Carolina Hurricanes
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Career Team Stat

Game Center
	Cleveland Monsters 9-1-0, 18pts	4	FINAL	3	Chicago Wolves 5-4-1, 11pts

Team Stats
W4	Streak	W1
4-1-0	Home Record	3-2-1
5-0-0	Home Record	2-2-0
9-1-0	Last 10 Games	5-4-1
5.60	Goals Per Game	6.00
3.30	Goals Against Per Game	5.90
39.13%	Power Play Percentage	21.88%
72.73%	Penalty Kill Percentage	63.64%

Toronto Marlies
2-7-1, 5pts

FINAL

Chicago Wolves
5-4-1, 11pts

Team Stats
L1	Streak	W1
1-4-0	Home Record	3-2-1
1-3-1	Home Record	2-2-0
2-7-1	Last 10 Games	5-4-1
5.10	Goals Per Game	6.00
8.80	Goals Against Per Game	5.90
20.00%	Power Play Percentage	21.88%
78.79%	Penalty Kill Percentage	63.64%

Chicago Wolves
5-4-1, 11pts

Day 27

Milwaukee Admirals
2-9-0, 4pts

Team Stats
W1	Streak	W1
3-2-1	Home Record	2-3-0
2-2-0	Away Record	0-6-0
5-4-1	Last 10 Games	2-8-0
6.00	Goals Per Game	5.00
5.90	Goals Against Per Game	5.00
21.88%	Power Play Percentage	21.43%
63.64%	Penalty Kill Percentage	63.16%

Syracuse Crunch
0-11-0, 0pts

Day 29

Chicago Wolves
5-4-1, 11pts

Team Stats
L11	Streak	W1
0-5-0	Home Record	3-2-1
0-6-0	Away Record	2-2-0
0-10-0	Last 10 Games	5-4-1
2.36	Goals Per Game	6.00
6.91	Goals Against Per Game	6.00
14.81%	Power Play Percentage	21.88%
44.44%	Penalty Kill Percentage	63.64%

Chicago Wolves
5-4-1, 11pts

Day 31

San Jose Barracuda
8-3-1, 17pts

Team Stats
W1	Streak	W1
3-2-1	Home Record	3-1-1
2-2-0	Away Record	5-2-0
5-4-1	Last 10 Games	8-2-0
6.00	Goals Per Game	5.50
5.90	Goals Against Per Game	5.50
21.88%	Power Play Percentage	25.00%
63.64%	Penalty Kill Percentage	74.29%

Team Leaders
	Goals Brendan Perlini 12	Assists Andrew Agozzino 15

	Points Brendan Perlini 23	Plus/Minus Mark Friedman 9

	Wins Zachary Emond 5	Save Percentage Zachary Emond 0.819

Team Stats
Goals For 60 6.00 GFG	Shots For 280 28.00 Avg	Power Play Percentage 21.9% 7 GF	Offensive Zone Start 32.2%
Goals Against 59 5.90 GAA	Shots Against 287 28.70 Avg	Penalty Kill Percentage 63.6%% 12 GA	Defensive Zone Start 31.4%

