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
Jiri Kulich	20	C/RW	99.00	62	40	79	77	80	85	67	72	67	64	68	66	72	58	50	1	50	68	22	1,500,000$/4yrs
Cole Schwindt	0	C/RW	100.00	70	40	83	73	83	79	74	68	70	64	64	68	68	67	58	1	50	67	25	1,200,000$/2yrs
Conor Geekie	28	C/LW/RW	99.00	70	44	69	69	75	69	64	68	64	65	62	67	66	51	50	1	50	64	22	886,667$/1yrs
Jacob Melanson	0	RW	100.00	78	56	64	69	77	72	65	65	43	59	59	61	64	55	50	1	50	62	23	975,000$/1yrs
T.J. Tynan (A)	0	C	99.00	52	46	64	71	51	71	71	64	44	67	53	65	63	70	63	1	50	62	34	1,500,000$/1yrs
Francesco Pinelli	0	C	100.00	62	44	65	69	64	65	63	64	43	60	57	61	63	51	50	1	50	60	23	975,000$/1yrs
David Edstrom (R)	0	C	100.00	64	42	67	64	65	62	60	61	57	60	60	61	60	50	50	1	50	60	21	975,000$/3yrs
Anton Wahlberg	0	C/LW	99.00	65	41	72	65	68	59	56	63	48	63	55	61	61	50	50	1	50	60	20	975,000$/2yrs
Colby Barlow (R)	0	LW	100.00	61	41	71	64	64	62	61	61	42	60	60	60	60	50	50	1	50	59	21	975,000$/3yrs
Eddie Genborg (R)	0	C/LW/RW	100.00	63	40	68	63	65	60	60	60	41	60	60	60	60	50	50	1	50	59	19	975,000$/3yrs
Brett Chorske	0	C/LW/RW	100.00	65	41	72	59	71	64	62	58	41	55	54	59	57	52	50	1	50	58	25	1,000,000$/3yrs
Dillon Dube (R)	0	C/LW/RW	100.00	53	41	69	64	57	64	62	60	42	57	57	54	60	53	50	1	50	57	27	1,200,000$/3yrs
Zeev Buium (R)	2	D	100.00	64	53	71	75	76	85	73	75	40	75	70	63	73	54	50	1	50	69	20	975,000$/3yrs
Sam Rinzel	6	D	99.00	64	40	68	74	81	87	67	71	40	67	65	69	70	55	50	1	50	68	22	975,000$/2yrs
Theo Lindstein (R)	41	D	100.00	60	40	73	73	80	83	60	66	40	62	64	66	67	54	50	1	50	66	21	975,000$/3yrs
Elias Petterssen 2 (R)	26	D	100.00	63	40	72	69	72	75	62	64	40	61	61	62	65	51	50	1	50	63	22	975,000$/2yrs
Topias Vilen	0	D	100.00	62	42	72	68	65	64	62	63	40	63	53	68	61	51	50	1	50	62	23	975,000$/1yrs
Tristan Bertucci (R)	0	D	100.00	62	44	63	69	63	60	60	62	40	61	60	66	62	50	50	1	50	61	20	975,000$/3yrs
Scratches
Danny Zhilkin	0	C	100.00	60	42	72	62	66	63	61	61	58	55	54	51	59	50	50	1	50	57	22	975,000$/2yrs
Mason Geertsen	0	LW/RW	100.00	62	52	57	56	72	69	67	56	41	51	51	56	55	65	55	1	50	57	31	1,500,000$/3yrs
Keean Washkurak	0	C/LW	100.00	56	55	58	65	62	66	64	60	57	54	54	53	60	52	50	1	50	57	24	1,500,000$/3yrs
Jacob Perreault	0	RW	100.00	58	42	68	64	63	62	60	56	41	53	52	53	57	52	50	1	50	56	24	975,000$/2yrs
