Ultimate Hockey League - 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
A.J. Greer	0	LW	100.00	80	74	72	70	79	49	98	60	57	63	62	70	54	61	58	72	75	72	28	850,000$/1yrs
Markus Nurmi (R)	0	LW/RW	100.00	67	77	86	63	77	56	83	59	77	54	61	59	58	46	46	66	75	66	27	650,000$/1yrs
Patrik Puistola (R)	0	LW/RW	100.00	65	65	92	66	65	57	86	62	59	58	62	52	59	39	39	66	75	66	24	450,000$/2yrs
Peter Cehlarik	0	LW/RW	100.00	66	73	86	63	73	56	82	61	73	56	61	46	58	47	47	65	75	65	29	500,000$/1yrs
Ty Mueller (R)	0	C	100.00	65	66	89	65	66	55	83	60	68	57	57	60	55	35	35	65	75	65	22	500,000$/1yrs
Vinzenz Rohrer (R)	0	C/RW	100.00	64	61	91	63	61	56	87	57	30	49	59	56	56	39	39	63	75	63	20	625,000$/1yrs
Nate Danielson (R)	0	C	100.00	66	69	87	64	69	56	84	59	76	57	58	55	55	38	38	63	75	63	20	875,000$/2yrs
Ben King (R)	0	C/RW	100.00	66	75	80	63	75	54	82	55	57	47	58	46	55	38	38	62	75	62	23	550,000$/2yrs
Logan Brown	0	C	100.00	68	82	87	64	82	57	51	61	85	57	61	52	59	43	43	62	75	62	27	775,000$/1yrs
Keean Washkurak (R)	0	C/LW	100.00	64	65	82	63	65	53	85	52	32	46	53	56	49	39	39	61	75	61	23	525,000$/1yrs
Danny Nelson (R)	0	C/LW	100.00	67	77	77	63	78	55	76	55	61	46	58	44	54	32	32	61	75	61	19	625,000$/2yrs
Zachary Nehring (R)	0	RW	100.00	65	67	82	64	68	54	81	53	41	47	53	58	50	32	32	61	75	61	20	550,000$/2yrs
Yegor Zamula	0	D	100.00	62	40	91	70	76	65	76	59	40	66	59	84	54	59	58	73	75	73	25	1,700,000$/2yrs
Nolan Allan (R)	0	D	100.00	63	45	80	74	73	62	68	57	41	63	57	72	55	56	57	65	75	65	22	825,000$/2yrs
William Trudeau (R)	0	D	100.00	65	74	79	64	73	54	86	48	25	38	41	68	39	40	40	62	75	62	22	856,667$/1yrs
Maxime Lajoie (R)	0	D	100.00	65	69	84	65	69	56	83	52	25	46	42	65	40	48	48	62	75	62	27	775,000$/1yrs
Robin Salo (R)	0	D	100.00	65	67	89	66	67	58	87	52	25	46	42	60	40	42	42	62	75	62	26	550,000$/2yrs
Theo Lindstein (R)	0	D	100.00	65	69	88	64	69	53	83	47	25	36	41	70	39	36	36	61	75	61	20	725,000$/2yrs
Scratches
Jonah Gadjovich (R)	0	LW	100.00	77	85	88	68	79	46	51	56	54	58	63	67	54	59	58	62	75	62	26	775,000$/2yrs
Cross Hanas (R)	0	C/LW/RW	100.00	65	66	88	63	66	53	79	52	34	46	53	54	49	38	38	60	75	60	23	859,167$/1yrs
Jakub Brabenec (R)	0	C/LW	100.00	66	67	91	62	67	53	77	52	36	46	53	48	49	37	37	60	75	60	21	828,333$/2yrs
Olle Lycksell (R)	0	C/LW/RW	100.00	59	55	78	67	59	62	70	64	55	65	61	67	60	61	58	59	75	59	25	787,500$/1yrs
Samuel Savoie (R)	0	C/LW	100.00	65	66	89	64	66	54	85	51	34	46	48	48	46	38	38	58	75	58	21	550,000$/1yrs
Wade Allison (R)	0	LW/RW	100.00	65	75	61	62	75	53	57	47	43	41	47	45	45	40	40	55	75	55	27	450,000$/1yrs
Cal Thomas (R)	0	D	100.00	65	66	85	63	66	53	75	46	25	36	40	65	38	33	33	59	75	59	22	500,000$/3yrs
Kevin Korchinski (R)	0	D	76.33	57	53	71	75	69	68	83	59	41	63	57	66	55	55	54	59	75	59	21	875,000$/1yrs
Nate Clurman (R)	0	D	100.00	65	74	80	65	75	54	62	49	25	43	41	67	39	39	39	59	75	59	27	775,000$/1yrs
Mattias Havelid (R)	0	D	100.00	65	64	87	64	64	54	68	48	25	39	41	56	39	34	34	57	75	57	21	725,000$/1yrs
Andrew Gibson (R)	0	D	100.00	65	77	67	63	77	53	74	47	25	36	41	56	38	36	36	57	75	57	20	725,000$/2yrs
Alexander Alexeyev	0	D	100.00	57	45	76	57	86	63	12	54	41	60	60	67	55	59	59	56	75	56	25	825,000$/1yrs
Carter Berger (R)	0	D	100.00	65	73	85	64	73	52	82	46	25	36	41	47	39	34	34	56	75	56	25	450,000$/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
Yaroslav Askarov	0	99.50	67	73	65	72	74	71	73	68	68	67	70	59	55	64	75	64	23	925,000$/1yrs
Nick Malik	0	100.00	55	76	66	62	55	62	55	55	55	55	48	38	38	52	75	52	23	450,000$/1yrs
Scratches
Michael Hrabal	0	100.00	52	74	64	72	52	59	52	52	52	52	45	33	33	51	75	51	20	725,000$/2yrs
Olof Glifford	0	100.00	51	52	55	70	51	58	51	51	51	51	44	30	30	49	75	49	20	450,000$/2yrs
Vyacheslav Buteyets	0	100.00	41	76	65	68	41	48	41	41	41	41	34	35	35	45	75	45	23	450,000$/1yrs
Jesper Wallstedt	0	100.00	34	67	62	67	34	41	34	34	34	34	27	35	35	42	75	42	22	925,000$/1yrs

