Ligue de Hockey Virtuelle de l'Outaouais

Roster

Player	#	POS	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Tyler Pitlick	0	C/RW	100.00	67	42	74	68	70	76	76	66	45	64	62	67	65	86	66	1	50	66	33	2,500,000$/4yrs
Jayson Megna	0	C	100.00	62	42	74	68	62	72	73	66	66	65	60	68	64	85	69	1	50	65	35	2,500,000$/4yrs
Max Sasson	0	C	100.00	63	41	77	73	71	74	64	66	60	63	62	66	67	52	50	1	50	64	24	870,000$/2yrs
Jack Studnicka	0	C/RW	100.00	63	45	71	71	67	75	71	67	53	63	60	63	66	70	54	1	50	64	26	1,250,000$/1yrs
Matthew Phillips	0	RW	100.00	57	40	77	75	56	72	69	67	44	65	61	67	68	64	52	1	50	64	27	1,200,000$/3yrs
Joe Snively	0	C	100.00	54	42	72	67	57	71	69	65	45	65	59	57	64	59	50	1	50	61	29	1,200,000$/1yrs
Gabriel Fortier	0	LW	100.00	59	41	72	66	61	66	64	61	59	56	56	58	61	53	50	1	50	59	25	1,250,000$/1yrs
Jonathan Gruden	0	C/LW	100.00	58	48	62	66	60	66	64	61	59	56	55	57	61	53	50	1	50	58	25	1,250,000$/1yrs
Matvey Petrov	0	LW	100.00	61	42	70	64	64	63	61	60	42	53	55	54	59	50	50	1	50	57	22	975,000$/2yrs
Jaycob Megna	0	D	100.00	69	44	75	64	78	78	76	65	40	61	56	72	61	80	61	1	50	67	32	3,500,000$/4yrs
Jarred Tinordi	0	D	100.00	70	56	66	63	79	75	75	62	40	58	56	70	60	87	69	1	50	66	33	3,000,000$/4yrs
Chad Ruhwedel	0	D	100.00	62	43	74	69	66	75	77	64	40	61	57	70	63	87	70	1	50	66	35	3,000,000$/4yrs
Spencer Stastney	0	D	100.00	56	40	79	75	75	83	67	66	40	62	61	70	67	59	50	1	50	66	25	950,000$/1yrs
Kevin Gravel	0	D	100.00	59	42	72	64	69	73	72	62	40	58	55	67	60	80	65	1	50	64	33	1,500,000$/4yrs
William Lagesson	0	D	100.00	65	48	69	68	71	72	68	62	40	57	56	70	62	69	53	1	50	64	29	1,400,000$/3yrs
Ian Mitchell	0	D	100.00	58	41	72	72	60	68	65	63	40	63	56	68	64	60	50	1	50	63	26	1,500,000$/1yrs
Jack Ahcan	0	D	100.00	55	43	70	71	58	70	68	67	40	65	56	69	64	57	50	1	50	63	28	1,400,000$/4yrs
Mikko Kokkonen	0	D	100.00	62	42	71	66	64	64	63	61	40	57	54	66	60	52	50	1	50	61	24	1,350,000$/3yrs
Scratches
Clark Bishop	0	C	100.00	59	45	66	65	63	70	69	63	61	59	58	59	62	59	54	1	50	60	29	1,600,000$/4yrs
Bogdan Trineyev	0	RW	100.00	66	42	71	65	66	64	63	61	42	54	56	63	61	51	50	1	50	59	23	975,000$/2yrs
Ryder Rolston	0	RW	100.00	64	41	73	64	66	64	63	62	42	59	57	59	61	51	50	1	50	59	23	975,000$/2yrs
Nathan Légaré	0	RW	100.00	61	55	57	63	65	65	63	62	42	53	57	55	61	52	50	1	50	58	24	1,400,000$/4yrs
Ryan McAllister	0	C	100.00	59	40	70	65	62	60	58	58	42	55	54	55	59	51	50	1	50	57	23	975,000$/2yrs
Bryce McConnell-Barker (R)	0	C	100.00	61	42	64	62	62	61	60	58	41	55	55	58	58	50	50	1	50	57	21	975,000$/3yrs
Simon Robertsson (R)	0	RW	100.00	62	41	67	62	62	60	58	58	41	55	55	59	58	50	50	1	50	57	22	975,000$/3yrs
Alex Beaucage	0	RW	100.00	62	41	71	65	65	62	59	56	40	52	51	58	57	51	50	1	50	57	23	1,250,000$/4yrs
Josh Filmon (R)	0	LW	100.00	58	40	66	61	60	58	56	54	40	53	53	54	56	50	50	1	50	55	21	975,000$/3yrs
Owen Lindmark (R)	0	C	100.00	59	40	66	60	61	60	58	54	47	54	53	55	55	51	50	1	50	55	24	975,000$/1yrs
Blake McLaughlin	0	C/LW/RW	100.00	57	40	71	65	60	62	59	55	40	51	51	54	57	53	50	1	50	55	25	950,000$/1yrs
Luca Cagnoni (R)	0	D	100.00	58	41	66	69	60	58	57	64	40	62	64	64	66	50	50	1	50	61	20	975,000$/3yrs
Ryan Ufko (R)	0	D	100.00	59	41	69	67	60	63	62	62	40	61	59	64	63	50	50	1	50	61	22	975,000$/3yrs
Jorian Donovan (R)	0	D	100.00	59	48	58	62	62	61	60	60	40	55	55	59	59	50	50	1	50	58	21	975,000$/3yrs

