Vegas Golden Knights
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Career Team Stat

Game Center
	Chicago Wolves 6-2-1, 13pts	4	FINAL	3	Binghamton Senators 5-2-1, 11pts

Team Stats
W1	Streak	OTL1
2-1-1	Home Record	2-1-1
4-1-0	Home Record	3-1-0
6-2-1	Last 10 Games	5-2-1
2.33	Goals Per Game	2.75
1.67	Goals Against Per Game	2.50
10.26%	Power Play Percentage	13.51%
91.84%	Penalty Kill Percentage	81.40%

Toronto Marlies
2-4-1, 5pts

FINAL

Chicago Wolves
6-2-1, 13pts

Team Stats
L3	Streak	W1
1-3-1	Home Record	2-1-1
1-1-0	Home Record	4-1-0
2-4-1	Last 10 Games	6-2-1
1.43	Goals Per Game	2.33
2.43	Goals Against Per Game	1.67
6.67%	Power Play Percentage	10.26%
88.89%	Penalty Kill Percentage	91.84%

Binghamton Senators
5-2-1, 11pts

Day 28

Chicago Wolves
6-2-1, 13pts

Team Stats
OTL1	Streak	W1
2-1-1	Home Record	2-1-1
3-1-0	Away Record	4-1-0
5-2-1	Last 10 Games	6-2-1
2.75	Goals Per Game	2.33
2.50	Goals Against Per Game	2.33
13.51%	Power Play Percentage	10.26%
81.40%	Penalty Kill Percentage	91.84%

Syracuse Crunch
4-4-0, 8pts

Day 33

Chicago Wolves
6-2-1, 13pts

Team Stats
W1	Streak	W1
3-2-0	Home Record	2-1-1
1-2-0	Away Record	4-1-0
4-4-0	Last 10 Games	6-2-1
2.00	Goals Per Game	2.33
2.13	Goals Against Per Game	2.33
15.79%	Power Play Percentage	10.26%
92.86%	Penalty Kill Percentage	91.84%

Chicago Wolves
6-2-1, 13pts

Day 36

Worcester Sharks
9-1-0, 18pts

Team Stats
W1	Streak	L1
2-1-1	Home Record	3-1-0
4-1-0	Away Record	6-0-0
6-2-1	Last 10 Games	9-1-0
2.33	Goals Per Game	2.80
1.67	Goals Against Per Game	2.80
10.26%	Power Play Percentage	12.82%
91.84%	Penalty Kill Percentage	92.16%

Team Leaders
	Goals Dryden Hunt 4	Assists Tanner Jeannot 5

	Points Tanner Jeannot 7	Plus/Minus Brad Malone 5

	Wins Michael Hutchinson 6	Save Percentage Michael Hutchinson 0.92

Team Stats
Goals For 21 2.33 GFG	Shots For 170 18.89 Avg	Power Play Percentage 10.3% 4 GF	Offensive Zone Start 40.0%
Goals Against 15 1.67 GAA	Shots Against 175 19.44 Avg	Penalty Kill Percentage 91.8%% 4 GA	Defensive Zone Start 39.6%

Team Info
	General Manager
	Division	Division 3
	Conference	Conference 1
	Captain	Yakov Trenin
	Assistant #1	Josh Brown
	Assistant #2	Tanner Jeannot
Arena Info
	Capacity	3,000
	Attendance	0
	Season Tickets	0
Roster Info
	Pro Team	40
	Farm Team	18
	Contract Limit	58 / 250
	Prospects	0