Gavin Hain	0	C	100.00	60	41	70	62	64	61	59	53	51	50	51	53	55	52	50	1	50	55	23	975,000$/1yrs
Reece Newkirk	0	C/LW/RW	100.00	58	42	69	65	60	62	59	54	41	51	50	56	56	53	50	1	50	55	25	1,500,000$/3yrs
Tim Gettinger	0	LW	100.00	58	41	66	53	66	62	60	55	41	52	52	52	53	55	50	1	50	54	28	1,200,000$/4yrs
Brandon Coe	0	RW	100.00	55	41	64	57	59	57	54	53	40	51	51	52	53	51	50	1	50	53	24	1,000,000$/2yrs
Ronan Seeley	0	D	100.00	59	40	74	68	65	65	63	64	40	56	60	60	64	51	50	1	50	61	23	1,000,000$/4yrs
Aaron Ness (C)	0	D	100.00	53	42	69	66	59	67	65	58	40	53	53	62	59	75	70	1	50	60	36	1,500,000$/3yrs
Tyrel Bauer	0	D	100.00	64	60	53	62	71	65	63	59	40	51	52	62	58	52	50	1	50	59	24	975,000$/1yrs
Xavier Bernard	0	D	100.00	63	41	62	62	68	62	59	53	40	50	50	64	55	53	50	1	50	58	26	1,500,000$/3yrs
Tory Dello	0	D	100.00	52	41	64	58	57	61	59	54	40	51	51	55	53	58	50	1	50	55	29	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
James Reimer	0	98.00	69	81	80	77	76	78	78	80	79	75	75	91	84	1	50	72	38	5,000,000$/3yrs
Sebastian Cossa	0	100.00	68	64	69	75	71	73	75	73	74	69	60	51	50	1	50	65	23	1,200,000$/3yrs
Scratches
Jakub Skarek	0	100.00	67	63	64	68	66	65	67	64	66	66	57	54	50	1	50	60	26	2,000,000$/4yrs
Mads Sogaard	0	100.00	64	61	63	72	65	63	63	59	63	64	56	53	50	1	50	58	25	1,600,000$/1yrs
Jan Bednar	0	100.00	62	57	57	65	61	59	60	58	61	59	54	50	50	1	50	55	23	1,200,000$/4yrs
Scott Ratzlaff	0	100.00	63	55	54	63	60	59	60	56	60	59	54	50	50	1	50	54	21	975,000$/3yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Joel Quenneville	81	75	84	77	95	82	1	CAN	65	4	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	Cole Schwindt	Griffins (DET)	C/RW	15	11	11	22	16	0	0	27	67	21	36	16.42%	4	19.13	1	1	2	0	1	1	30	1	0	60.67%	0	1	1.47	0	1	1.87
2	Conor Geekie	Griffins (DET)	C/LW/RW	15	10	6	16	4	14	10	26	50	12	24	20.00%	10	19.84	1	0	1	0	0	0	41	3	0	57.14%	0	0	1.07	0	0	1.03
3	Jiri Kulich	Griffins (DET)	C/RW	15	6	10	16	3	13	5	32	60	13	36	10.00%	6	20.11	0	1	1	0	0	0	39	0	0	54.05%	0	0	1.07	1	1	1.08
4	Jacob Melanson	Griffins (DET)	RW	15	6	6	12	11	25	5	36	35	16	23	17.14%	5	17.12	0	0	0	0	0	0	10	2	0	36.84%	0	0	0.80	0	0	0.82
5	Theo Lindstein	Griffins (DET)	D	15	0	9	9	9	2	0	17	22	9	11	0.00%	13	19.97	0	1	1	0	0	0	34	0	0	0.00%	0	0	0.60	0	0	0.75
6	Colby Barlow	Griffins (DET)	LW	15	2	6	8	5	0	0	21	34	10	11	5.88%	5	14.26	0	0	0	0	0	0	5	0	0	22.22%	0	0	0.53	0	0	0.70