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
Louis Domingue	0	100.00	69	71	72	67	69	69	71	67	69	70	69	68	61	1	50	64	33	2,200,000$/4yrs
Jesper Wallstedt	0	100.00	68	61	64	70	68	68	71	67	69	67	59	50	50	1	50	62	22	1,200,000$/2yrs
Scratches
Troy Grosenick	0	100.00	63	62	62	54	60	61	63	60	62	61	59	70	60	1	50	57	35	1,250,000$/4yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Travis Green	86	84	84	84	78	73	1	CAN	54	3	500,000$

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	MP	PPG	PPA	PPP	PKG	PKA	PKP	PKM	GW	GT	FO%	EG	HT	P/GP	PSG	PSS	GSAVG
1	Jack Studnicka	Monsters (CLB)	C/RW	30	10	19	29	-2	15	5	39	94	32	52	10.64%	9	21.55	2	10	12	0	0	0	7	1	1	50.89%	0	0	0.97	3	7	0.87
2	Tyler Pitlick	Monsters (CLB)	C/RW	30	10	14	24	-6	12	0	48	70	32	53	14.29%	17	21.59	5	6	11	0	0	0	55	1	1	46.22%	0	0	0.80	1	9	0.68
3	Gabriel Fortier	Monsters (CLB)	LW	30	9	11	20	-6	25	5	45	43	12	26	20.93%	12	22.56	5	4	9	0	0	0	48	2	0	58.97%	0	0	0.67	0	0	0.41
4	Max Sasson	Monsters (CLB)	C	30	10	9	19	6	8	0	36	59	9	43	16.95%	16	18.71	0	0	0	2	0	2	32	1	1	50.81%	0	0	0.63	2	2	0.67
5	Jayson Megna	Monsters (CLB)	C	30	4	15	19	-8	14	10	35	47	16	43	8.51%	2	18.25	0	6	6	0	0	0	22	0	0	54.51%	0	0	0.63	3	9	0.42
6	Matthew Phillips	Monsters (CLB)	RW	30	8	8	16	-2	4	0	44	49	18	36	16.33%	8	17.03	4	2	6	0	0	0	0	0	2	62.50%	0	0	0.53	1	5	0.53
7	Jaycob Megna	Monsters (CLB)	D	30	2	12	14	-1	4	0	39	33	15	14	6.06%	32	23.27	2	4	6	0	0	0	63	0	0	0.00%	0	0	0.47	0	0	0.44
8	Matvey Petrov	Monsters (CLB)	LW	30	6	5	11	6	10	0	44	29	7	10	20.69%	8	17.74	0	0	0	0	2	2	23	3	0	33.33%	0	0	0.37	0	0	0.36
9	Jonathan Gruden	Monsters (CLB)	C/LW	30	7	1	8	-8	18	10	43	28	6	13	25.00%	7	18.78	2	0	2	0	0	0	28	1	0	54.55%	0	0	0.27	0	0	0.10