Show or Hide Columns

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	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary
1	Yakov Trenin (C)	X	X	X		100.00	86	42	85	67	82	77	84	69	75	66	68	63	71	67	64	68	46	67	0	26	3	1,571,000$
2	Ryan Poehling	X				100.00	78	35	93	67	80	83	86	65	77	63	62	68	67	63	65	81	46	66	0	24	2	1,693,000$
3	Tanner Jeannot (A)		X			100.00	89	36	91	64	82	79	77	65	56	64	68	67	62	69	65	53	46	66	0	26	1	2,364,000$
4	Brad Malone	X	X			100.00	76	61	61	62	84	77	82	61	79	63	62	64	63	80	78	38	46	65	0	34	2	0$
5	Dryden Hunt		X			100.00	91	36	82	63	73	81	84	65	62	67	64	61	65	71	66	42	46	65	0	27	4	2,339,000$
6	Emil Bemstrom		X	X		100.00	68	35	95	69	73	77	81	68	63	66	67	62	68	63	64	61	46	65	0	24	4	0$
7	Kyle Rau		X			100.00	76	46	79	63	65	76	78	65	68	64	59	66	63	77	69	51	43	64	0	30	2	4,085,000$
8	Angus Crookshank		X			100.00	65	37	88	67	69	74	79	65	73	67	61	59	55	63	64	69	46	63	0	23	2	1,457,000$
9	Dominik Bokk		X			100.00	68	39	87	66	77	85	81	63	56	62	64	59	63	61	62	81	46	63	0	23	3	2,536,000$
10	Nikita Jevpalovs			X		100.00	69	37	85	61	79	85	86	63	55	62	59	56	61	73	66	38	46	63	0	29	3	0$
11	Marko Dano		X			100.00	67	46	79	66	73	77	72	63	57	60	59	62	65	73	67	68	46	62	0	28	4	1,432,000$
12	Josh Brown (A)				X	100.00	83	53	74	60	94	79	82	60	30	65	58	67	49	73	68	42	43	67	0	29	3	2,602,000$
13	William Borgen				X	100.00	83	39	87	58	84	87	80	57	30	58	54	64	47	69	65	60	46	65	0	26	2	0$
14	Carl Dahlstrom				X	100.00	85	73	91	56	91	72	68	59	30	65	54	62	50	71	66	68	46	64	0	28	1	2,378,000$
15	Reece Willcox				X	100.00	78	36	91	58	87	81	86	57	30	56	52	59	46	73	67	48	46	64	0	29	3	3,753,000$
16	Aaron Ness				X	100.00	62	35	95	63	67	69	68	61	30	63	57	64	52	79	68	59	46	63	0	33	3	0$
17	Mark Alt				X	100.00	58	37	71	56	87	80	76	58	30	59	56	62	48	78	73	56	46	63	0	31	4	2,200,000$
Scratches
1	Matt Read			X		100.00	59	42	83	63	68	80	85	61	57	59	62	56	60	85	75	23	31	62	0	37	4	2,730,000$
2	Sean Malone	X				100.00	50	63	56	63	74	86	75	62	73	68	60	57	62	71	66	47	31	62	0	28	3	1,969,000$
3	Dennis Yan		X			100.00	68	46	79	59	77	82	87	57	55	56	58	59	57	67	64	65	31	61	0	26	3	2,011,000$
4	German Rubtsov	X				100.00	58	35	95	62	76	70	73	63	67	61	59	57	63	65	62	81	31	61	0	25	3	0$
5	Kirill Maksimov		X	X		100.00	76	52	78	57	85	87	90	58	55	56	54	57	54	63	62	56	31	61	0	24	2	2,010,000$
6	Damien Giroux	X				100.00	63	52	72	59	68	80	85	57	72	61	58	56	59	61	63	49	31	60	0	23	4	1,359,000$
7	Jeremy Gregoire	X				100.00	64	71	56	56	73	82	87	56	60	58	56	57	56	71	69	47	31	60	0	28	4	1,239,000$
8	Matthew Strome		X			100.00	79	35	92	60	88	70	75	58	54	57	60	56	54	63	62	61	31	60	0	24	3	1,237,000$
9	Reese Johnson			X		100.00	87	62	58	61	76	70	73	59	54	57	60	58	61	65	63	54	31	60	0	25	2	1,222,000$
10	Nicholas Jones	X				100.00	59	39	86	57	68	85	90	56	61	58	55	54	56	69	65	42	31	59	0	27	2	0$
11	Justin Brazeau			X		100.00	84	76	89	53	93	66	71	53	52	51	56	59	55	65	63	44	31	58	0	25	3	1,908,000$
12	Manuel Wiederer			X		100.00	63	42	85	59	72	67	72	57	61	58	54	56	57	69	65	53	31	58	0	26	5	1,147,000$
13	Lukas Craggs		X			100.00	63	51	76	54	72	60	65	54	55	54	56	54	55	69	65	42	31	56	0	27	1	1,744,000$
14	Tim Heed				X	100.00	56	35	95	65	68	77	73	62	30	67	58	55	50	78	70	46	31	62	0	32	5	0$
15	Bode Wilde				X	100.00	63	39	86	57	79	77	73	61	30	56	53	59	45	61	64	77	31	61	0	23	2	0$
16	Joe Morrow				X	100.00	65	56	69	57	74	78	73	58	30	56	53	54	46	77	69	66	31	60	0	30	1	3,003,000$
17	Matt Mangene				X	100.00	61	38	76	59	70	78	73	56	30	59	52	54	46	80	72	27	31	60	0	34	4	1,859,000$
18	Olivier Leblanc				X	100.00	59	37	66	56	69	65	61	54	30	55	51	53	45	69	65	48	31	56	0	27	4	0$

