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Full Scoreboard ยป |
Dallas Stars 14-18-6, 34pts · 11th in Western Conference |
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 | # | 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 | 66 | 15 | 93 | 91 | 85 | 98 | 94 | 93 | 90 | 95 | 94 | 68 | 83 | 78 | 79 | 76 | 43 | 83 | 30 | 12,500,000$/4yrs | |||
![]() | 0 | C/RW | 100.00 | 62 | 17 | 95 | 85 | 72 | 82 | 93 | 89 | 85 | 94 | 82 | 80 | 53 | 75 | 76 | 73 | 43 | 79 | 30 | 11,000,000$/3yrs | |||
![]() | 0 | LW | 100.00 | 31 | 1 | 99 | 96 | 58 | 96 | 98 | 96 | 31 | 99 | 98 | 43 | 43 | 73 | 74 | 78 | 43 | 79 | 30 | 8,250,000$/4yrs | |||
![]() | 0 | LW/RW | 100.00 | 91 | 8 | 86 | 87 | 58 | 67 | 64 | 72 | 16 | 92 | 92 | 67 | 25 | 58 | 58 | 71 | 43 | 72 | 24 | 3,500,000$/3yrs | |||
![]() | 0 | LW/RW | 100.00 | 62 | 13 | 99 | 85 | 68 | 62 | 76 | 77 | 28 | 70 | 91 | 68 | 25 | 65 | 65 | 67 | 43 | 71 | 28 | 2,500,000$/2yrs | |||
![]() | 0 | C/LW | 100.00 | 31 | 1 | 99 | 82 | 69 | 78 | 80 | 82 | 85 | 83 | 89 | 56 | 25 | 58 | 58 | 61 | 43 | 71 | 25 | 3,500,000$/3yrs | |||
![]() | 0 | LW | 100.00 | 62 | 24 | 63 | 79 | 71 | 78 | 86 | 62 | 46 | 76 | 65 | 69 | 75 | 77 | 78 | 66 | 46 | 69 | 33 | 685,000$/1yrs | |||
![]() | 0 | LW/RW | 100.00 | 47 | 23 | 99 | 84 | 70 | 78 | 77 | 75 | 47 | 70 | 75 | 61 | 67 | 70 | 70 | 59 | 43 | 69 | 30 | 685,000$/1yrs | |||
![]() | 0 | C/RW | 100.00 | 60 | 4 | 99 | 75 | 72 | 92 | 62 | 64 | 86 | 55 | 65 | 81 | 63 | 82 | 84 | 67 | 43 | 68 | 34 | 900,000$/3yrs | |||
![]() | 0 | C/LW | 100.00 | 54 | 14 | 99 | 80 | 79 | 79 | 99 | 58 | 85 | 60 | 59 | 78 | 75 | 75 | 77 | 62 | 43 | 68 | 31 | 775,000$/3yrs | |||
![]() | 40 | LW/RW | 100.00 | 51 | 2 | 99 | 82 | 75 | 80 | 93 | 67 | 32 | 75 | 78 | 41 | 25 | 75 | 77 | 70 | 46 | 68 | 31 | 685,000$/1yrs | |||
![]() | 0 | RW | 100.00 | 74 | 38 | 75 | 83 | 79 | 83 | 94 | 61 | 25 | 62 | 71 | 62 | 25 | 68 | 69 | 72 | 46 | 67 | 32 | 800,000$/1yrs | |||
![]() | 0 | D | 98.00 | 87 | 36 | 67 | 80 | 86 | 90 | 90 | 76 | 25 | 80 | 62 | 84 | 63 | 83 | 83 | 53 | 43 | 78 | 28 | 8,000,000$/4yrs | |||
![]() | 0 | D | 98.00 | 35 | 1 | 99 | 95 | 81 | 99 | 97 | 90 | 25 | 82 | 66 | 82 | 25 | 76 | 77 | 54 | 43 | 77 | 26 | 7,000,000$/4yrs | |||
![]() | 0 | D | 100.00 | 75 | 17 | 89 | 84 | 85 | 90 | 99 | 63 | 25 | 61 | 47 | 84 | 25 | 72 | 74 | 65 | 43 | 75 | 32 | 4,000,000$/1yrs | |||
![]() | 0 | D | 100.00 | 60 | 4 | 97 | 84 | 72 | 98 | 89 | 76 | 25 | 72 | 48 | 76 | 25 | 72 | 73 | 65 | 43 | 73 | 33 | 1,000,000$/1yrs | |||
![]() | 0 | D | 100.00 | 44 | 17 | 90 | 80 | 63 | 80 | 95 | 87 | 27 | 88 | 74 | 72 | 61 | 60 | 60 | 74 | 43 | 72 | 25 | 2,000,000$/4yrs | |||
![]() | 8 | D | 100.00 | 72 | 6 | 96 | 83 | 77 | 90 | 79 | 58 | 25 | 52 | 47 | 78 | 25 | 73 | 74 | 55 | 22 | 71 | 33 | 685,000$/1yrs | |||
Scratches | ||||||||||||||||||||||||||