7	Sam Rinzel	Griffins (DET)	D	15	1	7	8	9	8	0	24	36	21	25	2.78%	27	24.83	1	1	2	0	0	0	40	0	0	0.00%	0	0	0.53	0	0	0.81
8	Zeev Buium	Griffins (DET)	D	15	2	6	8	11	38	10	14	39	19	14	5.13%	12	19.55	1	1	2	0	0	0	9	0	0	0.00%	0	0	0.53	0	0	0.45
9	Francesco Pinelli	Griffins (DET)	C	15	4	3	7	2	6	0	23	31	15	29	12.90%	2	13.54	0	0	0	0	0	0	4	1	0	36.97%	0	0	0.47	0	0	0.53
10	T.J. Tynan	Griffins (DET)	C	15	2	4	6	5	20	10	25	23	7	11	8.70%	2	18.86	0	1	1	1	0	1	26	1	1	47.06%	0	0	0.40	1	1	0.33
11	Topias Vilen	Griffins (DET)	D	15	0	5	5	6	16	10	12	9	8	7	0.00%	17	21.40	0	0	0	0	1	1	39	0	0	0.00%	0	0	0.33	0	0	0.29
12	Anton Wahlberg	Griffins (DET)	C/LW	12	2	2	4	7	6	0	20	14	6	8	14.29%	4	15.09	0	0	0	0	0	0	0	0	1	47.06%	0	0	0.33	0	0	0.50
13	David Edstrom	Griffins (DET)	C	15	1	2	3	-1	0	0	24	25	6	15	4.00%	3	8.28	0	0	0	0	0	0	0	0	0	59.46%	0	0	0.20	0	0	0.30
14	Eddie Genborg	Griffins (DET)	C/LW/RW	15	2	1	3	2	2	0	10	26	5	14	7.69%	4	12.71	0	0	0	0	0	0	0	1	0	20.00%	0	0	0.20	0	0	0.34
15	Dillon Dube	Griffins (DET)	C/LW/RW	15	2	0	2	0	4	0	8	21	8	8	9.52%	1	9.14	0	0	0	0	0	0	0	1	0	100.00%	0	0	0.13	0	0	0.19
16	Elias Petterssen 2	Griffins (DET)	D	14	0	1	1	0	33	15	17	17	5	3	0.00%	13	17.82	0	0	0	0	0	0	2	0	0	0.00%	0	0	0.07	0	0	-0.20
17	Tristan Bertucci	Griffins (DET)	D	15	0	1	1	2	4	0	17	10	2	7	0.00%	18	17.09	0	0	0	0	0	0	31	0	0	0.00%	0	0	0.07	0	0	0.16
18	Brett Chorske	Griffins (DET)	C/LW/RW	15	0	0	0	-1	2	0	17	7	2	8	0.00%	0	8.15	0	0	0	0	0	0	0	0	0	33.33%	0	0	0.00	0	0	0.02
19	Keean Washkurak	Griffins (DET)	C/LW	3	0	0	0	-1	0	0	1	1	0	0	0.00%	1	9.49	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.09
20	Ronan Seeley	Griffins (DET)	D	1	0	0	0	0	0	0	0	0	0	0	0.00%	0	13.88	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.05
Team Total or Average			270	51	80	131	89	193	65	371	527	185	290	9.68%	147	16.42	4	6	10	306	1	2	3	316	10	2	52.69%	873	108	89	0	1	0.59	2	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	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	James Reimer	Griffins (DET)	14	10	4	0	0.909	2.47	825	6	0	34	373	181	1	0	0.667	3	14	1	1	1	1
2	Sebastian Cossa	Griffins (DET)	2	1	0	0	0.941	2.31	78	0	0	3	51	27	0	0	0.000	0	1	14	1	0	0
Team Total or Average		16	11	4	0	0.913	2.46	904	6	0	37	424	208	1	0	0.667	3	15	15	2	1	1