	TEAM AVERAGE					100.00	70	46	81	61	77	77	78	60	52	60	58	59	56	70	66	55	38	62

Show or Hide Columns

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	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary
1	Michael Hutchinson	100.00	80	84	89	83	79	78	80	79	78	80	79	80	86	51	46	78	0	33	2	4,527,775$
2	Tyler Parks	100.00	71	79	77	92	70	69	71	70	69	71	70	77	83	36	46	73	0	31	2	0$
Scratches
1	Lukas Dostal	100.00	73	88	86	75	72	71	73	72	71	73	72	61	65	64	31	71	0	23	5	1,762,849$
2	Veini Vehvilainen	100.00	70	82	80	71	69	68	70	69	68	70	69	67	71	58	31	69	0	26	4	2,957,617$
3	Dylan Wells	100.00	62	71	69	78	61	60	62	61	60	62	61	65	69	58	31	63	0	25	2	0$

	TEAM AVERAGE	100.00	71	81	80	80	70	69	71	70	69	71	70	70	75	53	37	71

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

Show or Hide Columns

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	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Tanner Jeannot	Chicago Wolves (VGK)	LW	9	2	5	7	-3	10	0	13	13	20	2	21	10.00%	3	228	25.42	1	2	3	7	29	0	0	0	0	38	0	0	21.43%	14	0.61	1	1	0	0	0	1	0	1
2	Emil Bemstrom	Chicago Wolves (VGK)	LW/RW	9	2	4	6	4	4	0	4	9	19	3	14	10.53%	0	202	22.50	0	0	0	5	32	0	0	0	2	23	0	1	58.82%	17	0.59	0	0	0	0	0	0	0	0
3	Yakov Trenin	Chicago Wolves (VGK)	C/LW/RW	9	2	4	6	-3	4	0	19	15	21	7	16	9.52%	2	227	25.25	0	1	1	7	28	0	0	0	0	40	0	0	51.13%	266	0.53	0	1	0	0	0	0	2	0
4	Brad Malone	Chicago Wolves (VGK)	C/LW	9	2	3	5	5	12	0	19	15	20	1	10	10.00%	0	176	19.56	0	0	0	5	30	0	0	0	0	1	1	0	52.63%	19	0.57	0	0	0	0	0	1	1	0
5	Josh Brown	Chicago Wolves (VGK)	D	9	1	4	5	-1	43	15	20	13	7	3	10	14.29%	7	205	22.86	1	2	3	5	34	0	0	0	0	29	1	0	0%	0	0.49	0	0	1	1	1	1	0	0
6	Dryden Hunt	Chicago Wolves (VGK)	LW	9	4	0	4	-2	2	0	6	11	13	1	9	30.77%	1	87	9.77	0	0	0	0	0	0	0	0	0	0	2	0	75.00%	8	0.91	0	0	0	0	0	0	0	2
7	Nikita Jevpalovs	Chicago Wolves (VGK)	RW	9	2	2	4	4	0	0	9	6	13	2	3	15.38%	1	174	19.36	0	0	0	2	31	0	0	0	0	0	0	0	50.00%	10	0.46	0	0	0	0	0	0	0	2
8	Ryan Poehling	Chicago Wolves (VGK)	C	9	2	2	4	4	4	0	13	24	21	10	7	9.52%	2	231	25.67	0	0	0	2	27	0	0	0	2	28	1	0	50.85%	234	0.35	0	1	0	0	0	0	2	0
9	Aaron Ness	Chicago Wolves (VGK)	D	9	0	3	3	4	8	0	14	9	0	1	1	0%	9	140	15.64	0	0	0	0	0	0	0	0	0	19	0	0	0%	0	0.43	0	0	0	0	0	0	0	0
10	Carl Dahlstrom	Chicago Wolves (VGK)	D	9	0	3	3	4	16	0	10	5	3	3	3	0%	6	192	21.34	0	0	0	1	29	0	1	1	0	34	0	0	0%	0	0.31	0	0	0	0	0	0	0	0
11	Kyle Rau	Chicago Wolves (VGK)	LW	9	1	2	3	4	7	5	5	13	7	2	4	14.29%	1	82	9.12	0	0	0	1	11	1	0	1	1	9	0	0	53.33%	45	0.73	0	0	0	1	0	1	0	0
12	William Borgen	Chicago Wolves (VGK)	D	9	2	1	3	-2	16	0	23	3	6	1	6	33.33%	6	217	24.22	2	1	3	6	34	0	0	0	0	35	0	1	0%	0	0.28	0	0	0	0	0	1	0	0
13	Angus Crookshank	Chicago Wolves (VGK)	LW	9	0	2	2	1	0	0	1	4	2	2	2	0%	0	24	2.73	0	1	1	1	5	0	0	0	0	0	0	0	54.55%	11	1.63	0	0	0	0	0	0	0	0
14	Dominik Bokk	Chicago Wolves (VGK)	LW	9	1	1	2	4	2	0	1	4	9	4	5	11.11%	0	74	8.33	0	0	0	0	0	0	0	0	0	0	1	0	25.00%	4	0.53	0	0	0	0	0	0	1	0
15	Mark Alt	Chicago Wolves (VGK)	D	9	0	1	1	4	8	0	13	4	1	2	3	0%	5	141	15.71	0	0	0	0	3	0	0	0	0	13	0	0	0%	0	0.14	0	0	0	0	0	0	0	0
16	Marko Dano	Chicago Wolves (VGK)	LW	9	0	1	1	1	2	0	2	2	0	0	0	0%	0	30	3.35	0	0	0	0	1	0	1	1	0	23	0	0	57.14%	14	0.66	0	0	0	0	0	0	0	0
17	Reece Willcox	Chicago Wolves (VGK)	D	9	0	0	0	4	6	0	11	5	8	1	3	0%	6	189	21.01	0	0	0	6	29	0	0	0	0	34	0	0	0%	0	0	0	0	0	0	0	0	0	0
Team Total or Average				153	21	38	59	32	144	20	183	155	170	45	117	12.35%	49	2626	17.17	4	7	11	48	332	1	2	3	5	333	6	2	51.09%	642	0.45	1	3	1	2	1	5	6	5