![]() | 0 | RW | 100.00 | 59 | 35 | 67 | 70 | 72 | 77 | 85 | 64 | 63 | 54 | 63 | 56 | 66 | 40 | 40 | 90 | 20 | 60 | 20 | 925,000$/3yrs | |||
![]() | 0 | D | 100.00 | 56 | 12 | 94 | 86 | 66 | 98 | 87 | 91 | 25 | 86 | 50 | 77 | 42 | 68 | 70 | 68 | 24 | 74 | 32 | 3,250,000$/2yrs | |||
TEAM AVERAGE | 99.80 | 59 | 14 | 90 | 84 | 73 | 85 | 87 | 75 | 45 | 75 | 71 | 69 | 46 | 70 | 71 | 67 | 40 | 72 |
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 | 99.00 | 95 | 85 | 86 | 68 | 83 | 99 | 99 | 99 | 99 | 86 | 95 | 62 | 62 | 59 | 53 | 84 | 25 | 9,000,000$/3yrs |
![]() | 0 | 100.00 | 80 | 81 | 81 | 73 | 78 | 83 | 85 | 84 | 85 | 80 | 80 | 88 | 94 | 81 | 53 | 79 | 38 | 700,000$/1yrs |
Scratches | ||||||||||||||||||||
TEAM AVERAGE | 99.50 | 88 | 83 | 84 | 71 | 81 | 91 | 92 | 92 | 92 | 83 | 88 | 75 | 78 | 70 | 53 | 82 |
Coaches Name | PH | DF | OF | PD | EX | LD | PO | CNT | Age | Contract | Salary |
---|---|---|---|---|---|---|---|---|---|---|---|
Jared Bednar | 65 | 83 | 87 | 64 | 77 | 58 | 63 | CAN | 50 | 5 | 2,500,000$ |
General Manager | Jeff Shirley |
---|
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 | AMG | PPG | PPA | PPP | PPM | PKG | PKA | PKP | PKM | GW | GT | FO% | FOT | GA | TA | EG | HT | P/20 | PSG | PSS |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Johnny Gaudreau | 0 | LW | 38 | 15 | 21 | 36 | 4 | 4 | 0 | 30 | 172 | 51 | 83 | 8.72% | 12 | 20.57 | 3 | 10 | 13 | 85 | 1 | 0 | 1 | 70 | 3 | 0 | 48.98% | 49 | 47 | 10 | 0 | 0 | 0.92 | 0 | 0 | |
2 | Mika Zibanejad | 0 | C | 38 | 9 | 27 | 36 | 1 | 16 | 0 | 63 | 130 | 30 | 83 | 6.92% | 20 | 20.57 | 5 | 8 | 13 | 85 | 0 | 0 | 0 | 67 | 0 | 1 | 57.66% | 985 | 29 | 15 | 0 | 0 | 0.92 | 1 | 2 | |
3 | Ryan Strome | 0 | C/RW | 38 | 11 | 13 | 24 | -14 | 0 | 0 | 69 | 124 | 36 | 58 | 8.87% | 15 | 18.89 | 4 | 4 | 8 | 85 | 0 | 0 | 0 | 73 | 3 | 0 | 56.51% | 538 | 27 | 12 | 0 | 0 | 0.67 | 0 | 2 | |
4 | Sam Steel | 0 | C/LW | 38 | 10 | 9 | 19 | -5 | 0 | 0 | 21 | 125 | 33 | 75 | 8.00% | 15 | 15.55 | 1 | 2 | 3 | 83 | 0 | 0 | 0 | 14 | 0 | 0 | 53.57% | 420 | 25 | 11 | 0 | 0 | 0.64 | 0 | 0 | |
5 | Zach Werenski | 0 | D | 38 | 2 | 15 | 17 | -13 | 0 | 0 | 8 | 87 | 33 | 34 | 2.30% | 49 | 24.30 | 0 | 3 | 3 | 89 | 0 | 0 | 0 | 78 | 0 | 0 | 0.00% | 0 | 18 | 30 | 0 | 0 | 0.37 | 0 | 0 | |
6 | Dominik Kubalik | 0 | LW/RW | 38 | 8 | 7 | 15 | 8 | 4 | 0 | 58 | 41 | 18 | 19 | 19.51% | 8 | 18.02 | 1 | 2 | 3 | 82 | 0 | 0 | 0 | 0 | 0 | 1 | 27.69% | 65 | 3 | 11 | 0 | 0 | 0.44 | 0 | 0 | |
7 | Kailer Yamamoto | 0 | LW/RW | 38 | 9 | 6 | 15 | -11 | 12 | 0 | 84 | 108 | 26 | 60 | 8.33% | 22 | 18.07 | 1 | 1 | 2 | 82 | 0 | 0 | 0 | 64 | 3 | 2 | 24.55% | 167 | 41 | 19 | 0 | 0 | 0.44 | 1 | 1 | |
8 | Darnell Nurse | 0 | D | 38 | 5 | 6 | 11 | -15 | 58 | 0 | 108 | 50 | 21 | 16 | 10.00% | 51 | 23.71 | 3 | 1 | 4 | 89 | 0 | 0 | 0 | 49 | 0 | 0 | 0.00% | 0 | 9 | 25 | 1 | 0 | 0.24 | 0 | 0 | |
