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Cleveland Monsters 0-0-0, 0pts · 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 | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 24 | C | 100.00 | 68 | 39 | 77 | 57 | 67 | 71 | 74 | 68 | 74 | 57 | 64 | 72 | 60 | 77 | 75 | 0 | 92 | 73 | 31 | 800,000$/1yrs | |||
![]() | 0 | LW | 100.00 | 91 | 47 | 76 | 55 | 70 | 75 | 83 | 59 | 62 | 59 | 63 | 69 | 61 | 77 | 75 | 0 | 89 | 73 | 31 | ||||
![]() | 0 | LW/RW | 100.00 | 87 | 44 | 50 | 52 | 92 | 67 | 66 | 70 | 69 | 56 | 55 | 73 | 55 | 79 | 74 | 0 | 63 | 72 | 32 | ||||
![]() | 0 | LW/RW | 100.00 | 83 | 36 | 91 | 60 | 77 | 74 | 88 | 57 | 55 | 58 | 64 | 61 | 60 | 70 | 69 | 0 | 90 | 72 | 27 | 800,000$/1yrs | |||
![]() | 0 | C/LW/RW | 100.00 | 78 | 39 | 87 | 57 | 84 | 74 | 84 | 57 | 61 | 61 | 56 | 63 | 57 | 72 | 70 | 0 | 91 | 71 | 28 | 1,200,000$/1yrs | |||
![]() | 0 | C/RW | 100.00 | 64 | 33 | 93 | 59 | 76 | 73 | 76 | 59 | 59 | 60 | 62 | 57 | 60 | 68 | 67 | 0 | 88 | 71 | 26 | ||||
![]() | 15 | RW | 100.00 | 62 | 38 | 88 | 53 | 73 | 64 | 67 | 64 | 35 | 54 | 54 | 68 | 54 | 68 | 67 | 0 | 85 | 68 | 25 | 775,000$/1yrs | |||
![]() | 19 | C | 100.00 | 62 | 33 | 95 | 55 | 76 | 72 | 67 | 58 | 55 | 63 | 50 | 56 | 54 | 66 | 66 | 0 | 88 | 68 | 25 | 950,000$/1yrs | |||
![]() | 0 | RW | 100.00 | 70 | 40 | 75 | 55 | 72 | 64 | 67 | 61 | 38 | 56 | 54 | 64 | 55 | 63 | 60 | 0 | 45 | 68 | 23 | ||||
![]() | 0 | LW | 100.00 | 59 | 38 | 93 | 57 | 76 | 64 | 67 | 59 | 38 | 57 | 55 | 62 | 56 | 59 | 58 | 0 | 87 | 67 | 22 | 855,000$/2yrs | |||
![]() | 0 | C | 100.00 | 73 | 42 | 70 | 54 | 91 | 62 | 66 | 60 | 61 | 51 | 55 | 64 | 53 | 61 | 58 | 0 | 90 | 67 | 22 | 870,000$/1yrs | |||
![]() | 0 | LW | 100.00 | 70 | 32 | 95 | 55 | 68 | 67 | 68 | 51 | 30 | 50 | 50 | 56 | 50 | 69 | 69 | 0 | 94 | 64 | 27 | ||||
![]() | 77 | D | 100.00 | 67 | 32 | 86 | 60 | 66 | 87 | 84 | 59 | 30 | 63 | 59 | 73 | 59 | 70 | 69 | 0 | 90 | 75 | 27 | 800,000$/1yrs | |||
![]() | 15 | D | 100.00 | 79 | 57 | 64 | 55 | 85 | 75 | 75 | 57 | 30 | 63 | 53 | 66 | 55 | 72 | 69 | 0 | 91 | 72 | 28 | ||||
![]() | 0 | D | 100.00 | 79 | 46 | 69 | 55 | 80 | 82 | 71 | 55 | 30 | 55 | 50 | 80 | 53 | 68 | 65 | 0 | 50 | 71 | 25 | 800,000$/1yrs | |||
![]() | 0 | D | 100.00 | 72 | 41 | 72 | 51 | 87 | 67 | 67 | 50 | 30 | 57 | 52 | 75 | 55 | 80 | 78 | 0 | 54 | 70 | 33 | 800,000$/1yrs | |||
![]() | 0 | D | 100.00 | 63 | 40 | 87 | 49 | 85 | 66 | 66 | 48 | 30 | 54 | 52 | 75 | 53 | 80 | 79 | 0 | 91 | 69 | 33 | 800,000$/1yrs | |||
![]() | 0 | D | 100.00 | 62 | 39 | 88 | 52 | 76 | 63 | 66 | 50 | 30 | 57 | 52 | 63 | 55 | 59 | 58 | 0 | 85 | 67 | 21 | 850,000$/1yrs | |||
Scratches | ||||||||||||||||||||||||||
![]() | 22 | C | 100.00 | 64 | 41 | 85 | 59 | 93 | 70 | 67 | 64 | 68 | 62 | 60 | 68 | 61 | 70 | 69 | 0 | 20 | 73 | 27 | ||||
![]() | 0 | C | 100.00 | 68 | 39 | 77 | 53 | 70 | 63 | 65 | 62 | 39 | 54 | 52 | 66 | 53 | 64 | 62 | 0 | 19 | 67 | 24 | ||||
![]() | 0 | RW | 100.00 | 81 | 41 | 62 | 57 | 81 | 64 | 82 | 54 | 49 | 57 | 55 | 64 | 56 | 59 | 55 | 0 | 85 | 67 | 21 | 867,500$/2yrs | |||
![]() | 0 | C | 100.00 | 68 | 33 | 95 | 55 | 80 | 71 | 67 | 50 | 74 | 60 | 50 | 57 | 50 | 69 | 68 | 0 | 82 | 66 | 26 | ||||
![]() | 0 | LW/RW | 100.00 | 61 | 40 | 89 | 52 | 86 | 62 | 65 | 62 | 45 | 53 | 51 | 65 | 52 | 63 | 61 | 0 | 20 | 66 | 23 | ||||