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary

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%	FOT	EG	HT	P/20	PSG	PSS
1	A.J. Greer	Gargoyles (MOU)	LW	55	25	31	56	4	50	0	178	218	69	167	11.47%	11	19.94	4	7	11	0	0	0	146	4	1	57.78%	424	0	1	1.02	1	3
2	Nolan Allan	Gargoyles (MOU)	D	44	18	35	53	-2	22	0	84	128	33	57	14.06%	65	22.71	7	7	14	0	0	0	121	1	0	0.00%	0	0	0	1.06	0	0
3	Ty Mueller	Gargoyles (MOU)	C	55	15	37	52	5	8	0	30	127	37	108	11.81%	3	17.19	3	10	13	0	0	0	62	0	0	52.06%	1262	0	0	1.10	1	3
4	Markus Nurmi	Gargoyles (MOU)	LW/RW	55	13	32	45	14	21	5	67	170	56	160	7.65%	7	18.86	2	5	7	0	0	0	123	1	0	61.99%	171	1	0	0.87	1	3
5	Yegor Zamula	Gargoyles (MOU)	D	49	12	31	43	-10	18	0	63	127	28	74	9.45%	72	23.33	6	8	14	0	0	0	115	2	0	0.00%	0	0	0	0.75	0	0
6	Peter Cehlarik	Gargoyles (MOU)	LW/RW	55	16	26	42	5	34	0	86	139	52	104	11.51%	7	17.95	2	7	9	0	0	0	51	2	1	51.09%	92	0	0	0.85	2	2
7	Theo Lindstein	Gargoyles (MOU)	D	54	9	27	36	25	32	0	76	74	37	55	12.16%	78	17.27	0	0	0	0	0	0	65	1	0	0.00%	0	0	0	0.77	0	0
8	Nate Danielson	Gargoyles (MOU)	C	50	18	17	35	3	20	0	53	152	37	91	11.84%	6	16.24	3	2	5	0	0	0	0	2	1	57.87%	1061	0	0	0.86	0	0
9	Vinzenz Rohrer	Gargoyles (MOU)	C/RW	55	13	13	26	4	2	0	36	111	29	85	11.71%	10	13.39	0	1	1	1	0	1	6	2	0	34.17%	720	0	0	0.71	0	0
10	Maxime Lajoie	Gargoyles (MOU)	D	55	6	20	26	-17	50	0	103	73	33	52	8.22%	81	21.09	2	5	7	0	1	1	136	1	0	0.00%	0	0	0	0.45	0	0
11	Patrik Puistola	Gargoyles (MOU)	LW/RW	42	12	14	26	-8	8	0	21	131	37	91	9.16%	2	19.37	3	4	7	0	1	1	122	0	0	40.91%	88	1	0	0.64	0	1
12	Logan Brown	Gargoyles (MOU)	C	42	8	16	24	11	16	0	66	52	13	36	15.38%	13	14.50	0	0	0	0	0	0	17	2	0	63.25%	302	0	0	0.79	0	0
13	Robin Salo	Gargoyles (MOU)	D	55	4	18	22	-21	34	0	88	64	23	43	6.25%	62	21.78	2	1	3	0	0	0	146	0	0	0.00%	0	0	0	0.37	0	0
14	Jonah Gadjovich	Gargoyles (MOU)	LW	40	11	8	19	9	44	10	70	83	24	84	13.25%	7	11.58	0	0	0	0	0	0	32	3	0	50.00%	72	0	0	0.82	0	0