9 | Reid Boucher | 0 | LW/RW | 38 | 4 | 5 | 9 | -8 | 0 | 0 | 22 | 87 | 33 | 60 | 4.60% | 13 | 13.03 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 26.09% | 23 | 11 | 7 | 0 | 0 | 0.36 | 0 | 0 | |
10 | Mac Hollowell | 0 | D | 38 | 4 | 4 | 8 | 4 | 20 | 0 | 11 | 57 | 27 | 18 | 7.02% | 31 | 18.87 | 1 | 0 | 1 | 81 | 0 | 1 | 1 | 38 | 0 | 0 | 0.00% | 0 | 19 | 21 | 0 | 0 | 0.22 | 0 | 0 | |
11 | Antoine Roussel | 0 | LW | 38 | 2 | 4 | 6 | -7 | 34 | 0 | 57 | 38 | 10 | 24 | 5.26% | 17 | 13.21 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 4 | 0 | 0 | 47.37% | 19 | 4 | 12 | 0 | 0 | 0.24 | 0 | 0 | |
12 | Jake Gardiner | 0 | D | 38 | 0 | 6 | 6 | -6 | 4 | 0 | 15 | 23 | 20 | 12 | 0.00% | 51 | 16.59 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 9 | 0 | 0 | 0.00% | 0 | 2 | 15 | 0 | 0 | 0.19 | 0 | 0 | |
13 | Riley Sheahan | 0 | C/LW | 38 | 4 | 2 | 6 | -5 | 0 | 0 | 13 | 26 | 8 | 21 | 15.38% | 9 | 8.21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 55.56% | 9 | 0 | 7 | 0 | 0 | 0.38 | 0 | 0 | |
14 | Torey Krug | 0 | D | 25 | 1 | 5 | 6 | 3 | 0 | 0 | 27 | 36 | 16 | 13 | 2.78% | 27 | 19.89 | 0 | 0 | 0 | 62 | 0 | 0 | 0 | 48 | 0 | 0 | 0.00% | 0 | 2 | 23 | 0 | 0 | 0.24 | 0 | 0 | |
15 | Brett Connolly | 40 | LW/RW | 38 | 2 | 4 | 6 | -8 | 2 | 0 | 14 | 27 | 7 | 15 | 7.41% | 7 | 14.04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 44.00% | 25 | 8 | 5 | 0 | 0 | 0.22 | 0 | 0 | |
16 | Zack Kassian | 0 | RW | 38 | 3 | 2 | 5 | -4 | 10 | 0 | 33 | 65 | 18 | 28 | 4.62% | 4 | 8.19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20.00% | 10 | 12 | 5 | 0 | 0 | 0.32 | 0 | 0 | |
17 | Brandon Sutter | 0 | C/RW | 38 | 1 | 3 | 4 | -4 | 0 | 0 | 13 | 27 | 15 | 27 | 3.70% | 8 | 8.81 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 2 | 0 | 0 | 63.56% | 118 | 10 | 5 | 0 | 0 | 0.24 | 0 | 0 | |
18 | Brenden Dillon | 0 | D | 38 | 0 | 3 | 3 | -7 | 16 | 0 | 54 | 25 | 11 | 17 | 0.00% | 40 | 18.65 | 0 | 1 | 1 | 19 | 0 | 0 | 0 | 71 | 0 | 0 | 0.00% | 0 | 3 | 32 | 0 | 0 | 0.08 | 0 | 0 | |
19 | Kevin Connauton | 8 | D | 14 | 0 | 0 | 0 | -1 | 0 | 0 | 12 | 8 | 5 | 8 | 0.00% | 10 | 15.14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.00% | 0 | 0 | 8 | 0 | 0 | 0.00 | 0 | 0 | |
Team Total or Average | 685 | 90 | 142 | 232 | -88 | 180 | 0 | 712 | 1256 | 418 | 671 | 7.17% | 409 | 16.53 | 19 | 32 | 51 | 863 | 1 | 1 | 2 | 596 | 12 | 4 | 53.05% | 2428 | 270 | 273 | 1 | 0 | 0.41 | 2 | 5 |
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 | Carter Hart | Stars | 38 | 14 | 18 | 5 | 0.912 | 2.88 | 2168 | 2 | 1 | 104 | 1177 | 469 | 2 | 2 | 1.000 | 6 | 38 | 0 | 2 | 4 | 5 |
2 | Marc-Andre Fleury | Stars | 4 | 0 | 0 | 1 | 0.929 | 2.27 | 132 | 0 | 0 | 5 | 70 | 30 | 0 | 0 | 0.000 | 0 | 0 | 38 | 0 | 0 | 0 |
Team Total or Average | 42 | 14 | 18 | 6 | 0.913 | 2.84 | 2300 | 2 | 1 | 109 | 1247 | 499 | 2 | 2 | 1.000 | 6 | 38 | 38 | 2 | 4 | 5 |