![]() | 0 | C | 100.00 | 61 | 38 | 89 | 55 | 69 | 62 | 66 | 59 | 43 | 52 | 55 | 63 | 54 | 59 | 58 | 0 | 20 | 66 | 21 | 858,333$/1yrs | |||
![]() | 12 | LW | 100.00 | 77 | 41 | 63 | 50 | 74 | 60 | 71 | 62 | 66 | 54 | 45 | 65 | 49 | 62 | 59 | 0 | 20 | 65 | 25 | ||||
![]() | 0 | C | 100.00 | 60 | 38 | 90 | 48 | 68 | 58 | 69 | 61 | 67 | 47 | 47 | 64 | 47 | 61 | 60 | 0 | 20 | 63 | 23 | ||||
![]() | 48 | D | 100.00 | 61 | 38 | 89 | 54 | 74 | 68 | 67 | 52 | 30 | 60 | 53 | 73 | 56 | 77 | 76 | 0 | 87 | 71 | 31 | ||||
![]() | 0 | D | 100.00 | 80 | 42 | 58 | 50 | 80 | 64 | 66 | 48 | 30 | 55 | 51 | 69 | 53 | 70 | 66 | 0 | 28 | 67 | 27 | ||||
![]() | 0 | D | 100.00 | 67 | 41 | 80 | 47 | 90 | 62 | 65 | 47 | 30 | 52 | 51 | 66 | 51 | 64 | 62 | 0 | 84 | 65 | 24 | 875,000$/1yrs | |||
![]() | 58 | D | 100.00 | 63 | 39 | 85 | 46 | 77 | 61 | 88 | 45 | 30 | 51 | 48 | 66 | 50 | 64 | 63 | 0 | 20 | 64 | 26 |
Priority | Type | Description |
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1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
Goalie | # | CON | SK | DU | EN | SZ | AG | RB | SC | HS | RT | PH | PS | EX | LD | PO | MO | OV | AGE | CONTRACT |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | 40 | 100.00 | 63 | 76 | 72 | 82 | 77 | 72 | 80 | 86 | 82 | 85 | 80 | 69 | 73 | 0 | 88 | 81 | 26 | |
![]() | 0 | 100.00 | 72 | 71 | 70 | 74 | 78 | 70 | 79 | 84 | 78 | 84 | 79 | 75 | 79 | 0 | 40 | 80 | 29 | 825,000$/1yrs |
Scratches | ||||||||||||||||||||
![]() | 45 | 100.00 | 75 | 70 | 72 | 74 | 79 | 76 | 77 | 75 | 76 | 74 | 76 | 67 | 67 | 0 | 88 | 78 | 24 | 800,000$/1yrs |
![]() | 0 | 100.00 | 73 | 58 | 70 | 75 | 73 | 76 | 73 | 73 | 76 | 76 | 74 | 60 | 59 | 0 | 42 | 75 | 20 |
Coaches Name | PH | DF | OF | PD | EX | LD | PO | CNT | Age | Contract | Salary |
---|---|---|---|---|---|---|---|---|---|---|---|
Lane Lambert | 93 | 84 | 82 | 95 | 78 | 74 | 86 | CAN | 61 | 4 | 1,000,000$ |
General Manager |
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Priority | Type | Description |
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1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
# | Player Name | Team Name | # | POS | GP | G | A | P | +/- | PIM | PIM5 | HIT | SHT | OSB | OSM | SHT% | SB | AMG | PPG | PPA | PPP | PPM | PKG | PKA | PKP | PKM | GW | GT | FO% | FOT | GA | TA | EG | HT | P/20 | PSG | PSS |
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Priority | Type | Description |
---|---|---|
1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
# | Goalie Name | Team Name | GP | W | L | OTL | PCT | GAA | MP | PIM | SO | GA | SA | SAR | A | EG | PS % | PSA | ST | BG | S1 | S2 | S3 |
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Player Name | POS | Age | Cap Hit | 2020-21 | 2021-22 | 2022-23 | 2023-24 | 2024-25 | 2025-26 | 2026-27 | 2027-28 |
---|---|---|---|---|---|---|---|---|---|---|---|
Alex Petrovic | D | 33 | 800,000$ | 800,000$ | UFA | ||||||
Cade Webber | D | 24 | 875,000$ | 875,000$ | |||||||
Calle Rosen | D | 31 | 0$ | UFA | |||||||
Christian Fischer | C/LW/RW | 28 | 1,200,000$ | 1,200,000$ | UFA | ||||||
Cole Koepke | LW/RW | 27 | 800,000$ | 800,000$ | UFA | ||||||
Colten Ellis | G | 24 | 800,000$ | 800,000$ | |||||||
Daniel Torgersson | LW/RW | 23 | 0$ | RFA | |||||||
