your software? He’s still WJC eligible, good size, and had a nice little year in the SHL last season along with strong, productive showings internationally for Sweden. I’d take him over a few of the ‘top overagers’ you’ve listed (the ones I’ve seen play).