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 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 9 | 10 | -1 | 2 | 0.333 | 9 | 14 | 23 | 1 | 0 | 2 | 5 | 2 | 0 | 108 | 33 | 35 | 40 | 0 | 91 | 37 | 12 | 58 | 4 | 1 | 25.00% | 6 | 3 | 50.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 64 | 40 | 66 | 25 | 49 | 24 | 53.3% | 8.3% | 89.0% | 97.3 | Unlucky | |
2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 8 | -1 | 2 | 0.500 | 7 | 10 | 17 | 0 | 0 | 1 | 5 | 1 | 0 | 69 | 15 | 28 | 26 | 0 | 62 | 13 | 6 | 36 | 9 | 4 | 44.44% | 3 | 2 | 33.33% | 1 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 41 | 24 | 43 | 17 | 34 | 18 | 33.3% | 10.1% | 87.1% | 97.2 | FUN | |
3 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 0 | 4 | 0.667 | 6 | 11 | 17 | 0 | 1 | 4 | 0 | 2 | 0 | 95 | 42 | 26 | 27 | 0 | 89 | 33 | 10 | 51 | 7 | 1 | 14.29% | 5 | 0 | 100.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 67 | 42 | 62 | 26 | 49 | 24 | 45.5% | 6.3% | 93.3% | 99.6 | DULL | |
4 | 3 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 4 | 8 | -4 | 3 | 0.500 | 4 | 5 | 9 | 0 | 0 | 1 | 1 | 1 | 1 | 93 | 33 | 24 | 33 | 5 | 112 | 39 | 18 | 62 | 6 | 2 | 33.33% | 9 | 1 | 88.89% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 66 | 40 | 68 | 26 | 50 | 24 | 22.2% | 4.3% | 92.9% | 97.2 | DULL | |
5 | 5 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 13 | 15 | -2 | 5 | 0.500 | 13 | 21 | 34 | 0 | 0 | 6 | 4 | 3 | 0 | 140 | 40 | 48 | 52 | 0 | 177 | 52 | 30 | 87 | 11 | 3 | 27.27% | 15 | 5 | 66.67% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 103 | 59 | 112 | 44 | 83 | 41 | 50.0% | 9.3% | 91.5% | 100.8 | LUCKY | |
6 | 4 | 0 | 2 | 0 | 1 | 0 | 1 | 0 | 8 | 9 | -1 | 4 | 0.500 | 8 | 12 | 20 | 0 | 0 | 2 | 2 | 2 | 2 | 120 | 38 | 53 | 28 | 4 | 138 | 49 | 18 | 80 | 17 | 1 | 5.88% | 9 | 1 | 88.89% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 90 | 52 | 87 | 35 | 68 | 35 | 46.7% | 6.7% | 93.5% | 100.1 | DULL | |
7 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 3 | 2 | 1.000 | 5 | 8 | 13 | 0 | 0 | 1 | 4 | 0 | 0 | 34 | 11 | 11 | 12 | 0 | 29 | 9 | 8 | 17 | 4 | 1 | 25.00% | 4 | 1 | 75.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 22 | 13 | 20 | 8 | 16 | 8 | 80.0% | 14.7% | 93.1% | 107.8 | LUCKY | |
8 | 4 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 8 | 13 | -5 | 1 | 0.125 | 8 | 13 | 21 | 0 | 0 | 3 | 2 | 3 | 0 | 139 | 47 | 36 | 56 | 0 | 124 | 36 | 18 | 69 | 17 | 4 | 23.53% | 9 | 2 | 77.78% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 87 | 50 | 86 | 34 | 67 | 35 | 26.7% | 5.8% | 89.5% | 95.3 | Unlucky | |
9 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 7 | 9 | -2 | 2 | 0.333 | 7 | 12 | 19 | 1 | 0 | 1 | 3 | 3 | 0 | 116 | 36 | 37 | 43 | 0 | 95 | 45 | 20 | 56 | 6 | 1 | 16.67% | 10 | 2 | 80.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 67 | 41 | 61 | 24 | 51 | 26 | 46.2% | 6.0% | 90.5% | 96.6 | Unlucky | |
