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
Conor Sheary	0	LW/RW	99.00	63	41	85	74	65	81	83	71	52	70	66	70	70	79	69	1	50	69	34	1,300,000$/1yrs
Noah Gregor	0	LW/RW	99.00	72	56	71	74	80	80	78	70	49	65	65	67	70	76	66	1	50	68	27	1,000,000$/3yrs
Tyler Motte	0	C/LW/RW	99.00	71	40	82	68	69	75	79	64	57	59	59	67	64	83	70	1	50	65	31	1,000,000$/4yrs
Laurent Dauphin	0	C/LW/RW	99.00	60	43	65	70	59	70	69	66	45	65	59	67	65	64	55	1	50	63	31	1,000,000$/4yrs
Cameron Hughes	0	C/LW/RW	100.00	59	45	64	69	61	71	69	67	45	67	60	65	66	59	52	1	50	63	29	1,000,000$/4yrs
Brian Pinho	0	C/LW/RW	100.00	57	41	74	66	61	73	72	65	43	62	59	58	63	65	55	1	50	62	31	1,000,000$/4yrs
Filip Hallander (R)	0	C/LW/RW	99.00	54	40	71	69	78	80	64	63	40	59	58	62	63	66	56	1	50	62	26	1,000,000$/4yrs
Joona Koppanen	0	C/LW/RW	100.00	65	41	73	64	68	69	67	61	58	61	54	65	60	57	51	1	50	61	28	1,000,000$/4yrs
Jack Becker	0	C/LW/RW	100.00	64	43	68	63	69	71	70	63	43	59	56	63	61	59	50	1	50	61	29	1,300,000$/1yrs
Mitch McLain	0	C/LW/RW	100.00	55	56	57	64	63	73	72	65	45	60	60	55	63	65	57	1	50	61	32	1,000,000$/4yrs
Jacob Gaucher	0	C/LW	100.00	63	44	67	61	69	67	65	64	43	60	60	56	62	53	50	1	50	60	25	1,000,000$/4yrs
Nikita Pavlychev	0	C	100.00	61	45	64	62	68	70	68	62	58	59	55	59	60	59	50	1	50	60	29	1,100,000$/4yrs
Cole Hutson (R)	0	D	99.00	60	40	73	75	74	83	60	67	40	66	65	65	69	54	50	1	50	66	20	975,000$/2yrs
Henry Thrun	0	D	99.00	64	49	72	69	75	75	70	67	40	64	58	70	65	56	50	1	50	66	25	1,000,000$/2yrs
Robert Bortuzzo	0	D	100.00	56	57	62	64	74	75	72	60	40	56	55	62	59	89	76	1	50	63	37	1,000,000$/4yrs
Michael Callahan	0	D	100.00	64	42	70	67	64	67	65	61	40	57	52	68	60	54	50	1	50	62	26	1,000,000$/4yrs
Carter Yakemchuk (R)	0	D	99.00	60	42	65	64	70	60	60	65	40	63	62	60	64	50	50	1	50	61	20	975,000$/2yrs
Cavan Fitzgerald	0	D	100.00	56	44	65	66	63	68	66	61	40	54	56	63	61	59	52	1	50	60	29	975,000$/4yrs
Scratches
Chris Terry	0	C/LW/RW	100.00	52	43	69	62	60	67	66	63	43	60	57	52	61	75	68	1	50	60	37	1,300,000$/1yrs
Justin Janicke (R)	0	C/LW/RW	100.00	60	40	68	64	62	60	60	60	41	60	60	60	60	50	50	1	50	59	22	1,000,000$/4yrs
Jacob Doty	0	C/LW/RW	100.00	61	53	57	55	71	71	70	56	41	51	52	53	55	68	60	1	50	57	33	1,000,000$/4yrs
C.J. Smith	0	C/LW/RW	100.00	54	40	70	62	58	66	64	56	42	55	51	55	57	64	55	1	50	56	31	1,000,000$/4yrs
David Gust	0	C/LW/RW	100.00	51	41	59	59	53	63	62	57	42	56	54	53	57	65	54	1	50	56	32	1,000,000$/4yrs
Jett Jones	0	C/LW/RW	100.00	65	40	70	57	72	60	57	53	40	50	50	54	54	51	50	1	50	55	23	1,000,000$/4yrs
Scott Reedy	0	C/LW/RW	100.00	61	40	71	58	68	64	61	54	40	50	51	54	54	55	50	1	50	55	27	1,000,000$/4yrs
Jeff Kubiak	0	C/LW/RW	100.00	53	42	58	54	59	63	62	54	42	52	52	51	53	65	54	1	50	54	32	1,000,000$/4yrs
Ryan Dzingel	0	C/LW/RW	100.00	51	40	62	55	54	59	57	52	42	51	51	51	53	65	52	1	50	53	34	1,100,000$/1yrs
Brett Budgell	0	C/LW/RW	100.00	55	40	63	57	57	55	53	52	40	50	50	52	52	52	50	1	50	52	25	1,000,000$/4yrs
Darren Brady	0	D	100.00	55	40	64	59	58	60	58	53	40	51	50	60	54	56	50	1	50	56	30	1,000,000$/4yrs
Andy Welinski	0	D	100.00	52	40	60	55	57	61	59	52	40	51	50	55	52	65	52	1	50	55	33	1,100,000$/1yrs
Jake Boltmann (R)	0	D	100.00	56	40	66	61	61	57	55	52	40	51	50	57	54	51	50	1	50	55	24	1,000,000$/4yrs
Cole Krygier	0	D	100.00	55	41	60	58	58	57	54	53	40	51	51	55	53	53	50	1	50	54	26	1,100,000$/1yrs

