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Seattle Kraken 0-0-0, 0pts · 1st in Western Conference |
Player | # | POS | CON | CK | FG | DI | SK | ST | EN | DU | PH | FO | PA | SC | DF | PS | EX | LD | PO | MO | OV | AGE | CONTRACT | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 0 | C | 100.00 | 63 | 36 | 87 | 77 | 82 | 85 | 75 | 72 | 74 | 76 | 73 | 67 | 75 | 67 | 69 | 79 | 75 | 0 | 24 | 4,437,500$/3yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 84 | 66 | 69 | 67 | 93 | 76 | 82 | 66 | 63 | 62 | 65 | 61 | 67 | 65 | 66 | 75 | 75 | 0 | 25 | 2,000,000$/1yrs | |||
![]() | 0 | C | 100.00 | 63 | 37 | 91 | 75 | 87 | 85 | 80 | 76 | 64 | 72 | 82 | 63 | 77 | 65 | 71 | 82 | 75 | 0 | 24 | 3,437,000$/1yrs | |||
![]() | 0 | LW/RW | 100.00 | 92 | 83 | 65 | 66 | 84 | 82 | 85 | 64 | 57 | 63 | 61 | 74 | 62 | 68 | 70 | 37 | 75 | 0 | 27 | 2,665,000$/1yrs | |||
![]() | 23 | LW | 100.00 | 76 | 74 | 71 | 73 | 80 | 88 | 61 | 75 | 72 | 79 | 81 | 68 | 76 | 79 | 92 | 68 | 75 | 0 | 31 | 9,290,000$/4yrs | |||
![]() | 0 | C | 100.00 | 68 | 37 | 89 | 72 | 85 | 84 | 81 | 71 | 79 | 70 | 67 | 64 | 72 | 65 | 68 | 85 | 75 | 0 | 25 | 2,500,000$/1yrs | |||
![]() | 0 | C | 100.00 | 64 | 33 | 88 | 77 | 67 | 90 | 72 | 78 | 74 | 76 | 73 | 64 | 79 | 58 | 68 | 94 | 75 | 0 | 19 | 950,000$/2yrs | |||
![]() | 97 | LW/RW | 100.00 | 69 | 38 | 78 | 73 | 76 | 88 | 87 | 74 | 57 | 76 | 76 | 75 | 72 | 85 | 80 | 34 | 75 | 0 | 34 | 6,500,000$/3yrs | |||
![]() | 0 | LW | 100.00 | 69 | 34 | 93 | 73 | 68 | 86 | 87 | 72 | 59 | 70 | 66 | 65 | 69 | 71 | 70 | 68 | 75 | 0 | 27 | 2,000,000$/1yrs | |||
![]() | 25 | C/LW/RW | 100.00 | 72 | 36 | 92 | 70 | 79 | 87 | 83 | 68 | 63 | 71 | 65 | 79 | 67 | 70 | 69 | 42 | 75 | 0 | 28 | 3,250,000$/2yrs | |||
![]() | 0 | D | 100.00 | 60 | 36 | 90 | 67 | 65 | 81 | 74 | 66 | 30 | 71 | 53 | 59 | 49 | 66 | 68 | 70 | 75 | 0 | 25 | 1,150,000$/1yrs | |||
![]() | 49 | D | 100.00 | 93 | 81 | 72 | 62 | 85 | 86 | 85 | 58 | 30 | 72 | 57 | 78 | 48 | 87 | 75 | 45 | 75 | 0 | 34 | 2,850,000$/2yrs | |||
![]() | 11 | D | 100.00 | 75 | 58 | 78 | 67 | 79 | 92 | 88 | 63 | 30 | 77 | 56 | 86 | 50 | 71 | 69 | 62 | 75 | 0 | 28 | 4,025,175$/1yrs | |||
![]() | 12 | D | 100.00 | 70 | 36 | 81 | 66 | 67 | 89 | 82 | 61 | 30 | 73 | 59 | 86 | 50 | 69 | 70 | 49 | 75 | 0 | 27 | 3,000,000$/1yrs | |||
Scratches | ||||||||||||||||||||||||||
![]() | 0 | C | 100.00 | 75 | 45 | 84 | 67 | 69 | 78 | 89 | 64 | 75 | 61 | 62 | 83 | 63 | 65 | 67 | 60 | 75 | 0 | 25 | 1,180,000$/1yrs | |||
![]() | 0 | LW/RW | 100.00 | 70 | 39 | 95 | 69 | 83 | 80 | 87 | 67 | 55 | 60 | 65 | 76 | 64 | 63 | 66 | 81 | 75 | 0 | 23 | 850,000$/1yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 80 | 57 | 84 | 72 | 74 | 87 | 90 | 73 | 62 | 74 | 70 | 65 | 71 | 73 | 72 | 62 | 75 | 0 | 28 | 5,000,000$/4yrs | |||
![]() | 0 | D | 100.00 | 71 | 35 | 87 | 69 | 74 | 90 | 85 | 66 | 30 | 73 | 58 | 76 | 52 | 67 | 66 | 76 | 75 | 0 | 25 | 3,100,000$/1yrs | |||
![]() | 0 | D | 100.00 | 66 | 41 | 89 | 67 | 68 | 83 | 85 | 65 | 30 | 73 | 59 | 63 | 50 | 63 | 65 | 62 | 75 | 0 | 23 | ||||
TEAM AVERAGE | 100.00 | 73 | 47 | 83 | 70 | 77 | 85 | 82 | 68 | 54 | 71 | 66 | 71 | 64 | 69 | 71 | 65 | 75 | 0 |
Priority | Type | Description |
