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Full Scoreboard »» |
Floride Panthers 10-1-1, 21pts · 1st in Conference Est |
Player | # | POS | CON | CK | FG | DI | SK | ST | EN | DU | PH | FO | PA | SC | DF | PS | EX | LD | PO | MO | OV | AGE | CONTRACT | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 0 | RW | 100.00 | 65 | 55 | 67 | 82 | 66 | 67 | 64 | 76 | 36 | 63 | 73 | 61 | 48 | 76 | 80 | 69 | 50 | 0 | 30 | 6,500,000$/5yrs | |||
![]() | 0 | C | 100.00 | 65 | 42 | 80 | 88 | 73 | 78 | 97 | 79 | 83 | 84 | 68 | 70 | 85 | 76 | 80 | 74 | 50 | 0 | 30 | 7,800,000$/3yrs | |||
![]() | 0 | LW/RW | 100.00 | 65 | 43 | 90 | 88 | 79 | 80 | 73 | 91 | 41 | 84 | 88 | 60 | 85 | 68 | 68 | 81 | 50 | 0 | 24 | 8,700,000$/4yrs | |||
![]() | 0 | C/LW | 100.00 | 93 | 78 | 78 | 70 | 70 | 59 | 89 | 59 | 52 | 62 | 66 | 76 | 25 | 58 | 59 | 69 | 50 | 0 | 26 | 825,000$/2yrs | |||
![]() | 0 | C | 97.00 | 81 | 55 | 92 | 80 | 74 | 81 | 97 | 82 | 84 | 82 | 84 | 86 | 75 | 82 | 82 | 84 | 50 | 0 | 33 | 8,000,000$/2yrs | |||
![]() | 0 | RW | 100.00 | 57 | 41 | 88 | 83 | 73 | 63 | 99 | 67 | 36 | 72 | 70 | 64 | 58 | 92 | 92 | 70 | 50 | 0 | 34 | 1,500,000$/1yrs | |||
![]() | 0 | LW/RW | 100.00 | 99 | 99 | 55 | 73 | 84 | 56 | 98 | 58 | 33 | 58 | 58 | 69 | 25 | 72 | 73 | 64 | 50 | 0 | 31 | 1,750,000$/4yrs | |||
![]() | 0 | LW | 100.00 | 65 | 42 | 89 | 85 | 68 | 74 | 86 | 73 | 50 | 78 | 75 | 78 | 37 | 71 | 72 | 76 | 50 | 0 | 28 | 4,000,000$/3yrs | |||
![]() | 0 | C/LW | 100.00 | 76 | 55 | 85 | 85 | 82 | 66 | 96 | 61 | 83 | 60 | 66 | 82 | 37 | 71 | 72 | 71 | 50 | 0 | 28 | 3,250,000$/3yrs | |||
![]() | 0 | C/LW | 100.00 | 75 | 57 | 81 | 83 | 70 | 59 | 89 | 64 | 46 | 64 | 72 | 68 | 61 | 66 | 66 | 71 | 50 | 0 | 26 | 1,200,000$/1yrs | |||
![]() | 0 | C/RW | 100.00 | 69 | 42 | 89 | 77 | 66 | 59 | 95 | 62 | 68 | 59 | 70 | 80 | 63 | 71 | 72 | 71 | 50 | 0 | 35 | 1,250,000$/1yrs | |||
![]() | 0 | C | 100.00 | 80 | 44 | 80 | 86 | 70 | 86 | 96 | 96 | 64 | 88 | 93 | 73 | 70 | 63 | 63 | 87 | 50 | 0 | 20 | 925,000$/1yrs | |||
![]() | 0 | D | 99.00 | 89 | 93 | 58 | 80 | 83 | 75 | 64 | 60 | 25 | 64 | 49 | 90 | 25 | 70 | 71 | 66 | 50 | 0 | 30 | 4,500,000$/4yrs | |||
![]() | 0 | D | 100.00 | 76 | 44 | 92 | 70 | 75 | 70 | 71 | 59 | 25 | 52 | 50 | 80 | 25 | 64 | 64 | 62 | 50 | 0 | 29 | 750,000$/1yrs | |||
![]() | 0 | D | 100.00 | 67 | 42 | 93 | 71 | 69 | 64 | 64 | 66 | 25 | 48 | 50 | 68 | 25 | 45 | 45 | 60 | 50 | 0 | 23 | 850,000$/2yrs | |||
![]() | 0 | D | 99.00 | 85 | 57 | 87 | 80 | 82 | 75 | 82 | 58 | 25 | 50 | 50 | 94 | 25 | 70 | 72 | 66 | 50 | 0 | 30 | 3,000,000$/2yrs | |||
![]() | 0 | D | 100.00 | 78 | 44 | 84 | 77 | 75 | 72 | 83 | 60 | 25 | 49 | 48 | 89 | 25 | 65 | 68 | 63 | 50 | 0 | 32 | 2,750,000$/3yrs | |||
![]() | 0 | D | 100.00 | 90 | 68 | 83 | 75 | 75 | 70 | 76 | 60 | 25 | 55 | 48 | 84 | 25 | 59 | 59 | 65 | 50 | 0 | 25 | 900,000$/1yrs | |||
Scratches | ||||||||||||||||||||||||||