10 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 3 | -1 | 1 | 0.500 | 2 | 2 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 47 | 23 | 9 | 10 | 5 | 33 | 7 | 2 | 24 | 3 | 1 | 33.33% | 1 | 1 | 0.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 30 | 20 | 16 | 7 | 17 | 10 | 33.3% | 4.3% | 90.9% | 95.2 | Unlucky | |
11 | 3 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 9 | 6 | 3 | 5 | 0.833 | 9 | 12 | 21 | 0 | 0 | 1 | 6 | 2 | 0 | 99 | 38 | 29 | 30 | 2 | 99 | 31 | 12 | 65 | 6 | 0 | 0.00% | 6 | 1 | 83.33% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 65 | 40 | 67 | 25 | 50 | 26 | 64.3% | 9.1% | 93.9% | 103.0 | LUCKY | |
12 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 4 | 10 | -6 | 0 | 0.000 | 4 | 6 | 10 | 0 | 0 | 1 | 1 | 2 | 0 | 65 | 21 | 28 | 16 | 0 | 72 | 17 | 6 | 33 | 11 | 0 | 0.00% | 3 | 1 | 66.67% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 46 | 27 | 40 | 17 | 33 | 17 | 30.8% | 6.2% | 86.1% | 92.3 | Unlucky | |
13 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 2 | 1 | 2 | 1.000 | 3 | 5 | 8 | 0 | 0 | 0 | 1 | 1 | 1 | 36 | 13 | 5 | 17 | 1 | 34 | 5 | 6 | 15 | 0 | 0 | 0.00% | 3 | 0 | 100.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 22 | 14 | 20 | 9 | 16 | 7 | 60.0% | 8.3% | 94.1% | 102.5 | DULL | |
14 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 7 | -2 | 1 | 0.250 | 5 | 8 | 13 | 0 | 0 | 2 | 1 | 2 | 0 | 68 | 25 | 16 | 27 | 0 | 65 | 24 | 10 | 43 | 1 | 0 | 0.00% | 5 | 0 | 100.00% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 41 | 24 | 46 | 17 | 34 | 16 | 41.7% | 7.4% | 89.2% | 96.6 | Unlucky | |
15 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | -1 | 0 | 0.000 | 2 | 3 | 5 | 0 | 0 | 0 | 0 | 2 | 0 | 27 | 6 | 12 | 9 | 0 | 29 | 12 | 6 | 16 | 1 | 0 | 0.00% | 3 | 1 | 66.67% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 19 | 11 | 22 | 9 | 17 | 8 | 50.0% | 7.4% | 89.7% | 97.1 | Unlucky | |
_Vs Division | 19 | 4 | 8 | 0 | 1 | 4 | 2 | 0 | 42 | 51 | -9 | 18 | 0.474 | 42 | 63 | 105 | 0 | 0 | 13 | 15 | 11 | 3 | 591 | 196 | 190 | 199 | 11 | 650 | 207 | 96 | 363 | 57 | 10 | 17.54% | 48 | 10 | 79.17% | 0 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 414 | 243 | 422 | 166 | 321 | 163 | 43.8% | 7.1% | 92.2% | 99.3 | DULL | |
_Vs Conference | 30 | 9 | 14 | 0 | 1 | 4 | 2 | 0 | 71 | 84 | -13 | 28 | 0.467 | 71 | 110 | 181 | 2 | 1 | 21 | 28 | 19 | 3 | 979 | 322 | 316 | 335 | 11 | 987 | 335 | 144 | 564 | 83 | 17 | 20.48% | 72 | 17 | 76.39% | 1 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 656 | 392 | 655 | 259 | 506 | 257 | 44.6% | 7.3% | 91.5% | 98.7 | DULL | |
_Since Last GM Reset | 38 | 10 | 18 | 0 | 2 | 6 | 2 | 0 | 92 | 111 | -19 | 34 | 0.447 | 92 | 142 | 234 | 2 | 1 | 27 | 35 | 26 | 4 | 1256 | 421 | 397 | 426 | 17 | 1249 | 409 | 182 | 712 | 103 | 19 | 18.45% | 91 | 21 | 76.92% | 1 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 839 | 503 | 824 | 327 | 641 | 327 | 44.8% | 7.3% | 91.1% | 98.4 | Unlucky | |