Show or Hide Columns

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	Michael Hutchinson	Chicago Wolves (VGK)	9	6	2	1	0.920	1.53	549	0	2	14	175	0	0	0	0.333	3	9	0	2	0	1
Team Total or Average			9	6	2	1	0.920	1.53	549	0	2	14	175	0	0	0		3	9	0	2	0	1

Show or Hide Columns

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	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Waiver Possible	Contract	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Salary Year 5	Link
Aaron Ness	Chicago Wolves (VGK)	D	33	5/18/1990	No	184 Lbs	5 ft10	No	No	No	No	3	Pro & Farm		0$	0$	No					Link
Angus Crookshank	Chicago Wolves (VGK)	LW	23	10/2/1999	No	181 Lbs	5 ft11	No	No	No	No	2	Pro & Farm	1,457,000$	0$	0$	No	1,457,000$				Link
Bode Wilde	Chicago Wolves (VGK)	D	23	1/24/2000	No	192 Lbs	6 ft2	No	No	No	No	2	Pro & Farm		0$	0$	No					Link
Brad Malone	Chicago Wolves (VGK)	C/LW	34	5/20/1989	No	217 Lbs	6 ft2	No	No	No	No	2	Pro & Farm		0$	0$	No					Link
Carl Dahlstrom	Chicago Wolves (VGK)	D	28	1/28/1995	No	221 Lbs	6 ft4	No	No	No	No	1	Pro & Farm	2,378,000$	0$	0$	No					Link
Damien Giroux	Chicago Wolves (VGK)	C	23	3/3/2000	No	179 Lbs	5 ft11	No	No	No	No	4	Pro & Farm	1,359,000$	0$	0$	No	1,359,000$	1,359,000$	1,359,000$		Link
Dennis Yan	Chicago Wolves (VGK)	LW	26	4/14/1997	No	197 Lbs	6 ft1	No	No	No	No	3	Pro & Farm	2,011,000$	0$	0$	No	2,011,000$	2,011,000$			Link
Dominik Bokk	Chicago Wolves (VGK)	LW	23	2/3/2000	No	181 Lbs	6 ft2	No	No	No	No	3	Pro & Farm	2,536,000$	0$	0$	No	2,536,000$	2,536,000$			Link
Dryden Hunt	Chicago Wolves (VGK)	LW	27	11/24/1995	No	193 Lbs	6 ft0	No	No	No	No	4	Pro & Farm	2,339,000$	0$	0$	No	2,339,000$	2,339,000$	2,339,000$		Link / NHL Link
Dylan Wells	Chicago Wolves (VGK)	G	25	1/3/1998	No	190 Lbs	6 ft2	No	No	No	No	2	Pro & Farm		0$	0$	No					Link
Emil Bemstrom	Chicago Wolves (VGK)	LW/RW	24	6/1/1999	No	193 Lbs	6 ft0	No	No	No	No	4	Pro & Farm		0$	0$	No					Link / NHL Link
German Rubtsov	Chicago Wolves (VGK)	C	25	6/27/1998	No	178 Lbs	6 ft2	No	No	No	No	3	Pro & Farm		0$	0$	No					Link
Jeremy Gregoire	Chicago Wolves (VGK)	C	28	9/5/1995	No	193 Lbs	6 ft0	No	No	No	No	4	Pro & Farm	1,239,000$	0$	0$	No	1,239,000$	1,239,000$	1,239,000$		Link
Joe Morrow	Chicago Wolves (VGK)	D	30	12/9/1992	No	196 Lbs	6 ft0	No	No	No	No	1	Pro & Farm	3,003,000$	0$	0$	No					Link
Josh Brown	Chicago Wolves (VGK)	D	29	1/21/1994	No	221 Lbs	6 ft5	No	No	No	No	3	Pro & Farm	2,602,000$	0$	0$	No	2,602,000$	2,602,000$			Link / NHL Link
Justin Brazeau	Chicago Wolves (VGK)	RW	25	2/2/1998	No	220 Lbs	6 ft5	No	No	No	No	3	Pro & Farm	1,908,000$	0$	0$	No	1,908,000$	1,908,000$			Link
Kirill Maksimov	Chicago Wolves (VGK)	LW/RW	24	6/1/1999	No	207 Lbs	6 ft3	No	No	No	No	2	Pro & Farm	2,010,000$	0$	0$	No	2,010,000$				Link
Kyle Rau	Chicago Wolves (VGK)	LW	30	10/24/1992	No	176 Lbs	5 ft8	No	No	No	No	2	Pro & Farm	4,085,000$	0$	0$	No	4,085,000$				Link
Lukas Craggs	Chicago Wolves (VGK)	LW	27	5/16/1996	No	190 Lbs	6 ft0	No	No	No	No	1	Pro & Farm	1,744,000$	0$	0$	No					Link
Lukas Dostal	Chicago Wolves (VGK)	G	23	6/22/2000	No	174 Lbs	6 ft2	No	No	No	No	5	Pro & Farm	1,762,849$	0$	0$	No	1,762,849$	1,762,849$	1,762,849$	1,762,849$	Link
Manuel Wiederer	Chicago Wolves (VGK)	RW	26	11/21/1996	No	175 Lbs	6 ft1	No	No	No	No	5	Pro & Farm	1,147,000$	0$	0$	No	1,147,000$	1,147,000$	1,147,000$	1,147,000$	Link
Mark Alt	Chicago Wolves (VGK)	D	31	10/18/1991	No	201 Lbs	6 ft4	No	No	No	No	4	Pro & Farm	2,200,000$	0$	0$	No	2,200,000$	2,200,000$	2,200,000$		Link
Marko Dano	Chicago Wolves (VGK)	LW	28	11/30/1994	No	201 Lbs	5 ft11	No	No	No	No	4	Pro & Farm	1,432,000$	0$	0$	No	1,432,000$	1,432,000$	1,432,000$		Link
Matt Mangene	Chicago Wolves (VGK)	D	34	3/12/1989	No	190 Lbs	5 ft11	No	No	No	No	4	Pro & Farm	1,859,000$	0$	0$	No	1,859,000$	1,859,000$	1,859,000$		Link