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1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
Goalie | # | CON | SK | DU | EN | SZ | AG | RB | SC | HS | RT | PH | PS | EX | LD | PO | MO | OV | AGE | CONTRACT |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 0 | 100.00 | 93 | 82 | 77 | 89 | 92 | 91 | 93 | 92 | 91 | 93 | 92 | 70 | 81 | 54 | 75 | 0 | 28 | 4,900,000$/1yrs |
![]() | 0 | 100.00 | 78 | 83 | 79 | 82 | 77 | 76 | 78 | 77 | 76 | 78 | 77 | 63 | 71 | 84 | 75 | 0 | 23 | 4,500,000$/2yrs |
Scratches | ||||||||||||||||||||
![]() | 0 | 100.00 | 66 | 70 | 67 | 74 | 65 | 64 | 66 | 65 | 64 | 66 | 65 | 62 | 68 | 65 | 75 | 0 | 22 | 925,000$/2yrs |
![]() | 0 | 100.00 | 91 | 86 | 82 | 80 | 90 | 89 | 91 | 90 | 89 | 91 | 90 | 70 | 75 | 69 | 75 | 0 | 25 | |
TEAM AVERAGE | 100.00 | 82 | 80 | 76 | 81 | 81 | 80 | 82 | 81 | 80 | 82 | 81 | 66 | 74 | 68 | 75 | 0 |
Coaches Name | PH | DF | OF | PD | EX | LD | PO | CNT | Age | Contract | Salary |
---|---|---|---|---|---|---|---|---|---|---|---|
Kris Knoblauch | 78 | 80 | 83 | 79 | 68 | 64 | 81 | CAN | 45 | 2 | 1,150,000$ |
General Manager | Dominik Long |
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Priority | Type | Description |
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1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
# | Player Name | Team Name | # | POS | GP | G | A | P | +/- | PIM | PIM5 | HIT | SHT | OSB | OSM | SHT% | SB | MP | AMG | PPG | PPA | PPP | PPM | PKG | PKA | PKP | PKM | GW | GT | FO% | FOT | GA | TA | EG | HT | P/20 | PSG | PSS |
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Priority | Type | Description |
---|---|---|
1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
# | Goalie Name | Team Name | GP | W | L | OTL | PCT | GAA | MP | PIM | SO | GA | SA | SAR | A | EG | PS % | PSA | ST | BG | S1 | S2 | S3 |
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Projected Total Cap Hit | 68,509,632$ |
Projected Cap Space | 19,490,368$ |
Retains And Buyout Cap Hit | 0$ |
Salary Cap To Date | 0$ |
Players In Salary Cap | 23 |
LTIR Players | 0 |
Player Name | Pos | Age | Cap Hit | 2024-25 | 2025-26 | 2026-27 | 2027-28 | 2028-29 | 2029-30 | 2030-31 | 2031-32 |
---|---|---|---|---|---|---|---|---|---|---|---|
Adin Hill | D/G | 28 | 4,900,000$ | 4,900,000$ | RFA | ||||||
Aleksei Kolosov ![]() | D/G | 22 | 925,000$ | 925,000$ | 925,000$ | RFA | |||||
Alex Killorn ![]() | LW/RW | 34 | 6,500,000$ | 6,500,000$ | 6,500,000$ | 6,500,000$ | UFA | ||||
Alexandre Carrier | D | 27 | 3,000,000$ | 3,000,000$ | RFA | ||||||
Anthony Beauvillier | LW | 27 | 2,000,000$ | 2,000,000$ | RFA | ||||||
Carter Hart | C/G | 25 | 0$ | RFA | |||||||
Cody Glass | C | 25 | 2,500,000$ | 2,500,000$ | RFA | ||||||
Connor Bedard ![]() | C | 19 | 950,000$ | 950,000$ | 950,000$ | ||||||
Connor Dewar | C | 25 | 1,180,000$ | 1,180,000$ | RFA | ||||||
Filip Chytil | C/G | 24 | 4,437,500$ | 4,437,500$ | 4,437,500$ | 4,437,500$ | RFA | ||||
Gabriel Landeskog ![]() | LW | 31 | 9,290,000$ | 9,290,000$ | 9,290,000$ | 9,290,000$ | 9,290,000$ | UFA | |||
Gabriel Vilardi | C | 24 | 3,437,000$ | 3,437,000$ | RFA | ||||||
Henri Jokiharju | D | 25 | 3,100,000$ | 3,100,000$ | RFA | ||||||
Ivan Barbashev ![]() | C/LW/RW | 28 | 5,000,000$ | 5,000,000$ | 5,000,000$ | 5,000,000$ | 5,000,000$ | ||||
