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Full Scoreboard »» |
Philadelphia Flyers 33-13-2, 68pts · 1st in Eastern |
Player | # | POS | CON | CK | FG | DI | SK | ST | EN | DU | PH | FO | PA | SC | DF | PS | EX | LD | PO | MO | OV | AGE | CONTRACT | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 91 | C/LW/RW | 100.00 | 72 | 32 | 84 | 93 | 71 | 86 | 90 | 87 | 84 | 89 | 88 | 70 | 87 | 82 | 99 | 0 | 77 | 95 | 32 | 8,500,000$/2yrs | |||
![]() | 93 | C | 100.00 | 66 | 33 | 92 | 86 | 80 | 89 | 90 | 83 | 85 | 85 | 85 | 80 | 84 | 76 | 85 | 0 | 77 | 92 | 29 | 8,500,000$/6yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 79 | 34 | 88 | 83 | 89 | 84 | 90 | 79 | 85 | 74 | 86 | 93 | 82 | 73 | 71 | 0 | 77 | 91 | 27 | 3,150,000$/7yrs | |||
![]() | 17 | LW/RW | 100.00 | 69 | 33 | 77 | 76 | 77 | 86 | 90 | 78 | 57 | 79 | 79 | 77 | 78 | 82 | 89 | 0 | 77 | 87 | 33 | 4,450,000$/1yrs | |||
![]() | 71 | C/RW | 100.00 | 69 | 38 | 72 | 76 | 74 | 85 | 84 | 77 | 82 | 77 | 73 | 90 | 75 | 68 | 66 | 0 | 76 | 86 | 25 | 5,500,000$/4yrs | |||
![]() | 27 | LW/RW | 100.00 | 71 | 32 | 64 | 77 | 75 | 81 | 90 | 80 | 60 | 81 | 77 | 63 | 79 | 71 | 68 | 0 | 77 | 85 | 27 | 950,000$/1yrs | |||
![]() | 69 | C/RW | 100.00 | 82 | 34 | 91 | 74 | 87 | 82 | 90 | 71 | 85 | 70 | 75 | 83 | 73 | 77 | 76 | 0 | 76 | 85 | 30 | 1,000,000$/1yrs | |||
![]() | 18 | LW/RW | 100.00 | 78 | 33 | 91 | 73 | 75 | 83 | 83 | 76 | 56 | 78 | 76 | 69 | 76 | 79 | 87 | 0 | 77 | 85 | 31 | 5,500,000$/4yrs | |||
![]() | 0 | C/LW | 100.00 | 74 | 33 | 84 | 66 | 80 | 80 | 87 | 66 | 81 | 70 | 68 | 92 | 67 | 72 | 70 | 0 | 77 | 82 | 27 | 2,250,000$/5yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 60 | 33 | 96 | 69 | 80 | 81 | 90 | 72 | 60 | 72 | 75 | 63 | 74 | 80 | 79 | 0 | 70 | 81 | 32 | 1,200,000$/2yrs | |||
![]() | 0 | C/RW | 100.00 | 75 | 39 | 78 | 65 | 77 | 78 | 90 | 70 | 62 | 72 | 68 | 80 | 69 | 63 | 61 | 0 | 77 | 80 | 22 | 863,333$/1yrs | |||
![]() | 18 | C/LW | 100.00 | 80 | 62 | 50 | 68 | 70 | 74 | 72 | 69 | 60 | 71 | 67 | 74 | 68 | 66 | 61 | 0 | 76 | 79 | 24 | 800,000$/1yrs | |||
![]() | 59 | D | 100.00 | 67 | 33 | 80 | 82 | 79 | 98 | 90 | 80 | 30 | 91 | 79 | 81 | 80 | 81 | 95 | 0 | 77 | 93 | 32 | 9,000,000$/6yrs | |||
![]() | 23 | D | 98.00 | 59 | 32 | 87 | 86 | 70 | 95 | 90 | 82 | 30 | 90 | 75 | 90 | 78 | 68 | 66 | 0 | 72 | 93 | 24 | 9,500,000$/7yrs | |||
![]() | 6 | D | 100.00 | 83 | 34 | 90 | 62 | 90 | 84 | 89 | 72 | 30 | 71 | 64 | 93 | 68 | 84 | 83 | 0 | 75 | 86 | 34 | 6,000,000$/1yrs | |||
![]() | 2 | D | 100.00 | 99 | 57 | 54 | 66 | 88 | 83 | 90 | 71 | 30 | 74 | 61 | 87 | 66 | 82 | 77 | 0 | 76 | 86 | 33 | 1,250,000$/3yrs | |||
![]() | 0 | D | 100.00 | 77 | 33 | 91 | 65 | 83 | 90 | 90 | 71 | 30 | 73 | 62 | 87 | 66 | 74 | 73 | 0 | 77 | 85 | 29 | 3,750,000$/4yrs | |||
![]() | 7 | D | 100.00 | 74 | 33 | 75 | 64 | 79 | 88 | 90 | 69 | 30 | 70 | 62 | 91 | 66 | 80 | 77 | 0 | 77 | 84 | 32 | 1,250,000$/1yrs | |||
Scratches | ||||||||||||||||||||||||||
TEAM AVERAGE | 99.89 | 74 | 37 | 80 | 74 | 79 | 85 | 88 | 75 | 58 | 77 | 73 | 81 | 74 | 75 | 77 | 0 | 76 | 86 |
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 | 81 | 84 | 76 | 83 | 85 | 82 | 87 | 90 | 88 | 90 | 87 | 76 | 81 | 0 | 76 | 88 | 30 | 750,000$/1yrs |
![]() | 0 | 98.00 | 74 | 81 | 75 | 88 | 84 | 79 | 85 | 91 | 83 | 92 | 85 | 70 | 74 | 0 | 77 | 86 | 26 | 1,500,000$/1yrs |
Scratches | ||||||||||||||||||||
![]() | 0 | 96.00 | 97 | 95 | 85 | 85 | 97 | 86 | 96 | 93 | 93 | 93 | 96 | 76 | 80 | 0 | 77 | 94 | 29 | 6,166,666$/2yrs |
TEAM AVERAGE | 97.67 | 84 | 87 | 79 | 85 | 89 | 82 | 89 | 91 | 88 | 92 | 89 | 74 | 78 | 0 | 77 | 89 |
Coaches Name | PH | DF | OF | PD | EX | LD | PO | CNT | Age | Contract | Salary |
---|---|---|---|---|---|---|---|---|---|---|---|
Darryl Sutter | 80 | 87 | 91 | 78 | 99 | 90 | 87 | CAN | 62 | 2 | 1,000,000$ |
