Ligue Hockey General Manager Québec

Roster

Player	#	POS	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Julien Gauthier	0	RW	99.00	82	34	90	63	90	72	69	62	59	60	66	62	64	71	70	0	44	74	28	950,000$/3yrs
Martin Pospisil	0	RW	100.00	95	44	55	63	71	78	87	61	60	66	59	60	60	67	63	0	41	73	25	1,000,000$/1yrs
Pavol Regenda	0	LW/RW	100.00	75	42	67	57	87	67	71	64	48	58	58	67	58	68	65	0	44	71	25	950,000$/1yrs
Noel Gunler	0	LW/RW	100.00	68	39	77	56	70	65	67	62	49	55	57	65	56	64	62	0	37	69	24	800,000$/2yrs
Joakim Kemell (R)	0	RW	100.00	64	39	84	59	74	65	71	59	41	58	58	62	58	59	58	0	44	69	21	918,333$/1yrs
Ivan Ivan (R)	0	C	99.00	58	32	92	62	74	72	71	54	72	56	63	51	58	62	61	0	44	68	23	845,000$/1yrs
Jaydon Dureau (R)	0	LW	100.00	62	38	88	52	69	62	65	63	42	53	52	66	52	64	63	0	44	67	24	800,000$/1yrs
Chase Stillman (R)	0	RW	100.00	76	41	64	55	74	63	68	60	40	53	55	64	54	61	57	0	44	67	22	863,333$/1yrs
Justin Robidas (R)	0	C	100.00	56	37	92	63	64	68	60	61	67	56	52	61	62	58	57	0	44	67	22	825,000$/1yrs
Juha Jaaska	0	LW/RW	100.00	69	46	83	55	76	70	68	53	75	65	50	49	51	70	68	0	41	67	27	900,000$/2yrs
Anton Lundmark	0	LW/RW	100.00	55	38	99	56	79	65	89	54	47	55	57	66	56	64	64	0	43	67	24	975,000$/1yrs
William Lagesson	92	D	100.00	77	44	74	55	82	80	72	53	30	58	50	64	51	73	71	0	41	70	29	800,000$/2yrs
Kevin Connauton	7	D	100.00	82	42	55	50	80	68	67	49	30	55	52	77	54	84	79	0	34	70	35	800,000$/2yrs
Alexander Alexeyev	0	D	99.00	71	34	95	55	86	79	72	52	30	54	54	65	53	67	66	0	44	69	25	1,000,000$/2yrs
Jimmy Schuldt	4	D	100.00	67	40	80	50	80	66	67	49	30	56	53	72	54	75	73	0	37	69	30	800,000$/2yrs
Butrus Ghafari	0	D	100.00	74	40	68	58	75	63	60	58	65	54	48	69	59	68	65	0	37	69	29	800,000$/2yrs
Ronnie Attard	0	D	100.00	76	41	65	50	83	65	68	50	30	55	55	68	55	68	65	0	41	68	26	950,000$/1yrs
Cole McWard	0	D	100.00	60	38	91	50	76	64	67	49	30	56	52	66	54	64	63	0	42	67	24	800,000$/2yrs
Scratches
Alexander Nylander	92	RW	95.00	59	33	89	66	76	81	70	66	61	59	76	55	71	70	69	0	44	75	27	900,000$/2yrs
Max Comtois	0	C/LW	100.00	71	49	60	61	83	78	75	60	61	62	64	55	62	67	63	0	28	72	26
Corey Andonovski	0	RW	100.00	80	42	58	53	82	65	67	64	36	55	53	68	54	68	64	0	28	69	26	800,000$/1yrs
Mathias Laferriere	0	RW	100.00	62	39	88	54	75	64	67	63	39	56	53	67	54	66	65	0	28	68	25	900,000$/1yrs
Ondrej Pavel	0	C/LW/RW	100.00	72	41	71	53	85	64	67	63	37	53	54	67	53	66	63	0	28	68	25	850,000$/2yrs
Blake McLaughlin (R)	0	LW	100.00	62	38	88	52	70	63	65	63	39	54	51	67	52	66	65	0	28	67	25	900,000$/1yrs
Sam Morton	0	LW	100.00	61	38	89	54	71	63	67	62	42	49	59	66	54	64	63	0	31	67	26	850,000$/2yrs
Carson Focht (R)	0	C	100.00	63	38	86	52	71	61	66	62	65	45	57	65	51	62	61	0	36	66	25	800,000$/1yrs
Carson Bantle (R)	0	LW	100.00	75	42	66	51	88	61	65	62	36	50	51	65	51	62	59	0	36	66	23	850,000$/2yrs
Maxence Guenette	0	D	100.00	64	40	84	52	83	65	68	51	30	58	54	66	56	65	63	0	28	68	24	850,000$/2yrs
Dmitri Semykin	13	D	100.00	57	39	96	57	83	60	60	55	30	51	49	64	58	61	63	0	28	67	25	900,000$/2yrs
Luke Krys	0	D	100.00	67	39	79	49	75	64	66	48	30	55	52	67	53	66	64	0	36	67	25	800,000$/1yrs
Mikko Kokkonen (R)	0	D	100.00	64	39	85	49	77	63	66	49	30	55	52	66	54	64	63	0	28	66	24	800,000$/1yrs
Anton Malmstrom	0	D	100.00	67	40	80	48	83	63	65	47	30	53	51	67	52	66	64	0	28	66	25	900,000$/2yrs