![]() | 0 | C/RW | 100.00 | 70 | 71 | 69 | 77 | 71 | 74 | 79 | 60 | 75 | 48 | 62 | 67 | 59 | 67 | 70 | 63 | 50 | 0 | 31 | 1,350,000$/1yrs | |||
![]() | 0 | C/RW | 100.00 | 93 | 59 | 87 | 80 | 76 | 62 | 77 | 57 | 72 | 54 | 57 | 78 | 25 | 70 | 72 | 66 | 50 | 0 | 27 | 1,000,000$/3yrs | |||
![]() | 0 | LW/RW | 100.00 | 77 | 73 | 87 | 63 | 73 | 67 | 69 | 61 | 50 | 56 | 61 | 65 | 58 | 46 | 46 | 64 | 50 | 0 | 26 | 750,000$/1yrs | |||
![]() | 0 | C/RW | 100.00 | 88 | 89 | 79 | 69 | 90 | 57 | 74 | 60 | 69 | 56 | 57 | 77 | 25 | 59 | 60 | 64 | 50 | 0 | 27 | 750,000$/1yrs | |||
TEAM AVERAGE | 99.77 | 77 | 59 | 82 | 78 | 75 | 69 | 83 | 67 | 50 | 63 | 64 | 75 | 45 | 67 | 68 | 69 | 50 | 0 |
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 | # | CON | SK | DU | EN | SZ | AG | RB | SC | HS | RT | PH | PS | EX | LD | PO | MO | OV | AGE | CONTRACT |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 0 | 100.00 | 69 | 69 | 67 | 84 | 76 | 62 | 83 | 69 | 67 | 68 | 78 | 56 | 56 | 70 | 50 | 0 | 25 | 1,000,000$/2yrs |
![]() | 0 | 95.00 | 81 | 60 | 60 | 78 | 86 | 76 | 70 | 80 | 85 | 81 | 79 | 63 | 64 | 77 | 50 | 0 | 28 | 1,300,000$/1yrs |
Scratches | ||||||||||||||||||||
TEAM AVERAGE | 97.50 | 75 | 65 | 64 | 81 | 81 | 69 | 77 | 75 | 76 | 75 | 79 | 60 | 60 | 74 | 50 | 0 |
Player | # | POS | CON | CK | FG | DI | SK | ST | EN | DU | PH | FO | PA | SC | DF | PS | EX | LD | PO | MO | OV | AGE | CONTRACT | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 0 | RW | 100.00 | 72 | 64 | 89 | 70 | 64 | 63 | 63 | 63 | 50 | 65 | 58 | 63 | 55 | 44 | 44 | 63 | 57 | 0 | 23 | 830,000$/1yrs | |||
![]() | 0 | D | 100.00 | 82 | 44 | 86 | 78 | 62 | 78 | 60 | 68 | 25 | 62 | 51 | 78 | 25 | 47 | 47 | 66 | 57 | 0 | 24 | 825,000$/2yrs | |||
Scratches |
Goalie | # | CON | SK | DU | EN | SZ | AG | RB | SC | HS | RT | PH | PS | EX | LD | PO | MO | OV | AGE | CONTRACT | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Scratches |
Coaches Name | PH | DF | OF | PD | EX | LD | PO | CNT | Age | Contract | Salary |
---|
General Manager | Michel Pepin |
---|
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 |
# | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Tim Stutzle | 0 | C | 12 | 7 | 10 | 17 | 11 | 18 | 0 | 22 | 47 | 18 | 25 | 14.89% | 5 | 19.33 | 0 | 3 | 3 | 12 | 0 | 0 | 0 | 0 | 2 | 1 | 43.48% | 23 | 14 | 5 | 0 | 0 | 1.47 | 2 | 3 | ||
2 | Logan Couture | 0 | C | 12 | 7 | 9 | 16 | 9 | 0 | 0 | 18 | 47 | 16 | 23 | 14.89% | 5 | 22.45 | 0 | 4 | 4 | 18 | 0 | 0 | 0 | 28 | 1 | 1 | 50.32% | 316 | 9 | 3 | 0 | 1 | 1.19 | 0 | 3 | ||
3 | Patrik Laine | 0 | LW/RW | 12 | 2 | 12 | 14 | 4 | 2 | 0 | 13 | 50 | 14 | 36 | 4.00% | 1 | 18.76 | 1 | 3 | 4 | 19 | 0 | 0 | 0 | 18 | 0 | 0 | 47.06% | 17 | 12 | 2 | 0 | 0 | 1.24 | 0 | 3 | ||
4 | Evgeny Kuznetsov | 0 | C | 12 | 9 | 2 | 11 | 4 | 2 | 0 | 12 | 32 | 10 | 26 | 28.12% | 4 | 17.99 | 3 | 0 | 3 | 13 | 0 | 0 | 0 | 18 | 4 | 0 | 49.35% | 154 | 3 | 1 | 0 | 0 | 1.02 | 0 | 3 | ||
5 | Alex Iafallo | 0 | LW | 12 | 3 | 6 | 9 | 4 | 4 | 0 | 19 | 32 | 9 | 7 | 9.38% | 4 | 16.81 | 1 | 1 | 2 | 20 | 0 | 0 | 0 | 0 | 0 | 1 | 64.29% | 14 | 6 | 3 | 0 | 0 | 0.89 | 0 | 0 | ||