Daniil Tarasov | G | 26 | 0$ | ||||||||
Dennis Gilbert | D | 28 | 0$ | UFA | |||||||
Dmitri Samorukov | D | 26 | 0$ | ||||||||
Emil Bemstrom | C/RW | 26 | 0$ | ||||||||
Eric Comrie | G | 29 | 825,000$ | 825,000$ | UFA | ||||||
Fabian Wagner | C | 21 | 858,333$ | 858,333$ | RFA | ||||||
Jack St. Ivany | D | 25 | 800,000$ | 800,000$ | |||||||
Jaroslav Chmelar | RW | 21 | 867,500$ | 867,500$ | 867,500$ | RFA | |||||
Jere Innala | LW | 27 | 0$ | UFA | |||||||
Joseph LaBate | LW/RW | 32 | 0$ | UFA | |||||||
Kevin Gravel | D | 33 | 800,000$ | 800,000$ | UFA | ||||||
Kirill Kudryavtsev | D | 21 | 850,000$ | 850,000$ | RFA | ||||||
Liam Foudy | C | 25 | 950,000$ | 950,000$ | |||||||
Logan Brown | C | 27 | 0$ | UFA | |||||||
Marshall Rifai | D | 27 | 0$ | UFA | |||||||
Matej Pekar | LW | 25 | 0$ | ||||||||
Milo Roelens | C | 22 | 870,000$ | 870,000$ | RFA | ||||||
Nathan Walker | LW | 31 | 0$ | UFA | |||||||
Nick Blankenburg | D | 27 | 800,000$ | 800,000$ | UFA | ||||||
Noah Philp | C | 26 | 0$ | ||||||||
Reece Newkirk | C | 24 | 0$ | ||||||||
Ryan Francis | C | 23 | 0$ | RFA | |||||||
Sandis Vilmanis | LW | 22 | 855,000$ | 855,000$ | 855,000$ | RFA | |||||
Shane Bowers | RW | 25 | 775,000$ | 775,000$ | |||||||
Sheldon Dries | C | 31 | 800,000$ | 800,000$ | UFA | ||||||
Ty Young | G | 20 | 0$ | RFA | |||||||
Tyler Tullio | RW | 23 | 0$ | RFA |
Forward Lines | |||||||
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Goalies | |||||||
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Priority | Type | Description |
---|---|---|
1 | | or OR | Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar |
2 | && or AND | Logical "and". Filter the column for content that matches text from either side of the operator. |
3 | /\d/ | Add any regex to the query to use in the query ("mig" flags can be included /\w/mig ) |
4 | < <= >= > | Find alphabetical or numerical values less than or greater than or equal to the filtered query |
5 | ! or != | Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (= ), single (' ) or double quote (" ) to exactly not match a filter. |
6 | " or = | To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query |
7 | - or to | Find a range of values. Make sure there is a space before and after the dash (or the word "to") |
8 | ? | Wildcard for a single, non-space character. |
8 | * | Wildcard for zero or more non-space characters. |
9 | ~ | Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query |
10 | text | Any text entered in the filter will match text found within the column |
# | VS Team | GP | W | L | T | OTW | OTL | SOW | SOL | GF | GA | Diff | P | PCT | G | A | TP | SO | EG | GP1 | GP2 | GP3 | GP4 | SHF | SH1 | SP2 | SP3 | SP4 | SHA | SHB | Pim | Hit | PPA | PPG | PP% | PKA | PK GA | PK% | PK GF | W OF FO | T OF FO | OF FO% | W DF FO | T DF FO | DF FO% | W NT FO | T NT FO | NT FO% | PZ DF | PZ OF | PZ NT | PC DF | PC OF | PC NT | GF% | SH% | SV% | PDO | PDOBRK |
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Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00% | 0 | 0 | 0.00% | 0 | 0 | 0 | 0.00% | 0 | 0 | 0.00% | 0 | 0 | 0.00% | 0 | 0 | 0 | 0 | 0 | 0 | nan% | nan% | nan% | nan | Unlucky |
Puck Time | |
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Offensive Zone | NAN |