Team Info
	General Manager	Alain Bosch
	Division	North Division
	Conference	Eastern Conference
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,746
	Season Tickets	300
Roster Info
	Pro Team	20
	Farm Team	18
	Contract Limit	38 / 50
	Prospects	7
Team History
	This Season	5-4-1 (11PTS)
	History	14-75-1 (0.156%)
	Playoff Appearances	2
	Playoff Record (W-L)	0-0
	Stanley Cup	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	TA	SP	Age	Contract	Salary
1	Craig Smith	X		X		100.00	72	50	92	76	75	83	96	58	59	76	76	73	88	86	76	0	40	68	N	0	33	2	1,250,000$
2	Sheldon Dries	X	X			100.00	73	50	91	74	74	81	79	63	79	73	76	74	91	55	60	0	40	67		0	28	2	762,500$
3	Zach Sanford	X	X	X		100.00	80	50	89	74	74	84	85	59	74	74	75	74	88	60	66	0	40	67		0	28	2	775,000$
4	Andrew Agozzino		X			100.00	85	50	95	72	78	81	71	50	79	73	70	74	79	52	60	0	40	65		0	32	2	775,000$
5	Brendan Perlini		X	X		100.00	73	50	92	74	72	78	66	51	35	71	76	71	96	50	60	0	40	64		0	26	1	750,000$
6	Sheldon Rempal			X		100.00	60	50	95	70	68	79	71	50	35	70	70	72	79	52	60	0	40	62		0	27	2	765,000$
7	Brett Sutter	X				100.00	65	46	80	59	74	87	92	57	71	58	56	59	55	84	79	0	40	58		0	35	1	750,000$
8	Carson Gicewicz	X				100.00	76	54	72	59	86	73	75	56	71	57	55	58	56	67	63	0	40	57		0	25	3	900,000$
9	Ryan McGregor	X				100.00	59	35	83	58	68	82	86	57	66	62	56	59	57	63	62	0	40	57		0	23	2	775,000$
10	Nathan Todd	X				100.00	61	38	88	57	70	79	77	58	76	59	57	56	59	71	66	0	40	56		0	27	1	775,000$
11	Spencer Smallman		X	X		100.00	68	43	83	57	77	79	84	58	60	56	53	57	54	69	64	0	40	56		0	26	2	762,500$
12	Tanner Fritz			X		100.00	62	43	83	54	71	66	71	54	56	54	56	55	59	78	73	0	40	53		0	31	1	750,000$
13	Mark Friedman				X	100.00	76	50	87	73	77	84	73	50	35	74	71	79	82	51	60	0	40	68		0	27	2	775,000$
14	Jon Lizotte				X	100.00	90	50	75	70	70	85	70	50	35	70	70	72	73	50	60	0	40	65		0	28	1	750,000$
15	Aaron Ness				X	100.00	80	50	95	70	60	83	70	50	35	70	70	72	79	54	60	0	40	64		0	32	1	750,000$
16	Frederic Allard				X	100.00	60	50	95	70	76	83	61	50	35	70	70	77	78	50	60	0	40	64		0	25	1	750,000$
17	Dylan Blujus				X	100.00	73	52	76	56	82	77	72	55	30	53	54	57	46	73	67	0	40	57		0	28	1	750,000$
18	Chaz Reddekopp				X	100.00	78	73	57	52	87	71	75	53	30	51	50	56	45	67	63	0	40	56		0	26	2	750,000$
Scratches

	TEAM AVERAGE					100.00	72	49	85	65	74	80	76	54	53	65	65	66	70	63	64	0	40	61

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	Zachary Emond (R)	100.00	75	70	70	82	75	78	77	80	74	82	83	50	60	0	40	73	0	25	1	750,000$
2	Antoine Bibeau	100.00	68	74	72	84	67	66	68	67	66	68	67	73	77	0	40	67	0	28	1	750,000$
Scratches