6 | Brendan Gallagher | 0 | RW | 12 | 3 | 4 | 7 | 2 | 4 | 0 | 11 | 22 | 6 | 17 | 13.64% | 3 | 11.52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00% | 5 | 8 | 1 | 0 | 0 | 1.01 | 0 | 0 | ||
7 | Jan Rutta | 0 | D | 12 | 2 | 4 | 6 | 9 | 2 | 0 | 20 | 15 | 4 | 8 | 13.33% | 9 | 23.00 | 2 | 0 | 2 | 20 | 0 | 0 | 0 | 29 | 0 | 0 | 0.00% | 0 | 2 | 10 | 0 | 0 | 0.43 | 0 | 0 | ||
8 | Josh Manson | 0 | D | 12 | 1 | 3 | 4 | 2 | 31 | 15 | 24 | 16 | 4 | 7 | 6.25% | 12 | 23.45 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 11 | 1 | 0 | 0.00% | 0 | 5 | 10 | 0 | 0 | 0.28 | 0 | 0 | ||
9 | Radek Faksa | 0 | C/LW | 12 | 1 | 3 | 4 | 2 | 0 | 0 | 16 | 15 | 4 | 10 | 6.67% | 4 | 14.68 | 0 | 1 | 1 | 13 | 0 | 0 | 0 | 29 | 0 | 0 | 46.51% | 86 | 5 | 5 | 0 | 0 | 0.45 | 0 | 0 | ||
10 | Ryan Donato | 0 | C/LW | 12 | 2 | 2 | 4 | 2 | 2 | 0 | 14 | 22 | 3 | 12 | 9.09% | 2 | 11.10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58.33% | 12 | 7 | 1 | 0 | 0 | 0.60 | 0 | 0 | ||
11 | Dakota Joshua | 0 | C/LW | 12 | 2 | 1 | 3 | 1 | 2 | 0 | 12 | 17 | 4 | 3 | 11.76% | 4 | 9.69 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 100.00% | 1 | 6 | 1 | 0 | 0 | 0.52 | 0 | 0 | ||
12 | Nicolas Deslauriers | 0 | LW/RW | 9 | 0 | 3 | 3 | 1 | 2 | 0 | 7 | 11 | 2 | 4 | 0.00% | 2 | 9.52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00% | 2 | 0 | 1 | 0 | 0 | 0.70 | 0 | 0 | ||
13 | Derek Ryan | 0 | C/RW | 12 | 2 | 1 | 3 | 0 | 0 | 0 | 5 | 26 | 6 | 13 | 7.69% | 3 | 8.45 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52.50% | 40 | 5 | 0 | 0 | 0 | 0.59 | 1 | 2 | ||
14 | Scott Harrington | 0 | D | 12 | 1 | 1 | 2 | 8 | 0 | 0 | 11 | 7 | 7 | 5 | 14.29% | 14 | 20.56 | 1 | 0 | 1 | 12 | 0 | 0 | 0 | 18 | 0 | 0 | 0.00% | 0 | 0 | 5 | 0 | 0 | 0.16 | 0 | 0 | ||
15 | Derek Forbort | 0 | D | 12 | 0 | 2 | 2 | 1 | 11 | 5 | 14 | 23 | 4 | 7 | 0.00% | 18 | 25.95 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 27 | 0 | 0 | 0.00% | 0 | 1 | 12 | 0 | 0 | 0.13 | 0 | 0 | ||
16 | William Borgen | 0 | D | 12 | 0 | 2 | 2 | 6 | 16 | 10 | 12 | 5 | 3 | 2 | 0.00% | 7 | 13.35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00% | 0 | 0 | 5 | 0 | 0 | 0.25 | 0 | 0 | ||
17 | Phil Kessel | 0 | RW | 12 | 0 | 1 | 1 | 11 | 2 | 0 | 7 | 14 | 7 | 14 | 0.00% | 4 | 18.15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33.33% | 15 | 2 | 4 | 0 | 0 | 0.09 | 0 | 3 | ||
18 | Jack Rathbone | 0 | D | 12 | 0 | 1 | 1 | 7 | 2 | 0 | 5 | 4 | 4 | 3 | 0.00% | 9 | 15.33 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 12 | 0 | 0 | 0.00% | 0 | 0 | 3 | 0 | 0 | 0.11 | 0 | 0 | ||
19 | Curtis Lazar | 0 | C/RW | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0.00% | 0 | 9.37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100.00% | 1 | 1 | 0 | 0 | 0 | 0.00 | 0 | 0 | ||
