LSFH - Today's Games Farm

Roster

Player	#	POS	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Adam Ruzicka	64	C	100.00	76	39	92	67	90	83	75	65	78	66	68	64	67	63	65	0	50	72	23	791,667$/3yrs
Noah Gregor (R)	73	C/LW/RW	100.00	74	39	86	69	72	84	85	67	61	68	66	62	72	64	65	0	50	71	24	900,000$/2yrs
Juho Lammikko	91	LW	100.00	68	36	94	63	79	77	89	64	80	61	63	75	62	66	67	0	50	71	27	4,600,000$/1yrs
Joachim Blichfeld	86	LW/RW	100.00	64	38	81	67	79	82	83	66	57	62	67	61	69	64	66	0	50	70	24	790,000$/1yrs
Vasily Podkolzin	92	RW	100.00	64	35	82	72	75	81	83	70	56	64	67	58	71	61	64	0	50	70	21	1,775,000$/3yrs
Andrew Poturalski	12	C	100.00	58	36	93	64	69	87	88	66	77	68	62	59	65	68	73	0	50	69	29	820,833$/3yrs
Glenn Gawdin	42	C	100.00	71	39	87	63	76	80	82	64	75	65	58	61	59	65	67	0	50	68	26	773,333$/3yrs
Riley Barber (A)	38	RW	100.00	64	38	84	62	74	80	78	63	66	61	64	59	67	69	70	0	50	68	29	875,000$/2yrs
Dylan Holloway	55	C/LW	100.00	69	39	85	65	78	91	62	68	70	64	59	61	66	63	62	0	50	68	21	1,441,667$/3yrs
Gage Quinney	74	LW	100.00	60	37	92	61	72	89	68	63	56	61	64	59	62	67	69	0	50	67	27	1,062,500$/1yrs
Michal Teply	71	RW	100.00	72	38	94	57	81	79	83	55	61	59	56	60	57	61	63	0	50	66	21	919,167$/3yrs
Ben McCartney	52	LW	100.00	79	39	66	58	71	84	82	56	58	57	59	60	58	61	63	0	50	66	21	813,333$/3yrs
Kurtis MacDermid (A)	56	D	100.00	77	89	61	59	96	74	83	57	30	60	58	66	51	68	70	0	50	69	29	959,000$/3yrs
Jakub Galvas	76	D	100.00	62	35	81	63	68	83	87	62	30	61	58	66	52	63	67	0	50	67	23	925,000$/2yrs
Santeri Hatakka	61	D	100.00	60	38	83	64	76	77	74	60	30	65	58	64	50	61	63	0	50	67	22	925,000$/3yrs
Dmitri Samorukov	5	D	100.00	68	38	82	62	81	73	77	60	30	59	58	56	49	63	65	0	50	66	23	819,166$/2yrs
Scratches
Gerald Mayhew (C)	36	LW/RW	100.00	68	35	94	66	62	73	74	66	68	64	66	65	66	77	72	0	50	69	30	876,500$/2yrs
Ben Jones	18	C	100.00	71	39	73	61	72	80	87	59	66	58	60	55	62	64	65	0	50	66	24	1,002,000$/2yrs
David Cotton	45	C	100.00	71	39	85	57	80	79	82	58	62	56	59	58	61	65	67	0	50	66	25	1,093,000$/1yrs
Tyce Thompson	13	RW	100.00	63	40	87	59	72	79	66	58	67	61	57	62	56	63	65	0	50	65	23	883,333$/3yrs
Dave Gust	37	RW	100.00	56	37	88	56	65	85	86	58	62	59	60	54	59	68	70	0	50	64	29	758,333$/3yrs
Aarne Talvitie	8	C	100.00	63	37	92	55	72	68	88	54	61	55	56	57	58	63	65	0	50	63	24	925,000$/2yrs
Jamieson Rees	81	C	100.00	58	37	76	59	67	78	87	58	66	57	56	54	59	61	63	0	50	63	22	910,833$/3yrs
Lukas Rousek	11	RW	100.00	58	36	92	63	68	74	65	59	52	57	55	53	59	63	65	0	50	62	23	830,000$/2yrs
Josh Mahura	28	D	100.00	70	34	88	70	69	85	76	69	30	70	63	59	54	63	65	0	50	70	24	954,333$/3yrs
Cavan Fitzgerald	79	D	100.00	66	38	91	57	75	71	82	56	30	55	54	58	45	66	68	0	50	64	26	805,500$/2yrs
Ryan O'Rourke	20	D	100.00	61	54	71	56	70	79	84	58	30	57	53	56	45	59	62	0	50	63	20	894,167$/3yrs
Brennan Menell	62	D	100.00	61	34	73	57	68	66	67	59	30	58	52	57	45	65	67	0	50	62	25	806,000$/1yrs
Matthew Kessel (R)	51	D	100.00	72	38	83	54	81	66	64	52	30	56	50	52	45	62	64	0	50	61	22	850,000$/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
Ken Appleby	50	100.00	73	68	67	88	72	71	73	72	71	73	72	67	77	0	50	75	28	750,000$/3yrs
Adam Scheel	31	100.00	70	73	74	82	69	68	70	69	68	70	69	63	69	0	50	72	23	1,023,750$/2yrs
Scratches
Kirill Ustimenko	67	100.00	66	68	67	81	65	64	66	65	64	66	65	63	69	0	50	69	24	925,000$/1yrs
Ryan Fanti	34	100.00	67	64	65	82	66	65	67	66	65	67	66	63	69	0	50	69	23	933,333$/3yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Rémi Girard	75	75	75	75	75	75	75	Can	72	5	0$