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
Alexandar Georgiev	0	100.00	74	73	70	65	72	66	64	62	66	72	70	71	64	1	50	63	30	975,000$/4yrs
Jesper Vikman	0	98.00	64	59	59	67	64	62	63	59	63	62	53	52	50	1	50	57	24	1,100,000$/4yrs
Scratches
Kirill Gerasimyuk	0	100.00	64	55	54	58	60	63	67	65	65	59	53	50	50	1	50	57	22	1,000,000$/3yrs
Jeremy Brodeur	0	100.00	67	60	55	61	61	58	58	54	59	61	59	59	52	1	50	55	29	1,300,000$/1yrs
Jeremy Smith	0	100.00	56	56	53	57	55	55	55	53	56	55	59	75	66	1	50	52	37	1,000,000$/4yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Paul Coffey	68	70	79	75	68	64	1	CAN	65	2	700,000$

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	Noah Gregor	Barracuda (SJ )	LW/RW	15	8	7	15	-6	22	10	33	39	19	34	20.51%	8	21.99	5	2	7	0	0	0	7	2	0	45.28%	1	1	1.00	1	3	0.63
2	Conor Sheary	Barracuda (SJ )	LW/RW	15	6	7	13	0	2	0	26	45	17	25	13.33%	12	24.20	1	2	3	0	0	0	26	0	1	51.85%	1	0	0.87	2	3	0.91
3	Cole Hutson	Barracuda (SJ )	D	15	1	11	12	2	4	0	34	22	11	14	4.55%	21	29.78	0	3	3	0	1	1	27	0	1	0.00%	0	0	0.80	0	0	0.76
4	Tyler Motte	Barracuda (SJ )	C/LW/RW	15	4	4	8	1	0	0	26	19	7	10	21.05%	12	22.95	1	2	3	1	0	1	27	0	0	51.11%	0	0	0.53	0	0	0.55
5	Henry Thrun	Barracuda (SJ )	D	15	1	7	8	-12	14	10	35	25	10	17	4.00%	33	29.62	1	3	4	0	0	0	26	0	0	0.00%	0	0	0.53	0	0	0.20
6	Filip Hallander	Barracuda (SJ )	C/LW/RW	15	2	4	6	-7	7	5	26	15	13	11	13.33%	6	18.95	1	1	2	0	0	0	1	1	0	30.00%	0	0	0.40	0	0	0.16
7	Laurent Dauphin	Barracuda (SJ )	C/LW/RW	15	1	5	6	0	17	5	38	31	12	25	3.23%	3	18.98	0	1	1	0	0	0	4	1	0	38.32%	0	0	0.40	1	3	0.29
8	Brian Pinho	Barracuda (SJ )	C/LW/RW	15	3	2	5	-4	2	0	27	14	6	15	21.43%	1	15.84	0	1	1	0	0	0	13	0	0	38.95%	0	0	0.33	0	0	0.22
9	Cameron Hughes	Barracuda (SJ )	C/LW/RW	15	3	2	5	-9	8	0	26	24	12	14	12.50%	7	16.63	0	1	1	0	0	0	9	0	1	32.74%	0	0	0.33	0	1	0.10