Total | 38 | 10 | 18 | 0 | 2 | 6 | 2 | 0 | 92 | 111 | -19 | 34 | 0.447 | 92 | 142 | 234 | 2 | 1 | 27 | 35 | 26 | 4 | 1256 | 421 | 397 | 426 | 17 | 1249 | 409 | 182 | 712 | 103 | 19 | 18.45% | 91 | 21 | 76.92% | 1 | 495 | 938 | 52.77% | 506 | 948 | 53.38% | 287 | 512 | 56.05% | 839 | 503 | 824 | 327 | 641 | 327 | 44.8% | 7.3% | 91.1% | 98.4 | Unlucky |
Puck Time | |
---|---|
Offensive Zone | 22 |
Neutral Zone | 16 |
Defensive Zone | 21 |
Puck Time | |
---|---|
Offensive Zone Start | 938 |
Neutral Zone Start | 512 |
Defensive Zone Start | 948 |
Puck Time | |
---|---|
With Puck | 30 |
Without Puck | 30 |
Faceoffs | |
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Faceoffs Won | 1288 |
Faceoffs Lost | 1110 |
Team Average Shots after | League Average Shots after | |
---|---|---|
1st Period | 11.1 | 9.57 |
2nd Period | 21.5 | 20.31 |
3rd Period | 32.7 | 30.68 |
Overtime | 33.2 | 31.4 |
Goals in | Team Average Goals after | League Average Goals after |
---|---|---|
1st Period | 0.7 | 0.64 |
2nd Period | 1.6 | 1.65 |
3rd Period | 2.3 | 2.67 |
Overtime | 2.4 | 2.83 |
Even Strenght Goal | 70 |
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PP Goal | 19 |
PK Goal | 1 |
Empty Net Goal | 2 |
Home | Away | |
---|---|---|
Win | 6 | 8 |
Lost | 9 | 9 |
Overtime Lost | 3 | 3 |
PP Attempt | 103 |
---|---|
PP Goal | 19 |
PK Attempt | 91 |
PK Goal Against | 21 |
Home | |
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Shots For | 33.1 |
Shots Against | 32.9 |
Goals For | 2.4 |
Goals Against | 2.9 |
Hits | 18.7 |
Shots Blocked | 10.8 |
Pim | 4.8 |
Projected Total Cap Hit | 88,343,124$ |
Projected Cap Space | 1,656,876$ |
Retains And Buyout Cap Hit | 6,000,000$ |
Salary Cap To Date | 38,391,395$ |
Players In Salary Cap | 23 |
LTIR Players | 0 |
Player Name | Pos | Age | Cap Hit | 2018-2019 | 2019-2020 | 2020-2021 | 2021-2022 | 2022-2023 | 2023-2024 | 2024-2025 | 2025-2026 |
---|---|---|---|---|---|---|---|---|---|---|---|
Antoine Roussel ![]() | LW | 33 | 685,000$ | 685,000$ | |||||||
Brandon Sutter ![]() | C/RW | 34 | 900,000$ | 900,000$ | 900,000$ | 900,000$ | |||||
Brenden Dillon ![]() | D | 32 | 4,000,000$ | 4,000,000$ | |||||||
Brett Connolly ![]() | LW/RW | 31 | 685,000$ | 685,000$ | |||||||
Carter Hart ![]() | C | 25 | 9,000,000$ | 9,000,000$ | 9,000,000$ | 9,000,000$ | |||||
Cole Huckins ![]() | RW | 20 | 925,000$ | 925,000$ | 925,000$ | 925,000$ | |||||
Darnell Nurse | D | 28 | 8,000,000$ | 8,000,000$ | 8,000,000$ | 8,000,000$ | 8,000,000$ | ||||
Dominik Kubalik ![]() | LW/RW | 28 | 2,500,000$ | 2,500,000$ | 2,500,000$ | ||||||
Jake Gardiner | D | 33 | 1,000,000$ | 1,000,000$ | |||||||