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	Juho Lammikko	Phantoms (PHI)	LW	77	40	52	92	10	24	0	87	392	89	229	10.20%	58	20.02	13	13	26	0	1	1	137	3	3	50.45%	0	0	1.19	1	3	1.31
2	Vasily Podkolzin	Phantoms (PHI)	RW	82	34	47	81	2	20	0	98	343	87	232	9.91%	15	18.22	12	13	25	0	0	0	18	2	2	39.05%	1	0	0.99	1	3	1.05
3	Adam Ruzicka	Phantoms (PHI)	C	74	26	44	70	4	32	0	144	239	64	201	10.88%	32	19.99	6	17	23	0	0	0	139	3	0	55.31%	0	2	0.95	2	2	0.96
4	Andrew Poturalski	Phantoms (PHI)	C	82	22	46	68	0	4	0	48	235	79	196	9.36%	15	19.19	6	15	21	0	0	0	58	2	1	52.80%	0	0	0.83	1	1	0.83
5	Joachim Blichfeld	Phantoms (PHI)	LW/RW	82	23	45	68	-3	33	5	106	287	79	218	8.01%	19	19.60	7	12	19	0	0	0	138	1	0	44.05%	1	0	0.83	0	3	0.85
6	Josh Mahura	Phantoms (PHI)	D	79	15	45	60	1	54	0	128	156	50	126	9.62%	107	23.22	8	8	16	0	1	1	185	3	0	0.00%	0	0	0.76	0	0	0.67
7	Dylan Holloway	Phantoms (PHI)	C/LW	78	21	32	53	5	28	0	124	251	67	175	8.37%	16	16.11	1	5	6	0	0	0	67	2	1	46.90%	1	1	0.68	0	1	0.75
8	Jakub Galvas	Phantoms (PHI)	D	82	11	42	53	-12	22	0	78	135	38	116	8.15%	113	21.44	3	13	16	0	0	0	200	4	1	0.00%	0	0	0.65	0	0	0.57
9	Santeri Hatakka	Phantoms (PHI)	D	75	15	37	52	-8	28	0	83	137	41	85	10.95%	94	21.39	8	9	17	0	2	2	173	0	1	0.00%	0	0	0.69	0	0	0.61
10	Gerald Mayhew	Phantoms (PHI)	LW/RW	80	19	28	47	3	18	0	89	255	70	218	7.45%	19	17.03	2	5	7	1	0	1	144	2	1	42.05%	0	0	0.59	1	2	0.71
11	Kurtis MacDermid	Phantoms (PHI)	D	81	7	32	39	5	112	10	227	134	44	85	5.22%	137	21.70	3	5	8	0	0	0	128	1	0	0.00%	0	0	0.48	0	0	0.40
12	Glenn Gawdin	Phantoms (PHI)	C	80	14	20	34	-8	29	5	116	161	37	112	8.70%	24	13.96	0	1	1	1	1	2	152	2	0	52.68%	0	0	0.43	0	0	0.41
13	Noah Gregor	Phantoms (PHI)	C/LW/RW	27	10	20	30	-3	15	5	71	86	33	57	11.63%	5	19.99	3	9	12	0	0	0	53	1	0	43.90%	0	0	1.11	0	0	0.96
14	Gage Quinney	Phantoms (PHI)	LW	74	17	13	30	-7	8	0	25	120	26	86	14.17%	6	10.97	0	0	0	0	0	0	2	1	3	52.11%	0	0	0.41	0	0	0.38
15	Dmitri Samorukov	Phantoms (PHI)	D	82	11	15	26	-18	52	0	141	60	17	48	18.33%	82	17.15	1	2	3	0	0	0	75	1	1	0.00%	0	0	0.32	0	0	0.19
16	Riley Barber	Phantoms (PHI)	RW	82	11	13	24	-7	22	0	66	160	40	115	6.88%	8	11.97	0	1	1	0	0	0	3	2	0	54.00%	0	0	0.29	0	2	0.32
17	Cavan Fitzgerald	Phantoms (PHI)	D	74	3	15	18	-9	50	0	89	47	12	24	6.38%	92	16.12	0	0	0	0	0	0	73	1	0	0.00%	0	0	0.24	0	0	0.14
18	Ben Jones	Phantoms (PHI)	C	57	5	9	14	-9	32	10	60	61	16	33	8.20%	6	8.76	1	0	1	0	0	0	5	2	0	48.37%	0	0	0.25	0	0	0.12
19	Michal Teply	Phantoms (PHI)	RW	54	5	8	13	-22	4	0	30	55	15	40	9.09%	5	8.88	0	0	0	0	0	0	0	0	1	42.59%	0	0	0.24	0	0	0.11
20	David Cotton	Phantoms (PHI)	C	47	5	2	7	-4	6	0	17	32	14	34	15.63%	3	6.95	0	0	0	0	0	0	0	0	0	54.84%	0	0	0.15	0	0	0.14
21	Tyce Thompson	Phantoms (PHI)	RW	31	4	3	7	-9	2	0	8	47	13	24	8.51%	2	6.90	0	0	0	0	0	0	0	0	0	27.78%	0	0	0.23	0	0	0.19
22	Ryan O'Rourke	Phantoms (PHI)	D	11	0	6	6	-2	6	0	20	5	2	3	0.00%	12	17.69	0	2	2	0	0	0	14	0	0	0.00%	0	0	0.55	0	0	0.33
23	Jamieson Rees	Phantoms (PHI)	C	2	1	1	2	0	2	0	0	4	1	3	25.00%	0	17.66	0	1	1	0	0	0	3	1	0	56.60%	0	0	1.00	0	0	0.69
24	Ben McCartney	Phantoms (PHI)	LW	33	0	1	1	-11	10	0	24	15	8	13	0.00%	5	4.77	0	0	0	0	0	0	1	0	0	50.98%	0	0	0.03	0	0	-0.08
Team Total or Average			1526	319	576	895	-102	613	35	1879	3417	942	2473	9.34%	875	16.53	74	131	205	2819	2	5	7	1778	34	15	51.80%	8486	0	0	3	3	0.71	6	17