10	Spencer Stastney	Monsters (CLB)	D	30	1	7	8	-3	2	0	11	15	4	6	6.67%	22	18.33	1	0	1	0	0	0	41	0	0	0.00%	0	0	0.27	0	1	0.20
11	Jarred Tinordi	Monsters (CLB)	D	30	2	5	7	-3	50	20	21	36	17	19	5.56%	32	23.90	1	1	2	0	0	0	52	0	1	0.00%	0	0	0.23	0	0	-0.03
12	Chad Ruhwedel	Monsters (CLB)	D	30	0	7	7	-3	10	0	20	19	4	6	0.00%	12	18.70	0	2	2	0	1	1	41	0	0	0.00%	0	0	0.23	0	0	0.12
13	Kevin Gravel	Monsters (CLB)	D	30	0	4	4	-3	2	0	19	10	4	7	0.00%	13	14.95	0	0	0	0	0	0	0	0	0	50.00%	0	0	0.13	0	0	0.10
14	Joe Snively	Monsters (CLB)	C	7	0	1	1	1	0	0	4	2	0	0	0.00%	0	8.43	0	0	0	0	0	0	0	0	0	33.33%	0	0	0.14	0	1	0.16
15	Jack Ahcan	Monsters (CLB)	D	30	0	1	1	0	0	0	4	2	0	1	0.00%	3	8.85	0	0	0	0	0	0	4	0	0	0.00%	0	0	0.03	0	0	0.03
16	William Lagesson	Monsters (CLB)	D	30	0	0	0	-6	15	5	9	7	5	3	0.00%	5	11.04	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.13
17	Ian Mitchell	Monsters (CLB)	D	30	0	0	0	-2	2	0	5	1	0	1	0.00%	7	9.57	0	0	0	0	0	0	16	0	0	0.00%	0	0	0.00	1	1	-0.03
18	Mikko Kokkonen	Monsters (CLB)	D	30	0	0	0	0	0	0	0	0	0	0	0.00%	0	0.51	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	0.00
Team Total or Average			517	69	119	188	-40	191	55	466	544	181	333	12.68%	205	16.67	22	35	57	393	2	3	5	439	9	6	50.82%	856	150	137	0	0	0.44	11	35

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	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Louis Domingue	Monsters (CLB)	30	16	5	6	0.878	2.42	1714	0	3	69	564	292	0	0	0.710	31	30	0	3	1	1
2	Jesper Wallstedt	Monsters (CLB)	5	0	2	1	0.903	2.57	140	0	0	6	62	32	1	1	0.750	4	0	30	0	0	0
Team Total or Average		35	16	7	7	0.880	2.43	1855	0	3	75	626	324	1	1	0.714	35	30	30	3	1	1