	TEAM AVERAGE	100.00	72	72	71	83	71	72	73	74	70	75	75	62	69	0	40	70

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	FO%	FOT	GA	TA	HT	P/20	S1	S2	S3
1	Brendan Perlini	Chicago Wolves (CAR)	LW/RW	10	12	11	23	8	0	0	7	19	41	15	21	29.27%	2	194	19.49	4	1	5	10	28	0	0	0	0	2	1	16.67%	6	32	2	0	2.36	2	1	1
2	Sheldon Dries	Chicago Wolves (CAR)	C/LW	10	12	9	21	6	0	0	10	14	42	14	30	28.57%	7	186	18.62	2	0	2	8	25	0	0	0	0	0	3	59.82%	112	22	4	2	2.26	0	1	2
3	Andrew Agozzino	Chicago Wolves (CAR)	LW	10	3	15	18	3	2	0	18	16	20	9	16	15.00%	9	237	23.74	0	4	4	3	27	0	0	0	0	21	0	27.27%	11	18	7	0	1.52	1	0	1
4	Sheldon Rempal	Chicago Wolves (CAR)	RW	10	6	9	15	-4	0	0	6	23	37	16	16	16.22%	2	232	23.25	0	0	0	1	23	0	0	0	1	22	1	34.69%	98	22	8	1	1.29	1	1	0
5	Zach Sanford	Chicago Wolves (CAR)	C/LW/RW	10	9	6	15	3	2	0	18	5	24	10	11	37.50%	3	137	13.74	0	0	0	0	0	0	0	0	0	0	0	83.33%	6	21	4	2	2.18	1	1	1
6	Craig Smith	Chicago Wolves (CAR)	C/RW	10	4	9	13	0	0	0	6	9	28	14	18	14.29%	3	165	16.60	0	2	2	3	25	0	0	0	0	0	0	45.13%	113	19	3	0	1.57	0	0	1
7	Aaron Ness	Chicago Wolves (CAR)	D	10	5	6	11	-7	0	0	5	15	14	8	4	35.71%	23	252	25.25	1	0	1	2	24	1	0	1	1	35	0	0%	0	10	12	1	0.87	0	1	0
8	Mark Friedman	Chicago Wolves (CAR)	D	10	7	4	11	9	0	0	6	14	14	7	4	50.00%	14	157	15.70	0	0	0	0	0	0	0	0	0	0	0	0%	0	4	6	0	1.40	1	0	0
9	Frederic Allard	Chicago Wolves (CAR)	D	10	1	6	7	5	0	0	7	17	17	7	11	5.88%	17	203	20.31	0	2	2	4	29	0	0	0	0	24	0	0%	0	8	10	0	0.69	0	0	0
10	Brett Sutter	Chicago Wolves (CAR)	C	10	0	5	5	6	10	0	14	5	7	1	2	0%	2	193	19.40	0	2	2	1	29	0	0	0	0	6	0	67.50%	40	1	4	0	0.52	0	1	0
11	Carson Gicewicz	Chicago Wolves (CAR)	C	10	0	5	5	0	6	0	17	10	4	2	5	0%	3	134	13.40	0	0	0	0	0	0	0	0	0	0	0	47.06%	85	6	2	0	0.75	0	0	0
12	Jon Lizotte	Chicago Wolves (CAR)	D	10	0	5	5	9	4	0	11	10	8	1	3	0%	6	156	15.63	0	0	0	0	0	0	0	0	0	0	0	0%	0	9	6	0	0.64	0	0	0
13	Chaz Reddekopp	Chicago Wolves (CAR)	D	10	0	3	3	-8	16	0	22	5	4	2	2	0%	7	236	23.67	0	0	0	0	24	0	0	0	0	19	0	0%	0	0	9	0	0.25	0	0	0
14	Dylan Blujus	Chicago Wolves (CAR)	D	10	1	1	2	5	8	0	19	13	11	3	4	9.09%	7	199	19.94	0	0	0	2	29	0	0	0	0	18	0	0%	0	0	4	0	0.20	0	0	0
15	Ryan McGregor	Chicago Wolves (CAR)	C	10	0	2	2	-1	10	0	4	6	8	5	5	0%	0	161	16.19	0	0	0	0	0	0	1	1	0	21	0	50.00%	30	0	5	0	0.25	0	0	0
16	Spencer Smallman	Chicago Wolves (CAR)	LW/RW	10	0	1	1	-3	19	5	10	4	1	1	1	0%	5	102	10.24	0	0	0	0	3	0	1	1	1	22	0	42.86%	7	0	3	0	0.20	0	0	0
17	Nathan Todd	Chicago Wolves (CAR)	C	10	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0.06	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0	0	0	0	0	0
18	Tanner Fritz	Chicago Wolves (CAR)	RW	10	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0.06	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0	0	0	0	0	0
Team Total or Average				180	60	97	157	31	77	5	180	185	280	115	153	21.43%	110	2952	16.40	7	11	18	34	273	1	2	3	3	194	5	48.43%	508	172	89	6	1.06	6	6	6