Show or Hide Column

Filter Tips

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
Nico Daws	0	100.00	69	70	69	84	78	72	79	83	80	83	79	66	70	0	44	80	24	1,100,000$/1yrs
Dom DiVincentiis	0	97.00	74	62	70	78	71	75	73	74	75	76	74	60	59	0	47	76	21	858,333$/1yrs
Scratches
Filip Larsson	29	100.00	71	62	70	78	71	75	74	71	75	73	74	71	70	0	36	76	27	900,000$/1yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Dave Tippett	76	80	84	82	91	84	55	CAN	64	2	1,000,000$

General Manager

Player Stats

Show or Hide Column

Filter Tips

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	PKP	PKM	GW	GT	FO%	FOT	EG	HT	P/20	PSG	PSS
1	Alexander Nylander	Bears (WSH)	RW	12	6	8	14	5	2	0	9	52	7	34	11.54%	1	23.29	0	3	3	33	0	19	1	0	50.00%	28	0	0	1.00	0	1
2	Julien Gauthier	Bears (WSH)	RW	12	3	8	11	-1	18	0	30	53	10	38	5.66%	6	20.52	2	4	6	33	0	6	1	0	50.00%	18	0	0	0.89	0	1
3	Ivan Ivan	Bears (WSH)	C	12	5	4	9	5	0	0	5	23	8	21	21.74%	3	21.46	2	1	3	34	0	40	3	1	57.14%	364	0	0	0.70	0	0
4	Pavol Regenda	Bears (WSH)	LW/RW	12	5	3	8	6	2	0	35	40	11	18	12.50%	5	18.92	1	0	1	12	0	19	0	1	47.83%	23	0	0	0.70	0	0
5	Joakim Kemell	Bears (WSH)	RW	12	5	3	8	-2	0	0	12	34	11	16	14.71%	2	17.36	3	1	4	25	0	0	1	0	38.14%	215	0	0	0.77	0	0
6	Alexander Alexeyev	Bears (WSH)	D	12	2	5	7	-1	8	0	11	17	4	16	11.76%	25	24.87	1	2	3	37	0	32	0	0	0.00%	0	0	0	0.47	0	0
7	Martin Pospisil	Bears (WSH)	RW	12	3	4	7	-2	21	5	50	39	7	25	7.69%	2	16.62	1	3	4	23	0	0	0	0	46.67%	15	0	0	0.70	0	1
8	Anton Lundmark	Bears (WSH)	LW/RW	11	3	4	7	1	0	0	2	12	2	6	25.00%	1	9.32	2	1	3	9	0	9	0	0	28.57%	7	0	1	1.37	0	0
9	Kevin Connauton	Bears (WSH)	D	7	0	6	6	1	8	0	18	14	4	8	0.00%	6	19.29	0	5	5	20	0	9	0	0	0.00%	0	0	0	0.89	0	0
10	William Lagesson	Bears (WSH)	D	12	0	5	5	4	18	0	29	10	2	8	0.00%	17	20.78	0	2	2	22	0	17	0	0	0.00%	0	0	0	0.40	0	0
11	Ronnie Attard	Bears (WSH)	D	12	1	4	5	-1	2	0	30	15	8	7	6.67%	9	17.64	1	1	2	22	0	18	0	0	0.00%	0	0	0	0.47	0	0