Team Total or Average | 216 | 42 | 67 | 109 | 84 | 100 | 30 | 244 | 408 | 126 | 222 | 10.29% | 110 | 16.67 | 8 | 12 | 20 | 167 | 0 | 0 | 0 | 199 | 8 | 3 | 49.13% | 686 | 86 | 72 | 0 | 1 | 0.61 | 3 | 17 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Joonas Korpisalo | 8 | 7 | 0 | 1 | 0.922 | 2.22 | 487 | 0 | 0 | 18 | 231 | 104 | 2 | 0 | 0.833 | 6 | 8 | 4 | 0 | 2 | 0 | |
2 | Samuel Montembeault | 4 | 3 | 1 | 0 | 0.915 | 2.40 | 250 | 0 | 0 | 10 | 118 | 68 | 0 | 0 | 0.909 | 11 | 4 | 8 | 0 | 0 | 0 | |
Team Total or Average | 12 | 10 | 1 | 1 | 0.920 | 2.28 | 737 | 0 | 0 | 28 | 349 | 172 | 2 | 0 | 0.882 | 17 | 12 | 12 | 0 | 2 | 0 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 9 | 5 | 4 | 4 | 1.000 | 9 | 12 | 21 | 0 | 0 | 3 | 0 | 5 | 1 | 67 | 17 | 21 | 29 | 5 | 60 | 27 | 10 | 37 | 2 | 0 | 0.00% | 5 | 2 | 60.00% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 37 | 21 | 49 | 19 | 37 | 17 | 75.0% | 13.4% | 91.7% | 105.1 | LUCKY | |
2 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 3 | 3 | 4 | 1.000 | 6 | 9 | 15 | 0 | 0 | 1 | 1 | 3 | 2 | 62 | 25 | 15 | 22 | 6 | 55 | 20 | 21 | 29 | 1 | 1 | 100.00% | 8 | 3 | 62.50% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 41 | 23 | 44 | 19 | 38 | 19 | 100.0% | 9.7% | 94.5% | 104.2 | LUCKY | |
3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 6 | 1 | 3 | 0.750 | 7 | 11 | 18 | 0 | 0 | 0 | 4 | 3 | 0 | 71 | 14 | 30 | 27 | 6 | 56 | 14 | 20 | 55 | 5 | 3 | 60.00% | 5 | 0 | 100.00% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 43 | 24 | 42 | 19 | 39 | 19 | 40.0% | 9.9% | 89.3% | 99.1 | FUN | |
4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 3 | 5 | 4 | 1.000 | 8 | 11 | 19 | 0 | 0 | 3 | 1 | 4 | 0 | 69 | 30 | 21 | 18 | 0 | 52 | 10 | 15 | 51 | 6 | 2 | 33.33% | 5 | 2 | 60.00% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 46 | 26 | 36 | 18 | 37 | 17 | 85.7% | 11.6% | 94.2% | 105.8 | LUCKY | |
5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 1 | 2 | 1.000 | 4 | 7 | 11 | 0 | 0 | 0 | 2 | 2 | 0 | 35 | 10 | 10 | 15 | 0 | 32 | 14 | 6 | 18 | 3 | 1 | 33.33% | 3 | 2 | 33.33% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 20 | 12 | 21 | 8 | 17 | 8 | 75.0% | 11.4% | 90.6% | 102.1 | FUN | |
6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 2 | 2 | 1.000 | 4 | 7 | 11 | 0 | 0 | 1 | 1 | 2 | 0 | 36 | 17 | 15 | 4 | 0 | 25 | 6 | 6 | 14 | 4 | 1 | 25.00% | 3 | 1 | 66.67% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 21 | 13 | 21 | 9 | 17 | 8 | 75.0% | 11.1% | 92.0% | 103.1 | LUCKY | |
7 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 6 | 7 | -1 | 2 | 0.500 | 6 | 10 | 16 | 0 | 0 | 2 | 3 | 0 | 1 | 68 | 25 | 22 | 14 | 7 | 69 | 19 | 22 | 40 | 4 | 0 | 0.00% | 6 | 1 | 83.33% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 41 | 24 | 45 | 19 | 35 | 16 | 50.0% | 8.8% | 89.9% | 98.7 | Unlucky | |
_Vs Division | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY | |
_Vs Conference | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY | |
_Since Last GM Reset | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY | |
Total | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY |
Puck Time | |
---|---|
Offensive Zone | 21 |
Neutral Zone | 18 |
Defensive Zone | 21 |
Puck Time | |
---|---|
Offensive Zone Start | 237 |
Neutral Zone Start | 180 |
Defensive Zone Start | 263 |
Puck Time | |
---|---|
With Puck | 30 |
Without Puck | 30 |
Faceoffs | |
---|---|
Faceoffs Won | 337 |
Faceoffs Lost | 343 |
Team Average Shots after | League Average Shots after | |
---|---|---|
1st Period | 11.5 | 9.57 |
2nd Period | 22.7 | 20.31 |
3rd Period | 33.4 | 30.68 |
Overtime | 35.4 | 31.4 |
Goals in | Team Average Goals after | League Average Goals after |
---|---|---|
1st Period | 0.8 | 0.64 |
2nd Period | 1.8 | 1.65 |
3rd Period | 3.4 | 2.67 |
Overtime | 3.8 | 2.83 |
Even Strenght Goal | 36 |
---|---|
PP Goal | 8 |
PK Goal | 0 |
Empty Net Goal | 0 |
Home | Away | |
---|---|---|
Win | 5 | 5 |
Lost | 1 | 0 |
Overtime Lost | 0 | 1 |
PP Attempt | 25 |
---|---|
PP Goal | 8 |
PK Attempt | 35 |
PK Goal Against | 11 |
Home | |
---|---|
Shots For | 34.0 |
Shots Against | 29.1 |
Goals For | 3.7 |
Goals Against | 2.4 |
Hits | 20.3 |
Shots Blocked | 9.2 |
Pim | 8.3 |
Projected Total Cap Hit | 0$ |
Projected Cap Space | 82,500,000$ |
Retains And Buyout Cap Hit | 0$ |
Salary Cap To Date | 0$ |
Players In Salary Cap | 24 |
LTIR Players | 0 |
Panthers Roster | Pos | Age | Cap Hit | 2023-24 | 2024-25 | 2025-26 | 2026-27 | 2027-28 | 2028-29 | 2029-30 | 2030-31 |
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Alex Iafallo ![]() | LW | 28 | 4,000,000$ | 4,000,000$ | 4,000,000$ | 4,000,000$ | |||||
Brendan Gallagher ![]() | RW | 30 | 6,500,000$ | 6,500,000$ | 6,500,000$ | 6,500,000$ | 6,500,000$ | 6,500,000$ | |||
Chris Wagner ![]() | C/RW | 31 | 1,350,000$ | 1,350,000$ | |||||||
Curtis Lazar ![]() | C/RW | 27 | 1,000,000$ | 1,000,000$ | 1,000,000$ | 1,000,000$ | |||||
Dakota Joshua ![]() | C/LW | 26 | 825,000$ | 825,000$ | 825,000$ | ||||||
Derek Forbort ![]() | D | 30 | 3,000,000$ | 3,000,000$ | 3,000,000$ | ||||||
Derek Ryan ![]() | C/RW | 35 | 1,250,000$ | 1,250,000$ | |||||||
Evgeny Kuznetsov ![]() | C | 30 | 7,800,000$ | 7,800,000$ | 7,800,000$ | 7,800,000$ | |||||
Jack Rathbone ![]() | D | 23 | 850,000$ | 850,000$ | 850,000$ | ||||||
Jan Rutta ![]() | D | 32 | 2,750,000$ | 2,750,000$ | 2,750,000$ | 2,750,000$ | |||||
Joonas Korpisalo ![]() | G | 28 | 1,300,000$ | 1,300,000$ | |||||||
Josh Manson ![]() | D | 30 | 4,500,000$ | 4,500,000$ | 4,500,000$ | 4,500,000$ | 4,500,000$ | ||||
Justin Kirkland ![]() | LW/RW | 26 | 750,000$ | 750,000$ | |||||||
Logan Couture ![]() | C | 33 | 8,000,000$ | 8,000,000$ | 8,000,000$ | ||||||
Michael McCarron ![]() | C/RW | 27 | 750,000$ | 750,000$ | |||||||
Nicolas Deslauriers ![]() | LW/RW | 31 | 1,750,000$ | 1,750,000$ | 1,750,000$ | 1,750,000$ | 1,750,000$ | ||||
Patrik Laine ![]() | LW/RW | 24 | 8,700,000$ | 8,700,000$ | 8,700,000$ | 8,700,000$ | 8,700,000$ | ||||
Phil Kessel ![]() | RW | 34 | 1,500,000$ | 1,500,000$ | |||||||