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	GA	SA	SAR	A	PS %	ST	BG
1	Zachary Emond	Chicago Wolves (CAR)	10	5	4	1	0.819	5.11	564	48	265	144	1	0	10	0
2	Antoine Bibeau	Chicago Wolves (CAR)	2	0	0	0	0.500	18.33	36	11	22	8	0	0	0	10
Team Total or Average			12	5	4	1	0.794	5.90	600	59	287	152	1		10	10

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	Link
Aaron Ness	Chicago Wolves (CAR)	D	32	1990-05-18	No	184 Lbs	5 ft10	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Andrew Agozzino	Chicago Wolves (CAR)	LW	32	1991-01-03	No	187 Lbs	5 ft10	No	No	No	No	2	Pro & Farm	775,000$	0$	0$	No	775,000$		Link
Antoine Bibeau	Chicago Wolves (CAR)	G	28	1994-05-01	No	205 Lbs	6 ft3	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Brendan Perlini	Chicago Wolves (CAR)	LW/RW	26	1996-04-27	No	211 Lbs	6 ft3	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Brett Sutter	Chicago Wolves (CAR)	C	35	1987-06-02	No	200 Lbs	6 ft0	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Carson Gicewicz	Chicago Wolves (CAR)	C	25	1997-03-04	No	212 Lbs	6 ft3	No	No	No	No	3	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$	Link
Chaz Reddekopp	Chicago Wolves (CAR)	D	26	1997-01-01	No	219 Lbs	6 ft3	No	No	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$		Link
Craig Smith	Chicago Wolves (CAR)	C/RW	33	1989-09-05	No	206 Lbs	6 ft0	Yes	No	No	No	2	Pro & Farm	1,250,000$	0$	0$	No	1,250,000$		Link
Dylan Blujus	Chicago Wolves (CAR)	D	28	1994-01-22	No	191 Lbs	6 ft3	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Frederic Allard	Chicago Wolves (CAR)	D	25	1997-12-27	No	179 Lbs	6 ft1	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Jon Lizotte	Chicago Wolves (CAR)	D	28	1994-11-10	No	216 Lbs	6 ft1	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Mark Friedman	Chicago Wolves (CAR)	D	27	1995-12-25	No	185 Lbs	5 ft11	No	No	No	No	2	Pro & Farm	775,000$	0$	0$	No	775,000$		Link
Nathan Todd	Chicago Wolves (CAR)	C	27	1995-12-02	No	176 Lbs	6 ft0	No	No	No	No	1	Pro & Farm	775,000$	0$	0$	No			Link
Ryan McGregor	Chicago Wolves (CAR)	C	23	1999-01-29	No	168 Lbs	6 ft0	No	No	No	No	2	Pro & Farm	775,000$	0$	0$	No	775,000$		Link
Sheldon Dries	Chicago Wolves (CAR)	C/LW	28	1994-04-23	No	180 Lbs	5 ft9	No	No	No	No	2	Pro & Farm	762,500$	0$	0$	No	762,500$		Link
Sheldon Rempal	Chicago Wolves (CAR)	RW	27	1995-08-07	No	165 Lbs	5 ft10	No	No	No	No	2	Pro & Farm	765,000$	0$	0$	No	765,000$		Link
Spencer Smallman	Chicago Wolves (CAR)	LW/RW	26	1996-09-09	No	198 Lbs	6 ft1	No	No	No	No	2	Pro & Farm	762,500$	0$	0$	No	762,500$		Link
Tanner Fritz	Chicago Wolves (CAR)	RW	31	1991-08-20	No	192 Lbs	5 ft11	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Link
Zach Sanford	Chicago Wolves (CAR)	C/LW/RW	28	1994-11-09	No	207 Lbs	6 ft4	No	No	No	No	2	Pro & Farm	775,000$	0$	0$	No	775,000$		Link
Zachary Emond	Chicago Wolves (CAR)	G	25	1997-06-01 07:30:41	Yes	165 Lbs	6 ft3	No	No	No	No	1	Pro & Farm	750,000$	0$	0$	No