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	Ken Appleby	Phantoms (PHI)	54	25	23	1	0.903	3.41	2920	4	2	166	1705	0	2	1	0.923	13	49	24	4	0	2
2	Adam Scheel	Phantoms (PHI)	39	11	18	2	0.883	4.38	1919	4	1	140	1195	0	0	3	0.000	0	33	49	3	0	1
3	Kirill Ustimenko	Phantoms (PHI)	1	0	0	0	0.947	2.50	24	0	0	1	19	0	0	0	0.000	0	0	2	0	0	0
4	Ryan Fanti	Phantoms (PHI)	2	0	2	0	0.902	4.23	71	0	0	5	51	0	0	0	0.000	0	0	7	0	0	0
Team Total or Average		96	36	43	3	0.895	3.79	4936	8	3	312	2970	0	2	4	0.923	13	82	82	7	0	3

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20	2020-21
Aarne Talvitie	C	24	925,000$	925,000$	925,000$	RFA
Adam Ruzicka	C	23	791,667$	850,000$	762,500$	762,500$	UFA
Adam Scheel	G	23	1,023,750$	910,000$	1,137,500$	RFA
Andrew Poturalski	C	29	820,833$	937,500$	762,500$	762,500$	UFA
Ben Jones	C	24	1,002,000$	1,254,000$	750,000$	RFA
Ben McCartney	LW	21	813,333$	813,333$	813,333$	813,333$	RFA
Brennan Menell	D	25	806,000$	806,000$	RFA
Cavan Fitzgerald	D	26	805,500$	861,000$	750,000$	UFA
Dave Gust	RW	29	758,333$	750,000$	762,500$	762,500$	UFA
David Cotton	C	25	1,093,000$	1,093,000$	RFA
Dmitri Samorukov	D	23	819,166$	863,333$	775,000$	RFA
Dylan Holloway	C/LW	21	1,441,667$	1,441,667$	1,441,667$	1,441,667$	RFA
Gage Quinney	LW	27	1,062,500$	1,062,500$	UFA
Gerald Mayhew	LW/RW	30	876,500$	1,003,000$	750,000$	UFA
Glenn Gawdin	C	26	773,333$	795,000$	762,500$	762,500$	UFA
Jakub Galvas	D	23	925,000$	925,000$	925,000$	RFA
Jamieson Rees	C	22	910,833$	910,833$	910,833$	910,833$	RFA
Joachim Blichfeld	LW/RW	24	790,000$	790,000$	RFA
Josh Mahura	D	24	954,333$	1,188,000$	750,000$	925,000$	UFA
Juho Lammikko	LW	27	4,600,000$	4,600,000$	UFA
Ken Appleby	G	28	750,000$	700,000$	762,500$	762,500$	UFA
Kirill Ustimenko	G	24	925,000$	925,000$	RFA
Kurtis MacDermid	D	29	959,000$	902,000$	987,500$	987,500$	UFA
Lukas Rousek	RW	23	830,000$	830,000$	830,000$	RFA
Matthew Kessel	D	22	850,000$	700,000$	925,000$	925,000$	RFA
Michal Teply	RW	21	919,167$	919,167$	919,167$	919,167$	RFA
Noah Gregor	C/LW/RW	24	900,000$	850,000$	950,000$	RFA
Riley Barber	RW	29	875,000$	1,000,000$	750,000$	UFA
Ryan Fanti	G	23	933,333$	950,000$	925,000$	925,000$	RFA
Ryan O'Rourke	D	20	894,167$	894,167$	894,167$	894,167$	RFA
Santeri Hatakka	D	22	925,000$	925,000$	925,000$	925,000$	RFA
Tyce Thompson	RW	23	883,333$	1,125,000$	762,500$	762,500$	RFA
Vasily Podkolzin	RW	21	1,775,000$	1,775,000$	1,775,000$	1,775,000$	RFA

Lines

Forward Lines
# - Juho Lammikko	# - Adam Ruzicka	# - Noah Gregor
# - Joachim Blichfeld	# - Andrew Poturalski	# - Vasily Podkolzin
# - Dylan Holloway	# - Riley Barber
# - Gage Quinney	# - Glenn Gawdin	# - Michal Teply

Defensive Pairings
# - Kurtis MacDermid
# - Jakub Galvas	# - Santeri Hatakka
# - Dmitri Samorukov	# - Glenn Gawdin

1st Power Play Unit
# - Juho Lammikko		# - Adam Ruzicka		# - Noah Gregor
# - Kurtis MacDermid

2nd Power Play Unit
# - Joachim Blichfeld		# - Andrew Poturalski		# - Vasily Podkolzin
# - Jakub Galvas			# - Santeri Hatakka

