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
Juha Jaaska	0	LW	100.00	61	43	69	67	74	71	59	63	49	62	59	62	63	51	50	1	50	61	27	1,000,000$/2yrs
Max McCormick	0	LW	100.00	56	44	67	65	60	69	68	62	44	57	58	58	61	68	64	1	50	60	33	975,000$/3yrs
Lukas Rousek	0	RW	100.00	56	43	69	66	60	68	67	62	44	65	54	59	61	54	50	1	50	60	26	975,000$/3yrs
Dylan Roobroeck	0	C	100.00	64	42	64	62	67	60	58	62	42	58	60	59	61	50	50	1	50	59	20	1,000,000$/2yrs
Curtis Douglas	0	C	100.00	70	63	52	55	77	66	65	61	59	58	54	59	57	53	50	1	50	59	25	1,250,000$/1yrs
Chase Wouters	0	C	100.00	58	48	63	65	62	68	66	62	59	60	55	58	61	53	50	1	50	59	25	950,000$/1yrs
Ondrej Pavel	0	C	100.00	63	46	63	62	66	65	63	60	42	56	54	58	59	52	50	1	50	58	24	975,000$/3yrs
Stefan Matteau	0	LW	100.00	58	41	67	58	64	65	63	55	40	51	51	54	55	64	58	1	50	56	31	1,200,000$/1yrs
Jordan Martel	0	RW	100.00	58	40	66	61	61	62	60	56	40	53	54	57	57	53	50	1	50	56	27	1,000,000$/2yrs
Riley Hughes (R)	0	C/LW/RW	100.00	59	41	67	59	62	62	60	57	40	54	54	55	57	52	50	1	50	56	25	975,000$/1yrs
Josiah Didier	0	D	100.00	61	45	64	64	66	72	71	60	40	52	54	66	59	65	61	1	50	62	32	975,000$/3yrs
Wyatte Wylie	0	D	100.00	60	42	72	66	63	66	64	61	40	55	54	65	61	53	50	1	50	61	25	1,250,000$/1yrs
Grant Hutton	0	D	100.00	60	43	68	61	68	67	65	58	40	54	51	62	57	59	52	1	50	60	29	1,000,000$/2yrs
Seth Helgeson	0	D	100.00	58	46	62	58	69	69	68	55	40	51	51	59	54	70	62	1	50	59	34	975,000$/3yrs
Djibril Touré	0	D	100.00	63	44	60	62	67	60	58	57	40	54	55	63	58	50	50	1	50	59	22	1,000,000$/2yrs
Ryan Jones	0	D	100.00	61	42	68	60	67	67	64	56	40	52	50	62	55	59	50	1	50	59	29	975,000$/3yrs
Jeremie Bucheler	0	D	100.00	63	40	69	60	68	62	59	55	40	51	51	61	56	52	50	1	50	58	25	1,000,000$/2yrs
Joe Arntsen	0	D	100.00	62	41	66	61	65	58	57	55	40	53	54	61	57	50	50	1	50	58	22	1,000,000$/2yrs
Scratches
Josh Prokop	0	RW	100.00	58	40	62	61	61	61	58	55	40	54	54	56	57	52	50	1	50	56	25	1,000,000$/2yrs
Keaton Mastrodonato	0	C/LW/RW	100.00	61	40	69	60	65	61	58	55	40	51	52	54	55	52	50	1	50	55	24	1,000,000$/2yrs
Sam Bitten	0	C/LW/RW	100.00	61	55	56	59	66	65	63	56	40	52	52	53	55	52	50	1	50	55	25	975,000$/4yrs
Connor Kurth (R)	0	RW	100.00	62	40	62	59	63	58	56	54	40	54	53	57	55	50	50	1	50	55	21	975,000$/1yrs
Jackson van de Leest	0	D	94.25	67	40	66	58	75	61	58	53	40	51	51	64	54	52	50	1	50	59	24	1,000,000$/2yrs
Grant Gabriele	0	D	100.00	60	40	69	62	64	64	62	53	40	51	51	62	55	56	50	1	50	58	28	1,000,000$/2yrs
Joe Fleming	0	D	100.00	62	49	56	62	64	60	58	57	40	54	53	62	57	50	50	1	50	58	22	1,000,000$/2yrs
Noah Warren (R)	0	D	100.00	64	42	64	58	69	58	57	58	40	54	53	59	56	50	50	1	50	58	20	865,000$/2yrs
Dyllan Gill	0	D	100.00	59	40	67	62	61	59	57	57	40	54	55	59	58	50	50	1	50	57	21	1,000,000$/2yrs
Filip Král	0	D	100.00	53	42	63	60	60	61	60	59	40	57	54	56	57	52	50	1	50	57	25	1,000,000$/2yrs
Jeremie Biakabutuka	0	D	100.00	61	40	66	60	64	59	57	55	40	54	53	59	56	51	50	1	50	57	23	1,000,000$/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
Malcolm Subban	0	100.00	68	70	72	68	69	67	69	66	67	69	69	65	58	1	50	63	31	4,000,000$/1yrs
Felix Sandström	0	100.00	73	66	65	66	68	67	68	66	67	69	61	57	51	1	50	62	28	975,000$/3yrs
Scratches
Jaxon Castor	0	100.00	62	59	57	63	60	57	57	54	58	59	57	55	51	1	50	54	28	975,000$/3yrs