Matt Read	Chicago Wolves (VGK)	RW	37	6/14/1986	No	188 Lbs	5 ft10	No	No	No	No	4	Pro & Farm	2,730,000$	0$	0$	No	2,730,000$	2,730,000$	2,730,000$		Link
Matthew Strome	Chicago Wolves (VGK)	LW	24	1/6/1999	No	206 Lbs	6 ft4	No	No	No	No	3	Pro & Farm	1,237,000$	0$	0$	No	1,237,000$	1,237,000$			Link
Michael Hutchinson	Chicago Wolves (VGK)	G	33	3/2/1990	No	198 Lbs	6 ft3	No	No	No	No	2	Pro & Farm	4,527,775$	0$	0$	No	4,527,775$				Link
Nicholas Jones	Chicago Wolves (VGK)	C	27	6/2/1996	No	176 Lbs	5 ft11	No	No	No	No	2	Pro & Farm		0$	0$	No					Link
Nikita Jevpalovs	Chicago Wolves (VGK)	RW	29	9/9/1994	No	210 Lbs	6 ft1	No	No	No	No	3	Pro & Farm		0$	0$	No					Link
Olivier Leblanc	Chicago Wolves (VGK)	D	27	2/17/1996	No	172 Lbs	6 ft0	No	No	No	No	4	Pro & Farm		0$	0$	No					Link
Reece Willcox	Chicago Wolves (VGK)	D	29	3/20/1994	No	205 Lbs	6 ft4	No	No	No	No	3	Pro & Farm	3,753,000$	0$	0$	No	3,753,000$	3,753,000$			Link
Reese Johnson	Chicago Wolves (VGK)	RW	25	7/10/1998	No	193 Lbs	6 ft1	No	No	No	No	2	Pro & Farm	1,222,000$	0$	0$	No	1,222,000$				Link / NHL Link
Ryan Poehling	Chicago Wolves (VGK)	C	24	1/3/1999	No	197 Lbs	6 ft2	No	No	No	No	2	Pro & Farm	1,693,000$	0$	0$	No	1,693,000$				Link / NHL Link
Sean Malone	Chicago Wolves (VGK)	C	28	4/30/1995	No	197 Lbs	6 ft0	No	No	No	No	3	Pro & Farm	1,969,000$	0$	0$	No	1,969,000$	1,969,000$			Link
Tanner Jeannot	Chicago Wolves (VGK)	LW	26	5/29/1997	No	208 Lbs	6 ft2	No	No	No	No	1	Pro & Farm	2,364,000$	0$	0$	No					Link / NHL Link
Tim Heed	Chicago Wolves (VGK)	D	32	1/27/1991	No	180 Lbs	5 ft11	No	No	No	No	5	Pro & Farm		0$	0$	No					Link
Tyler Parks	Chicago Wolves (VGK)	G	31	5/9/1992	No	198 Lbs	6 ft6	No	No	No	No	2	Pro & Farm		0$	0$	No					Link
Veini Vehvilainen	Chicago Wolves (VGK)	G	26	2/13/1997	No	181 Lbs	6 ft0	No	No	No	No	4	Pro & Farm	2,957,617$	0$	0$	No	2,957,617$	2,957,617$	2,957,617$		Link
William Borgen	Chicago Wolves (VGK)	D	26	12/19/1996	No	205 Lbs	6 ft3	No	No	No	No	2	Pro & Farm		0$	0$	No					Link
Yakov Trenin	Chicago Wolves (VGK)	C/LW/RW	26	1/13/1997	No	201 Lbs	6 ft2	No	No	No	No	3	Pro & Farm	1,571,000$	0$	0$	No	1,571,000$	1,571,000$			Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
40	27.48	194 Lbs	6 ft1	2.90	1,527,406$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Tanner Jeannot	Yakov Trenin	Emil Bemstrom	40	1	2	2
2	Brad Malone	Ryan Poehling	Nikita Jevpalovs	30	1	2	2
3	Dryden Hunt	Yakov Trenin	Tanner Jeannot	20	1	2	2
4	Dominik Bokk	Ryan Poehling	Emil Bemstrom	10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Josh Brown	William Borgen	40	1	2	2
2	Carl Dahlstrom	Reece Willcox	30	1	2	2
3	Mark Alt	Aaron Ness	20	1	2	2
4	Josh Brown	William Borgen	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Tanner Jeannot	Yakov Trenin	Emil Bemstrom	60	1	2	2
2	Brad Malone	Ryan Poehling	Nikita Jevpalovs	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Josh Brown	William Borgen	60	1	2	2
2	Carl Dahlstrom	Reece Willcox	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Yakov Trenin	Tanner Jeannot	60	1	2	2
2	Ryan Poehling	Emil Bemstrom	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Josh Brown	William Borgen	60	1	2	2
2	Carl Dahlstrom	Reece Willcox	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Yakov Trenin	60	1	2	2	Josh Brown	William Borgen	60	1	2	2
2	Tanner Jeannot	40	1	2	2	Carl Dahlstrom	Reece Willcox	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Yakov Trenin	Tanner Jeannot	60	1	2	2
2	Ryan Poehling	Emil Bemstrom	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Josh Brown	William Borgen	60	1	2	2
2	Carl Dahlstrom	Reece Willcox	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Tanner Jeannot	Yakov Trenin	Emil Bemstrom	Josh Brown	William Borgen