Goalies
# - Adam Scheel		# - Ken Appleby

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	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	Checkers	3	3	0	0	0	0	15	8	7	6	1.000	15	28	43	0	0	115	90	88	8	122	1048	1003	1087	38	105	20	26	73	18	5	27.78%	5	1	80.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	58.8%	12.3%	92.4%	104.7	LUCKY
2	Marlies	2	0	1	1	0	0	8	9	-1	2	0.500	8	15	23	0	0	115	90	88	8	68	1048	1003	1087	38	69	29	22	67	6	1	16.67%	11	5	54.55%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	63.6%	11.8%	87.0%	98.7	FUN
3	Americans	3	2	1	0	0	0	13	12	1	4	0.667	13	22	35	0	0	115	90	88	8	121	1048	1003	1087	38	98	28	18	63	14	4	28.57%	8	1	87.50%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	45.0%	10.7%	87.8%	98.5	FUN
4	Binghamton Senateurs	2	1	0	0	0	1	9	7	2	4	1.000	9	15	24	0	0	115	90	88	8	77	1048	1003	1087	38	92	26	16	43	5	0	0.00%	7	1	85.71%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	60.0%	11.7%	92.4%	104.1	LUCKY
5	Bears	6	2	3	0	1	0	17	22	-5	5	0.417	17	30	47	0	0	115	90	88	8	224	1048	1003	1087	38	248	64	34	139	21	5	23.81%	16	3	81.25%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	38.7%	7.6%	91.1%	98.7	Unlucky
6	Rockets	2	1	0	0	0	1	12	8	4	4	1.000	12	20	32	0	0	115	90	88	8	97	1048	1003	1087	38	65	23	18	63	3	0	0.00%	9	3	66.67%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	70.6%	12.4%	87.7%	100.1	FUN
7	Providence Bruins	2	0	2	0	0	0	7	11	-4	0	0.000	7	11	18	0	0	115	90	88	8	76	1048	1003	1087	38	71	21	8	46	9	1	11.11%	4	1	75.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	37.5%	9.2%	84.5%	93.7	FUN
8	Wolves	6	1	5	0	0	0	21	27	-6	2	0.167	21	38	59	0	0	115	90	88	8	266	1048	1003	1087	38	218	63	41	131	27	6	22.22%	17	2	88.24%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	37.5%	7.9%	87.6%	95.5	Unlucky
9	Eagles	1	0	1	0	0	0	3	4	-1	0	0.000	3	5	8	0	0	115	90	88	8	34	1048	1003	1087	38	40	12	8	22	2	0	0.00%	4	0	100.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	42.9%	8.8%	90.0%	98.8	Unlucky
10	Wolf Pack	6	3	2	1	0	0	23	21	2	8	0.667	23	46	69	0	1	115	90	88	8	225	1048	1003	1087	38	219	55	43	127	24	5	20.83%	18	5	72.22%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	52.9%	10.2%	90.4%	100.6	FUN
11	islanders	6	2	3	0	0	1	22	23	-1	6	0.500	22	40	62	0	0	115	90	88	8	229	1048	1003	1087	38	186	61	47	127	19	4	21.05%	17	5	70.59%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	50.0%	9.6%	87.6%	97.2	FUN
12	IceHogs	2	0	1	1	0	0	8	8	0	2	0.500	8	15	23	0	0	115	90	88	8	69	1048	1003	1087	38	63	20	8	37	6	0	0.00%	3	0	100.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	50.0%	11.6%	87.3%	98.9	FUN
13	Griffins	2	2	0	0	0	0	11	8	3	4	1.000	11	19	30	0	0	115	90	88	8	72	1048	1003	1087	38	69	14	12	46	9	3	33.33%	6	2	66.67%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	57.1%	15.3%	88.4%	103.7	FUN
14	Monsters	6	3	3	0	0	0	24	25	-1	6	0.500	24	42	66	2	0	115	90	88	8	212	1048	1003	1087	38	213	64	48	134	25	5	20.00%	24	7	70.83%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	51.4%	11.3%	88.3%	99.6	FUN
15	Barracuda	1	0	1	0	0	0	3	4	-1	0	0.000	3	5	8	0	0	115	90	88	8	44	1048	1003	1087	38	27	12	12	30	3	0	0.00%	5	0	100.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	42.9%	6.8%	85.2%	92.0	Unlucky
16	Condors	1	0	1	0	0	0	2	4	-2	0	0.000	2	4	6	0	0	115	90	88	8	37	1048	1003	1087	38	32	14	8	19	2	1	50.00%	4	0	100.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	20.0%	5.4%	87.5%	92.9	Unlucky
17	Iowa Wild	2	0	2	0	0	0	7	10	-3	0	0.000	7	11	18	0	0	115	90	88	8	91	1048	1003	1087	38	87	22	20	47	6	0	0.00%	10	3	70.00%	1	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	50.0%	7.7%	88.5%	96.2	Unlucky