Jordan Papirny	0	100.00	68	60	55	59	60	57	57	52	58	61	59	59	52	1	50	54	29	1,000,000$/2yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Joe Sacco	88	79	79	78	78	69	1	USA	56	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	MP	PPG	PPA	PPP	PKG	PKA	PKP	PKM	GW	GT	FO%	EG	HT	P/GP	PSG	PSS	GSAVG
1	Juha Jaaska	Phantoms (PHI)	LW	82	33	37	70	-20	38	10	122	222	70	130	14.86%	48	24.20	7	13	20	1	0	1	171	3	2	47.03%	0	1	0.85	3	9	0.71
2	Lukas Rousek	Phantoms (PHI)	RW	82	29	34	63	4	100	60	117	222	63	131	13.06%	47	21.98	9	10	19	0	1	1	163	5	3	41.60%	1	1	0.77	3	9	0.62
3	Max McCormick	Phantoms (PHI)	LW	82	22	28	50	4	31	5	113	173	58	107	12.72%	23	20.24	7	10	17	1	0	1	82	4	3	49.84%	0	0	0.61	3	8	0.59
4	Dylan Roobroeck	Phantoms (PHI)	C	77	14	32	46	1	59	15	109	112	34	68	12.50%	24	17.96	5	10	15	0	0	0	79	2	4	40.78%	1	0	0.60	1	3	0.40
5	Chase Wouters	Phantoms (PHI)	C	82	14	20	34	-4	60	20	120	144	51	90	9.72%	28	18.49	4	8	12	0	0	0	13	5	1	54.42%	0	1	0.41	1	1	0.34
6	Curtis Douglas	Phantoms (PHI)	C	82	8	20	28	-20	83	15	137	95	30	71	8.42%	23	17.58	2	10	12	0	0	0	22	3	1	54.87%	0	0	0.34	0	0	0.11
7	Wyatte Wylie	Phantoms (PHI)	D	73	6	15	21	-1	40	10	59	77	25	19	7.79%	78	21.40	3	6	9	0	0	0	138	3	0	0.00%	1	0	0.29	0	0	0.23
8	Jordan Martel	Phantoms (PHI)	RW	77	9	10	19	-1	25	5	95	76	27	51	11.84%	23	17.79	3	1	4	0	0	0	0	2	0	51.28%	0	0	0.25	0	0	0.23
9	Josiah Didier	Phantoms (PHI)	D	73	6	13	19	-11	62	20	78	69	33	31	8.70%	83	24.52	2	6	8	0	0	0	158	0	1	0.00%	0	0	0.26	0	0	0.11
10	Grant Hutton	Phantoms (PHI)	D	82	1	6	7	-4	32	10	45	33	15	15	3.03%	62	18.94	1	2	3	0	0	0	130	0	0	100.00%	0	0	0.09	0	0	0.03
11	Ondrej Pavel	Phantoms (PHI)	C	36	3	4	7	-12	0	0	27	17	6	10	17.65%	7	12.05	0	0	0	0	0	0	36	1	0	43.53%	0	0	0.19	0	0	0.07
12	Riley Hughes	Phantoms (PHI)	C/LW/RW	52	4	3	7	-3	14	0	53	25	8	6	16.00%	15	13.57	0	0	0	0	0	0	39	1	0	45.71%	0	0	0.13	0	0	0.09
13	Seth Helgeson	Phantoms (PHI)	D	66	0	6	6	-9	13	5	28	18	10	8	0.00%	20	14.42	0	0	0	0	0	0	63	0	0	45.45%	0	0	0.09	0	0	0.01
14	Djibril Touré	Phantoms (PHI)	D	78	0	3	3	-14	21	5	23	9	4	6	0.00%	22	10.32	0	1	1	0	0	0	17	0	0	0.00%	0	0	0.04	0	0	-0.07
15	Josh Prokop	Phantoms (PHI)	RW	43	2	1	3	-15	8	0	32	28	11	17	7.14%	6	11.94	1	0	1	0	0	0	0	0	0	58.33%	0	0	0.07	0	0	-0.03
16	Jackson van de Leest	Phantoms (PHI)	D	50	0	2	2	0	4	0	21	12	5	7	0.00%	15	12.52	0	0	0	0	1	1	43	0	0	0.00%	0	0	0.04	0	0	0.04
17	Grant Gabriele	Phantoms (PHI)	D	38	0	1	1	-3	13	5	17	6	3	4	0.00%	8	10.77	0	0	0	0	0	0	16	0	0	0.00%	0	0	0.03	0	0	-0.05
18	Stefan Matteau	Phantoms (PHI)	LW	19	0	1	1	-2	2	0	8	2	3	1	0.00%	2	7.18	0	0	0	0	0	0	22	0	0	50.00%	0	0	0.05	0	0	-0.00
19	Ryan Jones	Phantoms (PHI)	D	82	0	1	1	0	12	0	24	13	6	8	0.00%	26	12.34	0	0	0	0	0	0	41	0	0	0.00%	0	0	0.01	0	0	0.01
20	Jeremie Bucheler	Phantoms (PHI)	D	29	0	0	0	-2	0	0	7	0	0	0	0.00%	0	6.71	0	0	0	0	0	0	1	0	0	0.00%	0	0	0.00	0	0	-0.03
21	Joe Arntsen	Phantoms (PHI)	D	65	0	0	0	-5	4	0	10	1	1	1	0.00%	4	5.08	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.03
22	Joe Fleming	Phantoms (PHI)	D	23	0	0	0	0	2	0	2	0	0	0	0.00%	0	1.79	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.01
23	Noah Warren	Phantoms (PHI)	D	1	0	0	0	0	0	0	0	0	0	0	0.00%	0	2.50	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	-0.03
Team Total or Average			1374	151	237	388	-117	623	185	1247	1354	463	781	11.15%	564	16.18	44	77	121	1053	2	2	4	1243	29	15	48.01%	3470	327	381	3	3	0.35	11	30