Johnny Gaudreau | LW | 30 | 8,250,000$ | 8,250,000$ | 8,250,000$ | 8,250,000$ | 8,250,000$ | ||||
Kailer Yamamoto ![]() | LW/RW | 24 | 3,500,000$ | 3,500,000$ | 3,500,000$ | 3,500,000$ | |||||
Kevin Connauton ![]() | D | 33 | 685,000$ | 685,000$ | |||||||
Mac Hollowell ![]() | D | 25 | 2,000,000$ | 2,000,000$ | 2,000,000$ | 2,000,000$ | 2,000,000$ | ||||
Marc-Andre Fleury | C/LW | 38 | 700,000$ | 700,000$ | |||||||
Mika Zibanejad | C | 30 | 12,500,000$ | 12,500,000$ | 12,500,000$ | 12,500,000$ | 12,500,000$ | ||||
Reid Boucher ![]() | LW/RW | 30 | 685,000$ | 685,000$ | |||||||
Riley Sheahan ![]() | C/LW | 31 | 775,000$ | 775,000$ | 775,000$ | 775,000$ | |||||
Ryan Strome | C/RW | 30 | 11,000,000$ | 11,000,000$ | 11,000,000$ | 11,000,000$ | |||||
Sam Steel ![]() | C/LW | 25 | 3,500,000$ | 3,500,000$ | 3,500,000$ | 3,500,000$ | |||||
Torey Krug ![]() | D | 32 | 3,250,000$ | 3,250,000$ | 3,250,000$ | ||||||
Zach Werenski | D | 26 | 7,000,000$ | 7,000,000$ | 7,000,000$ | 7,000,000$ | 7,000,000$ | ||||
Zack Kassian ![]() | RW | 32 | 800,000$ | 800,000$ |
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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 |
Prospect | Team Name | Draft Year | Overall Pick | Information | Lien |
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Albert Wikman | 8 | 89 | |||
Brandon Svoboda | 8 | 65 | |||
Daniil Orlov | 6 | 65 | |||
Filip Bystedt | 7 | 18 | |||
Garrett Brown | 7 | 66 | |||
Kyle Kukkonen | 5 | 92 | |||
Vladmir Grudinin | 6 | 89 |
Date | Matchup | Result | Detail | |||||||||||
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2023-09-26 | @ | Stars4,Coyotes5 (OT) | RECAP | |||||||||||
2023-09-27 | @ | Stars2,Rangers3 | RECAP | |||||||||||
2023-09-29 | @ | Blues4,Stars0 | RECAP | |||||||||||
2023-10-01 | @ | Blues4,Stars3 (OT) | RECAP | |||||||||||
2023-10-03 | @ | Jets2,Stars3 (SO) | RECAP | |||||||||||
2023-10-05 | @ | Stars4,Blues2 | RECAP | |||||||||||
2023-10-07 | @ | Coyotes3,Stars2 | RECAP | |||||||||||
2023-10-10 | @ | Stars4,Jets3 (OT) | RECAP | |||||||||||
2023-10-11 | @ | Wild2,Stars1 (OT) | RECAP | |||||||||||
2023-10-13 | @ | Blues4,Stars3 | RECAP | |||||||||||
2023-10-16 | @ | Stars1,Avalanche5 | RECAP | |||||||||||
2023-10-18 | @ | Stars4,Wild2 | RECAP | |||||||||||
2023-10-20 | @ | Avalanche1,Stars2 (SO) | RECAP | |||||||||||
2023-10-21 | @ | Stars1,Coyotes2 | RECAP | |||||||||||
2023-10-23 | @ | Ducks3,Stars2 | RECAP | |||||||||||
2023-10-25 | @ | Coyotes3,Stars1 | RECAP | |||||||||||
2023-10-27 | @ | Stars5,Bruins2 | RECAP | |||||||||||
2023-10-29 | @ | Stars2,Rangers7 | RECAP | |||||||||||
2023-10-31 | @ | Stars5,Kings3 | RECAP | |||||||||||
2023-11-01 | @ | Stars0,Jets2 | RECAP | |||||||||||
2023-11-02 | @ | Canucks0,Stars1 | RECAP | |||||||||||
2023-11-04 | @ | Stars1,Avalanche2 (OT) | RECAP | |||||||||||
2023-11-06 | @ | Red Wings2,Stars3 (OT) | RECAP | |||||||||||