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Tanner Jeannot	Yakov Trenin	Emil Bemstrom	Josh Brown	William Borgen

Extra Forwards
Normal	PowerPlay	Penalty Kill
Kyle Rau, Angus Crookshank, Marko Dano	Kyle Rau, Angus Crookshank	Marko Dano

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Mark Alt, Aaron Ness, Carl Dahlstrom	Mark Alt	Aaron Ness, Carl Dahlstrom

Penalty Shots
Yakov Trenin, Tanner Jeannot, Ryan Poehling, Emil Bemstrom, Brad Malone

Goalie
#1 : Michael Hutchinson, #2 : Tyler Parks

Custom OT Lines Forwards
Yakov Trenin, Tanner Jeannot, Ryan Poehling, Emil Bemstrom, Brad Malone, Dryden Hunt, Dryden Hunt, Dominik Bokk, Kyle Rau, Nikita Jevpalovs, Angus Crookshank

Custom OT Lines Defensemen
Josh Brown, William Borgen, Carl Dahlstrom, Reece Willcox, Mark Alt

Show or Hide Columns

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

	Overall												Home											Visitor
#	VS Team	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	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
1	Binghamton Senators	1	0	0	0	1	0	0	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	4	3	1	2	1.000	4	7	11	0	0	5	7	8	2	11	45	63	59	6	30	12	14	22	1	0	0.00%	7	1	85.71%	1	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
2	Hershey Bears	2	1	0	0	0	0	0	1	4	4	0	1	0	0	0	0	0	0	1	1	2	-1	1	1	0	0	0	0	0	0	3	2	1	3	0.750	4	6	10	0	0	5	7	8	2	32	45	63	59	6	43	9	65	35	12	1	8.33%	16	0	100.00%	0	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
3	Rockford IceHogs	1	1	0	0	0	0	0	0	2	0	2	1	1	0	0	0	0	0	0	2	0	2	0	0	0	0	0	0	0	0	0	0	0	2	1.000	2	4	6	0	1	5	7	8	2	28	45	63	59	6	11	5	10	17	7	1	14.29%	5	0	100.00%	0	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
4	Syracuse Crunch	2	1	1	0	0	0	0	0	4	3	1	1	0	1	0	0	0	0	0	0	2	-2	1	1	0	0	0	0	0	0	4	1	3	2	0.500	4	8	12	0	0	5	7	8	2	38	45	63	59	6	35	11	27	39	7	1	14.29%	8	1	87.50%	0	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
5	Toronto Marlies	2	2	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	2	0	2	4	1.000	5	9	14	0	1	5	7	8	2	47	45	63	59	6	31	9	16	52	8	1	12.50%	7	0	100.00%	0	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
6	Wilkes-Barre/Scranton Penguins	1	0	1	0	0	0	0	0	2	4	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	2	4	6	0	0	5	7	8	2	14	45	63	59	6	25	3	12	18	4	0	0.00%	6	2	66.67%	0	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
	Total	9	5	2	0	1	0	0	1	21	15	6	4	2	1	0	0	0	0	1	6	5	1	5	3	1	0	1	0	0	0	15	10	5	13	0.722	21	38	59	0	2	5	7	8	2	170	45	63	59	6	175	49	144	183	39	4	10.26%	49	4	91.84%	1	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
	_Since Last GM Reset	9	5	2	0	1	0	0	1	21	15	6	4	2	1	0	0	0	0	1	6	5	1	5	3	1	0	1	0	0	0	15	10	5	13	0.722	21	38	59	0	2	5	7	8	2	170	45	63	59	6	175	49	144	183	39	4	10.26%	49	4	91.84%	1	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57
	_Vs Conference	6	2	2	0	1	0	0	1	14	14	0	2	0	1	0	0	0	0	1	1	4	-3	4	2	1	0	1	0	0	0	13	10	3	7	0.583	14	25	39	0	0	5	7	8	2	95	45	63	59	6	133	35	118	114	24	2	8.33%	37	4	89.19%	1	128	253	50.59%	135	250	54.00%	65	129	50.39%	210	141	223	69	115	57

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
9	13	W1	21	38	59	170	175	49	144	183	0	2

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
9	5	2	1	0	0	1	21	15

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	1	0	0	0	1	6	5

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
5	3	1	1	0	0	0	15	10

Last 10 Games
W	L	OTW	OTL	SOW	SOL
5	2	1	0	0	1

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
39	4	10.26%	49	4	91.84%	1

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
45	63	59	6	5	7	8	2

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
128	253	50.59%	135	250	54.00%	65	129	50.39%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
210	141	223	69	115	57