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	Malcolm Subban	Phantoms (PHI)	38	15	18	4	0.898	2.56	2226	2	3	95	930	503	2	0	0.692	13	37	40	4	0	1
2	Felix Sandström	Phantoms (PHI)	19	11	6	1	0.899	2.37	1113	0	3	44	436	250	2	0	0.700	10	18	35	2	2	1
3	Jordan Papirny	Phantoms (PHI)	1	0	0	0	0.800	3.16	19	0	0	1	5	3	0	0	0.000	0	0	6	0	0	0
Team Total or Average		58	26	24	5	0.898	2.50	3358	2	6	140	1371	756	4	0	0.696	23	55	81	6	2	2

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20	2020-21	2021-22
Chase Wouters	C	25	950,000$	975,000$	RFA
Connor Kurth	RW	21	975,000$	975,000$	RFA
Curtis Douglas	C	25	1,250,000$	1,250,000$	RFA
Djibril Touré	D	22	1,000,000$	1,000,000$	1,000,000$	RFA
Dylan Roobroeck	C	20	1,000,000$	1,000,000$	1,000,000$	RFA
Dyllan Gill	D	21	1,000,000$	1,000,000$	1,000,000$	RFA
Felix Sandström	G	28	975,000$	975,000$	975,000$	975,000$	UFA
Filip Král	D	25	1,000,000$	1,000,000$	1,000,000$	UFA
Grant Gabriele	D	28	1,000,000$	1,000,000$	1,000,000$	UFA
Grant Hutton	D	29	1,000,000$	1,000,000$	1,000,000$	UFA
Jackson van de Leest	D	24	1,000,000$	1,000,000$	1,000,000$	RFA
Jaxon Castor	G	28	975,000$	975,000$	975,000$	975,000$	UFA
Jeremie Biakabutuka	D	23	1,000,000$	1,000,000$	1,000,000$	RFA
Jeremie Bucheler	D	25	1,000,000$	1,000,000$	1,000,000$	RFA
Joe Arntsen	D	22	1,000,000$	1,000,000$	1,000,000$	RFA
Joe Fleming	D	22	1,000,000$	1,000,000$	1,000,000$	RFA
Jordan Martel	RW	27	1,000,000$	1,000,000$	1,000,000$	UFA
Jordan Papirny	G	29	1,000,000$	1,000,000$	1,000,000$	UFA
Josh Prokop	RW	25	1,000,000$	1,000,000$	1,000,000$	UFA
Josiah Didier	D	32	975,000$	975,000$	975,000$	975,000$	UFA
Juha Jaaska	LW	27	1,000,000$	1,000,000$	1,000,000$	UFA
Keaton Mastrodonato	C/LW/RW	24	1,000,000$	1,000,000$	1,000,000$	RFA
Lukas Rousek	RW	26	975,000$	975,000$	975,000$	975,000$	UFA
Malcolm Subban	G	31	4,000,000$	4,000,000$	UFA
Max McCormick	LW	33	975,000$	975,000$	975,000$	975,000$	UFA
Noah Warren	D	20	865,000$	975,000$	975,000$	RFA
Ondrej Pavel	C	24	975,000$	975,000$	975,000$	975,000$	UFA
Riley Hughes	C/LW/RW	25	975,000$	975,000$	RFA
Ryan Jones	D	29	975,000$	975,000$	975,000$	975,000$	UFA
Sam Bitten	C/LW/RW	25	975,000$	975,000$	975,000$	975,000$	975,000$	UFA
Seth Helgeson	D	34	975,000$	975,000$	975,000$	975,000$	UFA
Stefan Matteau	LW	31	1,200,000$	1,200,000$	UFA
Wyatte Wylie	D	25	1,250,000$	1,250,000$	RFA