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20	2020-21	2021-22
Alex Beaucage	RW	23	1,250,000$	1,250,000$	1,250,000$	1,250,000$	1,250,000$	UFA
Blake McLaughlin	C/LW/RW	25	950,000$	975,000$	RFA
Bogdan Trineyev	RW	23	975,000$	975,000$	975,000$	RFA
Bryce McConnell-Barker	C	21	975,000$	975,000$	975,000$	975,000$	RFA
Chad Ruhwedel	D	35	3,000,000$	3,000,000$	3,000,000$	3,000,000$	3,000,000$	UFA
Clark Bishop	C	29	1,600,000$	1,600,000$	1,600,000$	1,600,000$	1,600,000$	UFA
Gabriel Fortier	LW	25	1,250,000$	1,250,000$	RFA
Ian Mitchell	D	26	1,500,000$	1,500,000$	RFA
Jack Ahcan	D	28	1,400,000$	1,400,000$	1,400,000$	1,400,000$	1,400,000$	UFA
Jack Studnicka	C/RW	26	1,250,000$	1,250,000$	RFA
Jarred Tinordi	D	33	3,000,000$	3,000,000$	3,000,000$	3,000,000$	3,000,000$	UFA
Jaycob Megna	D	32	3,500,000$	3,500,000$	3,500,000$	3,500,000$	3,500,000$	UFA
Jayson Megna	C	35	2,500,000$	2,500,000$	2,500,000$	2,500,000$	2,500,000$	UFA
Jesper Wallstedt	G	22	1,200,000$	1,200,000$	1,200,000$	RFA
Joe Snively	C	29	1,200,000$	1,200,000$	UFA
Jonathan Gruden	C/LW	25	1,250,000$	1,250,000$	RFA
Jorian Donovan	D	21	975,000$	975,000$	975,000$	975,000$	RFA
Josh Filmon	LW	21	975,000$	975,000$	975,000$	975,000$	RFA
Kevin Gravel	D	33	1,500,000$	1,500,000$	1,500,000$	1,500,000$	1,500,000$	UFA
Louis Domingue	G	33	2,200,000$	2,200,000$	2,200,000$	2,200,000$	2,200,000$	UFA
Luca Cagnoni	D	20	975,000$	975,000$	975,000$	975,000$	RFA
Matthew Phillips	RW	27	1,200,000$	1,200,000$	1,200,000$	1,200,000$	UFA
Matvey Petrov	LW	22	975,000$	975,000$	975,000$	RFA
Max Sasson	C	24	870,000$	975,000$	975,000$	RFA
Mikko Kokkonen	D	24	1,350,000$	1,350,000$	1,350,000$	1,350,000$	RFA
Nathan Légaré	RW	24	1,400,000$	1,400,000$	1,400,000$	1,400,000$	1,400,000$	UFA
Owen Lindmark	C	24	975,000$	975,000$	RFA
Ryan McAllister	C	23	975,000$	975,000$	975,000$	RFA
Ryan Ufko	D	22	975,000$	975,000$	975,000$	975,000$	RFA
Ryder Rolston	RW	23	975,000$	975,000$	975,000$	RFA
Simon Robertsson	RW	22	975,000$	975,000$	975,000$	975,000$	RFA
Spencer Stastney	D	25	950,000$	975,000$	RFA
Troy Grosenick	G	35	1,250,000$	1,250,000$	1,250,000$	1,250,000$	1,250,000$	UFA
Tyler Pitlick	C/RW	33	2,500,000$	2,500,000$	2,500,000$	2,500,000$	2,500,000$	UFA
William Lagesson	D	29	1,400,000$	1,400,000$	1,400,000$	1,400,000$	UFA

Lines

Forward Lines
# - Gabriel Fortier	# - Tyler Pitlick	# - Jack Studnicka
# - Jonathan Gruden	# - Jayson Megna	# - Tyler Pitlick
# - Matvey Petrov	# - Max Sasson	# - Matthew Phillips
# - Gabriel Fortier	# - Tyler Pitlick	# - Jack Studnicka

Defensive Pairings
# - Jaycob Megna	# - Jarred Tinordi
# - Chad Ruhwedel	# - Spencer Stastney
# - Kevin Gravel	# - William Lagesson

1st Power Play Unit
# - Gabriel Fortier		# - Jack Studnicka		# - Tyler Pitlick
# - Jaycob Megna			# - Jarred Tinordi

2nd Power Play Unit
# - Jonathan Gruden		# - Jayson Megna		# - Matthew Phillips
# - Chad Ruhwedel			# - Spencer Stastney

