LSFH - Today's Games Farm

Roster

Player	#	POS	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Mark Kastelic	47	C	100.00	73	71	76	63	85	59	70	58	79	64	61	72	54	57	57	0	50	67	24	821,667$/1yrs
Adam Cracknell	14	C	100.00	55	57	66	62	70	66	64	62	60	59	61	63	60	83	69	0	50	65	38	750,000$/1yrs
Cole Schneider	25	C/LW	100.00	54	58	67	62	65	68	65	62	55	59	63	62	56	74	64	0	50	65	33	750,000$/1yrs
Marian Studenic	43	LW/RW	100.00	60	52	74	63	62	66	64	60	54	61	61	66	54	59	57	0	50	65	24	750,000$/2yrs
Laurent Dauphin	26	C	100.00	60	56	73	64	66	64	62	59	63	64	60	63	54	61	59	0	50	65	28	750,000$/1yrs
Alex Limoges	92	C/LW	100.00	54	57	67	61	65	66	64	62	55	60	62	61	54	62	57	0	50	64	26	780,000$/2yrs
Raphael Lavoie	50	RW	100.00	55	57	66	63	70	65	63	60	55	57	61	62	55	56	54	0	50	63	23	925,000$/2yrs
Garrett Pilon	40	RW	100.00	55	55	66	60	61	64	63	59	60	57	58	62	54	60	56	0	50	62	25	750,000$/2yrs
Jack Dugan	70	C/LW	100.00	55	58	65	58	67	64	63	58	55	57	57	61	54	60	56	0	50	62	25	750,000$/1yrs
Tyler Angle	59	C	100.00	55	55	66	60	57	67	64	58	65	57	58	59	55	56	54	0	50	62	22	925,000$/2yrs
Matej Blumel (R)	76	RW	100.00	52	52	66	60	62	62	60	57	52	57	58	58	51	54	51	0	50	61	23	925,000$/1yrs
Nathan Legare (R)	34	RW	100.00	56	55	64	61	65	66	65	57	55	56	57	58	54	55	53	0	50	61	22	905,000$/2yrs
Pierre-Olivier Joseph	73	D	100.00	63	45	73	70	68	70	72	60	40	69	60	72	54	57	57	0	50	69	24	825,000$/2yrs
Isaak Phillips	41	D	100.00	59	59	68	63	67	66	63	58	40	61	57	65	54	55	53	0	50	64	22	925,000$/2yrs
Danny Dekeyser	65	D	100.00	57	50	72	59	63	62	62	56	40	59	55	67	54	74	64	0	50	63	33	750,000$/1yrs
Jimmy Schuldt	3	D	100.00	55	56	65	60	66	67	65	58	40	58	56	64	54	65	59	0	50	63	28	750,000$/2yrs
Ole Bjorgvik-Holm	94	D	100.00	69	34	83	57	78	72	77	59	30	58	53	56	46	59	61	0	50	63	21	896,667$/3yrs
Tyler Wotherspoon	24	D	100.00	55	55	66	59	67	66	63	57	40	57	56	63	54	69	61	0	50	63	30	762,500$/2yrs
Scratches
Ryan Carpenter	22	C/RW	100.00	67	59	75	62	72	63	71	59	67	64	60	68	54	68	61	0	50	66	32	750,000$/2yrs
Bokondji Imama	16	LW	100.00	59	55	60	56	71	64	64	56	55	56	56	60	54	63	58	0	50	61	27	750,000$/2yrs
Shawn Boudrias (R)	78	RW	100.00	54	54	64	54	67	54	54	54	55	54	54	58	54	58	55	0	50	58	24	750,000$/1yrs
Alex Laferriere (R)	56	C	100.00	51	51	65	52	55	52	51	51	52	51	51	57	51	52	50	0	50	55	21	875,000$/2yrs
Ville Koivunen (R)	63	C	100.00	52	51	65	52	55	53	53	51	67	51	51	57	51	49	48	0	50	55	20	880,000$/3yrs
Brett Berard (R)	52	C	100.00	51	51	65	51	52	51	51	51	52	51	51	57	51	51	49	0	50	55	21	925,000$/4yrs
David Goyette (R)	88	C	100.00	51	51	65	51	55	51	51	51	52	51	51	57	51	48	48	0	50	55	19	950,000$/4yrs
Aaron Ness	42	D	100.00	55	55	66	58	59	66	64	57	40	57	56	63	54	74	64	0	50	62	33	775,000$/1yrs
Brady Keeper	25	D	100.00	55	54	62	55	62	59	58	55	44	55	54	58	54	61	57	0	50	59	27	762,500$/2yrs
Xavier Bouchard (R)	82	D	100.00	55	54	67	55	66	57	57	54	40	55	54	61	54	56	54	0	50	59	23	750,000$/1yrs

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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	#	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Erik Kallgren	31	100.00	63	61	61	72	66	59	69	63	62	61	68	63	57	0	50	65	26	750,000$/2yrs
Maxime Lagace	33	100.00	72	52	53	77	57	79	57	64	60	58	52	53	52	0	50	64	30	750,000$/1yrs
Scratches
Dylan Ferguson	60	100.00	58	57	57	68	64	56	66	58	59	57	65	60	55	0	50	61	25	750,000$/1yrs
Strauss Mann	37	100.00	54	56	56	59	63	54	64	56	54	54	55	57	52	0	50	58	25	750,000$/1yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Stéphane Richer	75	75	75	75	75	75	75	Can	57	5	0$