Jordan Spence ![]() | D | 23 | 0$ | RFA | |||||||
Klim Kostin | C/LW/RW | 25 | 2,000,000$ | 2,000,000$ | RFA | ||||||
Luke Schenn | D | 34 | 2,850,000$ | 2,850,000$ | 2,850,000$ | UFA | |||||
Marcus Pettersson | D | 28 | 4,025,175$ | 4,025,175$ | RFA | ||||||
Mason Appleton | C/LW/RW | 28 | 3,250,000$ | 3,250,000$ | 3,250,000$ | ||||||
Scott Perunovich | D | 25 | 1,150,000$ | 1,150,000$ | RFA | ||||||
Simon Holmstrom ![]() | LW/RW | 23 | 850,000$ | 850,000$ | RFA | ||||||
Spencer Knight | D/G | 23 | 4,500,000$ | 4,500,000$ | 4,500,000$ | UFA | |||||
Tanner Jeannot | LW/RW | 27 | 2,665,000$ | 2,665,000$ | RFA |
Forward Lines | |||||||
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Defensive Pairings | |||||||
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1st Power Play Unit | |||||||
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2nd Power Play Unit | |||||||
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Goalies | |||||||
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Priority | Type | Description |
---|---|---|
1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
# | VS Team | GP | W | L | T | OTW | OTL | SOW | SOL | GF | GA | Diff | P | PCT | G | A | TP | SO | EG | GP1 | GP2 | GP3 | GP4 | SHF | SH1 | SP2 | SP3 | SP4 | SHA | SHB | Pim | Hit | PPA | PPG | PP% | PKA | PK GA | PK% | PK GF | W OF FO | T OF FO | OF FO% | W DF FO | T DF FO | DF FO% | W NT FO | T NT FO | NT FO% | PZ DF | PZ OF | PZ NT | PC DF | PC OF | PC NT | GF% | SH% | SV% | PDO | PDOBRK |
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Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00% | 0 | 0 | 0.00% | 0 | 0 | 0 | 0.00% | 0 | 0 | 0.00% | 0 | 0 | 0.00% | 0 | 0 | 0 | 0 | 0 | 0 | 0.0% | 0.0% | 0.0% | 0.0 | Unlucky |
Puck Time | |
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Offensive Zone | 0 |
Neutral Zone | 0 |
Defensive Zone | 0 |
Puck Time | |
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Offensive Zone Start | 0 |
Neutral Zone Start | 0 |
Defensive Zone Start | 0 |
Puck Time | |
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With Puck | 0 |
Without Puck | 0 |
Faceoffs | |
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Faceoffs Won | 0 |
Faceoffs Lost | 0 |
Team Average Shots after | League Average Shots after | |
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1st Period | 0.0 | 9.57 |
2nd Period | 0.0 | 20.31 |
3rd Period | 0.0 | 30.68 |
Overtime | 0.0 | 31.4 |
Goals in | Team Average Goals after | League Average Goals after |
---|---|---|
1st Period | 0.0 | 0.64 |
2nd Period | 0.0 | 1.65 |
3rd Period | 0.0 | 2.67 |
Overtime | 0.0 | 2.83 |
Even Strenght Goal | 0 |
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PP Goal | 0 |
PK Goal | 0 |
Empty Net Goal | 0 |
Home | Away | |
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Win | 0 | 0 |
Lost | 0 | 0 |
Overtime Lost | 0 | 0 |
PP Attempt | 0 |
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PP Goal | 0 |
PK Attempt | 0 |
PK Goal Against | 0 |
Home | |
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Shots For | 0.0 |
Shots Against | 0.0 |
Goals For | 0.0 |
Goals Against | 0.0 |
Hits | 0.0 |
Shots Blocked | 0.0 |
Pim | 0.0 |