General Manager | Pierre-Etienne Jobin |
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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 |
# | 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 | Mika Zibanejad | 93 | C | 48 | 20 | 26 | 46 | 4 | 4 | 0 | 34 | 174 | 37 | 155 | 11.49% | 10 | 19.63 | 5 | 6 | 11 | 146 | 0 | 0 | 0 | 7 | 6 | 0 | 53.89% | 1169 | 0 | 0 | 0 | 0 | 0.98 | 2 | 4 | |
2 | Adam Fox | 23 | D | 47 | 9 | 34 | 43 | 12 | 22 | 0 | 34 | 85 | 12 | 49 | 10.59% | 61 | 23.45 | 6 | 9 | 15 | 150 | 0 | 3 | 3 | 127 | 2 | 1 | 0.00% | 0 | 0 | 0 | 0 | 1 | 0.78 | 0 | 0 | |
3 | Steven Stamkos | 91 | C/LW/RW | 48 | 13 | 27 | 40 | 1 | 22 | 0 | 60 | 170 | 52 | 115 | 7.65% | 6 | 19.41 | 0 | 11 | 11 | 146 | 0 | 0 | 0 | 0 | 2 | 5 | 48.31% | 89 | 0 | 0 | 0 | 0 | 0.86 | 2 | 4 | |
4 | Anthony Cirelli | 71 | C/RW | 48 | 12 | 23 | 35 | 0 | 24 | 10 | 52 | 140 | 42 | 88 | 8.57% | 11 | 18.04 | 4 | 9 | 13 | 110 | 1 | 0 | 1 | 76 | 2 | 0 | 49.44% | 1080 | 0 | 0 | 1 | 0 | 0.81 | 0 | 0 | |
5 | Nick Paul | 0 | C/LW/RW | 48 | 15 | 18 | 33 | 11 | 8 | 0 | 63 | 146 | 49 | 111 | 10.27% | 16 | 15.89 | 0 | 0 | 0 | 17 | 2 | 1 | 3 | 134 | 0 | 1 | 54.28% | 947 | 0 | 0 | 1 | 1 | 0.87 | 0 | 0 | |
6 | Roman Josi | 59 | D | 48 | 8 | 18 | 26 | -6 | 22 | 0 | 45 | 81 | 23 | 57 | 9.88% | 63 | 22.47 | 6 | 5 | 11 | 156 | 0 | 1 | 1 | 61 | 3 | 1 | 0.00% | 0 | 0 | 0 | 0 | 0 | 0.48 | 0 | 0 | |
7 | Ondrej Palat | 18 | LW/RW | 48 | 9 | 12 | 21 | -9 | 12 | 0 | 42 | 89 | 29 | 72 | 10.11% | 3 | 17.22 | 1 | 6 | 7 | 129 | 0 | 0 | 0 | 0 | 2 | 1 | 31.25% | 64 | 0 | 0 | 0 | 0 | 0.51 | 1 | 1 | |
8 | Cody Ceci | 0 | D | 48 | 4 | 15 | 19 | 16 | 56 | 0 | 89 | 43 | 14 | 24 | 9.30% | 54 | 21.63 | 1 | 4 | 5 | 115 | 0 | 2 | 2 | 121 | 0 | 0 | 0.00% | 0 | 0 | 0 | 1 | 0 | 0.37 | 0 | 0 | |
9 | Michael Bunting | 27 | LW/RW | 48 | 8 | 11 | 19 | -5 | 36 | 0 | 83 | 108 | 22 | 84 | 7.41% | 1 | 17.07 | 3 | 4 | 7 | 130 | 0 | 0 | 0 | 2 | 1 | 1 | 47.54% | 61 | 0 | 0 | 0 | 0 | 0.46 | 0 | 0 | |
10 | Alex Killorn | 17 | LW/RW | 48 | 8 | 10 | 18 | -2 | 16 | 0 | 93 | 81 | 28 | 66 | 9.88% | 6 | 19.45 | 3 | 2 | 5 | 145 | 0 | 0 | 0 | 19 | 3 | 0 | 25.00% | 64 | 0 | 0 | 0 | 0 | 0.39 | 2 | 3 | |
11 | Erik Johnson | 6 | D | 48 | 8 | 10 | 18 | -12 | 24 | 0 | 85 | 60 | 20 | 41 | 13.33% | 67 | 20.66 | 2 | 2 | 4 | 107 | 0 | 1 | 1 | 90 | 1 | 0 | 0.00% | 0 | 0 | 0 | 0 | 0 | 0.36 | 0 | 0 | |
12 | Dmitry Kulikov | 7 | D | 48 | 2 | 11 | 13 | 14 | 4 | 0 | 24 | 21 | 10 | 11 | 9.52% | 50 | 16.85 | 1 | 1 | 2 | 17 | 0 | 0 | 0 | 122 | 0 | 0 | 0.00% | 0 | 0 | 0 | 0 | 0 | 0.32 | 0 | 0 | |
13 | Jason Dickinson | 0 | C/LW | 48 | 4 | 8 | 12 | 14 | 10 | 0 | 32 | 54 | 16 | 32 | 7.41% | 11 | 11.62 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 162 | 2 | 0 | 46.67% | 135 | 0 | 0 | 0 | 0 | 0.43 | 0 | 0 | |
14 | Nick Bjugstad | 69 | C/RW | 28 | 7 | 4 | 11 | 7 | 6 | 0 | 24 | 30 | 6 | 16 | 23.33% | 3 | 10.76 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 79 | 0 | 0 | 50.65% | 154 | 0 | 0 | 0 | 0 | 0.73 | 0 | 0 | |
15 | Ty Dellandrea | 0 | C/RW | 31 | 5 | 5 | 10 | 13 | 8 | 0 | 21 | 40 | 10 | 18 | 12.50% | 3 | 12.71 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 69 | 1 | 0 | 50.00% | 26 | 0 | 0 | 0 | 0 | 0.51 | 0 | 0 | |
16 | Luke Schenn | 2 | D | 48 | 2 | 7 | 9 | 12 | 98 | 10 | 140 | 21 | 6 | 9 | 9.52% | 50 | 15.73 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 102 | 2 | 0 | 0.00% | 0 | 0 | 0 | 0 | 0 | 0.24 | 0 | 0 | |
17 | Marcus Johansson | 0 | C/LW/RW | 48 | 2 | 2 | 4 | 8 | 0 | 0 | 6 | 29 | 13 | 11 | 6.90% | 2 | 8.23 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 2 | 0 | 40.38% | 52 | 0 | 0 | 0 | 0 | 0.20 | 0 | 0 | |
18 | Mason Shaw | 18 | C/LW | 15 | 1 | 1 | 2 | 3 | 6 | 0 | 15 | 9 | 2 | 6 | 11.11% | 0 | 4.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 16.67% | 6 | 0 | 0 | 0 | 0 | 0.54 | 0 | 0 | |