18	Reign	1	0	1	0	0	0	3	5	-2	0	0.000	3	5	8	0	0	115	90	88	8	28	1048	1003	1087	38	30	10	12	15	5	2	40.00%	5	3	40.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	33.3%	10.7%	83.3%	94.0	FUN
19	Thunderbirds	1	0	1	0	0	0	1	3	-2	0	0.000	1	2	3	0	0	115	90	88	8	31	1048	1003	1087	38	43	14	10	27	2	0	0.00%	5	1	80.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	33.3%	3.2%	93.0%	96.2	DULL
20	Wranglers	2	1	1	0	0	0	6	7	-1	2	0.500	6	11	17	0	0	115	90	88	8	80	1048	1003	1087	38	86	24	6	41	8	2	25.00%	3	0	100.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	36.4%	7.5%	91.9%	99.4	DULL
21	Gulls	1	0	1	0	0	0	1	3	-2	0	0.000	1	2	3	0	0	115	90	88	8	24	1048	1003	1087	38	41	13	12	22	2	0	0.00%	6	1	83.33%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	33.3%	4.2%	92.7%	96.8	DULL
22	Texas Stars	1	0	1	0	0	0	3	4	-1	0	0.000	3	5	8	0	0	115	90	88	8	36	1048	1003	1087	38	50	15	10	21	2	1	50.00%	3	1	66.67%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	40.0%	8.3%	92.0%	100.3	DULL
23	Canucks	2	1	1	0	0	0	7	8	-1	2	0.500	7	14	21	0	0	115	90	88	8	72	1048	1003	1087	38	68	18	16	55	8	3	37.50%	8	3	62.50%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	44.4%	9.7%	88.2%	98.0	FUN
24	Comets	6	2	3	0	1	0	20	23	-3	5	0.417	20	37	57	1	0	115	90	88	8	260	1048	1003	1087	38	233	64	41	115	25	5	20.00%	15	5	66.67%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	45.5%	7.7%	90.1%	97.8	Unlucky
25	W-B/Scranton Penguins	6	3	2	0	1	0	22	16	6	7	0.583	22	41	63	0	1	115	90	88	8	225	1048	1003	1087	38	189	54	41	107	23	6	26.09%	15	3	80.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	55.2%	9.8%	91.5%	101.3	LUCKY
26	Roadrunners	1	0	1	0	0	0	2	3	-1	0	0.000	2	3	5	0	0	115	90	88	8	30	1048	1003	1087	38	49	12	16	25	1	0	0.00%	7	2	71.43%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	66.7%	6.7%	93.9%	100.5	DULL
27	Crunch	2	1	1	0	0	0	10	9	1	2	0.500	10	17	27	0	0	115	90	88	8	73	1048	1003	1087	38	80	14	10	39	8	2	25.00%	5	1	80.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	50.0%	13.7%	88.8%	102.4	FUN
28	Admirals	1	1	0	0	0	0	4	2	2	2	1.000	4	8	12	0	0	115	90	88	8	45	1048	1003	1087	38	37	6	8	28	5	0	0.00%	4	1	75.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	80.0%	8.9%	94.6%	103.5	Unlucky
29	Moose	3	1	2	0	0	0	8	9	-1	2	0.333	8	16	24	1	1	115	90	88	8	121	1048	1003	1087	38	73	29	12	58	20	4	20.00%	6	1	83.33%	1	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	33.3%	6.6%	87.7%	94.3	Unlucky
30	Silver Knights	1	0	1	0	0	0	3	5	-2	0	0.000	3	6	9	0	0	115	90	88	8	42	1048	1003	1087	38	42	7	10	24	6	2	33.33%	5	2	60.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	25.0%	7.1%	88.1%	95.2	Unlucky
31	Firebirds	1	0	1	0	0	0	4	8	-4	0	0.000	4	7	11	0	0	115	90	88	8	31	1048	1003	1087	38	51	8	12	31	4	1	25.00%	5	2	60.00%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	33.3%	12.9%	84.3%	97.2	FUN
	_Vs Division	42	16	21	1	3	1	149	157	-8	39	0.464	149	274	423	3	2	115	90	88	8	1641	1048	1003	1087	38	1506	425	295	880	164	36	21.95%	122	30	75.41%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	47.1%	9.1%	89.6%	98.7	FUN
	_Vs Conference	60	26	26	2	3	3	234	229	5	65	0.542	234	421	655	3	2	115	90	88	8	2347	1048	1003	1087	38	2155	600	425	1320	236	52	22.03%	177	45	74.58%	0	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	49.7%	10.0%	89.4%	99.3	FUN
	_Since Last GM Reset	82	30	43	3	3	3	299	316	-17	75	0.457	299	540	839	4	3	115	90	88	8	3162	1048	1003	1087	38	2974	836	605	1822	318	68	21.38%	260	65	75.00%	2	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	47.9%	9.5%	89.4%	98.8	FUN
	Total	82	30	43	3	3	3	299	316	-17	75	0.457	299	540	839	4	3	115	90	88	8	3162	1048	1003	1087	38	2974	836	605	1822	318	68	21.38%	260	65	75.00%	2	1699	3268	51.99%	1598	3048	52.43%	730	1390	52.52%	1962	1352	1919	611	1066	531	47.9%	9.5%	89.4%	98.8	FUN