Lines

Forward Lines
# - Juha Jaaska	# - Dylan Roobroeck	# - Lukas Rousek
# - Max McCormick	# - Chase Wouters	# - Jordan Martel
# - Juha Jaaska	# - Curtis Douglas	# - Lukas Rousek
# - Juha Jaaska	# - Ondrej Pavel	# - Lukas Rousek

Defensive Pairings
# - Josiah Didier	# - Wyatte Wylie
# - Grant Hutton	# - Josiah Didier
# - Djibril Touré	# - Seth Helgeson

1st Power Play Unit
# - Juha Jaaska		# - Dylan Roobroeck		# - Lukas Rousek
# - Josiah Didier			# - Wyatte Wylie

2nd Power Play Unit
# - Max McCormick		# - Chase Wouters		# - Jordan Martel
# - Grant Hutton			# - Josiah Didier

Goalies
# - Felix Sandström		# - Malcolm Subban

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	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	Gulls	2	0	1	0	1	0	0	2	4	-2	1	0.250	2	4	6	0	15	62	81	15	30	286	566	643	45	14	4	4	40	2	0	0.00%	2	2	0.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	6.7%	71.4%	78.1	Unlucky
2	Moose	2	2	0	0	0	0	0	4	1	3	4	1.000	4	6	10	1	15	62	81	15	25	286	566	643	45	33	12	4	27	2	0	0.00%	2	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	80.0%	16.0%	97.0%	113.0	LUCKY
3	Bruins	4	1	3	0	0	0	0	9	17	-8	2	0.250	9	15	24	0	15	62	81	15	83	286	566	643	45	140	46	58	62	12	4	33.33%	14	4	71.43%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	27.8%	10.8%	87.9%	98.7	FUN
4	Americans	2	1	1	0	0	0	0	2	3	-1	2	0.500	2	4	6	0	15	62	81	15	20	286	566	643	45	36	13	16	26	2	1	50.00%	8	2	75.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	10.0%	91.7%	101.7	LUCKY
5	Heat	2	2	0	0	0	0	0	6	1	5	4	1.000	6	9	15	1	15	62	81	15	43	286	566	643	45	29	11	17	34	5	2	40.00%	6	1	83.33%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	100.0%	14.0%	96.6%	110.5	LUCKY
6	Wolves	4	1	2	1	0	0	0	7	11	-4	4	0.500	7	14	21	0	15	62	81	15	70	286	566	643	45	141	41	22	70	7	2	28.57%	11	1	90.91%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	33.3%	10.0%	92.2%	102.2	LUCKY
7	IceHogs	3	0	2	0	0	0	1	4	11	-7	1	0.167	4	5	9	0	15	62	81	15	53	286	566	643	45	98	29	17	60	4	1	25.00%	6	1	83.33%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	23.1%	7.5%	88.8%	96.3	Unlucky
8	Eagles	2	1	0	1	0	0	0	3	1	2	4	1.000	3	4	7	1	15	62	81	15	36	286	566	643	45	35	8	2	34	1	0	0.00%	1	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	75.0%	8.3%	97.1%	105.5	DULL
9	Monsters	2	0	2	0	0	0	0	2	5	-3	0	0.000	2	2	4	0	15	62	81	15	40	286	566	643	45	39	16	20	34	1	1	100.00%	10	4	60.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	5.0%	87.2%	92.2	Unlucky
10	Stars	2	2	0	0	0	0	0	4	1	3	4	1.000	4	6	10	1	15	62	81	15	45	286	566	643	45	25	15	5	36	4	1	25.00%	0	0	0.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	75.0%	8.9%	96.0%	104.9	Unlucky
11	Griffins	2	0	2	0	0	0	0	3	8	-5	0	0.000	3	5	8	0	15	62	81	15	48	286	566	643	45	85	13	18	34	2	0	0.00%	9	3	66.67%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	37.5%	6.3%	90.6%	96.8	Unlucky
12	Condors	2	0	0	0	2	0	0	5	7	-2	2	0.500	5	7	12	0	15	62	81	15	39	286	566	643	45	53	20	17	41	6	2	33.33%	6	2	66.67%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	37.5%	12.8%	86.8%	99.6	FUN
13	Checkers 1	4	2	2	0	0	0	0	7	6	1	4	0.500	7	13	20	0	15	62	81	15	60	286	566	643	45	62	13	35	60	9	3	33.33%	15	2	86.67%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	11.7%	90.3%	102.0	FUN
14	Reign	2	1	0	0	1	0	0	5	4	1	3	0.750	5	7	12	0	15	62	81	15	47	286	566	643	45	53	16	8	38	3	1	33.33%	4	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	10.6%	92.5%	103.1	LUCKY
15	Wild	2	1	0	0	0	0	1	3	2	1	3	0.750	3	6	9	1	15	62	81	15	46	286	566	643	45	22	6	4	29	3	1	33.33%	2	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	6.5%	90.9%	97.4	Unlucky
16	Rocket	2	1	0	1	0	0	0	8	5	3	4	1.000	8	14	22	0	15	62	81	15	34	286	566	643	45	29	9	25	29	8	5	62.50%	5	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	37.5%	23.5%	82.8%	106.3	FUN