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
20	28.00	192 Lbs	6 ft1	1.55	790,750$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Andrew Agozzino	Craig Smith	Zach Sanford	25	1	2	2
2	Brendan Perlini	Sheldon Dries	Brett Sutter	25	1	2	2
3	Ryan McGregor	Carson Gicewicz	Sheldon Rempal	25	1	2	2
4	Andrew Agozzino	Sheldon Rempal	Spencer Smallman	25	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Mark Friedman	Jon Lizotte	25	1	2	2
2	Chaz Reddekopp	Aaron Ness	25	1	2	2
3	Frederic Allard	Dylan Blujus	25	1	2	2
4	Chaz Reddekopp	Aaron Ness	25	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Sheldon Rempal	Craig Smith	Sheldon Dries	50	1	2	2
2	Andrew Agozzino	Brett Sutter	Brendan Perlini	50	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Frederic Allard	Dylan Blujus	50	1	2	2
2	Chaz Reddekopp	Aaron Ness	50	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Sheldon Rempal	Andrew Agozzino	50	1	2	2
2	Ryan McGregor	Spencer Smallman	50	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Frederic Allard	Aaron Ness	50	1	2	2
2	Chaz Reddekopp	Dylan Blujus	50	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Sheldon Rempal	50	1	2	2	Aaron Ness	Frederic Allard	50	1	2	2
2	Andrew Agozzino	50	1	2	2	Chaz Reddekopp	Mark Friedman	50	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Sheldon Rempal	Zach Sanford	50	1	2	2
2	Andrew Agozzino	Craig Smith	50	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Frederic Allard	Dylan Blujus	50	1	2	2
2	Chaz Reddekopp	Aaron Ness	50	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Zach Sanford	Craig Smith	Sheldon Dries	Mark Friedman	Jon Lizotte

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Zach Sanford	Craig Smith	Sheldon Dries	Mark Friedman	Jon Lizotte

Extra Forwards
Normal	PowerPlay	Penalty Kill
Sheldon Rempal, Brett Sutter, Ryan McGregor	Sheldon Rempal, Spencer Smallman	Sheldon Rempal

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Chaz Reddekopp, Aaron Ness, Frederic Allard	Chaz Reddekopp	Dylan Blujus, Chaz Reddekopp

Penalty Shots
Brett Sutter, Ryan McGregor, Spencer Smallman, Carson Gicewicz, Sheldon Rempal

Goalie
#1 : Zachary Emond, #2 : Antoine Bibeau

Custom OT Lines Forwards
Brett Sutter, Ryan McGregor, Spencer Smallman, Sheldon Dries, Sheldon Rempal, Zach Sanford, Zach Sanford, Craig Smith, Andrew Agozzino, Brendan Perlini, Carson Gicewicz

Custom OT Lines Defensemen
Aaron Ness, Frederic Allard, Chaz Reddekopp, Dylan Blujus, Jon Lizotte