General Manager

Player Stats

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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	MP	PPG	PPA	PPP	PKG	PKA	PKP	PKM	GW	GT	FO%	EG	HT	P/GP	PSG	PSS	GSAVG

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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

Salary

Player Name	POS	Age	Cap Hit	2023-24	2024-25	2025-26	2026-27	2027-28
Aaron Ness	D	33	775,000$	775,000$	UFA
Adam Cracknell	C	38	750,000$	750,000$	UFA
Alex Laferriere	C	21	875,000$	875,000$	875,000$	RFA
Alex Limoges	C/LW	26	780,000$	780,000$	775,000$	UFA
Bokondji Imama	LW	27	750,000$	750,000$	775,000$	UFA
Brady Keeper	D	27	762,500$	762,500$	775,000$	UFA
Brett Berard	C	21	925,000$	925,000$	925,000$	925,000$	925,000$	RFA
Cole Schneider	C/LW	33	750,000$	750,000$	UFA
Danny Dekeyser	D	33	750,000$	750,000$	UFA
David Goyette	C	19	950,000$	950,000$	950,000$	950,000$	950,000$	RFA
Dylan Ferguson	G	25	750,000$	750,000$	RFA
Erik Kallgren	G	26	750,000$	750,000$	775,000$	UFA
Garrett Pilon	RW	25	750,000$	750,000$	775,000$	UFA
Isaak Phillips	D	22	925,000$	925,000$	925,000$	RFA
Jack Dugan	C/LW	25	750,000$	750,000$	RFA
Jimmy Schuldt	D	28	750,000$	775,000$	775,000$	UFA
Laurent Dauphin	C	28	750,000$	750,000$	UFA
Marian Studenic	LW/RW	24	750,000$	750,000$	775,000$	RFA
Mark Kastelic	C	24	821,667$	821,667$	RFA
Matej Blumel	RW	23	925,000$	925,000$	RFA
Maxime Lagace	G	30	750,000$	750,000$	UFA
Nathan Legare	RW	22	905,000$	905,000$	905,000$	RFA
Ole Bjorgvik-Holm	D	21	896,667$	896,667$	896,667$	896,667$	RFA
Pierre-Olivier Joseph	D	24	825,000$	825,000$	825,000$	RFA
Raphael Lavoie	RW	23	925,000$	925,000$	874,125$	RFA
Ryan Carpenter	C/RW	32	750,000$	750,000$	775,000$	UFA
Shawn Boudrias	RW	24	750,000$	750,000$	RFA
Strauss Mann	G	25	750,000$	925,000$	RFA
Tyler Angle	C	22	925,000$	925,000$	925,000$	RFA
Tyler Wotherspoon	D	30	762,500$	762,500$	762,500$	UFA
Ville Koivunen	C	20	880,000$	880,000$	880,000$	880,000$	RFA
Xavier Bouchard	D	23	750,000$	750,000$	RFA

Lines

Forward Lines

Defensive Pairings

1st Power Play Unit

2nd Power Play Unit

Goalies

Team Stats

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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
	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
Offensive Zone	NAN
Neutral Zone	NAN
Defensive Zone	NAN

	Puck Time
Offensive Zone Start	0
Neutral Zone Start	0
Defensive Zone Start	0

	Puck Time
With Puck	NAN
Without Puck	NAN

	Faceoffs
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
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
PP Goal	0
PK Attempt	0
PK Goal Against	0

	Home
Shots For	nan
Shots Against	nan
Goals For	nan
Goals Against	nan
Hits	nan
Shots Blocked	nan
Pim	nan

Schedule

Date	Matchup			Result	Detail

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
0$	0$	0$	75,000,000$

Arena	About us
	Name
	City	Cleveland
	Capacity	3000
	Season Ticket Holders	10%

Arena Capacity - Ticket Price Attendance - %

Arena Capacity	2000	1000
Ticket Price	27$	16$	$	$	$
Attendance	0.00%	0.00%	0.00%	0.00%	0.00%
Attendance PCT	0.00%	0.00%	0.00%	0.00%	0.00%

Income

Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
41	0 - 0.00%	0$	0$	3000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
0$	0$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
0$	0$	0$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	0$	0$

Team Total Estimate
Estimated Season Expenses	Estimated Season Salary Cap	Current Bank Account	Projected Bank Account
0$	0$	0$	0$

Sponsors
TV Rights	Primary Sponsor	Secondary Sponsor	Secondary Sponsor

Depth Chart

Rookie

Injured

Cold Streak

Hot Streak

Left Wing

Center

Right Wing

Defense #1

Defense #2

Goalie

Transactions

Injuries

No Injury or Suspension.

Game Center

TRANSACTIONS

Team Info


	Head Coach	Stéphane Richer
	Division	METROPOLITAINE
	City	Cleveland
	Stadium Capacity	3,000

Monsters Affiliation


	General Manager
	Head Coach	Emily Castonguay
	Stadium
	Capacity	17,000

Team Leaders

Expanded Player Leaderboard

Team Stats

Goals For 0 nan GFG	Goals Against 0 nan GAA
Power Play Percentage nan% 0 GF	Penalty Kill Percentage nan% 0 GA
Expanded Team Stats

Team Captain - Alternate Captains

Captain

Alternate Captain