12	Butrus Ghafari	Bears (WSH)	D	7	0	5	5	0	4	0	16	4	2	4	0.00%	6	14.04	0	0	0	1	0	0	0	0	0.00%	0	0	0	1.02	0	0
13	Justin Robidas	Bears (WSH)	C	12	2	2	4	-2	0	0	4	34	13	29	5.88%	2	19.07	1	2	3	30	0	22	0	0	55.44%	285	0	0	0.35	0	0
14	Cole McWard	Bears (WSH)	D	11	1	2	3	2	0	0	2	5	1	2	20.00%	7	9.53	0	1	1	10	0	16	0	0	0.00%	0	0	0	0.57	0	0
15	Juha Jaaska	Bears (WSH)	LW/RW	7	1	2	3	1	10	10	1	5	2	4	20.00%	1	11.81	0	0	0	0	0	0	0	0	100.00%	2	0	0	0.73	0	0
16	Jaydon Dureau	Bears (WSH)	LW	12	2	0	2	1	0	0	1	4	1	2	50.00%	0	1.81	1	0	1	2	0	13	0	0	0.00%	1	0	0	1.84	0	0
17	Luke Krys	Bears (WSH)	D	5	1	1	2	-1	0	0	1	3	0	1	33.33%	9	18.51	0	1	1	7	0	15	1	0	0.00%	0	0	0	0.43	0	0
18	Jimmy Schuldt	Bears (WSH)	D	7	0	1	1	0	5	5	3	2	1	6	0.00%	10	15.95	0	0	0	2	0	8	0	0	0.00%	1	0	0	0.18	0	0
19	Carson Focht	Bears (WSH)	C	5	0	1	1	1	0	0	0	0	0	1	0.00%	0	0.79	0	0	0	0	0	2	0	0	80.00%	5	0	0	5.06	0	0
20	Noel Gunler	Bears (WSH)	LW/RW	7	0	1	1	-4	0	0	5	13	4	11	0.00%	1	13.82	0	0	0	0	0	0	0	0	40.00%	5	0	0	0.21	0	0
21	Chase Stillman	Bears (WSH)	RW	12	0	1	1	1	4	0	8	5	1	1	0.00%	2	3.95	0	0	0	0	0	0	0	0	31.75%	63	0	0	0.42	0	0
22	Sam Morton	Bears (WSH)	LW	1	0	0	0	0	0	0	0	2	1	1	0.00%	0	14.60	0	0	0	0	0	0	0	0	0.00%	2	0	0	0.00	0	0
23	Carson Bantle	Bears (WSH)	LW	5	0	0	0	-2	0	0	3	14	5	7	0.00%	0	14.30	0	0	0	0	0	0	0	0	50.00%	4	0	0	0.00	0	0
Team Total or Average			217	40	70	110	12	102	20	275	400	105	266	10.00%	115	15.62	15	27	42	330	0	253	7	2	50.19%	1038	0	1	0.65	0	3

Show or Hide Column

Filter Tips

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	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Dom DiVincentiis	Bears (WSH)	7	4	1	2	0.923	2.41	424	2	0	17	222	0	1	0.667	3	7	5	2	0	0
2	Nico Daws	Bears (WSH)	5	3	2	0	0.942	2.21	299	0	0	11	190	0	0	0.000	0	5	6	1	0	1
Team Total or Average		12	7	3	2	0.932	2.32	723	2	0	28	412	0	1	0.667	3	12	11	3	0	1