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	Charlotte Checkers	1	1	0	0	0	0	0	0	9	6	3	1	1	0	0	0	0	0	0	9	6	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	9	16	25	0	0	27	23	10	0	27	109	78	92	1	30	6	10	23	3	1	33.33%	5	0	100.00%	1	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
2	Cleveland Monsters	1	0	1	0	0	0	0	0	3	4	-1	1	0	1	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	4	7	0	0	27	23	10	0	31	109	78	92	1	21	1	4	15	1	0	0.00%	2	0	100.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
3	Coachella Valley Firebirds	1	0	1	0	0	0	0	0	3	4	-1	1	0	1	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	4	7	0	0	27	23	10	0	25	109	78	92	1	24	11	0	21	2	0	0.00%	0	0	0%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
4	Grand Rapids Griffins	1	1	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	6	4	2	0	0	0	0	0	0	0	0	0	0	0	2	1.000	6	10	16	0	0	27	23	10	0	27	109	78	92	1	23	13	12	20	2	1	50.00%	6	2	66.67%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
5	Henderson Silver Knights	1	1	0	0	0	0	0	0	10	5	5	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	10	5	5	2	1.000	10	15	25	0	0	27	23	10	0	26	109	78	92	1	30	22	19	21	3	1	33.33%	4	2	50.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
6	Ontario Reign	1	0	0	0	0	1	0	0	4	5	-1	1	0	0	0	0	1	0	0	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	4	8	12	0	0	27	23	10	0	34	109	78	92	1	30	11	6	23	7	1	14.29%	3	0	100.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
7	Providence Bruins	1	0	1	0	0	0	0	0	2	11	-9	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	11	-9	0	0.000	2	3	5	0	0	27	23	10	0	23	109	78	92	1	34	14	6	17	4	0	0.00%	3	2	33.33%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
8	Texas Stars	1	1	0	0	0	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	3	2	1	2	1.000	3	5	8	0	0	27	23	10	0	25	109	78	92	1	22	8	4	14	4	2	50.00%	2	1	50.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
9	Toronto Marlies	1	1	0	0	0	0	0	0	16	7	9	1	1	0	0	0	0	0	0	16	7	9	0	0	0	0	0	0	0	0	0	0	0	2	1.000	16	25	41	0	0	27	23	10	0	32	109	78	92	1	35	8	8	16	3	0	0.00%	4	2	50.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
10	Tucson Roadrunners	1	0	1	0	0	0	0	0	4	11	-7	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	4	11	-7	0	0.000	4	7	11	0	0	27	23	10	0	30	109	78	92	1	38	16	8	10	3	1	33.33%	4	3	25.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
	Total	10	5	4	0	0	1	0	0	60	59	1	6	3	2	0	0	1	0	0	41	30	11	4	2	2	0	0	0	0	0	19	29	-10	11	0.550	60	97	157	0	0	27	23	10	0	280	109	78	92	1	287	110	77	180	32	7	21.88%	33	12	63.64%	1	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
	_Since Last GM Reset	10	5	4	0	0	1	0	0	60	59	1	6	3	2	0	0	1	0	0	41	30	11	4	2	2	0	0	0	0	0	19	29	-10	11	0.550	60	97	157	0	0	27	23	10	0	280	109	78	92	1	287	110	77	180	32	7	21.88%	33	12	63.64%	1	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
	_Vs Conference	5	3	2	0	0	0	0	0	23	27	-4	3	2	1	0	0	0	0	0	18	14	4	2	1	1	0	0	0	0	0	5	13	-8	6	0.600	23	38	61	0	0	27	23	10	0	127	109	78	92	1	133	52	32	95	15	4	26.67%	16	5	68.75%	1	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
	_Vs Division	1	0	0	0	0	0	0	0	3	4	-1	1	0	0	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	4	7	0	0	27	23	10	0	31	109	78	92	1	21	1	4	15	1	0	0.00%	2	0	100.00%	0	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
10	11	W1	60	97	157	280	287	110	77	180	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
10	5	4	0	1	0	0	60	59

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
6	3	2	0	1	0	0	41	30

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	2	0	0	0	0	19	29

Last 10 Games
W	L	OTW	OTL	SOW	SOL
5	4	0	1	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
32	7	21.88%	33	12	63.64%	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
109	78	92	1	27	23	10	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
86	163	52.76%	71	159	44.65%	89	184	48.37%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
219	138	219	80	162	79