Team Total or Average | 793 | 137 | 242 | 379 | 81 | 378 | 20 | 942 | 1381 | 391 | 965 | 9.92% | 417 | 17.12 | 32 | 59 | 91 | 1377 | 5 | 9 | 14 | 1180 | 29 | 10 | 51.00% | 3847 | 0 | 0 | 3 | 2 | 0.56 | 7 | 12 |
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 | Connor Hellebuyck | 37 | 25 | 10 | 2 | 0.911 | 2.45 | 2225 | 4 | 3 | 91 | 1021 | 0 | 2 | 1 | 0.778 | 9 | 37 | 10 | 4 | 2 | 0 | |
2 | Adin Hill | 12 | 8 | 3 | 0 | 0.909 | 2.29 | 682 | 2 | 0 | 26 | 287 | 0 | 1 | 1 | 0.750 | 4 | 11 | 37 | 0 | 0 | 1 | |
Team Total or Average | 49 | 33 | 13 | 2 | 0.911 | 2.41 | 2908 | 6 | 3 | 117 | 1308 | 0 | 3 | 2 | 0.769 | 13 | 48 | 47 | 4 | 2 | 1 |
Projected Total Cap Hit | 79,009,954$ |
Projected Cap Space | 3,490,046$ |
Retains And Buyout Cap Hit | 0$ |
Salary Cap To Date | 47,783,824$ |
Players In Salary Cap | 20 |
LTIR Players | 1 |
Flyers Roster | Pos | Age | Cap Hit | 2022-23 | 2023-24 | 2024-25 | 2025-26 | 2026-27 | 2027-28 | 2028-29 | 2029-30 |
---|---|---|---|---|---|---|---|---|---|---|---|
Adam Fox | D | 24 | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | 9,500,000$ | UFA |
Adin Hill | G | 26 | 1,500,000$ | 1,500,000$ | ![]() | ||||||
Alex Killorn | LW/RW | 33 | 4,450,000$ | 4,450,000$ | UFA | ||||||
Anthony Cirelli | C/RW | 25 | 5,500,000$ | 5,500,000$ | 5,500,000$ | 5,500,000$ | 5,500,000$ | UFA | |||
Cody Ceci | D | 29 | 3,750,000$ | 3,750,000$ | 3,750,000$ | 3,750,000$ | 3,750,000$ | UFA | |||
Connor Hellebuyck ![]() | G | 29 | 6,166,666$ | 6,166,666$ | 6,166,666$ | UFA | |||||
Dmitry Kulikov ![]() | D | 32 | 1,250,000$ | 1,250,000$ | UFA | ||||||
Erik Johnson | D | 34 | 6,000,000$ | 6,000,000$ | UFA | ||||||
Jason Dickinson | C/LW | 27 | 2,250,000$ | 2,250,000$ | 2,500,000$ | 2,500,000$ | 2,500,000$ | 2,500,000$ | UFA | ||
Luke Schenn ![]() | D | 33 | 1,250,000$ | 1,250,000$ | 1,250,000$ | 1,250,000$ | UFA | ||||
Marcus Johansson ![]() | C/LW/RW | 32 | 1,200,000$ | 1,200,000$ | 1,200,000$ | UFA | |||||
Mason Shaw ![]() | C/LW | 24 | 800,000$ | 800,000$ | RFA | ||||||
Michael Bunting ![]() | LW/RW | 27 | 950,000$ | 950,000$ | ![]() | ||||||
Mika Zibanejad | C | 29 | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | 8,500,000$ | UFA | |
Nick Bjugstad ![]() | C/RW | 30 | 1,000,000$ | 1,000,000$ | UFA | ||||||
Nick Paul | C/LW/RW | 27 | 3,150,000$ | 3,150,000$ | 3,150,000$ | 3,150,000$ | 3,150,000$ | 3,150,000$ | 3,150,000$ | 3,150,000$ | UFA |
Ondrej Palat | LW/RW | 31 | 5,500,000$ | 5,500,000$ | 5,500,000$ | 5,500,000$ | 5,500,000$ | UFA | |||
Roman Josi | D | 32 | 9,000,000$ | 9,000,000$ | 9,000,000$ | 9,000,000$ | 9,000,000$ | 9,000,000$ | 9,000,000$ | UFA | |
Scott Wedgewood ![]() | G | 30 | 750,000$ | 750,000$ | UFA | ||||||
Steven Stamkos | C/LW/RW | 32 | 8,500,000$ | 8,500,000$ | 8,500,000$ | UFA | |||||
Ty Dellandrea ![]() | C/RW | 22 | 863,333$ | 863,333$ | RFA |
Non-Roster | Pos | Age | Cap Hit | 2022-23 | 2023-24 | 2024-25 | 2025-26 | 2026-27 | 2027-28 | 2028-29 | 2029-30 |
---|---|---|---|---|---|---|---|---|---|---|---|
Adam Scheel ![]() | G | 23 | 925,000$ | 925,000$ | RFA | ||||||
Alex Galchenyuk ![]() | C/LW | 28 | 750,000$ | 750,000$ | UFA | ||||||
Amir Miftakhov ![]() | G | 22 | 838,333$ | 838,333$ | 838,333$ | RFA | |||||
Andrew Agozzino ![]() | C/LW | 32 | 750,000$ | 750,000$ | UFA | ||||||
Andrew Poturalski ![]() | C/RW | 29 | 750,000$ | 750,000$ | UFA | ||||||
Bennett MacArthur ![]() | LW | 21 | 859,167$ | 859,167$ | 859,167$ | 859,167$ | RFA | ||||
Brandon Pirri ![]() | LW/RW | 31 | 750,000$ | 750,000$ | 750,000$ | UFA | |||||
Cavan Fitzgerald ![]() | D | 26 | 750,000$ | 750,000$ | ![]() | ||||||
Curtis Douglas ![]() | C | 22 | 750,000$ | 750,000$ | RFA | ||||||