10	Carter Yakemchuk	Barracuda (SJ )	D	15	0	4	4	-4	4	0	28	8	2	7	0.00%	14	22.19	0	0	0	0	0	0	15	0	0	0.00%	0	0	0.27	0	0	0.14
11	Joona Koppanen	Barracuda (SJ )	C/LW/RW	15	1	2	3	-5	2	0	26	14	2	7	7.14%	6	14.17	0	0	0	0	0	0	0	0	0	40.00%	0	0	0.20	0	0	0.12
12	Michael Callahan	Barracuda (SJ )	D	15	0	2	2	0	6	0	6	4	4	1	0.00%	6	10.60	0	0	0	0	0	0	7	0	0	0.00%	0	0	0.13	0	0	0.05
13	Nikita Pavlychev	Barracuda (SJ )	C	15	1	0	1	-3	0	0	1	2	1	2	50.00%	0	2.50	0	0	0	0	0	0	0	0	0	28.57%	0	0	0.07	0	0	-0.01
14	Jack Becker	Barracuda (SJ )	C/LW/RW	15	1	0	1	-6	0	0	16	18	7	9	5.56%	4	12.60	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.07	0	0	0.05
15	Jacob Gaucher	Barracuda (SJ )	C/LW	14	0	0	0	-3	0	0	5	4	0	4	0.00%	1	7.29	0	0	0	0	0	0	8	0	0	43.75%	0	0	0.00	0	0	-0.06
16	Cavan Fitzgerald	Barracuda (SJ )	D	15	0	0	0	-10	8	0	7	13	5	2	0.00%	6	16.40	0	0	0	0	0	0	10	0	0	0.00%	0	0	0.00	0	0	-0.25
17	Chris Terry	Barracuda (SJ )	C/LW/RW	1	0	0	0	0	0	0	0	0	0	0	0.00%	0	4.10	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	0.00
18	Robert Bortuzzo	Barracuda (SJ )	D	13	0	0	0	-6	7	5	5	1	4	1	0.00%	6	13.10	0	0	0	0	0	0	11	0	0	0.00%	0	0	0.00	0	0	-0.22
19	Darren Brady	Barracuda (SJ )	D	2	0	0	0	0	0	0	0	0	0	0	0.00%	0	4.36	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	0.00
20	Mitch McLain	Barracuda (SJ )	C/LW/RW	15	0	0	0	-3	7	5	3	10	2	9	0.00%	1	3.12	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.08
Team Total or Average			270	32	57	89	-75	110	40	368	308	134	207	10.39%	147	16.64	9	16	25	317	1	1	2	197	4	3	41.48%	757	75	104	2	1	0.40	4	10

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	SO	GA	SA	SAR	EG	PS %	PSA	ST	BG	S1	S3
1	Jesper Vikman	Barracuda (SJ )	12	3	5	3	0.890	2.80	685	0	32	291	155	0	0.500	10	11	4	1	0
2	Alexandar Georgiev	Barracuda (SJ )	4	2	2	0	0.864	4.17	230	0	16	118	56	0	0.000	0	4	11	0	0
Team Total or Average		16	5	7	3	0.883	3.15	915	0	48	409	211	0	0.500	10	15	15	1	0