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	Link
2	8	Chicago Wolves	4	Tucson Roadrunners	11	AL		BoxScore
4	24	Charlotte Checkers	6	Chicago Wolves	9	BW		BoxScore
6	45	Chicago Wolves	2	Providence Bruins	11	AL		BoxScore
7	51	Coachella Valley Firebirds	4	Chicago Wolves	3	BL		BoxScore
9	69	Grand Rapids Griffins	4	Chicago Wolves	6	BW		BoxScore
14	99	Ontario Reign	5	Chicago Wolves	4	BL	X	BoxScore
16	115	Chicago Wolves	3	Texas Stars	2	AW		BoxScore
18	127	Chicago Wolves	10	Henderson Silver Knights	5	AW		BoxScore
20	143	Cleveland Monsters	4	Chicago Wolves	3	BL		BoxScore
23	164	Toronto Marlies	7	Chicago Wolves	16	BW		BoxScore
27	189	Chicago Wolves	-	Milwaukee Admirals	-
29	199	Syracuse Crunch	-	Chicago Wolves	-
31	215	Chicago Wolves	-	San Jose Barracuda	-
33	228	Chicago Wolves	-	San Diego Gulls	-
34	240	Rochester Americans	-	Chicago Wolves	-
36	251	Chicago Wolves	-	Iowa Wild	-
38	269	Syracuse Crunch	-	Chicago Wolves	-
41	291	Grand Rapids Griffins	-	Chicago Wolves	-
43	304	Chicago Wolves	-	Syracuse Crunch	-
45	314	Chicago Wolves	-	Grand Rapids Griffins	-
47	331	San Diego Gulls	-	Chicago Wolves	-
50	357	Charlotte Checkers	-	Chicago Wolves	-
52	366	Chicago Wolves	-	Cleveland Monsters	-
55	392	Hershey Bears	-	Chicago Wolves	-
57	403	Chicago Wolves	-	Texas Stars	-
59	419	Chicago Wolves	-	San Jose Barracuda	-
60	427	Charlotte Checkers	-	Chicago Wolves	-
63	455	Springfield Thunderbirds	-	Chicago Wolves	-
65	464	Chicago Wolves	-	Laval Rockets	-
68	484	Chicago Wolves	-	Hartford Wolf Pack	-
69	494	Toronto Marlies	-	Chicago Wolves	-
72	508	Chicago Wolves	-	Scranton Penguins	-
74	524	Cleveland Monsters	-	Chicago Wolves	-
78	550	Henderson Silver Knights	-	Chicago Wolves	-
80	559	Chicago Wolves	-	Springfield Thunderbirds	-
83	585	San Jose Barracuda	-	Chicago Wolves	-
86	604	Chicago Wolves	-	Iowa Wild	-
88	617	Iowa Wild	-	Chicago Wolves	-
90	633	Chicago Wolves	-	Charlotte Checkers	-
92	647	Texas Stars	-	Chicago Wolves	-
95	668	Chicago Wolves	-	Tucson Roadrunners	-
96	680	Chicago Wolves	-	Abbotsford Canucks	-
97	686	Providence Bruins	-	Chicago Wolves	-
101	712	Springfield Thunderbirds	-	Chicago Wolves	-
103	724	Chicago Wolves	-	San Diego Gulls	-
106	745	Bakersfield Condors	-	Chicago Wolves	-
108	762	Chicago Wolves	-	Milwaukee Admirals	-
110	776	Stockton Heat	-	Chicago Wolves	-
112	794	Chicago Wolves	-	Utica Comets	-
114	808	Ontario Reign	-	Chicago Wolves	-
116	827	Chicago Wolves	-	Ontario Reign	-
118	840	Chicago Wolves	-	Rockford IceDogs	-
119	847	Tucson Roadrunners	-	Chicago Wolves	-
122	867	Chicago Wolves	-	Lehigh Valley Phantoms	-
123	878	Colorado Eagles	-	Chicago Wolves	-
126	895	Chicago Wolves	-	Providence Bruins	-
128	910	Utica Comets	-	Chicago Wolves	-
132	932	Chicago Wolves	-	Manitoba Moose	-
133	942	Rochester Americans	-	Chicago Wolves	-
136	962	Chicago Wolves	-	Henderson Silver Knights	-
137	975	Milwaukee Admirals	-	Chicago Wolves	-
142	1001	Chicago Wolves	-	Toronto Marlies	-
143	1006	Coachella Valley Firebirds	-	Chicago Wolves	-
146	1031	Chicago Wolves	-	Coachella Valley Firebirds	-
147	1039	Abbotsford Canucks	-	Chicago Wolves	-
151	1065	Rockford IceDogs	-	Chicago Wolves	-
153	1081	Chicago Wolves	-	Stockton Heat	-
155	1097	Rockford IceDogs	-	Chicago Wolves	-
Trade Deadline --- Trades can’t be done after this day is simulated!
157	1112	Chicago Wolves	-	Rochester Americans	-
158	1123	Chicago Wolves	-	Bellerive Senators	-
160	1134	Abbotsford Canucks	-	Chicago Wolves	-
163	1157	Bridgeport Islanders	-	Chicago Wolves	-
166	1174	Chicago Wolves	-	Stockton Heat	-
168	1184	Chicago Wolves	-	Tucson Roadrunners	-
170	1197	Bellerive Senators	-	Chicago Wolves	-
171	1204	Chicago Wolves	-	Bakersfield Condors	-
174	1231	Bellerive Senators	-	Chicago Wolves	-
176	1239	Chicago Wolves	-	Bakersfield Condors	-
178	1257	Chicago Wolves	-	Colorado Eagles	-
179	1267	Manitoba Moose	-	Chicago Wolves	-
183	1286	Chicago Wolves	-	Colorado Eagles	-
185	1303	Manitoba Moose	-	Chicago Wolves	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	15
Attendance	11,135	5,342
Attendance PCT	92.79%	89.03%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
35	2746 - 91.54%	39,938$	239,626$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
2,018,928$	15,815,000$	0$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	2,018,928$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
1,397,818$	164	84,122$	13,796,008$