Salary

Player Name	POS	Age	Cap Hit	2020-21	2021-22	2022-23	2023-24
Alexander Alexeyev	D	25	1,000,000$	1,000,000$	1,000,000$	RFA
Alexander Nylander	RW	27	900,000$	900,000$	900,000$	UFA
Anton Lundmark	LW/RW	24	975,000$	975,000$	RFA
Anton Malmstrom	D	25	900,000$	900,000$	900,000$	RFA
Blake McLaughlin	LW	25	900,000$	900,000$	RFA
Butrus Ghafari	D	29	800,000$	800,000$	800,000$	UFA
Carson Bantle	LW	23	850,000$	850,000$	850,000$	RFA
Carson Focht	C	25	800,000$	800,000$	RFA
Chase Stillman	RW	22	863,333$	863,333$	RFA
Cole McWard	D	24	800,000$	800,000$	800,000$	RFA
Corey Andonovski	RW	26	800,000$	800,000$	RFA
Dmitri Semykin	D	25	900,000$	900,000$	900,000$	RFA
Dom DiVincentiis	G	21	858,333$	858,333$	RFA
Filip Larsson	G	27	900,000$	900,000$	UFA
Ivan Ivan	C	23	845,000$	845,000$	RFA
Jaydon Dureau	LW	24	800,000$	800,000$	RFA
Jimmy Schuldt	D	30	800,000$	800,000$	800,000$	UFA
Joakim Kemell	RW	21	918,333$	918,333$	RFA
Juha Jaaska	LW/RW	27	900,000$	900,000$	900,000$	UFA
Julien Gauthier	RW	28	950,000$	950,000$	950,000$	950,000$	UFA
Justin Robidas	C	22	825,000$	825,000$	RFA
Kevin Connauton	D	35	800,000$	800,000$	800,000$	UFA
Luke Krys	D	25	800,000$	800,000$	RFA
Martin Pospisil	RW	25	1,000,000$	1,000,000$	RFA
Mathias Laferriere	RW	25	900,000$	900,000$	RFA
Max Comtois	C/LW	26	0$	RFA
Maxence Guenette	D	24	850,000$	850,000$	850,000$	RFA
Mikko Kokkonen	D	24	800,000$	800,000$	RFA
Nico Daws	G	24	1,100,000$	1,100,000$	RFA
Noel Gunler	LW/RW	24	800,000$	800,000$	800,000$	RFA
Ondrej Pavel	C/LW/RW	25	850,000$	850,000$	850,000$	RFA
Pavol Regenda	LW/RW	25	950,000$	950,000$	RFA
Ronnie Attard	D	26	950,000$	950,000$	RFA
Sam Morton	LW	26	850,000$	850,000$	850,000$	UFA
William Lagesson	D	29	800,000$	800,000$	800,000$	UFA

Lines

Forward Lines
# - Pavol Regenda	# - Ivan Ivan
# - Noel Gunler	# - Joakim Kemell	# - Julien Gauthier
# - Juha Jaaska	# - Justin Robidas	# - Martin Pospisil
# - Julien Gauthier	# - Chase Stillman

Defensive Pairings
# - Butrus Ghafari	# - Alexander Alexeyev
# - William Lagesson	# - Kevin Connauton
# - Jimmy Schuldt	# - Ronnie Attard

1st Power Play Unit
# - Joakim Kemell		# - Ivan Ivan
# - Ronnie Attard			# - Alexander Alexeyev

2nd Power Play Unit
# - Martin Pospisil		# - Justin Robidas		# - Julien Gauthier
# - William Lagesson			# - Kevin Connauton