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20	2020-21	2021-22
Alexandar Georgiev	G	30	975,000$	975,000$	975,000$	975,000$	975,000$	UFA
Andy Welinski	D	33	1,100,000$	1,100,000$	UFA
Brett Budgell	C/LW/RW	25	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Brian Pinho	C/LW/RW	31	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
C.J. Smith	C/LW/RW	31	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Cameron Hughes	C/LW/RW	29	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Carter Yakemchuk	D	20	975,000$	975,000$	975,000$	RFA
Cavan Fitzgerald	D	29	975,000$	975,000$	975,000$	975,000$	975,000$	UFA
Chris Terry	C/LW/RW	37	1,300,000$	1,300,000$	UFA
Cole Hutson	D	20	975,000$	975,000$	975,000$	RFA
Cole Krygier	D	26	1,100,000$	1,100,000$	RFA
Conor Sheary	LW/RW	34	1,300,000$	1,300,000$	UFA
Darren Brady	D	30	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
David Gust	C/LW/RW	32	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Filip Hallander	C/LW/RW	26	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Henry Thrun	D	25	1,000,000$	1,000,000$	1,000,000$	RFA
Jack Becker	C/LW/RW	29	1,300,000$	1,300,000$	UFA
Jacob Doty	C/LW/RW	33	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Jacob Gaucher	C/LW	25	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Jake Boltmann	D	24	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Jeff Kubiak	C/LW/RW	32	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Jeremy Brodeur	G	29	1,300,000$	1,300,000$	UFA
Jeremy Smith	G	37	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Jesper Vikman	G	24	1,100,000$	1,100,000$	1,100,000$	1,100,000$	1,100,000$	UFA
Jett Jones	C/LW/RW	23	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Joona Koppanen	C/LW/RW	28	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Justin Janicke	C/LW/RW	22	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	RFA
Kirill Gerasimyuk	G	22	1,000,000$	1,000,000$	1,000,000$	1,000,000$	RFA
Laurent Dauphin	C/LW/RW	31	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Michael Callahan	D	26	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Mitch McLain	C/LW/RW	32	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Nikita Pavlychev	C	29	1,100,000$	1,100,000$	1,100,000$	1,100,000$	1,100,000$	UFA
Noah Gregor	LW/RW	27	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Robert Bortuzzo	D	37	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Ryan Dzingel	C/LW/RW	34	1,100,000$	1,100,000$	UFA
Scott Reedy	C/LW/RW	27	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Tyler Motte	C/LW/RW	31	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA

Lines

Forward Lines
# - Conor Sheary	# - Laurent Dauphin	# - Tyler Motte
# - Noah Gregor	# - Cameron Hughes	# - Filip Hallander
# - Filip Hallander	# - Brian Pinho	# - Conor Sheary
# - Joona Koppanen	# - Noah Gregor	# - Jack Becker

Defensive Pairings
# - Cole Hutson	# - Henry Thrun
# - Carter Yakemchuk	# - Henry Thrun
# - Cole Hutson	# - Henry Thrun

1st Power Play Unit
# - Conor Sheary		# - Laurent Dauphin		# - Tyler Motte
# - Cole Hutson			# - Henry Thrun

2nd Power Play Unit
# - Noah Gregor		# - Cameron Hughes		# - Conor Sheary
# - Carter Yakemchuk			# - Henry Thrun