	Puck Time
Offensive Zone	23
Neutral Zone	13
Defensive Zone	23

	Puck Time
Offensive Zone Start	3268
Neutral Zone Start	1390
Defensive Zone Start	3048

	Puck Time
With Puck	30
Without Puck	29

	Faceoffs
Faceoffs Won	4027
Faceoffs Lost	3679

	Team Average Shots after	League Average Shots after
1st Period	12.8	9.57
2nd Period	25.0	20.31
3rd Period	38.3	30.68
Overtime	38.7	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	1.4	0.64
2nd Period	2.5	1.65
3rd Period	3.6	2.67
Overtime	3.7	2.83


Even Strenght Goal	225
PP Goal	68
PK Goal	2
Empty Net Goal	4

	Home	Away
Win	17	19
Lost	22	21
Overtime Lost	2	1


PP Attempt	318
PP Goal	68
PK Attempt	260
PK Goal Against	65

	Home
Shots For	38.6
Shots Against	36.3
Goals For	3.6
Goals Against	3.9
Hits	22.2
Shots Blocked	10.2
Pim	7.4

Schedule

Date	Matchup			Result	Detail
Mon, Oct 17	UTI	@	LEV	UTI4,LEV3	RECAP
Thu, Oct 20	LEV	@	CHW	LEV4,CHW2	RECAP
Sat, Oct 22	BRI	@	LEV	BRI3,LEV2	RECAP
Mon, Oct 24	LEV	@	BRI	LEV4,BRI3 (SO)	RECAP
Thu, Oct 27	CLE	@	LEV	CLE5,LEV4	RECAP
Sun, Oct 30	LEV	@	WBS	LEV6,WBS0	RECAP
Tue, Nov 1	WBS	@	LEV	WBS5,LEV4 (OT)	RECAP
Thu, Nov 3	LEV	@	HER	LEV1,HER5	RECAP
Sat, Nov 5	LEV	@	UTI	LEV4,UTI3	RECAP
Mon, Nov 7	HER	@	LEV	HER1,LEV3	RECAP
Wed, Nov 9	LEV	@	CLE	LEV3,CLE5	RECAP
Thu, Nov 10	MAN	@	LEV	MAN0,LEV2	RECAP
Mon, Nov 14	HAR	@	LEV	HAR3,LEV5	RECAP
Fri, Nov 18	LEV	@	HAR	LEV4,HAR7	RECAP
Mon, Nov 21	CHW	@	LEV	CHW4,LEV1	RECAP
Wed, Nov 23	HAR	@	LEV	HAR3,LEV4 (OT)	RECAP
Thu, Nov 24	LEV	@	WBS	LEV6,WBS3	RECAP
Mon, Nov 28	SPR	@	LEV	SPR3,LEV1	RECAP
Wed, Nov 30	LEV	@	CHA	LEV5,CHA3	RECAP
Sat, Dec 3	BRI	@	LEV	BRI5,LEV2	RECAP
Tue, Dec 6	LEV	@	HAR	LEV2,HAR0	RECAP
Wed, Dec 7	ROC	@	LEV	ROC3,LEV6	RECAP
Mon, Dec 12	HAR	@	LEV	HAR3,LEV4	RECAP
Wed, Dec 14	LEV	@	WRA	LEV4,WRA1	RECAP
Sat, Dec 17	BRI	@	LEV	BRI4,LEV6	RECAP
Mon, Dec 19	MAR	@	LEV	MAR6,LEV4	RECAP
Wed, Dec 21	LEV	@	ROC	LEV4,ROC3	RECAP
Fri, Dec 23	LEV	@	ABB	LEV7,ABB3	RECAP
Sun, Dec 25	CHW	@	LEV	CHW3,LEV2	RECAP
Thu, Dec 29	LEV	@	ROC	LEV3,ROC6	RECAP
Sat, Dec 31	LEV	@	ONT	LEV3,ONT5	RECAP