17	Admirals	2	0	1	0	0	0	1	2	7	-5	1	0.250	2	3	5	0	15	62	81	15	36	286	566	643	45	26	11	24	35	2	0	0.00%	7	4	42.86%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	40.0%	5.6%	73.1%	78.6	Unlucky
18	Comets	2	2	0	0	0	0	0	4	1	3	4	1.000	4	6	10	1	15	62	81	15	32	286	566	643	45	15	6	4	34	2	1	50.00%	2	1	50.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	100.0%	12.5%	93.3%	105.8	LUCKY
19	Islanders	5	1	0	1	1	2	0	12	7	5	9	0.900	12	17	29	2	15	62	81	15	58	286	566	643	45	70	20	17	84	7	2	28.57%	6	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	58.8%	20.7%	90.0%	110.7	FUN
20	Wolf Pack	4	1	3	0	0	0	0	7	12	-5	2	0.250	7	10	17	1	15	62	81	15	82	286	566	643	45	127	46	61	77	7	0	0.00%	13	4	69.23%	1	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	46.7%	8.5%	90.6%	99.1	Unlucky
21	Belleville Senators	2	0	2	0	0	0	0	2	8	-6	0	0.000	2	4	6	0	15	62	81	15	32	286	566	643	45	38	14	34	34	3	1	33.33%	7	3	57.14%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	16.7%	6.3%	78.9%	85.2	Unlucky
22	Roadrunners	2	0	1	0	0	0	1	3	6	-3	1	0.250	3	6	9	0	15	62	81	15	32	286	566	643	45	31	9	27	22	5	1	20.00%	7	2	71.43%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	33.3%	9.4%	80.6%	90.0	FUN
23	Penguins	4	1	2	0	1	0	0	9	13	-4	3	0.375	9	14	23	0	15	62	81	15	68	286	566	643	45	136	56	33	74	5	1	20.00%	14	2	85.71%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	42.1%	13.2%	90.4%	103.7	FUN
24	Barracuda	2	0	2	0	0	0	0	1	5	-4	0	0.000	1	1	2	0	15	62	81	15	28	286	566	643	45	42	17	18	34	4	1	25.00%	9	3	66.67%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	0.0%	3.6%	88.1%	91.7	Unlucky
25	Thunderbirds	2	0	1	0	0	0	1	5	8	-3	1	0.250	5	8	13	0	15	62	81	15	35	286	566	643	45	76	31	38	31	2	1	50.00%	14	4	71.43%	1	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	14.3%	89.5%	103.8	FUN
26	Crunch	2	1	1	0	0	0	0	5	4	1	2	0.500	5	8	13	0	15	62	81	15	30	286	566	643	45	36	21	8	32	6	1	16.67%	4	2	50.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	66.7%	16.7%	88.9%	105.6	FUN
27	Marlies	2	1	1	0	0	0	0	8	6	2	2	0.500	8	15	23	0	15	62	81	15	41	286	566	643	45	67	33	38	33	7	3	42.86%	4	1	75.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	50.0%	19.5%	91.0%	110.6	FUN
28	Canucks	2	0	1	0	1	0	0	2	7	-5	1	0.250	2	3	5	0	15	62	81	15	40	286	566	643	45	64	24	15	31	7	2	28.57%	5	2	60.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	0.0%	5.0%	89.1%	94.1	Unlucky
29	Hershey Bears	7	3	3	0	0	1	0	17	19	-2	8	0.571	17	23	40	0	15	62	81	15	164	286	566	643	45	160	40	43	126	22	7	31.82%	19	7	63.16%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	45.5%	10.4%	88.1%	98.5	FUN
30	Silver Knights	2	1	0	0	0	1	0	10	5	5	4	1.000	10	14	24	0	15	62	81	15	35	286	566	643	45	24	7	11	29	4	1	25.00%	3	0	100.00%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	64.3%	28.6%	79.2%	107.7	FUN
31	Checkers 2	3	0	2	0	0	1	0	6	10	-4	2	0.333	6	7	13	0	15	62	81	15	73	286	566	643	45	88	30	42	50	7	3	42.86%	11	2	81.82%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	27.3%	8.2%	88.6%	96.9	Unlucky
	_Vs Division	20	10	15	2	2	3	0	44	57	-13	32	0.800	44	78	122	0	15	62	81	15	348	286	566	643	45	493	162	232	310	49	18	36.73%	66	17	74.24%	0	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	39.4%	12.6%	88.4%	101.1	FUN
	_Vs Conference	48	16	23	3	3	3	0	101	124	-23	47	0.490	101	162	263	4	15	62	81	15	854	286	566	643	45	1160	398	429	806	105	34	32.38%	137	35	74.45%	1	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	42.9%	11.8%	89.3%	101.1	FUN
	_Since Last GM Reset	82	26	35	4	7	5	5	167	205	-38	82	0.500	167	260	427	9	15	62	81	15	1505	286	566	643	45	1894	637	685	1380	161	49	30.43%	226	59	73.89%	2	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	44.7%	11.1%	89.2%	100.3	FUN
	Total	82	26	35	4	7	5	5	167	205	-38	82	0.500	167	260	427	9	15	62	81	15	1505	286	566	643	45	1894	637	685	1380	161	49	30.43%	226	59	73.89%	2	584	1198	48.75%	718	1457	49.28%	404	880	45.91%	1401	541	1623	899	1952	961	44.7%	11.1%	89.2%	100.3	FUN