Goalies
# - Jesper Vikman		# - Alexandar Georgiev

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	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	Heat	1	0	1	0	0	0	0	0	2	-2	0	0.000	0	0	0	7	10	14	5	5	80	100	120	18	20	5	6	34	1	0	0.00%	3	2	33.33%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	0.0%	0.0%	90.0%	90.0	Unlucky
2	IceHogs	1	1	0	0	0	0	0	4	3	1	2	1.000	4	8	12	7	10	14	5	17	80	100	120	18	33	15	2	24	4	1	25.00%	1	0	100.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	50.0%	23.5%	90.9%	114.4	FUN
3	Eagles	2	1	1	0	0	0	0	4	5	-1	2	0.500	4	7	11	7	10	14	5	53	80	100	120	18	46	13	13	49	6	3	50.00%	4	1	75.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	20.0%	7.5%	89.1%	96.7	Unlucky
4	Condors	1	0	1	0	0	0	0	3	11	-8	0	0.000	3	5	8	7	10	14	5	40	80	100	120	18	34	7	35	16	4	1	25.00%	5	3	40.00%	1	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	20.0%	7.5%	67.6%	75.1	Unlucky
5	Checkers 1	2	0	1	0	0	0	1	2	6	-4	1	0.250	2	4	6	7	10	14	5	32	80	100	120	18	40	9	6	42	5	1	20.00%	3	1	66.67%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	16.7%	6.3%	85.0%	91.3	Unlucky
6	Reign	1	0	1	0	0	0	0	2	4	-2	0	0.000	2	4	6	7	10	14	5	46	80	100	120	18	30	11	15	21	2	0	0.00%	5	1	80.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	40.0%	4.3%	86.7%	91.0	Unlucky
7	Wolf Pack	1	0	0	0	0	1	0	2	1	1	2	1.000	2	1	3	7	10	14	5	17	80	100	120	18	6	0	2	27	1	0	0.00%	1	0	100.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	66.7%	11.8%	83.3%	95.1	FUN
8	Belleville Senators	1	0	1	0	0	0	0	2	3	-1	0	0.000	2	4	6	7	10	14	5	10	80	100	120	18	28	14	6	26	1	0	0.00%	3	2	33.33%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	66.7%	20.0%	89.3%	109.3	FUN
9	Thunderbirds	3	1	1	1	0	0	0	8	7	1	4	0.667	8	15	23	7	10	14	5	46	80	100	120	18	101	51	15	82	10	1	10.00%	5	0	100.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	50.0%	17.4%	93.1%	110.5	LUCKY
10	Crunch	1	0	0	0	1	0	0	3	4	-1	1	0.500	3	5	8	7	10	14	5	18	80	100	120	18	29	10	6	26	1	0	0.00%	3	0	100.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	42.9%	16.7%	86.2%	102.9	FUN
11	Checkers 2	1	0	0	0	0	0	1	3	4	-1	1	0.500	3	4	7	7	10	14	5	24	80	100	120	18	42	12	4	21	5	2	40.00%	2	0	100.00%	0	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	20.0%	12.5%	90.5%	103.0	FUN
	_Vs Conference	10	3	5	1	0	0	1	24	36	-12	9	0.450	24	43	67	7	10	14	5	231	80	100	120	18	306	114	90	247	32	8	25.00%	25	7	72.00%	1	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	35.6%	10.4%	88.2%	98.6	FUN
	_Since Last GM Reset	15	3	7	1	1	1	2	33	50	-17	13	0.433	33	57	90	7	10	14	5	308	80	100	120	18	409	147	110	368	40	9	22.50%	35	10	71.43%	1	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	37.5%	10.7%	87.8%	98.5	FUN
	Total	15	3	7	1	1	1	2	33	50	-17	13	0.433	33	57	90	7	10	14	5	308	80	100	120	18	409	147	110	368	40	9	22.50%	35	10	71.43%	1	127	276	46.01%	122	290	42.07%	65	176	36.93%	277	118	299	153	338	174	37.5%	10.7%	87.8%	98.5	FUN

	Puck Time
Offensive Zone	18
Neutral Zone	22
Defensive Zone	19

	Puck Time
Offensive Zone Start	276
Neutral Zone Start	176
Defensive Zone Start	290

	Puck Time
With Puck	29
Without Puck	31

	Faceoffs
Faceoffs Won	314
Faceoffs Lost	428

	Team Average Shots after	League Average Shots after
1st Period	5.3	9.57
2nd Period	12.0	20.31
3rd Period	20.0	30.68
Overtime	21.2	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	0.5	0.64
2nd Period	1.1	1.65
3rd Period	2.1	2.67
Overtime	2.4	2.83


Even Strenght Goal	23
PP Goal	9
PK Goal	1
Empty Net Goal	0

	Home	Away
Win	2	3
Lost	5	2
Overtime Lost	1	2


PP Attempt	40
PP Goal	9
PK Attempt	35
PK Goal Against	10

	Home
Shots For	20.5
Shots Against	27.3
Goals For	2.2
Goals Against	3.3
Hits	24.5
Shots Blocked	9.8
Pim	7.3