15	William Trudeau	Gargoyles (MOU)	D	55	1	16	17	9	35	5	105	60	23	47	1.67%	87	21.34	0	5	5	0	0	0	139	0	0	0.00%	0	0	0	0.29	0	0
16	Zachary Nehring	Gargoyles (MOU)	RW	53	3	14	17	-7	18	0	40	78	17	55	3.85%	8	11.93	0	0	0	0	0	0	7	0	0	33.33%	36	0	0	0.54	0	0
17	Danny Nelson	Gargoyles (MOU)	C/LW	55	9	4	13	1	12	0	54	52	18	36	17.31%	2	10.34	0	0	0	0	0	0	16	3	0	55.56%	99	0	0	0.46	0	0
18	Keean Washkurak	Gargoyles (MOU)	C/LW	44	4	6	10	-6	0	0	24	31	8	38	12.90%	6	9.54	0	0	0	0	0	0	59	0	0	36.00%	225	0	0	0.48	0	0
19	Ben King	Gargoyles (MOU)	C/RW	50	3	7	10	-2	16	0	47	49	14	39	6.12%	3	12.02	0	0	0	0	0	0	18	0	1	51.22%	82	0	0	0.33	0	0
20	Alexander Alexeyev	Gargoyles (MOU)	D	8	1	4	5	-3	2	0	12	12	4	4	8.33%	12	17.55	0	0	0	0	0	0	13	0	0	0.00%	0	0	0	0.71	0	0
21	Cross Hanas	Gargoyles (MOU)	C/LW/RW	16	4	1	5	-4	4	0	16	28	8	16	14.29%	0	10.46	0	0	0	0	0	0	7	1	1	57.14%	14	0	0	0.60	0	0
22	Jakub Brabenec	Gargoyles (MOU)	C/LW	21	1	4	5	-10	6	0	11	17	4	11	5.88%	0	8.60	0	0	0	0	0	0	6	0	0	33.77%	154	0	0	0.55	0	0
23	Kevin Korchinski	Gargoyles (MOU)	D	10	0	5	5	-1	2	0	9	10	6	8	0.00%	15	16.48	0	0	0	0	0	0	6	0	0	0.00%	0	0	0	0.61	0	0
24	Cal Thomas	Gargoyles (MOU)	D	5	0	1	1	-1	0	0	9	0	2	1	0.00%	8	17.43	0	0	0	0	0	0	5	0	0	0.00%	0	0	0	0.23	0	0
25	Nate Clurman	Gargoyles (MOU)	D	6	0	1	1	-2	2	0	10	3	2	2	0.00%	8	17.60	0	0	0	0	0	0	7	0	0	0.00%	0	0	0	0.19	0	0
Team Total or Average			1029	206	388	594	-4	456	20	1358	1989	614	1464	10.36%	573	16.69	34	62	96	1481	1	2	3	1436	25	5	50.56%	4802	2	1	0.69	5	12

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	Yaroslav Askarov	Gargoyles (MOU)	50	23	18	7	0.897	3.42	2859	6	1	163	1577	0	1	1	0.500	12	49	5	3	1	1
2	Nick Malik	Gargoyles (MOU)	11	1	6	0	0.874	4.57	446	2	0	34	270	0	0	1	0.000	0	6	49	0	0	0
3	Michael Hrabal	Gargoyles (MOU)	1	0	0	0	0.833	3.53	17	0	0	1	6	0	0	0	0.000	0	0	1	0	0	0
Team Total or Average		62	24	24	7	0.893	3.58	3323	8	1	198	1853	0	1	2	0.500	12	55	55	3	1	1