	Puck Time
Offensive Zone	17
Neutral Zone	23
Defensive Zone	19

	Puck Time
Offensive Zone Start	1198
Neutral Zone Start	880
Defensive Zone Start	1457

	Puck Time
With Puck	29
Without Puck	31

	Faceoffs
Faceoffs Won	1706
Faceoffs Lost	1829

	Team Average Shots after	League Average Shots after
1st Period	3.5	9.57
2nd Period	10.4	20.31
3rd Period	18.2	30.68
Overtime	18.8	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	0.2	0.64
2nd Period	0.9	1.65
3rd Period	1.9	2.67
Overtime	2.1	2.83


Even Strenght Goal	116
PP Goal	49
PK Goal	2
Empty Net Goal	0

	Home	Away
Win	16	19
Lost	19	16
Overtime Lost	6	6


PP Attempt	161
PP Goal	49
PK Attempt	226
PK Goal Against	59

	Home
Shots For	18.4
Shots Against	23.1
Goals For	2.0
Goals Against	2.5
Hits	16.8
Shots Blocked	7.8
Pim	8.4

Schedule

Date	Matchup			Result	Detail
Wed, Oct 8	WAS	@	PHI	WAS4,PHI1	RECAP
Fri, Oct 10	PHI	@	NYI	PHI4,NYI1	RECAP
Tue, Oct 14	WAS	@	PHI	WAS2,PHI3 (SO)	RECAP
Thu, Oct 16	PHI	@	WAS	PHI2,WAS4	RECAP
Sat, Oct 18	CAR	@	PHI	CAR2,PHI3	RECAP
Sun, Oct 19	PHI	@	WAS	PHI3,WAS2	RECAP
Wed, Oct 22	FLO	@	PHI	FLO1,PHI2	RECAP
Sat, Oct 25	NYI	@	PHI	NYI0,PHI1 (OT)	RECAP
Mon, Oct 27	PHI	@	CAR	PHI1,CAR3	RECAP
Wed, Oct 29	NYR	@	PHI	NYR4,PHI0	RECAP
Sat, Nov 1	PHI	@	FLO	PHI1,FLO2	RECAP
Sun, Nov 2	WAS	@	PHI	WAS1,PHI3	RECAP
Wed, Nov 5	CHI	@	PHI	CHI3,PHI2 (SO)	RECAP
Thu, Nov 6	PHI	@	SEA	PHI2,SEA1 (SO)	RECAP
Sat, Nov 8	PHI	@	BOS	PHI1,BOS5	RECAP
Mon, Nov 10	PHO	@	PHI	PHO3,PHI1	RECAP
Wed, Nov 12	PHI	@	DET	PHI2,DET4	RECAP
Thu, Nov 13	COL	@	PHI	COL0,PHI1	RECAP
Sun, Nov 16	BUF	@	PHI	BUF2,PHI0	RECAP
Tue, Nov 18	PHI	@	VAN	PHI0,VAN4	RECAP
Wed, Nov 19	CAL	@	PHI	CAL1,PHI4	RECAP
Fri, Nov 21	PHI	@	BUF	PHI2,BUF1	RECAP
Sun, Nov 23	PHI	@	NYR	PHI2,NYR4	RECAP
Tue, Nov 25	CHI	@	PHI	CHI2,PHI3 (SO)	RECAP
Fri, Nov 28	PHI	@	WPG	PHI2,WPG1	RECAP
Sat, Nov 29	NYI	@	PHI	NYI0,PHI1 (SO)	RECAP
Tue, Dec 2	PHI	@	FLO	PHI4,FLO2	RECAP
Wed, Dec 3	WAS	@	PHI	WAS3,PHI1	RECAP
Sat, Dec 6	PHI	@	CHI	PHI7,CHI3	RECAP
Sun, Dec 7	WPG	@	PHI	WPG0,PHI2	RECAP
Thu, Dec 11	LOS	@	PHI	LOS1,PHI0 (OT)	RECAP
Sat, Dec 13	PHI	@	WAS	PHI4,WAS3	RECAP