Radek Faksa ![]() | C/LW | 28 | 3,250,000$ | 3,250,000$ | 3,250,000$ | 3,250,000$ | |||||
Ryan Donato ![]() | C/LW | 26 | 1,200,000$ | 1,200,000$ | |||||||
Samuel Montembeault ![]() | G | 25 | 1,000,000$ | 1,000,000$ | 1,000,000$ | ||||||
Scott Harrington ![]() | D | 29 | 750,000$ | 750,000$ | |||||||
Tim Stutzle ![]() | C | 20 | 925,000$ | 925,000$ | |||||||
William Borgen ![]() | D | 25 | 900,000$ | 900,000$ |
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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1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 9 | 5 | 4 | 4 | 1.000 | 9 | 12 | 21 | 0 | 0 | 3 | 0 | 5 | 1 | 67 | 17 | 21 | 29 | 5 | 60 | 27 | 10 | 37 | 2 | 0 | 0.00% | 5 | 2 | 60.00% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 37 | 21 | 49 | 19 | 37 | 17 | 75.0% | 13.4% | 91.7% | 105.1 | LUCKY | |
2 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 3 | 3 | 4 | 1.000 | 6 | 9 | 15 | 0 | 0 | 1 | 1 | 3 | 2 | 62 | 25 | 15 | 22 | 6 | 55 | 20 | 21 | 29 | 1 | 1 | 100.00% | 8 | 3 | 62.50% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 41 | 23 | 44 | 19 | 38 | 19 | 100.0% | 9.7% | 94.5% | 104.2 | LUCKY | |
3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 6 | 1 | 3 | 0.750 | 7 | 11 | 18 | 0 | 0 | 0 | 4 | 3 | 0 | 71 | 14 | 30 | 27 | 6 | 56 | 14 | 20 | 55 | 5 | 3 | 60.00% | 5 | 0 | 100.00% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 43 | 24 | 42 | 19 | 39 | 19 | 40.0% | 9.9% | 89.3% | 99.1 | FUN | |
4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 3 | 5 | 4 | 1.000 | 8 | 11 | 19 | 0 | 0 | 3 | 1 | 4 | 0 | 69 | 30 | 21 | 18 | 0 | 52 | 10 | 15 | 51 | 6 | 2 | 33.33% | 5 | 2 | 60.00% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 46 | 26 | 36 | 18 | 37 | 17 | 85.7% | 11.6% | 94.2% | 105.8 | LUCKY | |
5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 1 | 2 | 1.000 | 4 | 7 | 11 | 0 | 0 | 0 | 2 | 2 | 0 | 35 | 10 | 10 | 15 | 0 | 32 | 14 | 6 | 18 | 3 | 1 | 33.33% | 3 | 2 | 33.33% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 20 | 12 | 21 | 8 | 17 | 8 | 75.0% | 11.4% | 90.6% | 102.1 | FUN | |
6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 2 | 2 | 1.000 | 4 | 7 | 11 | 0 | 0 | 1 | 1 | 2 | 0 | 36 | 17 | 15 | 4 | 0 | 25 | 6 | 6 | 14 | 4 | 1 | 25.00% | 3 | 1 | 66.67% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 21 | 13 | 21 | 9 | 17 | 8 | 75.0% | 11.1% | 92.0% | 103.1 | LUCKY | |
7 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 6 | 7 | -1 | 2 | 0.500 | 6 | 10 | 16 | 0 | 0 | 2 | 3 | 0 | 1 | 68 | 25 | 22 | 14 | 7 | 69 | 19 | 22 | 40 | 4 | 0 | 0.00% | 6 | 1 | 83.33% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 41 | 24 | 45 | 19 | 35 | 16 | 50.0% | 8.8% | 89.9% | 98.7 | Unlucky | |
_Vs Division | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY | |
_Vs Conference | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY | |
_Since Last GM Reset | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY | |
Total | 12 | 7 | 1 | 0 | 1 | 0 | 2 | 1 | 44 | 29 | 15 | 21 | 0.875 | 44 | 67 | 111 | 0 | 0 | 10 | 12 | 19 | 4 | 408 | 138 | 134 | 129 | 24 | 349 | 110 | 100 | 244 | 25 | 8 | 32.00% | 35 | 11 | 68.57% | 0 | 110 | 237 | 46.41% | 139 | 263 | 52.85% | 88 | 180 | 48.89% | 253 | 145 | 260 | 114 | 223 | 107 | 66.7% | 10.8% | 91.7% | 102.5 | LUCKY |
Puck Time | |
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Offensive Zone | 21 |
Neutral Zone | 18 |
Defensive Zone | 21 |
Puck Time | |
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Offensive Zone Start | 237 |
Neutral Zone Start | 180 |
Defensive Zone Start | 263 |
Puck Time | |
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With Puck | 30 |
Without Puck | 30 |
Faceoffs | |
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Faceoffs Won | 337 |
Faceoffs Lost | 343 |
Team Average Shots after | League Average Shots after | |
---|---|---|
1st Period | 11.5 | 9.57 |
2nd Period | 22.7 | 20.31 |
3rd Period | 33.4 | 30.68 |
Overtime | 35.4 | 31.4 |
Goals in | Team Average Goals after | League Average Goals after |
---|---|---|
1st Period | 0.8 | 0.64 |
2nd Period | 1.8 | 1.65 |
3rd Period | 3.4 | 2.67 |
Overtime | 3.8 | 2.83 |
Even Strenght Goal | 36 |
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PP Goal | 8 |
PK Goal | 0 |
Empty Net Goal | 0 |
Home | Away | |
---|---|---|
Win | 5 | 5 |
Lost | 1 | 0 |
Overtime Lost | 0 | 1 |
PP Attempt | 25 |
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PP Goal | 8 |
PK Attempt | 35 |
PK Goal Against | 11 |
Home | |
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Shots For | 34.0 |
Shots Against | 29.1 |
Goals For | 3.7 |
Goals Against | 2.4 |
Hits | 20.3 |
Shots Blocked | 9.2 |
Pim | 8.3 |
Date | Matchup | Result | Detail | ||
---|---|---|---|---|---|
2023-08-12 | @ | Panthers4,Lightning1 | RECAP | ||
2023-08-13 | @ | Sabres2,Panthers4 | RECAP | ||
2023-08-15 | @ | Canadiens1,Panthers3 | RECAP | ||
2023-08-17 | @ | Red Wings2,Panthers4 | RECAP | ||
2023-08-19 | @ | Panthers3,Red Wings4 (SO) | RECAP | ||
2023-08-22 | @ | Bruins3,Panthers4 (SO) | RECAP | ||
2023-08-23 | @ | Panthers3,Canadiens2 (SO) | RECAP | ||
2023-08-24 | @ | Panthers5,Bruins2 | RECAP | ||
2023-08-25 | @ | Senators3,Panthers1 | RECAP | ||
2023-08-28 | @ | Lightning2,Panthers4 | RECAP | ||
2023-08-29 | @ | Panthers4,Maple Leafs3 | RECAP | ||
2023-08-30 | @ | Panthers5,Senators4 (OT) | RECAP | ||
2023-08-31 | @ | ||||
2023-09-02 | @ |
Salary Cap | |||
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Players Total Salaries | Retained Salary | Total Cap Hit | Estimated Cap Space |
64,600,000$ | 0$ | 0$ | 82,500,000$ |
Arena | Goal Horn | About us | |
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![]() | Name | FLA Live Arena | |
City | Floride | ||
Capacity | 18000 | ||
Season Ticket Holders | 40% |
Arena Capacity - Ticket Price Attendance - % | |||||
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Arena Capacity | 6000 | 5000 | 2000 | 4000 | 1000 |