Goalies
# - Louis Domingue		# - Jesper Wallstedt

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	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	Moose	2	0	0	1	0	0	1	3	3	0	3	0.750	3	4	7	0	0	12	23	35	16	27	129	228	231	45	20	6	4	32	6	2	33.33%	2	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	25.0%	11.1%	85.0%	96.1	FUN
2	Americans	2	1	1	0	0	0	0	6	8	-2	2	0.500	6	9	15	0	1	12	23	35	16	42	129	228	231	45	39	18	30	27	7	3	42.86%	10	2	80.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	33.3%	14.3%	79.5%	93.8	FUN
3	Wolves	1	0	0	0	0	1	0	3	2	1	2	1.000	3	3	6	0	0	12	23	35	16	31	129	228	231	45	42	18	4	13	2	0	0.00%	2	0	100.00%	1	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	60.0%	9.7%	95.2%	104.9	LUCKY
4	Eagles	1	1	0	0	0	0	0	4	0	4	2	1.000	4	7	11	0	1	12	23	35	16	19	129	228	231	45	21	4	17	21	2	2	100.00%	1	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	100.0%	21.1%	100.0%	121.1	LUCKY
5	Reign	1	0	0	0	0	1	0	2	1	1	2	1.000	2	2	4	0	0	12	23	35	16	17	129	228	231	45	23	8	2	11	0	0	0.00%	1	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	66.7%	11.8%	95.7%	107.4	LUCKY
6	Rocket	3	0	0	2	0	0	1	6	5	1	5	0.833	6	10	16	0	0	12	23	35	16	57	129	228	231	45	40	5	8	44	7	1	14.29%	4	1	75.00%	1	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	55.6%	10.5%	87.5%	98.0	FUN
7	Comets	2	1	0	0	1	0	0	3	2	1	3	0.750	3	4	7	0	1	12	23	35	16	36	129	228	231	45	26	7	13	34	4	1	25.00%	4	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	50.0%	8.3%	92.3%	100.6	DULL
8	Islanders	1	1	0	0	0	0	0	3	1	2	2	1.000	3	5	8	0	0	12	23	35	16	12	129	228	231	45	7	4	6	17	2	2	100.00%	3	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	50.0%	25.0%	85.7%	110.7	FUN
9	Belleville Senators	4	0	1	0	1	1	1	12	16	-4	4	0.500	12	21	33	0	0	12	23	35	16	98	129	228	231	45	105	36	40	57	10	3	30.00%	15	6	60.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	47.4%	12.2%	84.8%	97.0	FUN
10	Roadrunners	2	0	1	0	0	1	0	4	5	-1	2	0.500	4	4	8	0	0	12	23	35	16	22	129	228	231	45	34	13	2	34	2	1	50.00%	1	1	0.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	42.9%	18.2%	85.3%	103.5	FUN
11	Penguins	1	1	0	0	0	0	0	5	3	2	2	1.000	5	10	15	0	0	12	23	35	16	32	129	228	231	45	36	7	8	12	2	1	50.00%	4	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	57.1%	15.6%	91.7%	107.3	LUCKY
12	Barracuda	4	1	1	1	1	0	0	12	11	1	5	0.625	12	20	32	0	0	12	23	35	16	91	129	228	231	45	92	30	26	75	6	2	33.33%	8	3	62.50%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	55.6%	13.2%	88.0%	101.2	FUN
13	Crunch	1	0	1	0	0	0	0	1	3	-2	0	0.000	1	2	3	0	0	12	23	35	16	17	129	228	231	45	16	5	9	13	1	0	0.00%	2	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	25.0%	5.9%	81.3%	87.1	Unlucky
14	Canucks	3	0	2	0	0	0	1	10	16	-6	1	0.167	10	16	26	1	0	12	23	35	16	71	129	228	231	45	87	18	23	59	6	3	50.00%	9	3	66.67%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	35.0%	14.1%	81.6%	95.7	FUN
15	Checkers 2	2	0	0	1	0	1	0	6	4	2	4	1.000	6	8	14	0	0	12	23	35	16	26	129	228	231	45	39	32	10	44	3	1	33.33%	5	0	100.00%	0	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	55.6%	23.1%	89.7%	112.8	FUN
	_Vs Division	5	2	5	2	2	1	3	14	8	6	15	1.500	14	22	36	0	1	12	23	35	16	111	129	228	231	45	111	36	31	76	10	4	40.00%	13	0	100.00%	1	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	55.6%	12.6%	92.8%	105.4	LUCKY
	_Vs Conference	18	4	5	2	2	2	3	49	56	-7	21	0.583	49	80	129	1	2	12	23	35	16	396	129	228	231	45	398	118	141	276	41	14	34.15%	53	12	77.36%	2	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	44.3%	12.4%	85.9%	98.3	FUN
	_Since Last GM Reset	30	6	7	5	3	5	4	80	80	0	39	0.650	80	125	205	1	3	12	23	35	16	598	129	228	231	45	627	211	202	493	60	22	36.67%	71	16	77.46%	2	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	47.5%	13.4%	87.2%	100.6	FUN
	Total	30	6	7	5	3	5	4	80	80	0	39	0.650	80	125	205	1	3	12	23	35	16	598	129	228	231	45	627	211	202	493	60	22	36.67%	71	16	77.46%	2	221	432	51.16%	241	465	51.83%	180	328	54.88%	547	224	571	331	738	367	47.5%	13.4%	87.2%	100.6	FUN