Dylan Sikura ![]() | C/LW/RW | 27 | 1,100,000$ | 1,100,000$ | 1,100,000$ | 1,100,000$ | UFA | ||||
Gemel Smith ![]() | C/LW/RW | 28 | 900,000$ | 900,000$ | 900,000$ | 900,000$ | UFA | ||||
Greg McKegg ![]() | C/LW/RW | 30 | 1,000,000$ | 1,000,000$ | UFA | ||||||
Hayden Hodgson ![]() | RW | 26 | 750,000$ | 750,000$ | ![]() | ||||||
Jake Walman ![]() | D | 26 | 750,000$ | 750,000$ | ![]() | ||||||
Jason Demers ![]() | D | 34 | 750,000$ | 750,000$ | UFA | ||||||
Jeff Malott ![]() | LW | 26 | 800,000$ | 800,000$ | 800,000$ | ![]() | |||||
Jordan Frasca ![]() | C | 21 | 859,167$ | 859,167$ | 859,167$ | 859,167$ | RFA | ||||
Kirill Ustimenko ![]() | G | 23 | 750,000$ | 750,000$ | 750,000$ | ![]() | |||||
Lane Pederson ![]() | C/RW | 25 | 900,000$ | 900,000$ | ![]() | ||||||
Linden Marshall ![]() | G | 24 | 750,000$ | 750,000$ | 750,000$ | ![]() | |||||
Luke Glendening ![]() | C/LW/RW | 33 | 1,250,000$ | 1,250,000$ | UFA | ||||||
Mareks Mitens ![]() | G | 24 | 750,000$ | 750,000$ | 750,000$ | ![]() | |||||
Maxim Cajkovic ![]() | LW/RW | 22 | 750,000$ | 750,000$ | RFA | ||||||
Nick Abruzzese ![]() | C/LW | 23 | 850,000$ | 850,000$ | RFA | ||||||
Nick Seeler ![]() | D | 29 | 775,000$ | 775,000$ | 775,000$ | UFA | |||||
Nolan Burke ![]() | C | 20 | 861,667$ | 861,667$ | 861,667$ | 861,667$ | RFA | ||||
Nolan Stevens ![]() | C/LW | 26 | 750,000$ | 750,000$ | ![]() | ||||||
Paul Cotter ![]() | C/LW | 23 | 760,000$ | 760,000$ | RFA | ||||||
Peetro Seppala ![]() | D | 22 | 855,000$ | 855,000$ | 855,000$ | RFA | |||||
Robin Salo ![]() | D | 24 | 1,250,000$ | 1,250,000$ | 1,250,000$ | ![]() | |||||
Simon Johansson ![]() | D | 23 | 855,000$ | 855,000$ | 855,000$ | ![]() | |||||
T.J. Tynan ![]() | C | 30 | 900,000$ | 900,000$ | UFA | ||||||
Tyler Wotherspoon ![]() | D | 29 | 750,000$ | 750,000$ | 750,000$ | UFA | |||||
Victor Rask ![]() | C/LW | 29 | 1,000,000$ | 1,000,000$ | UFA | ||||||
Vladislav Kolyachonok ![]() | D | 21 | 800,000$ | 800,000$ | RFA | ||||||
Wade Allison ![]() | RW | 25 | 1,100,000$ | 1,100,000$ | 1,100,000$ | ![]() | |||||
Will Cranley ![]() | G | 20 | 750,000$ | 750,000$ | 750,000$ | 750,000$ | RFA |
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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 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 2 | 1.000 | 2 | 4 | 6 | 0 | 1 | 0 | 1 | 1 | 0 | 21 | 6 | 8 | 7 | 0 | 30 | 5 | 4 | 21 | 1 | 0 | 0.00% | 2 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 22 | 15 | 24 | 7 | 13 | 6 | 100.0% | 9.5% | 100.0% | 109.5 | LUCKY | |
2 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 0 | 4 | 0.667 | 10 | 18 | 28 | 0 | 0 | 2 | 5 | 3 | 0 | 72 | 22 | 24 | 26 | 0 | 92 | 23 | 32 | 47 | 10 | 3 | 30.00% | 16 | 3 | 81.25% | 1 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 73 | 50 | 69 | 22 | 37 | 19 | 50.0% | 13.9% | 89.1% | 103.0 | FUN | |
3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 3 | 3 | 4 | 1.000 | 6 | 11 | 17 | 0 | 0 | 0 | 3 | 3 | 0 | 66 | 24 | 22 | 20 | 0 | 50 | 19 | 12 | 44 | 9 | 3 | 33.33% | 6 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 46 | 31 | 46 | 14 | 26 | 13 | 50.0% | 9.1% | 94.0% | 103.1 | LUCKY | |
4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 3 | 5 | 4 | 1.000 | 8 | 14 | 22 | 0 | 0 | 2 | 4 | 2 | 0 | 62 | 11 | 36 | 15 | 0 | 68 | 18 | 25 | 32 | 7 | 3 | 42.86% | 10 | 0 | 100.00% | 1 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 45 | 30 | 49 | 15 | 25 | 12 | 62.5% | 12.9% | 95.6% | 108.5 | LUCKY | |
5 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 6 | 1 | 3 | 0.750 | 7 | 12 | 19 | 0 | 0 | 2 | 1 | 4 | 0 | 61 | 29 | 12 | 20 | 0 | 59 | 31 | 14 | 39 | 5 | 1 | 20.00% | 7 | 2 | 71.43% | 1 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 49 | 34 | 47 | 14 | 23 | 11 | 60.0% | 11.5% | 89.8% | 101.3 | FUN | |