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
1	Brendan Perlini	92	69	58	127	-66	6	138	147	362	19.06%	59	1729	18.79	19	12	31	75	3	0	3	9	2	1	35.06%	5	1.47
2	Andrew Agozzino	92	39	61	100	-114	6	194	186	280	13.93%	64	1795	19.52	2	6	8	9	0	0	0	1	0	0	52.33%	2	1.11
3	Sheldon Dries	83	59	35	94	-28	8	73	81	267	22.10%	33	1059	12.76	5	2	7	16	0	0	0	0	3	0	53.07%	4	1.77
4	Frederik Gauthier	82	36	53	89	-91	10	133	188	252	14.29%	84	1541	18.80	6	15	21	56	0	1	1	12	3	0	53.14%	1	1.15
5	Sheldon Rempal	92	40	47	87	-84	4	51	136	280	14.29%	54	1560	16.96	7	6	13	30	0	0	0	3	2	0	34.30%	2	1.12

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
1	Antoine Bibeau	84	8	43	2	0.729	8.01	3236	4	0	432	1592	796	6	0	0	0
2	Zachary Emond	79	6	32	1	0.746	7.73	2289	0	0	295	1163	570	5	0	0	0

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
Regular Season
4	82	8	71	0	1	2	0	0	309	668	-359	41	8	31	0	1	1	0	0	168	314	-146	41	0	40	0	0	1	0	0	141	354	-213	20	309	494	803	0	0	107	106	95	1	2325	778	825	717	5	2468	953	510	1633	258	52	20.16%	256	104	59.38%	5	787	1453	54.16%	676	1272	53.14%	849	1559	54.46%	1818	1161	1805	659	1300	639
5	10	5	4	0	0	1	0	0	60	59	1	6	3	2	0	0	1	0	0	41	30	11	4	2	2	0	0	0	0	0	19	29	-10	11	60	97	157	0	0	27	23	10	0	280	109	78	92	1	287	110	77	180	32	7	21.88%	33	12	63.64%	1	86	163	52.76%	71	159	44.65%	89	184	48.37%	219	138	219	80	162	79
Total Regular Season	92	13	75	0	1	3	0	0	369	727	-358	47	11	33	0	1	2	0	0	209	344	-135	45	2	42	0	0	1	0	0	160	383	-223	31	369	591	960	0	0	134	129	105	1	2605	887	903	809	6	2755	1063	587	1813	290	59	20.34%	289	116	59.86%	6	873	1616	54.02%	747	1431	52.20%	938	1743	53.82%	2038	1299	2025	740	1463	719
Playoff
Total Playoff	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0%	0	0	0	0%	0	0	0%	0	0	0%	0	0	0	0	0	0

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: 10 \| W: 5 \| L: 4 \| OTL: 1 \| P: 11 GF: 60 \| GA: 59 \| PP%: 21.88% \| PK%: 63.64% GM : Alain Bosch \| Morale : 40 \| Team Overall : 62 Next Games #189 vs Milwaukee Admirals