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20	2020-21	2021-22
Aaron Ness	D	36	1,500,000$	1,500,000$	1,500,000$	1,500,000$	UFA
Anton Wahlberg	C/LW	20	975,000$	975,000$	975,000$	RFA
Brandon Coe	RW	24	1,000,000$	1,000,000$	1,000,000$	RFA
Brett Chorske	C/LW/RW	25	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Colby Barlow	LW	21	975,000$	975,000$	975,000$	975,000$	RFA
Cole Schwindt	C/RW	25	1,200,000$	1,200,000$	1,200,000$	RFA
Conor Geekie	C/LW/RW	22	886,667$	975,000$	RFA
Danny Zhilkin	C	22	975,000$	975,000$	975,000$	RFA
David Edstrom	C	21	975,000$	975,000$	975,000$	975,000$	RFA
Dillon Dube	C/LW/RW	27	1,200,000$	1,200,000$	1,200,000$	1,200,000$	UFA
Eddie Genborg	C/LW/RW	19	975,000$	975,000$	975,000$	975,000$	RFA
Elias Petterssen 2	D	22	975,000$	975,000$	975,000$	RFA
Francesco Pinelli	C	23	975,000$	975,000$	RFA
Gavin Hain	C	23	975,000$	975,000$	RFA
Jacob Melanson	RW	23	975,000$	975,000$	RFA
Jacob Perreault	RW	24	975,000$	975,000$	975,000$	RFA
Jakub Skarek	G	26	2,000,000$	2,000,000$	2,000,000$	2,000,000$	2,000,000$	UFA
James Reimer	G	38	5,000,000$	5,000,000$	5,000,000$	5,000,000$	UFA
Jan Bednar	G	23	1,200,000$	1,200,000$	1,200,000$	1,200,000$	1,200,000$	UFA
Jiri Kulich	C/RW	22	1,500,000$	1,500,000$	1,500,000$	1,500,000$	1,500,000$	RFA
Keean Washkurak	C/LW	24	1,500,000$	1,500,000$	1,500,000$	1,500,000$	UFA
Mads Sogaard	G	25	1,600,000$	1,600,000$	RFA
Mason Geertsen	LW/RW	31	1,500,000$	1,500,000$	1,500,000$	1,500,000$	UFA
Reece Newkirk	C/LW/RW	25	1,500,000$	1,500,000$	1,500,000$	1,500,000$	UFA
Ronan Seeley	D	23	1,000,000$	1,000,000$	1,000,000$	1,000,000$	1,000,000$	UFA
Sam Rinzel	D	22	975,000$	975,000$	975,000$	RFA
Scott Ratzlaff	G	21	975,000$	975,000$	975,000$	975,000$	RFA
Sebastian Cossa	G	23	1,200,000$	1,200,000$	1,200,000$	1,200,000$	RFA
T.J. Tynan	C	34	1,500,000$	1,500,000$	UFA
Theo Lindstein	D	21	975,000$	975,000$	975,000$	975,000$	RFA
Tim Gettinger	LW	28	1,200,000$	1,200,000$	1,200,000$	1,200,000$	1,200,000$	UFA
Topias Vilen	D	23	975,000$	975,000$	RFA
Tory Dello	D	29	1,100,000$	1,100,000$	UFA
Tristan Bertucci	D	20	975,000$	975,000$	975,000$	975,000$	RFA
Tyrel Bauer	D	24	975,000$	975,000$	RFA
Xavier Bernard	D	26	1,500,000$	1,500,000$	1,500,000$	1,500,000$	UFA
Zeev Buium	D	20	975,000$	975,000$	975,000$	975,000$	RFA