Neutral Zone | NAN |
Defensive Zone | NAN |
Puck Time | |
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Offensive Zone Start | 0 |
Neutral Zone Start | 0 |
Defensive Zone Start | 0 |
Puck Time | |
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With Puck | NAN |
Without Puck | NAN |
Faceoffs | |
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Faceoffs Won | 0 |
Faceoffs Lost | 0 |
Team Average Shots after | League Average Shots after | |
---|---|---|
1st Period | nan | 9.57 |
2nd Period | nan | 20.31 |
3rd Period | nan | 30.68 |
Overtime | nan | 31.4 |
Goals in | Team Average Goals after | League Average Goals after |
---|---|---|
1st Period | nan | 0.64 |
2nd Period | nan | 1.65 |
3rd Period | nan | 2.67 |
Overtime | nan | 2.83 |
Even Strenght Goal | 0 |
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PP Goal | 0 |
PK Goal | 0 |
Empty Net Goal | 0 |
Home | Away | |
---|---|---|
Win | 0 | 0 |
Lost | 0 | 0 |
Overtime Lost | 0 | 0 |
PP Attempt | 0 |
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PP Goal | 0 |
PK Attempt | 0 |
PK Goal Against | 0 |
Home | |
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Shots For | nan |
Shots Against | nan |
Goals For | nan |
Goals Against | nan |
Hits | nan |
Shots Blocked | nan |
Pim | nan |
Date | Matchup | Result | Detail |
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Salary Cap | |||
---|---|---|---|
Players Total Salaries | Retained Salary | Total Cap Hit | Estimated Cap Space |
1,370,083$ | 0$ | 0$ | 75,000,000$ |
Arena | About us | |
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![]() | Name | |
City | Cleveland | |
Capacity | 3000 | |
Season Ticket Holders | 0% |
Arena Capacity - Ticket Price Attendance - % | |||||
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Arena Capacity | 2000 | 1000 | |||
Ticket Price | 35$ | 0$ | $ | $ | $ |
Attendance | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
Attendance PCT | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
Income | |||||
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Home Games Left | Average Attendance - % | Average Income per Game | Year to Date Revenue | Arena Capacity | Team Popularity |
36 | 0 - 0.00% | 0$ | 0$ | 3000 | 100 |
Expenses | |||
---|---|---|---|
Players Total Salaries | Players Total Average Salaries | Coaches Salaries | Special Salary Cap Value |
1,370,083$ | 1,370,083$ | 0$ | 0$ |
Year To Date Expenses | Salary Cap Per Days | Salary Cap To Date | Luxury Taxe Total |
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0$ | 0$ | 0$ | 0$ |
Estimate | |||
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Estimated Season Revenue | Remaining Season Days | Expenses Per Days | Estimated Season Expenses |
0$ | 0 | 0$ | 0$ |
Team Total Estimate | |||
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Estimated Season Expenses | Estimated Season Salary Cap | Current Bank Account | Projected Bank Account |
0$ | 0$ | 0$ | 0$ |
Sponsors | |||
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TV Rights | Primary Sponsor | Secondary Sponsor | Secondary Sponsor |
Left Wing | Center | Right Wing |
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Defense #1 | Defense #2 | Goalie |
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