6 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 3 | 5 | 4 | 1.000 | 8 | 14 | 22 | 0 | 0 | 6 | 1 | 1 | 0 | 54 | 19 | 14 | 21 | 0 | 51 | 11 | 30 | 40 | 3 | 1 | 33.33% | 13 | 2 | 84.62% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 44 | 29 | 50 | 15 | 24 | 12 | 87.5% | 14.8% | 94.1% | 108.9 | LUCKY | |
7 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 5 | 6 | -1 | 2 | 0.500 | 5 | 7 | 12 | 1 | 0 | 2 | 0 | 2 | 2 | 69 | 27 | 29 | 12 | 5 | 55 | 13 | 21 | 28 | 12 | 2 | 16.67% | 8 | 2 | 75.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 56 | 41 | 43 | 14 | 25 | 13 | 42.9% | 7.2% | 89.1% | 96.3 | Unlucky | |
8 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | -2 | 0 | 0.000 | 4 | 7 | 11 | 1 | 0 | 2 | 2 | 0 | 0 | 23 | 6 | 8 | 9 | 0 | 37 | 14 | 4 | 26 | 2 | 1 | 50.00% | 2 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 23 | 15 | 23 | 6 | 13 | 6 | 33.3% | 17.4% | 83.8% | 101.2 | FUN | |
9 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 2 | 1.000 | 2 | 4 | 6 | 0 | 0 | 2 | 0 | 0 | 0 | 31 | 15 | 6 | 10 | 0 | 28 | 10 | 6 | 25 | 0 | 0 | 0.00% | 3 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 22 | 15 | 25 | 6 | 12 | 6 | 66.7% | 6.5% | 96.4% | 102.9 | DULL | |
10 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 8 | 5 | 8 | 1.000 | 13 | 23 | 36 | 0 | 0 | 7 | 4 | 2 | 0 | 114 | 42 | 37 | 35 | 0 | 92 | 23 | 32 | 87 | 13 | 3 | 23.08% | 15 | 1 | 93.33% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 101 | 70 | 89 | 27 | 48 | 25 | 58.8% | 11.4% | 91.3% | 102.7 | LUCKY | |
11 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 2 | 1 | 2 | 1.000 | 3 | 4 | 7 | 0 | 0 | 0 | 1 | 1 | 2 | 26 | 6 | 11 | 8 | 4 | 34 | 5 | 4 | 26 | 4 | 0 | 0.00% | 2 | 1 | 50.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 27 | 19 | 24 | 7 | 12 | 6 | 75.0% | 11.5% | 94.1% | 105.7 | LUCKY | |
12 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | -6 | 0 | 0.000 | 1 | 2 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 43 | 11 | 13 | 19 | 0 | 62 | 21 | 18 | 41 | 7 | 0 | 0.00% | 9 | 1 | 88.89% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 46 | 31 | 49 | 14 | 24 | 11 | 14.3% | 2.3% | 88.7% | 91.0 | Unlucky | |
13 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 2 | 1.000 | 3 | 6 | 9 | 0 | 1 | 0 | 1 | 2 | 0 | 30 | 7 | 4 | 19 | 0 | 25 | 8 | 2 | 25 | 4 | 2 | 50.00% | 1 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 21 | 14 | 24 | 7 | 13 | 6 | 100.0% | 10.0% | 100.0% | 110.0 | LUCKY | |
14 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | -2 | 2 | 0.500 | 4 | 7 | 11 | 0 | 0 | 2 | 1 | 1 | 0 | 53 | 24 | 18 | 11 | 0 | 45 | 27 | 8 | 40 | 7 | 1 | 14.29% | 3 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 49 | 33 | 44 | 14 | 26 | 13 | 33.3% | 7.5% | 86.7% | 94.2 | Unlucky | |
15 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 1 | 2 | 1.000 | 2 | 4 | 6 | 0 | 0 | 0 | 1 | 0 | 1 | 33 | 6 | 17 | 8 | 2 | 26 | 7 | 6 | 22 | 4 | 1 | 25.00% | 3 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 27 | 19 | 21 | 7 | 11 | 6 | 50.0% | 6.1% | 96.2% | 102.2 | DULL | |
16 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | -4 | 0 | 0.000 | 2 | 3 | 5 | 0 | 0 | 1 | 0 | 1 | 0 | 54 | 11 | 18 | 25 | 0 | 54 | 21 | 28 | 40 | 6 | 0 | 0.00% | 14 | 2 | 85.71% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 45 | 29 | 48 | 15 | 26 | 12 | 33.3% | 3.7% | 88.9% | 92.6 | Unlucky | |
17 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 7 | 8 | -1 | 2 | 0.500 | 7 | 9 | 16 | 0 | 0 | 4 | 1 | 1 | 1 | 56 | 15 | 16 | 24 | 4 | 55 | 32 | 20 | 43 | 3 | 1 | 33.33% | 10 | 1 | 90.00% | 1 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 48 | 33 | 50 | 13 | 25 | 12 | 46.2% | 12.5% | 85.5% | 98.0 | FUN | |