Salary Cap | |||
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Players Total Salaries | Retained Salary | Total Cap Hit | Estimated Cap Space |
70,309,675$ | 0$ | 68,509,632$ | 19,490,368$ |
Arena | Goal Horn | About us | |
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![]() | Name | Key Arena | |
City | Seattle | ||
Capacity | 18000 | ||
Season Ticket Holders | 35% |
Arena Capacity - Ticket Price Attendance - % | |||||
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Arena Capacity | 6000 | 5000 | 2000 | 4000 | 1000 |
Ticket Price | 125$ | 100$ | 75$ | 50$ | 200$ |
Attendance | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
Attendance PCT | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
Income | |||||
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Home Games Left | Average Attendance - % | Average Income per Game | Year to Date Revenue | Arena Capacity | Team Popularity |
41 | 0 - 0.00% | 0$ | 0$ | 18000 | 100 |
Expenses | |||
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Players Total Salaries | Players Total Average Salaries | Coaches Salaries | Special Salary Cap Value |
70,309,675$ | 70,309,675$ | 0$ | 0$ |
Year To Date Expenses | Salary Cap Per Days | Salary Cap To Date | Luxury Taxe Total |
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0$ | 356,821$ | 0$ | 0$ |
Estimate | |||
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Estimated Season Revenue | Remaining Season Days | Expenses Per Days | Estimated Season Expenses |
0$ | 192 | 372,186$ | 71,459,712$ |
Team Total Estimate | |||
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Estimated Season Expenses | Estimated Season Salary Cap | Current Bank Account | Projected Bank Account |
73,809,657$ | 68,509,632$ | 147,685,661$ | 73,876,004$ |
Left Wing | Center | Right Wing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Defense #1 | Defense #2 | Goalie | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Year | Ronde 1 | Ronde 2 | Ronde 3 | Ronde 4 | Ronde 5 | Ronde 6 | Ronde 7 |
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2025 | |||||||
2026 | |||||||
2027 | |||||||
2028 | |||||||
2029 |
Priority | Type | Description |
---|---|---|
1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
Prospect | Team Name | Draft Year | Overall Pick | Information | Lien |
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Aidan Hreschuk | 2021 | 97 | |||
Alexander Gordin | 2020 | 151 | |||
Arseni Koromyslov | 2022 | 87 | |||
Calle Odelius | 2022 | 34 | ELC 2022-23 (slide) | Link | |
Charlie Leddy | 2022 | 175 | |||
Colby Ambrosio | 2020 | 89 | |||
Daniil O.Ivanov | 2022 | 94 | |||
Dmitri Kostenko | 2021 | 76 | |||
Eamon Powell | 2020 | 62 | |||
Hugo Gabrielson | 2021 | 131 | |||
Jackson Kunz | 2021 | 163 | |||
Jacob Battaglia | 2024 | 73 | |||
Marek Vanacker | 2024 | 48 | |||
Martin Kaut | |||||
Oliver Moore | 2023 | 15 | |||
Prokhor Poltapov | 2021 | 28 | |||
Quinn Finley | 2022 | 135 | |||
Redmond Savage | 2021 | 98 | |||
Roman Kantserov | 2023 | 81 | |||
Topias Leinonen | 2022 | 88 | |||
Trevor Connelly | 2024 | 16 | |||
Vitali Kravstov | |||||
Will Skahan | 2024 | 72 | |||
Zeev Buium | 2024 | 4 |
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