Lines

Forward Lines
# - T.J. Tynan	# - Jiri Kulich	# - Conor Geekie
# - Anton Wahlberg	# - Cole Schwindt	# - Jacob Melanson
# - Colby Barlow	# - Francesco Pinelli	# - Eddie Genborg
# - Dillon Dube	# - David Edstrom	# - Brett Chorske

Defensive Pairings
# - Theo Lindstein	# - Sam Rinzel
# - Zeev Buium	# - Topias Vilen
# - Elias Petterssen 2	# - Tristan Bertucci

1st Power Play Unit
# - T.J. Tynan		# - Jiri Kulich		# - Conor Geekie
# - Zeev Buium			# - Sam Rinzel

2nd Power Play Unit
# - Anton Wahlberg		# - Cole Schwindt		# - Jacob Melanson
# - Theo Lindstein			# - Topias Vilen

Goalies
# - James Reimer		# - Sebastian Cossa

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	SOW	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	Moose	1	1	0	0	6	3	3	2	1.000	6	9	15	15	18	18	2	43	171	181	175	3	22	3	6	24	1	0	0.00%	3	0	100.00%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	66.7%	14.0%	86.4%	100.3	FUN
2	Heat	1	0	1	0	1	5	-4	0	0.000	1	1	2	15	18	18	2	41	171	181	175	3	25	9	28	26	2	0	0.00%	4	1	75.00%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	20.0%	2.4%	80.0%	82.4	Unlucky
3	Wolves	1	1	0	0	5	1	4	2	1.000	5	8	13	15	18	18	2	37	171	181	175	3	25	11	9	30	1	0	0.00%	2	0	100.00%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	83.3%	13.5%	96.0%	109.5	LUCKY
4	IceHogs	1	0	1	0	1	2	-1	0	0.000	1	2	3	15	18	18	2	31	171	181	175	3	28	10	19	27	1	0	0.00%	2	0	100.00%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	33.3%	3.2%	92.9%	96.1	DULL
5	Stars	4	4	0	0	14	6	8	8	1.000	14	23	37	15	18	18	2	128	171	181	175	3	104	41	50	98	11	1	9.09%	10	1	90.00%	1	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	72.2%	10.9%	94.2%	105.2	LUCKY
6	Belleville Senators	1	0	1	0	1	3	-2	0	0.000	1	1	2	15	18	18	2	32	171	181	175	3	38	15	11	15	3	0	0.00%	3	1	66.67%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	33.3%	3.1%	92.1%	95.2	DULL
7	Penguins	2	1	0	1	8	6	2	4	1.000	8	11	19	15	18	18	2	71	171	181	175	3	70	14	18	47	3	1	33.33%	9	2	77.78%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	63.6%	11.3%	91.4%	102.7	LUCKY
8	Thunderbirds	2	1	1	0	7	5	2	2	0.500	7	11	18	15	18	18	2	76	171	181	175	3	62	21	46	55	5	0	0.00%	8	1	87.50%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	63.6%	9.2%	91.9%	101.1	LUCKY
9	Crunch	1	1	0	0	4	3	1	2	1.000	4	6	10	15	18	18	2	33	171	181	175	3	27	15	8	26	3	0	0.00%	4	2	50.00%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	80.0%	12.1%	88.9%	101.0	FUN
10	Silver Knights	1	1	0	0	5	3	2	2	1.000	5	8	13	15	18	18	2	35	171	181	175	3	23	8	4	23	6	2	33.33%	2	0	100.00%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	50.0%	14.3%	87.0%	101.2	FUN
	_Vs Division	2	7	2	0	6	8	-2	14	3.500	6	9	15	15	18	18	2	76	171	181	175	3	48	17	32	49	8	2	25.00%	6	1	83.33%	0	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	36.4%	7.9%	83.3%	91.2	Unlucky
	_Vs Conference	10	7	3	0	34	24	10	14	0.700	34	54	88	15	18	18	2	354	171	181	175	3	264	92	153	253	26	3	11.54%	29	3	89.66%	1	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	59.6%	9.6%	90.9%	100.5	FUN
	_Since Last GM Reset	15	10	4	1	52	37	15	22	0.733	52	80	132	15	18	18	2	527	171	181	175	3	424	147	199	371	36	4	11.11%	47	8	82.98%	1	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	62.3%	9.9%	91.3%	101.1	LUCKY
	Total	15	10	4	1	52	37	15	22	0.733	52	80	132	15	18	18	2	527	171	181	175	3	424	147	199	371	36	4	11.11%	47	8	82.98%	1	197	358	55.03%	163	312	52.24%	100	201	49.75%	329	183	288	145	287	140	62.3%	9.9%	91.3%	101.1	LUCKY

	Puck Time
Offensive Zone	21
Neutral Zone	19
Defensive Zone	19

	Puck Time
Offensive Zone Start	358
Neutral Zone Start	201
Defensive Zone Start	312

	Puck Time
With Puck	31
Without Puck	29

	Faceoffs
Faceoffs Won	460
Faceoffs Lost	411

	Team Average Shots after	League Average Shots after
1st Period	11.4	9.57
2nd Period	23.5	20.31
3rd Period	35.1	30.68
Overtime	35.3	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	1.0	0.64
2nd Period	2.2	1.65
3rd Period	3.4	2.67
Overtime	3.5	2.83


Even Strenght Goal	47
PP Goal	4
PK Goal	1
Empty Net Goal	0

	Home	Away
Win	4	7
Lost	3	1
Overtime Lost	0	0


PP Attempt	36
PP Goal	4
PK Attempt	47
PK Goal Against	8

	Home
Shots For	35.1
Shots Against	28.3
Goals For	3.5
Goals Against	2.5
Hits	24.7
Shots Blocked	9.8
Pim	13.3