Schedule

Date	Matchup			Result	Detail
Sun, Oct 11	EDM	@	S J	EDM11,S J3	RECAP
Wed, Oct 14	LOS	@	S J	LOS4,S J2	RECAP
Fri, Oct 16	S J	@	COL	S J4,COL2	RECAP
Sat, Oct 17	S J	@	NYR	S J2,NYR1 (SO)	RECAP
Mon, Oct 19	STL	@	S J	STL3,S J4	RECAP
Wed, Oct 21	S J	@	SEA	S J3,SEA4 (SO)	RECAP
Fri, Oct 23	S J	@	CHI	S J4,CHI3	RECAP
Sun, Oct 25	FLO	@	S J	FLO4,S J1	RECAP
Tue, Oct 27	COL	@	S J	COL3,S J0	RECAP
Thu, Oct 29	S J	@	FLO	S J1,FLO2 (SO)	RECAP
Sat, Oct 31	STL	@	S J	STL2,S J3 (OT)	RECAP
Mon, Nov 2	S J	@	OTT	S J2,OTT3	RECAP
Wed, Nov 4	S J	@	STL	S J1,STL2	RECAP
Fri, Nov 6	CAL	@	S J	CAL2,S J0	RECAP
Sun, Nov 8	TAM	@	S J	TAM4,S J3 (OT)	RECAP
Tue, Nov 10	CAL	@	S J
Thu, Nov 12	S J	@	ANA
Sat, Nov 14	S J	@	TAM
Mon, Nov 16	S J	@	CAL
Wed, Nov 18	DET	@	S J
Fri, Nov 20	S J	@	CHI
Sun, Nov 22	TOR	@	S J
Tue, Nov 24	MIN	@	S J
Fri, Nov 27	EDM	@	S J
Sun, Nov 29	S J	@	WPG
Mon, Nov 30	WAS	@	S J
Thu, Dec 3	S J	@	VAN
Sat, Dec 5	PIT	@	S J
Sun, Dec 6	S J	@	ANA
Tue, Dec 8	S J	@	PIT
Thu, Dec 10	EDM	@	S J
Sat, Dec 12	S J	@	CAR
Mon, Dec 14	FLO	@	S J
Wed, Dec 16	NAS	@	S J
Thu, Dec 17	S J	@	PHO
Sat, Dec 19	LOS	@	S J
Tue, Dec 22	S J	@	NJD
Wed, Dec 23	BUF	@	S J
Sat, Dec 26	S J	@	WAS
Sun, Dec 27	DAL	@	S J
Tue, Dec 29	S J	@	CHI
Thu, Dec 31	VAN	@	S J
Sat, Jan 2	S J	@	CAL
Sun, Jan 3	MTL	@	S J
Tue, Jan 5	S J	@	PHI
Thu, Jan 7	BOS	@	S J
Sat, Jan 9	S J	@	NAS
Sun, Jan 10	CAR	@	S J
Tue, Jan 12	S J	@	MTL
Wed, Jan 13	CLB	@	S J
Fri, Jan 15	NJD	@	S J
Sun, Jan 17	S J	@	NYI
Wed, Jan 20	LOS	@	S J
Thu, Jan 21	S J	@	COL
Sun, Jan 24	PHO	@	S J
Tue, Jan 26	S J	@	BUF
Wed, Jan 27	PHI	@	S J
Fri, Jan 29	S J	@	NYI
Sat, Jan 30	NYI	@	S J
Tue, Feb 2	S J	@	MIN
Wed, Feb 3	PHO	@	S J
Fri, Feb 5	S J	@	DET
Sun, Feb 7	NYR	@	S J
Tue, Feb 9	S J	@	LOS
Wed, Feb 10	ANA	@	S J
Fri, Feb 12	S J	@	SEA
Sun, Feb 14	SEA	@	S J
Tue, Feb 16	S J	@	CLB
Thu, Feb 18	S J	@	PHO
Fri, Feb 19	ANA	@	S J
Sun, Feb 21	COL	@	S J
Tue, Feb 23	S J	@	DAL
Thu, Feb 25	S J	@	BOS
Fri, Feb 26	S J	@	TOR
Sat, Feb 27	WPG	@	S J
Wed, Mar 3	SEA	@	S J
Fri, Mar 5	S J	@	CLB
Sun, Mar 7	CHI	@	S J
Tue, Mar 9	S J	@	MIN
Wed, Mar 10	S J	@	LOS
Fri, Mar 12	CHI	@	S J
Sun, Mar 14	S J	@	EDM
Trade Deadline --- Trades can�t be done after this day is simulated!
Wed, Mar 17	OTT	@	S J
Thu, Mar 18	S J	@	EDM