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San Jose Sharks 0-0-0, 0pts · 3rd 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 | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 62 | LW/RW | 100.00 | 72 | 34 | 88 | 76 | 68 | 90 | 80 | 75 | 54 | 78 | 78 | 80 | 71 | 73 | 72 | 57 | 75 | 0 | 29 | 4,500,000$/3yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 68 | 35 | 92 | 73 | 75 | 87 | 90 | 79 | 63 | 76 | 82 | 64 | 80 | 82 | 76 | 44 | 75 | 0 | 32 | 7,500,000$/4yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 60 | 38 | 84 | 68 | 67 | 85 | 77 | 66 | 69 | 70 | 64 | 62 | 68 | 72 | 73 | 39 | 75 | 0 | 30 | 775,000$/2yrs | |||
![]() | 0 | LW | 100.00 | 76 | 39 | 94 | 65 | 83 | 80 | 84 | 64 | 61 | 63 | 62 | 73 | 61 | 72 | 70 | 36 | 75 | 0 | 29 | 950,000$/1yrs | |||
![]() | 0 | LW/RW | 100.00 | 71 | 38 | 86 | 66 | 69 | 77 | 86 | 63 | 61 | 62 | 60 | 72 | 63 | 69 | 70 | 36 | 75 | 0 | 28 | 775,000$/1yrs | |||
![]() | 0 | LW/RW | 100.00 | 64 | 37 | 92 | 74 | 73 | 81 | 82 | 72 | 56 | 68 | 70 | 62 | 76 | 70 | 71 | 72 | 75 | 0 | 28 | 1,900,000$/2yrs | |||
![]() | 55 | C | 100.00 | 67 | 57 | 79 | 82 | 85 | 91 | 89 | 81 | 78 | 79 | 83 | 67 | 82 | 80 | 78 | 68 | 75 | 0 | 31 | 8,500,000$/7yrs | |||
![]() | 0 | C | 100.00 | 75 | 44 | 89 | 65 | 90 | 79 | 88 | 64 | 62 | 61 | 63 | 80 | 64 | 66 | 69 | 47 | 75 | 0 | 25 | 1,350,000$/1yrs | |||
![]() | 17 | C | 100.00 | 80 | 77 | 79 | 68 | 91 | 86 | 90 | 67 | 80 | 72 | 65 | 89 | 63 | 79 | 88 | 52 | 75 | 0 | 31 | 3,250,000$/2yrs | |||
![]() | 88 | RW | 100.00 | 70 | 38 | 84 | 88 | 74 | 92 | 87 | 89 | 57 | 86 | 93 | 66 | 91 | 75 | 74 | 67 | 75 | 0 | 28 | 11,250,000$/7yrs | |||
![]() | 0 | LW/RW | 100.00 | 69 | 38 | 94 | 74 | 78 | 84 | 77 | 72 | 63 | 69 | 71 | 65 | 74 | 63 | 65 | 81 | 75 | 0 | 23 | 2,100,000$/2yrs | |||
![]() | 71 | C | 100.00 | 65 | 34 | 94 | 77 | 72 | 88 | 89 | 76 | 86 | 78 | 75 | 85 | 76 | 78 | 73 | 55 | 75 | 0 | 31 | 5,900,000$/3yrs | |||
![]() | 3 | D | 100.00 | 71 | 40 | 83 | 67 | 85 | 91 | 82 | 62 | 30 | 75 | 60 | 90 | 51 | 73 | 71 | 53 | 75 | 0 | 29 | 4,250,000$/3yrs | |||
![]() | 0 | D | 100.00 | 89 | 68 | 70 | 64 | 73 | 89 | 83 | 59 | 30 | 63 | 56 | 76 | 50 | 69 | 70 | 40 | 75 | 0 | 29 | 1,100,000$/2yrs | |||
![]() | 0 | D | 100.00 | 73 | 34 | 84 | 74 | 71 | 93 | 87 | 72 | 30 | 83 | 66 | 76 | 56 | 69 | 71 | 60 | 75 | 0 | 26 | 7,250,000$/8yrs | |||
![]() | 74 | D | 100.00 | 65 | 37 | 87 | 67 | 72 | 88 | 75 | 64 | 30 | 72 | 58 | 75 | 52 | 68 | 70 | 50 | 75 | 0 | 27 | 2,062,500$/1yrs | |||
![]() | 0 | D | 100.00 | 73 | 37 | 85 | 67 | 84 | 90 | 86 | 63 | 30 | 72 | 56 | 84 | 49 | 69 | 70 | 59 | 75 | 0 | 27 | 4,000,000$/4yrs | |||
![]() | 73 | D | 100.00 | 76 | 39 | 71 | 77 | 81 | 94 | 87 | 76 | 30 | 85 | 71 | 83 | 58 | 70 | 75 | 71 | 75 | 0 | 26 | 9,500,000$/6yrs | |||
Scratches | ||||||||||||||||||||||||||
![]() | 57 | RW | 100.00 | 56 | 37 | 83 | 64 | 67 | 89 | 87 | 65 | 62 | 63 | 66 | 61 | 64 | 69 | 71 | 36 | 75 | 0 | 28 | ||||