2023-11-09 | @ | Jets2,Stars1 | RECAP | |||||||||||
2023-11-10 | @ | Stars2,Kings4 | RECAP | |||||||||||
2023-11-12 | @ | Senators3,Stars2 (OT) | RECAP | |||||||||||
2023-11-14 | @ | Stars3,Ducks5 | RECAP | |||||||||||
2023-11-16 | @ | Stars2,Ducks1 | RECAP | |||||||||||
2023-11-18 | @ | Wild2,Stars4 | RECAP | |||||||||||
2023-11-20 | @ | Stars3,Blues1 | RECAP | |||||||||||
2023-11-22 | @ | Panthers4,Stars3 | RECAP | |||||||||||
2023-11-23 | @ | Stars5,Oilers4 | RECAP | |||||||||||
2023-11-25 | @ | Stars2,Panthers3 (OT) | RECAP | |||||||||||
2023-11-26 | @ | Devils3,Stars2 | RECAP | |||||||||||
2023-11-29 | @ | Canucks3,Stars4 | RECAP | |||||||||||
2023-11-30 | @ | Stars2,Oilers4 | RECAP | |||||||||||
2023-12-02 | @ | Kings3,Stars2 | RECAP | |||||||||||
2023-12-03 | @ | Stars1,Canucks3 | RECAP | |||||||||||
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Salary Cap | |||
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Players Total Salaries | Retained Salary | Total Cap Hit | Estimated Cap Space |
83,140,009$ | 6,000,000$ | 88,343,124$ | 1,656,876$ |
Arena | Goal Horn | About us | |
---|---|---|---|
![]() | Name | American Airlines Center | |
City | Dallas | ||
Capacity | 18000 | ||
Season Ticket Holders | 50% |
Arena Capacity - Ticket Price Attendance - % | |||||
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Arena Capacity | 6000 | 5000 | 2000 | 4000 | 1000 |
Ticket Price | 300$ | 150$ | 100$ | 75$ | 400$ |
Attendance | 83565 | 70106 | 27519 | 57042 | 16921 |
Attendance PCT | 77.38% | 77.90% | 76.44% | 79.23% | 94.01% |
Income | |||||
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Home Games Left | Average Attendance - % | Average Income per Game | Year to Date Revenue | Arena Capacity | Team Popularity |
23 | 14175 - 78.75% | 4,087,885$ | 73,581,936$ | 18000 | 100 |
Expenses | |||
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Players Total Salaries | Players Total Average Salaries | Coaches Salaries | Special Salary Cap Value |
83,140,009$ | 83,140,009$ | 0$ | 6,000,000$ |
Year To Date Expenses | Salary Cap Per Days | Salary Cap To Date | Luxury Taxe Total |
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39,929,956$ | 556,351$ | 38,391,395$ | 0$ |
Estimate | |||
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Estimated Season Revenue | Remaining Season Days | Expenses Per Days | Estimated Season Expenses |
94,021,363$ | 79 | 578,649$ | 45,713,271$ |
Team Total Estimate | |||
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Estimated Season Expenses | Estimated Season Salary Cap | Current Bank Account | Projected Bank Account |
48,050,144$ | 88,343,124$ | 967,968,193$ | 1,022,210,370$ |
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Year | Round 1 | Round 2 | Round 3 | Round 4 |
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