Mon, Dec 15	MIN	@	PHI	MIN0,PHI2	RECAP
Tue, Dec 16	PHI	@	TAM	PHI3,TAM1	RECAP
Thu, Dec 18	PHI	@	S J	PHI1,S J3	RECAP
Fri, Dec 19	EDM	@	PHI	EDM2,PHI1 (OT)	RECAP
Mon, Dec 22	PHI	@	PHO	PHI2,PHO3 (SO)	RECAP
Wed, Dec 24	NYR	@	PHI	NYR4,PHI3	RECAP
Sat, Dec 27	PHI	@	NYI	PHI4,NYI3 (SO)	RECAP
Mon, Dec 29	NJD	@	PHI	NJD0,PHI2	RECAP
Wed, Dec 31	PHI	@	EDM	PHI4,EDM5 (OT)	RECAP
Fri, Jan 2	PHI	@	TOR	PHI0,TOR2	RECAP
Sat, Jan 3	TAM	@	PHI	TAM3,PHI2	RECAP
Tue, Jan 6	PHI	@	COL	PHI2,COL1 (OT)	RECAP
Wed, Jan 7	CAR	@	PHI	CAR4,PHI0	RECAP
Sat, Jan 10	PHI	@	NYI	PHI2,NYI3 (OT)	RECAP
Sun, Jan 11	CLB	@	PHI	CLB3,PHI1	RECAP
Wed, Jan 14	DAL	@	PHI	DAL0,PHI2	RECAP
Fri, Jan 16	PHI	@	STL	PHI1,STL3	RECAP
Sat, Jan 17	PHI	@	PIT	PHI3,PIT1	RECAP
Mon, Jan 19	OTT	@	PHI	OTT6,PHI1	RECAP
Wed, Jan 21	PHI	@	NAS	PHI1,NAS2 (SO)	RECAP
Fri, Jan 23	FLO	@	PHI	FLO1,PHI0	RECAP
Sat, Jan 24	PHI	@	OTT	PHI1,OTT2	RECAP
Mon, Jan 26	ANA	@	PHI	ANA1,PHI0	RECAP
Thu, Jan 29	PHI	@	ANA	PHI2,ANA3 (OT)	RECAP
Fri, Jan 30	PHI	@	MTL	PHI6,MTL4	RECAP
Sun, Feb 1	SEA	@	PHI	SEA5,PHI3	RECAP
Tue, Feb 3	TOR	@	PHI	TOR4,PHI8	RECAP
Thu, Feb 5	PHI	@	CAL	PHI2,CAL0	RECAP
Sat, Feb 7	PHI	@	CLB	PHI1,CLB2	RECAP
Mon, Feb 9	VAN	@	PHI	VAN3,PHI2 (OT)	RECAP
Wed, Feb 11	BOS	@	PHI	BOS2,PHI4	RECAP
Sat, Feb 14	PHI	@	CAR	PHI3,CAR2 (OT)	RECAP
Sun, Feb 15	PIT	@	PHI	PIT7,PHI3	RECAP
Wed, Feb 18	PHI	@	NYR	PHI2,NYR0	RECAP
Thu, Feb 19	NAS	@	PHI	NAS5,PHI1	RECAP
Sun, Feb 22	STL	@	PHI	STL5,PHI4 (SO)	RECAP
Mon, Feb 23	PHI	@	LOS	PHI5,LOS3	RECAP
Tue, Feb 24	PHI	@	DAL	PHI2,DAL1	RECAP
Fri, Feb 27	PIT	@	PHI	PIT3,PHI2 (OT)	RECAP
Mon, Mar 2	PHI	@	PIT	PHI1,PIT2	RECAP
Tue, Mar 3	PHI	@	MIN	PHI1,MIN2 (SO)	RECAP
Wed, Mar 4	S J	@	PHI	S J2,PHI0	RECAP
Fri, Mar 6	PHI	@	SEA	PHI1,SEA4	RECAP
Sun, Mar 8	MTL	@	PHI	MTL1,PHI2 (OT)	RECAP
Thu, Mar 12	CHI	@	PHI	CHI5,PHI1	RECAP
Fri, Mar 13	PHI	@	BOS	PHI2,BOS3	RECAP
Sun, Mar 15	PHI	@	NJD	PHI2,NJD1	RECAP
Mon, Mar 16	PHI	@	CHI	PHI1,CHI3	RECAP
Thu, Mar 19	DET	@	PHI	DET4,PHI1	RECAP
Trade Deadline --- Trades can�t be done after this day is simulated!
Mon, Mar 23	BOS	@	PHI	BOS7,PHI2	RECAP