![]() | 47 | D | 100.00 | 68 | 33 | 87 | 65 | 67 | 87 | 75 | 62 | 30 | 71 | 54 | 65 | 49 | 90 | 80 | 26 | 75 | 0 | 38 | ||||
TEAM AVERAGE | 100.00 | 70 | 42 | 85 | 71 | 76 | 87 | 84 | 70 | 53 | 72 | 68 | 74 | 65 | 73 | 73 | 52 | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 35 | 100.00 | 98 | 87 | 82 | 89 | 97 | 96 | 98 | 97 | 96 | 98 | 97 | 76 | 85 | 54 | 75 | 0 | 30 | 5,000,000$/1yrs |
![]() | 0 | 100.00 | 88 | 79 | 74 | 98 | 87 | 86 | 88 | 87 | 86 | 88 | 87 | 71 | 84 | 59 | 75 | 0 | 30 | 2,500,000$/2yrs |
Scratches | ||||||||||||||||||||
![]() | 0 | 100.00 | 75 | 81 | 75 | 84 | 74 | 73 | 75 | 74 | 73 | 75 | 74 | 75 | 88 | 47 | 75 | 0 | 34 | |
TEAM AVERAGE | 100.00 | 87 | 82 | 77 | 90 | 86 | 85 | 87 | 86 | 85 | 87 | 86 | 74 | 86 | 53 | 75 | 0 |
Coaches Name | PH | DF | OF | PD | EX | LD | PO | CNT | Age | Contract | Salary |
---|---|---|---|---|---|---|---|---|---|---|---|
Mike Sullivan | 89 | 85 | 88 | 87 | 84 | 78 | 64 | USA | 56 | 3 | 5,000,000$ |
General Manager | Francois Burelle |
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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 |
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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 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 | 87,095,765$ |
Projected Cap Space | 904,235$ |
Retains And Buyout Cap Hit | 2,333,333$ |
Salary Cap To Date | 0$ |
Players In Salary Cap | 25 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|
Adam Lowry | C | 31 | 3,250,000$ | 3,250,000$ | 3,250,000$ | UFA | |||||
Adam Pelech ![]() | D | 29 | 4,250,000$ | 4,250,000$ | 4,250,000$ | 4,250,000$ | |||||
Alex Goligoski ![]() | D | 38 | 0$ | UFA | |||||||
Anthony Stolarz ![]() | D/G | 30 | 2,500,000$ | 2,500,000$ | 2,500,000$ | RFA | |||||
Artturi Lehkonen ![]() | LW/RW/G | 29 | 4,500,000$ | 4,500,000$ | 4,500,000$ | 4,500,000$ | |||||
Charlie McAvoy ![]() | D | 26 | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | ||
David Pastrnak ![]() | RW | 28 | 11,250,000$ | 11,250,000$ | 11,250,000$ | 11,250,000$ | 11,250,000$ | 11,250,000$ | 11,250,000$ | 11,250,000$ | UFA |
Eric Robinson | LW | 29 | 950,000$ | 950,000$ | |||||||
Ethan Bear | D | 27 | 2,062,500$ | 2,062,500$ | RFA | ||||||
Filip Hronek | D | 26 | 7,250,000$ | 7,250,000$ | 7,250,000$ | 7,250,000$ | 7,250,000$ | 7,250,000$ | 7,250,000$ | 7,250,000$ | 7,250,000$ |
Joel Kiviranta ![]() | LW/RW | 28 | 775,000$ | 775,000$ | RFA | ||||||
Jonas Siegenthaler ![]() | D | 27 | 4,000,000$ | 4,000,000$ | 4,000,000$ | 4,000,000$ | 4,000,000$ | ||||
Kyle Burroughs | D | 29 | 1,100,000$ | 1,100,000$ | 1,100,000$ | ||||||
Linus Ullmark ![]() | D/G | 30 | 5,000,000$ | 5,000,000$ | |||||||
Mark Scheifele ![]() | C | 31 | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | UFA |
Michael Hutchinson ![]() | LW/G | 34 | 0$ | UFA | |||||||