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20
A.J. Greer	LW	28	850,000$	850,000$	RFA
Alexander Alexeyev	D	25	825,000$	825,000$	RFA
Andrew Gibson	D	20	725,000$	725,000$	725,000$
Ben King	C/RW	23	550,000$	550,000$	550,000$
Cal Thomas	D	22	500,000$	500,000$	500,000$	500,000$
Carter Berger	D	25	450,000$	450,000$	RFA
Cross Hanas	C/LW/RW	23	859,167$	859,167$
Danny Nelson	C/LW	19	625,000$	625,000$	625,000$
Jakub Brabenec	C/LW	21	828,333$	828,333$	828,333$
Jesper Wallstedt	G	22	925,000$	925,000$
Jonah Gadjovich	LW	26	775,000$	775,000$	775,000$	RFA
Keean Washkurak	C/LW	23	525,000$	525,000$	RFA
Kevin Korchinski	D	21	875,000$	875,000$
Logan Brown	C	27	775,000$	775,000$	RFA
Markus Nurmi	LW/RW	27	650,000$	650,000$	RFA
Mattias Havelid	D	21	725,000$	725,000$
Maxime Lajoie	D	27	775,000$	775,000$	RFA
Michael Hrabal	G	20	725,000$	725,000$	725,000$
Nate Clurman	D	27	775,000$	775,000$	RFA
Nate Danielson	C	20	875,000$	875,000$	875,000$
Nick Malik	G	23	450,000$	450,000$
Nolan Allan	D	22	825,000$	825,000$	825,000$
Olle Lycksell	C/LW/RW	25	787,500$	787,500$	RFA
Olof Glifford	G	20	450,000$	450,000$	450,000$
Patrik Puistola	LW/RW	24	450,000$	450,000$	450,000$	RFA
Peter Cehlarik	LW/RW	29	500,000$	500,000$	RFA
Robin Salo	D	26	550,000$	550,000$	550,000$	RFA
Samuel Savoie	C/LW	21	550,000$	550,000$
Theo Lindstein	D	20	725,000$	725,000$	725,000$
Ty Mueller	C	22	500,000$	500,000$
Vinzenz Rohrer	C/RW	20	625,000$	625,000$
Vyacheslav Buteyets	G	23	450,000$	450,000$
Wade Allison	LW/RW	27	450,000$	450,000$	RFA
William Trudeau	D	22	856,667$	856,667$
Yaroslav Askarov	G	23	925,000$	925,000$
Yegor Zamula	D	25	1,700,000$	1,700,000$	1,700,000$	RFA
Zachary Nehring	RW	20	550,000$	550,000$	550,000$

Lines

Forward Lines
# - A.J. Greer	# - Ty Mueller	# - Markus Nurmi
# - Patrik Puistola	# - Vinzenz Rohrer	# - Peter Cehlarik
# - Danny Nelson	# - Nate Danielson	# - Ben King
# - Keean Washkurak	# - Logan Brown	# - Zachary Nehring

Defensive Pairings
# - Yegor Zamula	# - Nolan Allan
# - Maxime Lajoie	# - Robin Salo
# - William Trudeau	# - Theo Lindstein

1st Power Play Unit
# - A.J. Greer		# - Ty Mueller		# - Markus Nurmi
# - Yegor Zamula			# - Nolan Allan

2nd Power Play Unit
# - Patrik Puistola		# - Vinzenz Rohrer		# - Peter Cehlarik
# - Maxime Lajoie			# - Robin Salo