Last Played Games Show or Hide Columns

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

Day	Game	Visitor Team	Score	Home Team	Score	ST	OT	SO	Link
2	7	Chicago Wolves	2	Wilkes-Barre/Scranton Penguins	4	AL			BoxScore
5	28	Syracuse Crunch	2	Chicago Wolves	0	BL			BoxScore
9	48	Chicago Wolves	4	Syracuse Crunch	1	AW			BoxScore
11	58	Hershey Bears	2	Chicago Wolves	1	BL	X	X	BoxScore
15	79	Rockford IceHogs	0	Chicago Wolves	2	BW			BoxScore
16	83	Chicago Wolves	3	Hershey Bears	2	AW			BoxScore
19	98	Chicago Wolves	2	Toronto Marlies	0	AW			BoxScore
22	114	Chicago Wolves	4	Binghamton Senators	3	AW	X		BoxScore
23	124	Toronto Marlies	1	Chicago Wolves	3	BW			BoxScore
28	149	Binghamton Senators	-	Chicago Wolves	-
33	173	Syracuse Crunch	-	Chicago Wolves	-
36	192	Chicago Wolves	-	Worcester Sharks	-
38	202	Chicago Wolves	-	Syracuse Crunch	-
40	209	Chicago Wolves	-	Texas Stars	-
42	220	Norfolk Admirals	-	Chicago Wolves	-
46	245	Oklahoma City Barons	-	Chicago Wolves	-
47	249	Chicago Wolves	-	Hershey Bears	-
51	273	Chicago Wolves	-	Norfolk Admirals	-
53	284	Bridgeport Sound Tigers	-	Chicago Wolves	-
58	307	Chicago Wolves	-	Providence Bruins	-
60	317	Portland Pirates	-	Chicago Wolves	-
63	329	Chicago Wolves	-	Syracuse Crunch	-
65	345	Chicago Wolves	-	Oklahoma City Barons	-
66	352	St. John's	-	Chicago Wolves	-
71	376	Toronto Marlies	-	Chicago Wolves	-
73	386	Chicago Wolves	-	Texas Stars	-
77	407	Texas Stars	-	Chicago Wolves	-
79	421	Chicago Wolves	-	Abbotsford Heat	-
82	437	Milwaukee Admirals	-	Chicago Wolves	-
86	463	Norfolk Admirals	-	Chicago Wolves	-
90	481	Chicago Wolves	-	Toronto Marlies	-
92	491	Chicago Wolves	-	Springfield Falcons	-
95	506	Rockford IceHogs	-	Chicago Wolves	-
99	528	Chicago Wolves	-	Wilkes-Barre/Scranton Penguins	-
101	537	Rochester Americans	-	Chicago Wolves	-
103	552	Chicago Wolves	-	Hershey Bears	-
105	564	Wilkes-Barre/Scranton Penguins	-	Chicago Wolves	-
107	571	Chicago Wolves	-	Worcester Sharks	-
109	580	Chicago Wolves	-	San Antonio Rampage	-
113	601	Chicago Wolves	-	Charlotte Checkers	-
114	607	Grand Rapids Griffins	-	Chicago Wolves	-
119	635	San Antonio Rampage	-	Chicago Wolves	-
124	664	Albany Devils	-	Chicago Wolves	-
129	683	Chicago Wolves	-	Milwaukee Admirals	-
131	696	Chicago Wolves	-	Houston Aeros	-
132	699	Abbotsford Heat	-	Chicago Wolves	-
137	726	Hamilton Bulldogs	-	Chicago Wolves	-
140	743	Chicago Wolves	-	Albany Devils	-
142	757	Chicago Wolves	-	Chicago Wolves	-
147	784	Chicago Wolves	-	Rochester Americans	-
149	790	Springfield Falcons	-	Chicago Wolves	-
153	816	Palm Springs	-	Chicago Wolves	-