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
3,637,500$	0$	0$	75,000,000$

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

Arena Capacity - Ticket Price Attendance - %

Arena Capacity	2000	1000
Ticket Price	35$	18$	$	$	$
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
0	0 - 0.00%	0$	0$	3000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
3,637,500$	3,637,500$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
4,852,070$	0$	3,852,069$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	22	27,441$	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

Griffins55-16-11, 121pts

41Final

Phantoms35-35-12, 82pts

Bruins43-31-8, 94pts

72Final

Phantoms35-35-12, 82pts

Bruins43-31-8, 94pts

Tue, Mar 24

Phantoms35-35-12, 82pts

Sat, Oct 11

Islanders34-37-11, 79pts

Hershey Bears43-26-13, 99pts

Wed, Oct 15

Phantoms35-35-12, 82pts

TRANSACTIONS

Team Info


	Head Coach	Joe Sacco
	Division	Atlantic Division
	City	Lehigh Valley
	Stadium Capacity	3,000

Phantoms Affiliation


	General Manager	Luc Gregoire
	Head Coach	Scott Arniel
	Stadium	Wachovia Center
	Capacity	20,523

Team Leaders

GOALS Juha Jaaska Phantoms	33 GOALS
POINTS Juha Jaaska Phantoms	70 POINTS
WINS Malcolm Subban Phantoms	15 WINS
Expanded Player Leaderboard

Team Stats

Goals For 167 2.04 GFG	Goals Against 205 2.50 GAA
Power Play Percentage 30.4% 49 GF	Penalty Kill Percentage 73.9% 59 GA
Expanded Team Stats

Team Captain - Alternate Captains

Captain

Alternate Captain