Sun, Jan 1	BNG	@	LEV	BNG1,LEV2	RECAP
Wed, Jan 4	LEV	@	HER	LEV2,HER5	RECAP
Sat, Jan 7	WBS	@	LEV	WBS3,LEV1	RECAP
Mon, Jan 9	LEV	@	UTI	LEV4,UTI6	RECAP
Tue, Jan 10	LVL	@	LEV	LVL3,LEV4 (SO)	RECAP
Thu, Jan 12	WBS	@	LEV	WBS3,LEV1	RECAP
Sun, Jan 15	LEV	@	SDG	LEV1,SDG3	RECAP
Mon, Jan 16	MIL	@	LEV	MIL2,LEV4	RECAP
Wed, Jan 18	LEV	@	WRA	LEV2,WRA6	RECAP
Sat, Jan 21	LEV	@	ABB	LEV0,ABB5	RECAP
Mon, Jan 23	SYR	@	LEV	SYR3,LEV5	RECAP
Wed, Jan 25	LEV	@	LVL	LEV8,LVL5	RECAP
Thu, Jan 26	TUC	@	LEV	TUC3,LEV2	RECAP
Sat, Jan 28	LEV	@	EAG	LEV3,EAG4	RECAP
Mon, Jan 30	CHA	@	LEV	CHA2,LEV4	RECAP
Tue, Jan 31	LEV	@	IOW	LEV3,IOW5	RECAP
Thu, Feb 2	IOW	@	LEV	IOW5,LEV4	RECAP
Sat, Feb 4	LEV	@	RFD	LEV5,RFD4 (OT)	RECAP
Mon, Feb 6	LEV	@	HAR	LEV4,HAR5	RECAP
Wed, Feb 8	LEV	@	BNG	LEV7,BNG6 (SO)	RECAP
Thu, Feb 9	UTI	@	LEV	UTI5,LEV4	RECAP
Tue, Feb 14	FCV	@	LEV	FCV8,LEV4	RECAP
Wed, Feb 15	LEV	@	CLE	LEV6,CLE4	RECAP
Sun, Feb 19	MAN	@	LEV	MAN2,LEV1	RECAP
Tue, Feb 21	RFD	@	LEV	RFD4,LEV3	RECAP
Thu, Feb 23	LEV	@	TEX	LEV3,TEX4	RECAP
Fri, Feb 24	CHA	@	LEV	CHA3,LEV6	RECAP
Wed, Mar 1	LEV	@	HER	LEV6,HER3	RECAP
Thu, Mar 2	CHW	@	LEV	CHW5,LEV4	RECAP
Sat, Mar 4	LEV	@	MAN	LEV5,MAN7	RECAP
Tue, Mar 7	UTI	@	LEV	UTI2,LEV3	RECAP
Fri, Mar 10	LEV	@	UTI	LEV2,UTI3 (OT)	RECAP
Trade Deadline --- Trades can�t be done after this day is simulated!
Sat, Mar 11	PRO	@	LEV	PRO4,LEV3	RECAP
Tue, Mar 14	LEV	@	WBS	LEV4,WBS2	RECAP
Wed, Mar 15	LEV	@	CLE	LEV4,CLE6	RECAP
Thu, Mar 16	HER	@	LEV	HER5,LEV4 (OT)	RECAP
Sat, Mar 18	LEV	@	SYR	LEV5,SYR6	RECAP
Mon, Mar 20	HER	@	LEV	HER3,LEV1	RECAP
Wed, Mar 22	LEV	@	GRG	LEV4,GRG3	RECAP
Fri, Mar 24	LEV	@	MAR	LEV4,MAR3 (OT)	RECAP
Sun, Mar 26	BAK	@	LEV	BAK4,LEV2	RECAP
Tue, Mar 28	LEV	@	PRO	LEV4,PRO7	RECAP
Thu, Mar 30	SJB	@	LEV	SJB4,LEV3	RECAP
Tue, Apr 4	GRG	@	LEV	GRG5,LEV7	RECAP
Fri, Apr 7	HEN	@	LEV	HEN5,LEV3	RECAP
Sat, Apr 8	LEV	@	BRI	LEV2,BRI4	RECAP
Mon, Apr 10	LEV	@	CHW	LEV4,CHW5	RECAP
Wed, Apr 12	LEV	@	BRI	LEV6,BRI4	RECAP
Fri, Apr 14	LEV	@	CHW	LEV6,CHW8	RECAP
Sat, Apr 15	CLE	@	LEV	CLE2,LEV3	RECAP
Tue, Apr 18	CLE	@	LEV	CLE3,LEV4	RECAP