Morgan Barron ![]() | C | 25 | 1,350,000$ | 1,350,000$ | RFA | ||||||
Sheldon Rempal ![]() | RW | 28 | 0$ | RFA | |||||||
Sonny Milano | LW/RW | 28 | 1,900,000$ | 1,900,000$ | 1,900,000$ | ||||||
Tyler Toffoli | C/LW/RW/G | 32 | 7,500,000$ | 7,500,000$ | 7,500,000$ | 7,500,000$ | 7,500,000$ | UFA | |||
Vinnie Hinostroza | C/LW/RW | 30 | 775,000$ | 775,000$ | 775,000$ | ||||||
William Karlsson ![]() | C | 31 | 5,900,000$ | 5,900,000$ | 5,900,000$ | 5,900,000$ | UFA | ||||
Yegor Chinakhov | LW/RW | 23 | 2,100,000$ | 2,100,000$ | 2,100,000$ | RFA |
Forward Lines | |||||||
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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 |
89,637,500$ | 2,333,333$ | 87,095,765$ | 904,235$ |
Arena | Goal Horn | About us | |
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![]() | Name | SAP Center at San Jose | |
City | San Jose | ||
Capacity | 17562 | ||
Season Ticket Holders | 35% |
Arena Capacity - Ticket Price Attendance - % | |||||
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Arena Capacity | 6000 | 5000 | 2000 | 3562 | 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$ | 17562 | 95 |
Expenses | |||
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Players Total Salaries | Players Total Average Salaries | Coaches Salaries | Special Salary Cap Value |
89,637,500$ | 89,637,500$ | 0$ | 2,333,333$ |
Year To Date Expenses | Salary Cap Per Days | Salary Cap To Date | Luxury Taxe Total |
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0$ | 441,471$ | 0$ | 0$ |
Estimate | |||
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Estimated Season Revenue | Remaining Season Days | Expenses Per Days | Estimated Season Expenses |
0$ | 192 | 492,904$ | 94,637,568$ |
Team Total Estimate | |||
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Estimated Season Expenses | Estimated Season Salary Cap | Current Bank Account | Projected Bank Account |
97,053,423$ | 87,095,765$ | 166,692,474$ | 69,639,051$ |
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 Thompson | 2023 | 125 | |||
Alex Young | 2021 | 216 | |||
Alexander Perevalov | 2022 | 36 | |||
Andre Gasseau | 2021 | 184 | |||
Cameron O'Neill | 2023 | 221 | |||
Cole O'Hara | 2023 | 223 | |||
Eric Engstrand | 2020 | 186 | |||
Eric Hjorth | 2019 | 112 | |||
Hugo Ollas | 2021 | 206 | ELC 2024-25 | Link | |
Jake Livanavage | 2022 | 160 | |||
Jamiro Reber | 2024 | 186 | |||
Jordan Gustafson | 2023 | 184 | Link | ||
Justin Coté | 2022 | 188 | |||
Justin Kipkie | 2023 | 189 | |||
Kenny Connors | 2023 | 182 | |||
Kyle Crnkovic | 2020 | 155 | |||
Luke Mittlestadt | 2023 | 102 | |||
Markus Vidicek | 2022 | 120 | |||
Noah Laba | 2023 | 215 | |||
Owen Lindmark | 2019 | 143 | |||
Roddy Ross | 2019 | 174 | |||
Shamil Shmakov | |||||
Simon Kjellberg | 2018 | 167 | |||
Simon Zether | 2024 | 63 |
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