18 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 4 | -1 | 1 | 0.500 | 3 | 6 | 9 | 0 | 0 | 0 | 0 | 3 | 0 | 30 | 6 | 11 | 12 | 1 | 23 | 6 | 2 | 23 | 5 | 0 | 0.00% | 1 | 1 | 0.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 28 | 21 | 20 | 6 | 12 | 7 | 50.0% | 10.0% | 82.6% | 92.6 | FUN | |
19 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 6 | 6 | 0 | 2 | 0.500 | 6 | 10 | 16 | 0 | 0 | 1 | 2 | 2 | 1 | 60 | 20 | 19 | 18 | 3 | 69 | 15 | 35 | 55 | 6 | 1 | 16.67% | 9 | 2 | 77.78% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 51 | 36 | 46 | 14 | 23 | 12 | 55.6% | 10.0% | 91.3% | 101.3 | LUCKY | |
20 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 9 | 9 | 0 | 4 | 0.667 | 9 | 15 | 24 | 0 | 0 | 1 | 2 | 6 | 0 | 93 | 23 | 32 | 38 | 0 | 68 | 26 | 27 | 70 | 7 | 0 | 0.00% | 11 | 2 | 81.82% | 1 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 68 | 46 | 70 | 23 | 41 | 20 | 56.3% | 9.7% | 86.8% | 96.4 | FUN | |
21 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 4 | 1 | 2 | 1.000 | 5 | 6 | 11 | 0 | 0 | 0 | 3 | 1 | 2 | 46 | 15 | 12 | 18 | 3 | 34 | 8 | 12 | 17 | 5 | 0 | 0.00% | 6 | 1 | 83.33% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 29 | 20 | 21 | 7 | 14 | 7 | 62.5% | 10.9% | 88.2% | 99.1 | FUN | |
22 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | -2 | 2 | 0.500 | 4 | 7 | 11 | 0 | 0 | 1 | 2 | 1 | 0 | 55 | 16 | 27 | 12 | 0 | 49 | 27 | 12 | 27 | 8 | 1 | 12.50% | 6 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 49 | 33 | 46 | 15 | 24 | 12 | 33.3% | 7.3% | 87.8% | 95.0 | Unlucky | |
23 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 2 | 4 | 2 | 1.000 | 6 | 11 | 17 | 0 | 0 | 3 | 2 | 1 | 0 | 38 | 12 | 13 | 13 | 0 | 33 | 5 | 4 | 18 | 4 | 3 | 75.00% | 2 | 0 | 100.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 25 | 19 | 22 | 7 | 12 | 6 | 60.0% | 15.8% | 93.9% | 109.7 | LUCKY | |
24 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 11 | 4 | 7 | 4 | 0.667 | 11 | 21 | 32 | 0 | 1 | 4 | 4 | 3 | 0 | 93 | 34 | 30 | 29 | 0 | 70 | 17 | 14 | 69 | 14 | 3 | 21.43% | 7 | 2 | 71.43% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 75 | 52 | 64 | 23 | 39 | 21 | 80.0% | 11.8% | 94.3% | 106.1 | LUCKY | |
25 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 3 | 1 | 2 | 1.000 | 4 | 7 | 11 | 0 | 0 | 1 | 0 | 2 | 1 | 43 | 14 | 5 | 21 | 3 | 22 | 9 | 6 | 24 | 2 | 1 | 50.00% | 3 | 2 | 33.33% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 30 | 23 | 20 | 5 | 12 | 6 | 75.0% | 9.3% | 86.4% | 95.7 | FUN | |
26 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 3 | 6 | 4 | 1.000 | 9 | 13 | 22 | 0 | 0 | 2 | 3 | 4 | 0 | 73 | 21 | 27 | 25 | 0 | 49 | 11 | 16 | 37 | 9 | 0 | 0.00% | 7 | 1 | 85.71% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 57 | 44 | 39 | 13 | 22 | 12 | 81.8% | 12.3% | 93.9% | 106.2 | LUCKY | |
27 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 2 | 1 | 2 | 1.000 | 3 | 5 | 8 | 0 | 0 | 1 | 1 | 0 | 1 | 28 | 7 | 13 | 7 | 1 | 30 | 11 | 8 | 17 | 5 | 1 | 20.00% | 4 | 1 | 75.00% | 0 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 27 | 19 | 21 | 7 | 13 | 6 | 66.7% | 10.7% | 93.3% | 104.0 | LUCKY | |
_Vs Division | 17 | 11 | 3 | 0 | 1 | 1 | 1 | 0 | 56 | 45 | 11 | 27 | 0.794 | 56 | 96 | 152 | 1 | 0 | 17 | 16 | 21 | 3 | 514 | 168 | 175 | 167 | 8 | 456 | 143 | 157 | 327 | 56 | 13 | 23.21% | 70 | 12 | 82.86% | 4 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 425 | 297 | 389 | 122 | 214 | 108 | 56.6% | 10.9% | 90.1% | 101.0 | FUN | |
_Vs Conference | 30 | 17 | 8 | 0 | 2 | 1 | 2 | 0 | 96 | 79 | 17 | 43 | 0.717 | 96 | 165 | 261 | 2 | 1 | 32 | 29 | 31 | 5 | 896 | 306 | 294 | 288 | 15 | 810 | 277 | 256 | 629 | 97 | 23 | 23.71% | 110 | 17 | 84.55% | 5 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 743 | 516 | 688 | 216 | 382 | 195 | 54.1% | 10.7% | 90.2% | 101.0 | FUN | |