157	840	Chicago Wolves	-	Palm Springs	-
159	850	Houston Aeros	-	Chicago Wolves	-
162	868	Chicago Wolves	-	Peoria Rivermen	-
165	881	Adirondack Phantoms	-	Chicago Wolves	-
170	910	Binghamton Senators	-	Chicago Wolves	-
174	931	Chicago Wolves	-	Hamilton Bulldogs	-
177	943	Springfield Falcons	-	Chicago Wolves	-
180	957	Chicago Wolves	-	Lake Erie Monsters	-
184	975	Providence Bruins	-	Chicago Wolves	-
189	1002	Worcester Sharks	-	Chicago Wolves	-
195	1031	Hershey Bears	-	Chicago Wolves	-
Trade Deadline --- Trades can’t be done after this day is simulated!
201	1060	Charlotte Checkers	-	Chicago Wolves	-
203	1072	Chicago Wolves	-	Rockford IceHogs	-
206	1092	Lake Erie Monsters	-	Chicago Wolves	-
208	1099	Chicago Wolves	-	Portland Pirates	-
212	1123	Syracuse Crunch	-	Chicago Wolves	-
214	1129	Chicago Wolves	-	Adirondack Phantoms	-
216	1141	Chicago Wolves	-	Binghamton Senators	-
219	1156	Connecticut Whale	-	Chicago Wolves	-
221	1164	Chicago Wolves	-	Bridgeport Sound Tigers	-
223	1174	Chicago Wolves	-	Grand Rapids Griffins	-
226	1192	Oklahoma City Barons	-	Chicago Wolves	-
231	1218	Chicago Wolves	-	Manchester Monarchs	-
233	1226	Peoria Rivermen	-	Chicago Wolves	-
235	1240	Chicago Wolves	-	Connecticut Whale	-
239	1259	Wilkes-Barre/Scranton Penguins	-	Chicago Wolves	-
240	1260	Chicago Wolves	-	St. John's	-
242	1272	Chicago Wolves	-	Norfolk Admirals	-
248	1296	Manchester Monarchs	-	Chicago Wolves	-
250	1308	Chicago Wolves	-	Chicago Wolves	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	15
Attendance	0	0
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
37	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
0$	6,109,624$	6,109,624$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	0$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	227	24,245$	5,503,615$

Chicago Wolves Players Stat Leaders (Regular Season)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Chicago Wolves Goalies Stat Leaders (Regular Season)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

Chicago Wolves Career Team Stats

Show or Hide Columns

	Overall											Home											Visitor
Year	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	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

Chicago Wolves Players Stat Leaders (Play-Off)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Chicago Wolves Goalies Stat Leaders (Play-Off)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

	Chicago Wolves
	GP: 9 \| W: 6 \| L: 2 \| OTL: 1 \| P: 13 GF: 21 \| GA: 15 \| PP%: 10.26% \| PK%: 91.84% GM : \| Morale : 44 \| Team Overall : 63 Next Games #149 vs Binghamton Senators