Goalies
# - Dom DiVincentiis		# - Nico Daws

Team Stats

Show or Hide Column

Filter Tips

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	OTL	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	GP1	GP2	GP3	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	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	Phantoms	2	2	0	0	0	7	5	2	4	1.000	7	12	19	0	14	10	16	58	150	118	135	5	83	27	16	45	10	3	30.00%	7	1	85.71%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	50.0%	12.1%	94.0%	106.0	LUCKY
2	Comets	1	0	0	0	1	2	3	-1	1	0.500	2	3	5	0	14	10	16	46	150	118	135	5	22	7	15	20	8	1	12.50%	5	1	80.00%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	33.3%	4.3%	86.4%	90.7	Unlucky
3	Islanders	3	2	1	0	0	11	8	3	4	0.667	11	22	33	0	14	10	16	126	150	118	135	5	101	23	10	76	12	3	25.00%	4	0	100.00%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	50.0%	8.7%	92.1%	100.8	DULL
4	Wolf Pack	1	0	0	1	0	3	4	-1	1	0.500	3	5	8	0	14	10	16	22	150	118	135	5	22	8	9	33	2	2	100.00%	2	0	100.00%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	20.0%	13.6%	81.8%	95.5	FUN
5	Thunderbirds	1	1	0	0	0	3	1	2	2	1.000	3	6	9	0	14	10	16	30	150	118	135	5	34	6	14	15	1	1	100.00%	6	0	100.00%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	66.7%	10.0%	97.1%	107.1	LUCKY
6	Bruins	2	0	2	0	0	3	6	-3	0	0.000	3	5	8	1	14	10	16	58	150	118	135	5	90	31	21	38	3	0	0.00%	6	2	66.67%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	42.9%	5.2%	93.3%	98.5	DULL
7	Checkers	1	1	0	0	0	5	2	3	2	1.000	5	10	15	0	14	10	16	38	150	118	135	5	27	8	11	26	5	3	60.00%	3	1	66.67%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	66.7%	13.2%	92.6%	105.8	LUCKY
8	Penguins	1	1	0	0	0	6	1	5	2	1.000	6	10	16	0	14	10	16	27	150	118	135	5	34	17	14	28	4	2	50.00%	6	1	83.33%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	100.0%	22.2%	97.1%	119.3	LUCKY
	_Vs Division	3	7	3	1	0	8	8	0	15	2.500	8	15	23	1	14	10	16	96	150	118	135	5	117	39	32	64	8	3	37.50%	9	3	66.67%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	50.0%	8.3%	93.2%	101.5	DULL
	_Vs Conference	12	7	3	1	1	40	30	10	16	0.667	40	73	113	1	14	10	16	405	150	118	135	5	413	127	110	281	45	15	33.33%	39	6	84.62%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	51.0%	9.9%	92.7%	102.6	LUCKY
	_Since Last GM Reset	12	7	3	1	1	40	30	10	16	0.667	40	73	113	1	14	10	16	405	150	118	135	5	413	127	110	281	45	15	33.33%	39	6	84.62%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	51.0%	9.9%	92.7%	102.6	LUCKY
	Total	12	7	3	1	1	40	30	10	16	0.667	40	73	113	1	14	10	16	405	150	118	135	5	413	127	110	281	45	15	33.33%	39	6	84.62%	214	412	51.94%	211	443	47.63%	96	175	54.86%	274	183	291	92	159	77	51.0%	9.9%	92.7%	102.6	LUCKY

	Puck Time
Offensive Zone	22
Neutral Zone	13
Defensive Zone	24

	Puck Time
Offensive Zone Start	412
Neutral Zone Start	175
Defensive Zone Start	443

	Puck Time
With Puck	29
Without Puck	30

	Faceoffs
Faceoffs Won	521
Faceoffs Lost	509

	Team Average Shots after	League Average Shots after
1st Period	12.5	9.57
2nd Period	22.3	20.31
3rd Period	33.6	30.68
Overtime	34.0	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	1.2	0.64
2nd Period	2.0	1.65
3rd Period	3.3	2.67
Overtime	3.3	2.83


Even Strenght Goal	24
PP Goal	15
PK Goal	0
Empty Net Goal	1

	Home	Away
Win	3	4
Lost	1	2
Overtime Lost	2	0


PP Attempt	45
PP Goal	15
PK Attempt	39
PK Goal Against	6

	Home
Shots For	33.8
Shots Against	34.4
Goals For	3.3
Goals Against	2.5
Hits	23.4
Shots Blocked	10.6
Pim	9.2