_Since Last GM Reset | 48 | 25 | 13 | 0 | 4 | 2 | 4 | 0 | 147 | 119 | 28 | 68 | 0.708 | 147 | 250 | 397 | 2 | 3 | 47 | 45 | 47 | 11 | 1427 | 449 | 482 | 482 | 26 | 1310 | 423 | 402 | 983 | 162 | 32 | 19.75% | 180 | 27 | 85.00% | 5 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 1196 | 833 | 1103 | 349 | 608 | 310 | 55.6% | 10.3% | 90.9% | 101.2 | FUN | |
Total | 48 | 25 | 13 | 0 | 4 | 2 | 4 | 0 | 147 | 119 | 28 | 68 | 0.708 | 147 | 250 | 397 | 2 | 3 | 47 | 45 | 47 | 11 | 1427 | 449 | 482 | 482 | 26 | 1310 | 423 | 402 | 983 | 162 | 32 | 19.75% | 180 | 27 | 85.00% | 5 | 870 | 1700 | 51.18% | 867 | 1619 | 53.55% | 361 | 741 | 48.72% | 1196 | 833 | 1103 | 349 | 608 | 310 | 55.6% | 10.3% | 90.9% | 101.2 | FUN |
Puck Time | |
---|---|
Offensive Zone | 24 |
Neutral Zone | 12 |
Defensive Zone | 22 |
Puck Time | |
---|---|
Offensive Zone Start | 1700 |
Neutral Zone Start | 741 |
Defensive Zone Start | 1619 |
Puck Time | |
---|---|
With Puck | 31 |
Without Puck | 29 |
Faceoffs | |
---|---|
Faceoffs Won | 2098 |
Faceoffs Lost | 1962 |
Team Average Shots after | League Average Shots after | |
---|---|---|
1st Period | 9.4 | 9.57 |
2nd Period | 19.4 | 20.31 |
3rd Period | 29.4 | 30.68 |
Overtime | 30.0 | 31.4 |
Goals in | Team Average Goals after | League Average Goals after |
---|---|---|
1st Period | 1.0 | 0.64 |
2nd Period | 1.9 | 1.65 |
3rd Period | 2.9 | 2.67 |
Overtime | 3.1 | 2.83 |
Even Strenght Goal | 108 |
---|---|
PP Goal | 32 |
PK Goal | 5 |
Empty Net Goal | 2 |
Home | Away | |
---|---|---|
Win | 17 | 16 |
Lost | 6 | 7 |
Overtime Lost | 1 | 1 |
PP Attempt | 162 |
---|---|
PP Goal | 32 |
PK Attempt | 180 |
PK Goal Against | 27 |
Home | |
---|---|
Shots For | 29.7 |
Shots Against | 27.3 |
Goals For | 3.1 |
Goals Against | 2.5 |
Hits | 20.5 |
Shots Blocked | 8.8 |
Pim | 8.4 |
Date | Matchup | Result | Detail | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2022-10-13 | @ | Devils3,Flyers1 | RECAP | |||||||||||
2022-10-15 | @ | Canucks2,Flyers3 (SO) | RECAP | |||||||||||
2022-10-18 | @ | Flyers1,Lightning4 | RECAP | |||||||||||
2022-10-19 | @ | Flyers4,Panthers3 (OT) | RECAP | |||||||||||
2022-10-22 | @ | Flyers3,Predators0 | RECAP | |||||||||||
2022-10-23 | @ | Sharks1,Flyers5 | RECAP | |||||||||||
2022-10-27 | @ | Panthers3,Flyers2 | RECAP | |||||||||||
2022-10-29 | @ | Hurricanes3,Flyers5 | RECAP | |||||||||||
2022-11-01 | @ | Flyers3,Rangers5 | RECAP | |||||||||||
2022-11-02 | @ | Flyers6,Maple Leafs0 | RECAP | |||||||||||
2022-11-05 | @ | Flyers4,Senators2 | RECAP | |||||||||||
2022-11-08 | @ | Blues1,Flyers2 (OT) | RECAP | |||||||||||
2022-11-10 | @ | Flyers3,Blue Jackets2 | RECAP | |||||||||||
2022-11-12 | @ | Senators1,Flyers2 | RECAP | |||||||||||
2022-11-13 | @ | Stars2,Flyers6 | RECAP | |||||||||||
2022-11-15 | @ | Flyers2,Blue Jackets5 | RECAP | |||||||||||
2022-11-17 | @ | Flyers5,Bruins4 (SO) | RECAP | |||||||||||
2022-11-19 | @ | Flyers2,Canadiens1 | RECAP | |||||||||||
2022-11-21 | @ | Flames0,Flyers2 | RECAP | |||||||||||
2022-11-23 | @ | Flyers3,Capitals2 | RECAP | |||||||||||
2022-11-25 | @ | Penguins3,Flyers4 (OT) | RECAP | |||||||||||
2022-11-26 | @ | Flyers3,Islanders1 | RECAP | |||||||||||
2022-11-29 | @ | Islanders2,Flyers5 | RECAP | |||||||||||
2022-12-01 | @ | Lightning2,Flyers3 | RECAP | |||||||||||
2022-12-03 | @ | Devils3,Flyers4 | RECAP | |||||||||||
2022-12-05 | @ | Avalanche1,Flyers3 | RECAP | |||||||||||
2022-12-07 | @ | Capitals2,Flyers3 | RECAP | |||||||||||
2022-12-09 | @ | Flyers3,Golden Knights2 (OT) | RECAP | |||||||||||
2022-12-11 | @ | Flyers1,Coyotes3 | RECAP | |||||||||||
2022-12-13 | @ | Flyers5,Avalanche2 | RECAP | |||||||||||
2022-12-15 | @ | Flyers5,Devils4 | RECAP | |||||||||||
2022-12-17 | @ | Rangers1,Flyers2 (SO) | RECAP | |||||||||||
2022-12-20 | @ | Blue Jackets2,Flyers4 | RECAP | |||||||||||
2022-12-22 | @ | Flyers4,Maple Leafs1 | RECAP | |||||||||||