	Puck Time
Offensive Zone	18
Neutral Zone	24
Defensive Zone	19

	Puck Time
Offensive Zone Start	432
Neutral Zone Start	328
Defensive Zone Start	465

	Puck Time
With Puck	30
Without Puck	31

	Faceoffs
Faceoffs Won	642
Faceoffs Lost	583

	Team Average Shots after	League Average Shots after
1st Period	4.3	9.57
2nd Period	11.9	20.31
3rd Period	19.6	30.68
Overtime	21.1	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	0.4	0.64
2nd Period	1.2	1.65
3rd Period	2.3	2.67
Overtime	2.9	2.83


Even Strenght Goal	55
PP Goal	22
PK Goal	2
Empty Net Goal	1

	Home	Away
Win	12	4
Lost	0	7
Overtime Lost	3	4


PP Attempt	60
PP Goal	22
PK Attempt	71
PK Goal Against	16

	Home
Shots For	19.9
Shots Against	20.9
Goals For	2.7
Goals Against	2.7
Hits	16.4
Shots Blocked	7.0
Pim	6.7

Schedule

Date	Matchup			Result	Detail
Wed, Oct 8	S J	@	CLB	S J2,CLB4	RECAP
Thu, Oct 9	CLB	@	NJD	CLB1,NJD2 (OT)	RECAP
Sun, Oct 12	OTT	@	CLB	OTT4,CLB3 (OT)	RECAP
Tue, Oct 14	CLB	@	OTT	CLB4,OTT3 (SO)	RECAP
Thu, Oct 16	CLB	@	TAM	CLB1,TAM3	RECAP
Fri, Oct 17	CLB	@	VAN	CLB1,VAN4	RECAP
Sun, Oct 19	NJD	@	CLB	NJD0,CLB2	RECAP
Wed, Oct 22	S J	@	CLB	S J3,CLB4 (OT)	RECAP
Sat, Oct 25	CLB	@	CAR	CLB3,CAR2 (SO)	RECAP
Mon, Oct 27	VAN	@	CLB	VAN5,CLB4 (SO)	RECAP
Wed, Oct 29	CLB	@	S J	CLB2,S J3 (OT)	RECAP
Fri, Oct 31	WPG	@	CLB	WPG2,CLB1 (SO)	RECAP
Sun, Nov 2	CLB	@	MTL	CLB1,MTL2 (SO)	RECAP
Mon, Nov 3	CLB	@	OTT	CLB3,OTT4 (SO)	RECAP
Wed, Nov 5	BUF	@	CLB	BUF0,CLB2	RECAP
Thu, Nov 6	CLB	@	BUF	CLB4,BUF8	RECAP
Sun, Nov 9	MTL	@	CLB	MTL1,CLB2 (OT)	RECAP
Tue, Nov 11	NYI	@	CLB	NYI1,CLB3	RECAP
Thu, Nov 13	CLB	@	MTL	CLB3,MTL2 (OT)	RECAP
Fri, Nov 14	COL	@	CLB	COL0,CLB4	RECAP