Schedule

Date	Matchup			Result	Detail
2025-10-09	Bears	@	Phantoms	Bears4,Phantoms3	RECAP
2025-10-11	Islanders	@	Bears	Islanders3,Bears5	RECAP
2025-10-12	Bears	@	Islanders	Bears1,Islanders2	RECAP
2025-10-16	Bears	@	Bruins	Bears1,Bruins2	RECAP
2025-10-17	Penguins	@	Bears	Penguins1,Bears6	RECAP
2025-10-19	Thunderbirds	@	Bears	Thunderbirds1,Bears3	RECAP
2025-10-22	Bears	@	Checkers	Bears5,Checkers2	RECAP
2025-10-24	Wolf Pack	@	Bears	Wolf Pack4,Bears3 (OT)	RECAP
2025-10-25	Bears	@	Islanders	Bears5,Islanders3	RECAP
2025-10-28	Comets	@	Bears	Comets3,Bears2 (SO)	RECAP
2025-10-30	Bears	@	Phantoms	Bears3,Phantoms2	RECAP
2025-10-31	Bruins	@	Bears	Bruins4,Bears2	RECAP
2025-11-03	Bears	@	Checkers
2025-11-05	Bears	@	Rocket
2025-11-06	Bears	@	Thunderbirds
2025-11-08	Thunderbirds	@	Bears
2025-11-09	Bears	@	Wolf Pack
2025-11-13	Bears	@	Gulls
2025-11-14	Wranglers	@	Bears
2025-11-17	Bears	@	Monsters
2025-11-18	Islanders	@	Bears
2025-11-21	Bears	@	Wranglers
2025-11-22	Wolves	@	Bears
2025-11-28	Bears	@	Crunch
2025-11-29	Senators	@	Bears
2025-12-02	Bears	@	Islanders
2025-12-03	Stars	@	Bears
2025-12-06	Bears	@	Moose
2025-12-08	Bears	@	Thunderbirds
2025-12-09	Crunch	@	Bears
2025-12-12	Bears	@	Barracuda
2025-12-13	Admirals	@	Bears
2025-12-19	Griffins	@	Bears
2025-12-21	Bears	@	IceHogs
2025-12-23	Bears	@	Eagles
2025-12-24	Rocket	@	Bears
2025-12-28	Comets	@	Bears
2025-12-30	Bears	@	Phantoms
2026-01-02	Wolves	@	Bears
2026-01-04	Bears	@	Rocket
2026-01-08	Phantoms	@	Bears
2026-01-11	Condors	@	Bears
2026-01-14	Bears	@	Checkers
2026-01-17	Firebirds	@	Bears
2026-01-20	Bears	@	Reign
2026-01-22	Wild	@	Bears
2026-01-27	Bears	@	Wolf Pack
2026-01-28	Phantoms	@	Bears
2026-02-01	Checkers	@	Bears
2026-02-03	Bears	@	Senators
2026-02-04	Islanders	@	Bears
2026-02-08	Canucks	@	Bears
2026-02-12	Bears	@	Penguins
2026-02-14	Bruins	@	Bears
2026-02-16	Bears	@	Griffins
2026-02-18	Bears	@	Comets
2026-02-19	Monsters	@	Bears
2026-02-22	Bears	@	Wolf Pack
2026-02-24	Wolf Pack	@	Bears
2026-02-26	Bears	@	Marlies
2026-03-01	Marlies	@	Bears
2026-03-04	Bears	@	Penguins
2026-03-06	Bears	@	Americans
2026-03-07	Americans	@	Bears
Trade Deadline --- Trades can�t be done after this day is simulated!
2026-03-09	Bears	@	Bruins
2026-03-11	Silver Knights	@	Bears
2026-03-13	Bears	@	Roadrunners
2026-03-16	Penguins	@	Bears
2026-03-21	Penguins	@	Bears
2026-03-26	Thunderbirds	@	Bears
2026-03-31	Checkers	@	Bears
2026-04-01	Bears	@	Bruins

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
2,283,499$	0$	0$	75,000,000$

Arena	About us
	Name
	City	Hershey
	Capacity	3000
	Season Ticket Holders	0%

Arena Capacity - Ticket Price Attendance - %

Arena Capacity	2000	1000
Ticket Price	35$	0$	$	$	$
Attendance	0	0
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
30	0 - 0.00%	0$	0$	3000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
2,283,499$	2,283,499$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
479,005$	12,686$	334,563$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	166	18,242$	2,809,268$

Team Total Estimate
Estimated Season Expenses	Estimated Season Salary Cap	Current Bank Account	Projected Bank Account
2,809,268$	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

Max Comtois is suspended for 70 more games.
Dmitri Semykin is suspended for 70 more games.
Mathias Laferriere is suspended for 70 more games.
Mikko Kokkonen is suspended for 70 more games.
Corey Andonovski is suspended for 70 more games.
Maxence Guenette is suspended for 70 more games.
Anton Malmstrom is suspended for 70 more games.
Ondrej Pavel is suspended for 70 more games.
Blake McLaughlin is suspended for 70 more games.