2022-12-23 | @ | Flyers2,Hurricanes3 (OT) | RECAP | |||||||||||
2022-12-29 | @ | Flyers4,Sharks2 | RECAP | |||||||||||
2022-12-31 | @ | Flyers0,Kings3 | RECAP | |||||||||||
2023-01-02 | @ | Flyers4,Ducks3 | RECAP | |||||||||||
2023-01-05 | @ | Coyotes3,Flyers1 | RECAP | |||||||||||
2023-01-08 | @ | Maple Leafs3,Flyers1 | RECAP | |||||||||||
2023-01-11 | @ | Capitals1,Flyers3 | RECAP | |||||||||||
2023-01-14 | @ | Flyers4,Capitals3 | RECAP | |||||||||||
2023-01-16 | @ | Flyers2,Bruins4 | RECAP | |||||||||||
2023-01-17 | @ | Ducks3,Flyers0 | RECAP | |||||||||||
2023-01-19 | @ | Blackhawks4,Flyers3 (OT) | RECAP | |||||||||||
2023-01-21 | @ | Flyers4,Red Wings6 | RECAP | |||||||||||
2023-01-22 | @ | Jets4,Flyers5 (SO) | RECAP | |||||||||||
2023-01-24 | @ | Kings4,Flyers1 | RECAP | |||||||||||
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Trade Deadline --- Trades can’t be done after this day is simulated! | ||||||||||||||
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Salary Cap | |||
---|---|---|---|
Players Total Salaries | Retained Salary | Total Cap Hit | Estimated Cap Space |
81,829,999$ | 0$ | 79,009,954$ | 670,001$ |
Arena | Goal Horn | About us | |
---|---|---|---|
![]() | Name | Wells Fargo Center | |
City | Philadelphia | ||
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 | 139433 | 116670 | 46608 | 92382 | 22814 |
Attendance PCT | 96.83% | 97.23% | 97.10% | 96.23% | 95.06% |
Income | |||||
---|---|---|---|---|---|
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Home Games Left | Average Attendance - % | Average Income per Game | Year to Date Revenue | Arena Capacity | Team Popularity |
17 | 17413 - 96.74% | 1,533,705$ | 36,808,912$ | 18000 | 100 |
Expenses | |||
---|---|---|---|
Players Total Salaries | Players Total Average Salaries | Coaches Salaries | Special Salary Cap Value |
81,829,999$ | 81,829,999$ | 0$ | 0$ |
Year To Date Expenses | Salary Cap Per Days | Salary Cap To Date | Luxury Taxe Total |
---|---|---|---|
48,436,381$ | 400,335$ | 47,783,824$ | 0$ |
Estimate | |||
---|---|---|---|
Estimated Season Revenue | Remaining Season Days | Expenses Per Days | Estimated Season Expenses |
26,072,979$ | 78 | 438,254$ | 34,183,812$ |
Team Total Estimate | |||
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Estimated Season Expenses | Estimated Season Salary Cap | Current Bank Account | Projected Bank Account |
35,605,303$ | 81,829,999$ | 51,708,317$ | 42,175,993$ |
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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2023 | |||||||
2024 | |||||||
2025 | |||||||
2026 | |||||||
2027 |
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 |
---|---|---|---|---|---|
Aidan Hreschuk | 2021 | 82 | |||
Arseny Koromyslov | 2022 | 143 | |||
Brandon Lisowsky | 2022 | 167 | |||
Brayden Peters | 2020 | 187 | |||
Bryce Montgomery | 2021 | 172 | |||
Chase McLane | |||||
Dru Krebs | 2021 | 162 | |||
Garin Bjorklund | 2020 | 145 | |||
Henry Thrun | 2019 | 104 | |||
Jake Boltmann | |||||
Jake Karabela | 2022 | 122 | |||
James Fisher | 2022 | 218 | |||
Joni Jurmo | 2020 | 63 | |||
Juho Markkanen | 2020 | 181 | |||
Justin Janicke | 2021 | 183 | |||
Kasper Puutio | 2020 | 119 | |||
Kirill Gerasimyuk | 2021 | 119 | |||
Landon Sim | 2022 | 208 | |||
Logan Neaton | 2019 | 166 | |||
Marcus Davidsson | 6.0 - 5.0 | ||||
Matvey Petrov | 2021 | 151 | |||
Noah Laba | 2022 | 123 | |||
Olof Lindbom | 7.5 - 4.5 | ||||
Otto Salin | 2022 | 154 | |||
Parker Bell | 2022 | 186 | |||
Riku Tohila | 2022 | 216 | |||
Ryan Ufko | 2021 | 87 | |||
Sam Rinzel | 2022 | 58 | |||
Simon Forsmark | 2022 | 88 | |||
Tucker Robertson | 2022 | 110 | |||
Ty Smilanic | 2020 | 57 | |||
Yaroslav Likhachyov | 2019 | 197 |
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