Goalies
# - Nick Malik		# - Yaroslav Askarov

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	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT	GF%	SH%	SV%	PDO	PDOBRK
1	HK	2	1	0	1	0	0	2.50	2.50	0.00	3	0.750	2.5	4.0	6.5	0	0	84	61	57	5	21.0	634	647	602	27	41.0	14.5	10.00	23.5	2.50	0.50	20.00%	5.00	1.00	80.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	57.1%	11.9%	93.9%	105.8	LUCKY
2	Ninjas	2	0	2	0	0	0	3.50	5.50	-2.00	0	0.000	3.5	6.0	9.5	0	0	84	61	57	5	28.0	634	647	602	27	39.5	13.0	12.00	24.5	2.50	1.00	40.00%	6.00	1.00	83.33%	1	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	35.7%	12.5%	86.1%	98.6	FUN
3	Junior Wolves	2	2	0	0	0	0	6.00	3.00	3.00	4	1.000	6.0	11.0	17.0	0	1	84	61	57	5	41.0	634	647	602	27	32.5	10.5	8.00	23.0	3.50	0.50	14.29%	4.00	1.00	75.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	73.3%	14.6%	90.8%	105.4	FUN
4	Bulldogs	1	0	1	0	0	0	1.00	3.00	-2.00	0	0.000	1.0	2.0	3.0	0	0	84	61	57	5	28.0	634	647	602	27	27.0	13.0	12.00	20.0	3.00	0.00	0.00%	6.00	1.00	83.33%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	33.3%	3.6%	88.9%	92.5	Unlucky
5	Shockers	4	3	1	0	0	0	3.50	3.25	0.25	6	0.750	3.5	6.3	9.8	0	0	84	61	57	5	32.0	634	647	602	27	35.8	8.3	9.00	19.3	3.25	0.50	15.38%	4.25	0.75	82.35%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	322.25	215.50	320.75	106.25	188.50	94.00	54.5%	10.9%	90.9%	101.8	FUN
6	Sailors	1	0	1	0	0	0	4.00	6.00	-2.00	0	0.000	4.0	7.0	11.0	0	0	84	61	57	5	32.0	634	647	602	27	29.0	13.0	27.00	35.0	3.00	1.00	33.33%	6.00	1.00	83.33%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	37.5%	12.5%	79.3%	91.8	FUN
7	Devils	2	1	1	0	0	0	2.00	4.00	-2.00	2	0.500	2.0	3.0	5.0	0	0	84	61	57	5	21.5	634	647	602	27	39.5	5.0	6.00	21.0	1.50	0.00	0.00%	3.00	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	33.3%	9.3%	89.9%	99.2	FUN
8	Eagles	1	1	0	0	0	0	3.00	2.00	1.00	2	1.000	3.0	6.0	9.0	0	0	84	61	57	5	43.0	634	647	602	27	25.0	13.0	6.00	31.0	3.00	0.00	0.00%	2.00	1.00	50.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	75.0%	7.0%	92.0%	99.0	DULL
9	Ravens	2	0	2	0	0	0	3.00	5.00	-2.00	0	0.000	3.0	5.5	8.5	2	0	84	61	57	5	28.0	634	647	602	27	32.0	8.5	11.00	26.5	2.00	0.50	25.00%	5.00	0.50	90.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	35.7%	10.7%	84.4%	95.1	FUN
10	Venom	1	0	0	0	1	0	4.00	3.00	1.00	2	1.000	4.0	5.0	9.0	0	0	84	61	57	5	31.0	634	647	602	27	45.0	11.0	4.00	21.0	0.00	0.00	0.00%	2.00	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	57.1%	12.9%	93.3%	106.2	LUCKY
11	Indians	1	0	1	0	0	0	3.00	4.00	-1.00	0	0.000	3.0	5.0	8.0	0	0	84	61	57	5	27.0	634	647	602	27	31.0	15.0	25.00	19.0	3.00	0.00	0.00%	4.00	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	42.9%	11.1%	87.1%	98.2	FUN
12	Squirrels	1	0	1	0	0	0	2.00	3.00	-1.00	0	0.000	2.0	3.0	5.0	0	0	84	61	57	5	31.0	634	647	602	27	37.0	9.0	8.00	36.0	1.00	0.00	0.00%	4.00	2.00	50.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	66.7%	6.5%	91.9%	98.3	DULL
13	Blades	2	1	0	0	0	1	5.00	3.00	2.00	3	0.750	5.0	9.5	14.5	0	0	84	61	57	5	40.5	634	647	602	27	39.5	14.5	5.00	19.5	3.00	0.50	16.67%	2.50	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	60.0%	12.3%	92.4%	104.8	LUCKY
14	Sharknado	3	0	2	0	0	1	2.67	3.67	-1.00	1	0.167	2.7	5.3	8.0	0	0	84	61	57	5	31.3	634	647	602	27	35.3	5.3	8.67	22.3	3.33	0.00	0.00%	4.00	1.33	66.67%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	429.67	287.33	427.67	141.67	251.33	125.33	53.3%	8.5%	89.6%	98.1	Unlucky
15	Barking Spiders	1	0	0	1	0	0	4.00	5.00	-1.00	1	0.500	4.0	6.0	10.0	0	0	84	61	57	5	41.0	634	647	602	27	39.0	13.0	10.00	18.0	2.00	0.00	0.00%	5.00	1.00	80.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	50.0%	9.8%	87.2%	96.9	FUN