Ticket Price | 100$ | 60$ | 35$ | 25$ | 200$ |
Attendance | 0 | 0 | 0 | 0 | 0 |
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 |
1 | 0 - 0.00% | 0$ | 0$ | 18000 | 100 |
Expenses | |||
---|---|---|---|
Players Total Salaries | Players Total Average Salaries | Coaches Salaries | Special Salary Cap Value |
64,600,000$ | 64,600,000$ | 0$ | 0$ |
Year To Date Expenses | Salary Cap Per Days | Salary Cap To Date | Luxury Taxe Total |
---|---|---|---|
0$ | 0$ | 0$ | 0$ |
Estimate | |||
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Estimated Season Revenue | Remaining Season Days | Expenses Per Days | Estimated Season Expenses |
0$ | 4 | 0$ | 0$ |
Team Total Estimate | |||
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Estimated Season Expenses | Estimated Season Salary Cap | Current Bank Account | Projected Bank Account |
0$ | 0$ | -477,014,135$ | -477,014,135$ |
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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2024 | |||||||
2025 | |||||||
2026 | |||||||
2027 | |||||||
2028 |
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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1 ballo flo 2023 | |||||
Aaron Irving | |||||
Adam Wilsby | 2020 | 114 | |||
Andrei Altybarmakyan | |||||
Andrew Hammond | Link | ||||
Brinson Pasichnuk | Link | ||||
Caedan Bankier | 2021 | 90 | |||
Chris Driedger | Link | ||||
Colin Wilson | |||||
Cooper Moore | 2019 | 139 | |||
Daniel Laatsch | 2021 | 219 | |||
Danny Nelson | 2023 | 73 | |||
Dennis Busby | 2018 | 148 | |||
Dmitri Kuzmin | 2021 | 91 | |||
Dmitri Rashevsky | 2021 | 155 | |||
Drew Commesso | 2020 | 39 | |||
Graham Sward | 2022 | 155 | |||
Hoyt Stanley | 2023 | 122 | |||
Jack Kopacka | 2016 | 92 | |||
Jiri Felcman | 2023 | 110 | |||
Joshua Roy | 2021 | 128 | |||
Justin Robidas | 2021 | 118 | |||
Karlis Cukste | |||||
Kasper Kotkansalo | |||||
Leon Gawanke | |||||
Linus Nassen | 2016 | 103 | |||
Luca Sbisa | Link | ||||
Luke Devlin | 2022 | 187 | |||
Luke Henman | 2018 | 104 | |||
Martin Frk | Link | ||||
Matej Pekar | 2018 | 87 | |||
Mattias Havelid | 2022 | 59 | |||
Max Zimmer | 2016 | 117 | |||
Maxim Denezhkin | 2019 | 201 | |||
Maxim Strbak | 2023 | 69 | |||
Michael Fisher | 2022 | 91 | |||
Miska Kukkonen | 2018 | 106 | |||
Mitch Reinke | |||||
Mitchell Heard | |||||
Nate Schnarr | |||||
Niklas Hansson | |||||
Niklas Hjalmarsson | Link | ||||
Nikolaj Krag Christensen | 2016 | 207 | |||
Noah Carroll | 2016 | 177 | |||
Noah Laba | 2022 | 123 | |||
Patrick Russell | Link | ||||
Patrick Sanvido | |||||
Petter Hansson | |||||
Reese Laubach | 2022 | 219 | |||
Rocco Grimaldi | Link | ||||
Sergei Boikov | |||||
Sergey Zborovskiy | |||||
Sutter Muzzatti | 2023 | 154 | |||
Ty Taylor | 2018 | 210 | |||
Ville Rasanen | |||||
William Smith | 2023 | 5 | |||
Yegor Rykov | 2016 | 147 | |||
Zachary Emond | 2018 | 179 | |||
Zachary Nagelvoort | |||||
daniel oregan |
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