Schedule

Date	Matchup			Result	Detail
Mon, Oct 12	CHI	@	DET	CHI3,DET5	RECAP
Wed, Oct 14	WPG	@	DET	WPG3,DET6	RECAP
Thu, Oct 15	DET	@	DAL	DET3,DAL1	RECAP
Sat, Oct 17	CHI	@	DET	CHI2,DET1	RECAP
Mon, Oct 19	OTT	@	DET	OTT3,DET1	RECAP
Wed, Oct 21	DET	@	PIT	DET3,PIT2	RECAP
Fri, Oct 23	DET	@	STL	DET6,STL1	RECAP
Sun, Oct 25	DET	@	CAL	DET1,CAL5	RECAP
Tue, Oct 27	DET	@	PIT	DET5,PIT4 (SO)	RECAP
Thu, Oct 29	DAL	@	DET	DAL1,DET2	RECAP
Sat, Oct 31	DET	@	DAL	DET4,DAL3	RECAP
Mon, Nov 2	STL	@	DET	STL4,DET1	RECAP
Wed, Nov 4	TAM	@	DET	TAM3,DET4	RECAP
Fri, Nov 6	DET	@	CAR	DET5,CAR1	RECAP
Sun, Nov 8	DET	@	DAL	DET5,DAL1	RECAP
Tue, Nov 10	NYR	@	DET
Thu, Nov 12	DAL	@	DET
Sat, Nov 14	DET	@	STL
Mon, Nov 16	STL	@	DET
Wed, Nov 18	DET	@	S J
Fri, Nov 20	DET	@	OTT
Sun, Nov 22	SEA	@	DET
Tue, Nov 24	CHI	@	DET
Thu, Nov 26	DET	@	BOS
Sat, Nov 28	DET	@	NYI
Mon, Nov 30	PHO	@	DET
Tue, Dec 1	DET	@	TAM
Thu, Dec 3	CAR	@	DET
Sat, Dec 5	FLO	@	DET
Mon, Dec 7	DET	@	WAS
Tue, Dec 8	LOS	@	DET
Thu, Dec 10	DET	@	PIT
Sat, Dec 12	DET	@	EDM
Mon, Dec 14	EDM	@	DET
Wed, Dec 16	DET	@	BUF
Thu, Dec 17	DET	@	NYR
Sat, Dec 19	CAL	@	DET
Mon, Dec 21	BUF	@	DET
Wed, Dec 23	DET	@	ANA
Fri, Dec 25	WAS	@	DET
Sun, Dec 27	PIT	@	DET
Tue, Dec 29	DET	@	WPG
Thu, Dec 31	MTL	@	DET
Fri, Jan 1	DET	@	LOS
Mon, Jan 4	NJD	@	DET
Tue, Jan 5	DET	@	CLB
Thu, Jan 7	CLB	@	DET
Sat, Jan 9	DET	@	MTL
Sun, Jan 10	DET	@	NJD
Tue, Jan 12	BOS	@	DET
Thu, Jan 14	PHI	@	DET
Fri, Jan 15	DET	@	PHI
Sun, Jan 17	PHI	@	DET
Tue, Jan 19	DET	@	CHI
Thu, Jan 21	DET	@	NAS
Fri, Jan 22	WPG	@	DET
Sun, Jan 24	DET	@	CHI
Tue, Jan 26	MIN	@	DET
Wed, Jan 27	DET	@	CHI
Fri, Jan 29	CHI	@	DET
Sat, Jan 30	DET	@	WPG
Mon, Feb 1	DET	@	FLO
Wed, Feb 3	NYI	@	DET
Fri, Feb 5	S J	@	DET
Sun, Feb 7	DET	@	COL
Tue, Feb 9	COL	@	DET
Wed, Feb 10	DET	@	CHI
Fri, Feb 12	DET	@	VAN
Sun, Feb 14	TOR	@	DET
Tue, Feb 16	WPG	@	DET
Thu, Feb 18	DET	@	NAS
Sat, Feb 20	CHI	@	DET
Tue, Feb 23	ANA	@	DET
Wed, Feb 24	DET	@	MIN
Sat, Feb 27	NAS	@	DET
Mon, Mar 1	DET	@	MIN
Wed, Mar 3	NAS	@	DET
Sun, Mar 7	MIN	@	DET
Mon, Mar 8	DET	@	TOR
Tue, Mar 9	DET	@	SEA
Sat, Mar 13	OTT	@	DET
Sun, Mar 14	DET	@	PHO
Tue, Mar 16	DET	@	PHO
Trade Deadline --- Trades can�t be done after this day is simulated!
Fri, Mar 19	VAN	@	DET