16	Space Invaders	5	4	0	1	0	0	5.20	3.80	1.40	9	0.900	5.2	9.2	14.4	0	0	84	61	57	5	45.2	634	647	602	27	29.0	10.2	9.80	25.0	3.00	0.80	26.67%	4.00	0.60	85.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	257.80	172.40	256.60	85.00	150.80	75.20	57.9%	11.5%	86.9%	98.4	FUN
17	Checkers	6	5	1	0	0	0	5.17	1.67	3.50	10	0.833	5.2	9.5	14.7	0	0	84	61	57	5	42.2	634	647	602	27	24.0	6.5	4.00	21.0	4.00	0.83	20.83%	1.83	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	214.83	143.67	213.83	70.83	125.67	62.67	72.2%	12.3%	93.1%	105.3	LUCKY
18	Vikings	1	0	1	0	0	0	1.00	7.00	-6.00	0	0.000	1.0	2.0	3.0	0	0	84	61	57	5	24.0	634	647	602	27	39.0	12.0	2.00	38.0	1.00	0.00	0.00%	1.00	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	12.5%	4.2%	82.1%	86.2	Unlucky
19	Kangaroos	1	0	0	1	0	0	3.00	4.00	-1.00	1	0.500	3.0	6.0	9.0	0	0	84	61	57	5	31.0	634	647	602	27	34.0	5.0	10.00	28.0	5.00	1.00	20.00%	4.00	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	1,289.00	862.00	1,283.00	425.00	754.00	376.00	33.3%	9.7%	88.2%	97.9	FUN
20	Thistles	3	2	1	0	0	0	5.00	4.33	0.67	4	0.667	5.0	9.3	14.3	0	0	84	61	57	5	41.0	634	647	602	27	33.0	6.7	4.67	26.3	2.67	1.00	37.50%	2.00	0.67	66.67%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	429.67	287.33	427.67	141.67	251.33	125.33	52.2%	12.2%	86.9%	99.1	FUN
21	Barracudas	2	0	2	0	0	0	3.50	4.50	-1.00	0	0.000	3.5	6.5	10.0	0	0	84	61	57	5	37.5	634	647	602	27	38.0	7.0	11.00	19.0	2.50	0.50	20.00%	4.50	0.50	88.89%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	42.9%	9.3%	88.2%	97.5	FUN
22	Senators	2	0	2	0	0	0	1.00	4.50	-3.50	0	0.000	1.0	1.5	2.5	0	0	84	61	57	5	32.0	634	647	602	27	34.0	8.0	8.50	30.5	4.00	0.00	0.00%	3.00	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	18.2%	3.1%	86.8%	89.9	Unlucky
23	Assassins	2	2	0	0	0	0	4.00	2.50	1.50	4	1.000	4.0	6.5	10.5	0	0	84	61	57	5	27.0	634	647	602	27	35.5	9.5	7.00	20.5	2.00	1.00	50.00%	3.50	0.00	100.00%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	54.5%	14.8%	93.0%	107.8	LUCKY
24	Zolos	5	0	4	1	0	0	3.00	5.00	-2.00	1	0.100	3.0	5.2	8.2	0	0	84	61	57	5	32.8	634	647	602	27	36.0	9.0	4.40	24.6	4.60	0.60	13.04%	2.20	0.20	90.91%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	257.80	172.40	256.60	85.00	150.80	75.20	33.3%	9.1%	86.1%	95.3	FUN
25	Huskies	2	1	1	0	0	0	4.00	2.50	1.50	2	0.500	4.0	8.0	12.0	0	0	84	61	57	5	34.5	634	647	602	27	34.5	8.0	8.00	28.5	1.00	1.00	100.00%	3.00	0.50	83.33%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	644.50	431.00	641.50	212.50	377.00	188.00	60.0%	11.6%	92.8%	104.3	LUCKY
	_Vs Division	26	14	9	2	0	1	4.19	3.50	0.69	31	0.596	4.2	7.6	11.8	0	0	84	61	57	5	38.0	634	647	602	27	31.4	7.8	6.58	23.0	3.58	0.65	18.28%	2.96	0.50	83.12%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	49.58	33.15	49.35	16.35	29.00	14.46	54.1%	11.0%	88.9%	99.9	FUN
	_Vs Conference	33	16	12	3	1	1	3.88	3.55	0.33	38	0.576	3.9	6.9	10.8	0	0	84	61	57	5	36.1	634	647	602	27	32.5	8.8	7.21	24.4	3.21	0.58	17.92%	3.09	0.58	81.37%	0	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	39.06	26.12	38.88	12.88	22.85	11.39	52.7%	10.7%	89.1%	99.9	FUN
	_Since Last GM Reset	55	23	24	5	1	2	3.69	3.67	0.02	55	0.500	3.7	6.6	10.3	2	1	84	61	57	5	34.5	634	647	602	27	33.7	9.2	8.15	23.9	2.96	0.55	18.40%	3.45	0.51	85.26%	1	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	23.44	15.67	23.33	7.73	13.71	6.84	49.9%	10.7%	89.1%	99.8	FUN
	Total	55	23	24	5	1	2	3.69	3.67	0.02	55	0.500	3.7	6.6	10.3	2	1	84	61	57	5	34.5	634	647	602	27	33.7	9.2	8.15	23.9	2.96	0.55	18.40%	3.45	0.51	85.26%	1	978	1941	50.39%	1050	2016	52.08%	504	966	52.17%	23.44	15.67	23.33	7.73	13.71	6.84	49.9%	10.7%	89.1%	99.8	FUN