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
3,527,450$	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$	15$	$	$	$
Attendance	59925	38507
Attendance PCT	73.08%	93.92%	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	2401 - 80.03%	97,213$	3,985,718$	3000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
3,527,450$	3,441,174$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
587,915$	0$	585,631$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	18,965$	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

Oscar Klefbom is suspended indefinitely.

Game Center

Monsters39-37-6, 84pts

23Final

Phantoms36-43-3, 75pts

Monsters39-37-6, 84pts

34Final

Phantoms36-43-3, 75pts

Monsters39-37-6, 84pts

Wed, Apr 19

Phantoms36-43-3, 75pts

Fri, Oct 21

Wolves44-34-4, 92pts

islanders37-36-9, 83pts

Sun, Oct 23

Phantoms36-43-3, 75pts

TRANSACTIONS

Team Info


	Head Coach	Rémi Girard
	Division	METROPOLITAINE
	City	Lehigh Valley
	Stadium Capacity	3,000

Phantoms Affiliation


	General Manager	Frank Grimard
	Head Coach	Jon Cooper
	Stadium	Wachovia Center
	Capacity	17,000

Team Leaders

GOALS Juho Lammikko Phantoms	40 GOALS
POINTS Juho Lammikko Phantoms	92 POINTS
WINS Ken Appleby Phantoms	25 WINS
Expanded Player Leaderboard

Team Stats

Goals For 299 3.65 GFG	Goals Against 316 3.85 GAA
Power Play Percentage 21.4% 68 GF	Penalty Kill Percentage 75.0% 65 GA
Expanded Team Stats

Team Captain - Alternate Captains

Captain

Alternate Captain