Mon, Nov 17	CLB	@	OTT	CLB2,OTT5	RECAP
Tue, Nov 18	CLB	@	PHO	CLB2,PHO4	RECAP
Thu, Nov 20	PIT	@	CLB	PIT3,CLB5	RECAP
Sat, Nov 22	WPG	@	CLB	WPG1,CLB2 (OT)	RECAP
Tue, Nov 25	CLB	@	S J	CLB2,S J3	RECAP
Thu, Nov 27	SEA	@	CLB	SEA3,CLB4 (OT)	RECAP
Fri, Nov 28	CLB	@	SEA	CLB2,SEA1 (SO)	RECAP
Sun, Nov 30	LOS	@	CLB	LOS1,CLB2 (SO)	RECAP
Tue, Dec 2	CLB	@	VAN	CLB5,VAN7	RECAP
Thu, Dec 4	PHO	@	CLB	PHO1,CLB2 (SO)	RECAP
Mon, Dec 8	FLO	@	CLB
Wed, Dec 10	CLB	@	COL
Fri, Dec 12	CLB	@	NJD
Sun, Dec 14	ANA	@	CLB
Tue, Dec 16	NAS	@	CLB
Thu, Dec 18	CLB	@	FLO
Sat, Dec 20	CAR	@	CLB
Mon, Dec 22	CLB	@	STL
Wed, Dec 24	CLB	@	MIN
Fri, Dec 26	CLB	@	NAS
Sat, Dec 27	MIN	@	CLB
Wed, Dec 31	BUF	@	CLB
Sat, Jan 3	STL	@	CLB
Mon, Jan 5	CLB	@	WAS
Wed, Jan 7	OTT	@	CLB
Sat, Jan 10	MTL	@	CLB
Sun, Jan 11	CLB	@	PHI
Tue, Jan 13	EDM	@	CLB
Fri, Jan 16	CLB	@	LOS
Sat, Jan 17	CLB	@	ANA
Mon, Jan 19	WAS	@	CLB
Wed, Jan 21	CLB	@	BUF
Thu, Jan 22	CAL	@	CLB
Sun, Jan 25	CLB	@	DAL
Mon, Jan 26	NYR	@	CLB
Wed, Jan 28	CLB	@	WPG
Fri, Jan 30	VAN	@	CLB
Mon, Feb 2	CLB	@	NYI
Tue, Feb 3	TAM	@	CLB
Fri, Feb 6	CLB	@	CAR
Sat, Feb 7	PHI	@	CLB
Tue, Feb 10	CLB	@	DET
Wed, Feb 11	DET	@	CLB
Sat, Feb 14	CLB	@	PIT
Sun, Feb 15	TAM	@	CLB
Tue, Feb 17	CLB	@	BOS
Thu, Feb 19	CHI	@	CLB
Sun, Feb 22	CLB	@	CHI
Mon, Feb 23	CHI	@	CLB
Thu, Feb 26	BOS	@	CLB
Fri, Feb 27	CLB	@	CAL
Sat, Feb 28	CLB	@	NYR
Tue, Mar 3	NJD	@	CLB
Thu, Mar 5	CLB	@	EDM
Fri, Mar 6	CLB	@	CHI
Sun, Mar 8	DAL	@	CLB
Trade Deadline --- Trades can�t be done after this day is simulated!
Thu, Mar 12	TOR	@	CLB
Sun, Mar 15	CLB	@	TOR
Mon, Mar 16	S J	@	CLB
Thu, Mar 19	CLB	@	TOR
Sat, Mar 21	TOR	@	CLB
Mon, Mar 23	CLB	@	TAM