	Puck Time
Offensive Zone	23
Neutral Zone	13
Defensive Zone	23

	Puck Time
Offensive Zone Start	1941
Neutral Zone Start	966
Defensive Zone Start	2016

	Puck Time
With Puck	30
Without Puck	30

	Faceoffs
Faceoffs Won	2532
Faceoffs Lost	2391

	Team Average Shots after	League Average Shots after
1st Period	11.5	9.57
2nd Period	23.3	20.31
3rd Period	34.2	30.68
Overtime	34.7	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	1.5	0.64
2nd Period	2.6	1.65
3rd Period	3.7	2.67
Overtime	3.8	2.83


Even Strenght Goal	170
PP Goal	30
PK Goal	1
Empty Net Goal	2

	Home	Away
Win	15	9
Lost	11	13
Overtime Lost	2	5


PP Attempt	163
PP Goal	30
PK Attempt	190
PK Goal Against	28

	Home
Shots For	34.5
Shots Against	33.7
Goals For	3.7
Goals Against	3.7
Hits	23.9
Shots Blocked	9.2
Pim	8.1

Schedule

Date	Matchup			Result	Detail
3	GRG	@	ZOL	GRG4,ZOL6	RECAP
4	GRG	@	SHO	GRG5,SHO7	RECAP
7	GRG	@	RAV	GRG2,RAV4	RECAP
11	BAR	@	GRG	BAR5,GRG4	RECAP
12	CHE	@	GRG	CHE1,GRG5	RECAP
14	SAI	@	GRG	SAI6,GRG4	RECAP
15	ZOL	@	GRG	ZOL7,GRG4	RECAP
18	GRG	@	BAR	GRG3,BAR4	RECAP
20	GRG	@	DEV	GRG0,DEV5	RECAP
22	GRG	@	BKS	GRG4,BKS5 (OT)	RECAP
26	GRG	@	CHE	GRG3,CHE2	RECAP
29	THI	@	GRG	THI4,GRG6	RECAP
31	VIK	@	GRG	VIK7,GRG1	RECAP
34	CHE	@	GRG	CHE1,GRG9	RECAP
37	GRG	@	ASS	GRG4,ASS2	RECAP
38	GRG	@	KAN	GRG3,KAN4 (OT)	RECAP
41	GRG	@	OHK	GRG1,OHK2 (OT)	RECAP
46	SHO	@	GRG	SHO2,GRG3	RECAP
48	ASS	@	GRG	ASS3,GRG4	RECAP
49	ZOL	@	GRG	ZOL4,GRG2	RECAP
51	CAN	@	GRG	CAN2,GRG6	RECAP
54	DEV	@	GRG	DEV3,GRG4	RECAP
56	CHE	@	GRG	CHE1,GRG7	RECAP
58	SHA	@	GRG	SHA4,GRG3	RECAP
60	SPA	@	GRG	SPA4,GRG7	RECAP
62	SHO	@	GRG	SHO2,GRG3	RECAP
64	RAV	@	GRG	RAV6,GRG4	RECAP
65	SQU	@	GRG	SQU3,GRG2	RECAP
67	GRG	@	THI	GRG7,THI2	RECAP
69	GRG	@	CHE	GRG6,CHE2	RECAP
72	GRG	@	IND	GRG3,IND4	RECAP
73	GRG	@	SPA	GRG6,SPA7 (OT)	RECAP
75	VEN	@	GRG	VEN3,GRG4 (SO)	RECAP
76	THI	@	GRG	THI7,GRG2	RECAP
78	EAG	@	GRG	EAG2,GRG3	RECAP
80	GRG	@	SHA	GRG2,SHA3	RECAP
82	GRG	@	NIN	GRG4,NIN6	RECAP
84	GRG	@	BLA	GRG6,BLA1	RECAP
86	GRG	@	SEN	GRG2,SEN5	RECAP
90	GRG	@	ZOL	GRG3,ZOL5	RECAP
93	JWO	@	GRG	JWO6,GRG8	RECAP
95	SEN	@	GRG	SEN4,GRG0	RECAP
98	NIN	@	GRG	NIN5,GRG3	RECAP
99	SPA	@	GRG	SPA3,GRG4	RECAP
101	BLA	@	GRG	BLA5,GRG4 (SO)	RECAP
103	GRG	@	SPA	GRG4,SPA3	RECAP
105	GRG	@	ZOL	GRG2,ZOL3 (OT)	RECAP
107	GRG	@	JWO	GRG4,JWO0	RECAP
111	GRG	@	BUL	GRG1,BUL3	RECAP
114	GRG	@	SHO	GRG3,SHO2	RECAP
