NHL Draft Software: Final Rankings | Top200

ProspectsFanatic

Registered User
Nov 13, 2012
3,696
2,426
Final Rankings:
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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.

eiJei34.png

MjwTxGw.png

R3keEFD.png

7dBDzsJ.png


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.

gX0Ngss.png

ed2Xaqr.png


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
 
Last edited:
Jul 30, 2005
17,659
4,588
I mean, what is location, really
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

Registered User
Apr 18, 2017
465
235
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

Registered User
May 9, 2016
101
14
Lloydminster, AB
soupsonhockey.com
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.
 
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ProspectsFanatic

Registered User
Nov 13, 2012
3,696
2,426
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.
 
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Sens of Anarchy

Registered User
Jul 9, 2013
64,989
49,506
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
 
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ProspectsFanatic

Registered User
Nov 13, 2012
3,696
2,426
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

Registered User
Feb 24, 2017
3,971
1,324
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.
 
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atrud66

Tank Tabarnack
Aug 5, 2014
1,340
1,956
Montreal
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

Registered User
Nov 13, 2012
3,696
2,426
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.
 
Last edited:

TheGoldenJet

Registered User
Apr 2, 2008
9,419
4,507
Coquitlam, BC
Final Rankings:
Y90smQL.png

LGe3p6Z.png

3ij0DmO.png

VrzKhWK.png

KaK11kw.png

R4Rm5eK.png

JG7VeX5.png


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.

eiJei34.png

12zyETb.png

R3keEFD.png

7dBDzsJ.png


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.

gX0Ngss.png

ed2Xaqr.png


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!
 
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Maverick41

Cold-blooded Jelly Doughnut
Sponsor
Nov 9, 2005
3,868
2,204
Germany
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.
 
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TheGoldenJet

Registered User
Apr 2, 2008
9,419
4,507
Coquitlam, BC
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).
 
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