115	GRG	@	CAN	GRG2,CAN3	RECAP
117	GRG	@	CHE	GRG1,CHE3	RECAP
120	SHA	@	GRG	SHA4,GRG3 (SO)	RECAP
122	OHK	@	GRG	OHK3,GRG4	RECAP
125	GRG	@	SPA	GRG5,SPA2	RECAP
127	EAG	@	GRG
130	BUL	@	GRG
132	IND	@	GRG
133	KAN	@	GRG
136	GRG	@	SPA
139	GRG	@	SQU
141	GRG	@	VIK
145	GRG	@	WPK
149	GRG	@	EAG
152	GRG	@	SAI
154	GRG	@	OHK
159	SAI	@	GRG
160	THI	@	GRG
162	WPK	@	GRG
Trade Deadline --- Trades can�t be done after this day is simulated!
166	GRG	@	PIM
169	GRG	@	THI
171	BKS	@	GRG
172	SHA	@	GRG
174	VEN	@	GRG
177	SHO	@	GRG
179	WPK	@	GRG
182	PIM	@	GRG
185	GRG	@	SHA
188	GRG	@	VIK
191	GRG	@	SQU
193	GRG	@	WPK
194	GRG	@	VEN

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
2,425,667$	0$	0$	75,000,000$

Arena	About us
	Name
	City	New York
	Capacity	3000
	Season Ticket Holders	10%

Arena Capacity - Ticket Price Attendance - %

Arena Capacity	2000	1000
Ticket Price	35$	15$	$	$	$
Attendance	45119	22666
Attendance PCT	80.57%	80.95%	0.00%	0.00%	0.00%

Income

Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
13	2421 - 80.70%	68,541$	1,919,155$	3000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
2,425,667$	2,425,667$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
1,579,602$	12,052$	1,570,645$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
891,036$	68	12,503$	850,204$

Team Total Estimate
Estimated Season Expenses	Estimated Season Salary Cap	Current Bank Account	Projected Bank Account
850,204$	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

HK33-15-3, 69pts

34Final

Gargoyles24-24-7, 55pts

52Final

Space Invaders19-29-5, 43pts

Gargoyles24-24-7, 55pts

Tue, Apr 14

Space Invaders19-29-5, 43pts

Gargoyles24-24-7, 55pts

Sun, Dec 14

Shockers31-22-0, 62pts

Gargoyles24-24-7, 55pts

Wed, Dec 17

Ravens31-17-7, 69pts

TRANSACTIONS

Team Info


	Head Coach
	Division	LaFontaine
	City	New York
	Stadium Capacity	3,000

Gargoyles Affiliation


	General Manager	John Salerno
	Head Coach
	Stadium	Rougeau Center
	Capacity	18,000

Team Leaders

GOALS A.J. Greer Gargoyles	25 GOALS
POINTS A.J. Greer Gargoyles	56 POINTS
WINS Yaroslav Askarov Gargoyles	23 WINS
Expanded Player Leaderboard

Team Stats

Goals For 203 3.69 GFG	Goals Against 202 3.67 GAA
Power Play Percentage 18.4% 30 GF	Penalty Kill Percentage 85.3% 28 GA
Expanded Team Stats

Team Captain - Alternate Captains

Captain

Alternate Captain