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

Game Center
	Chicago Wolves 6-4-0, 12pts	3	FINAL	6	San Diego Gulls 7-4-0, 14pts

Team Stats
W1	Streak	L1
4-2-0	Home Record	3-2-0
2-2-0	Home Record	4-2-0
6-3-1	Last 10 Games	6-4-0
3.20	Goals Per Game	3.55
3.00	Goals Against Per Game	2.73
13.64%	Power Play Percentage	15.79%
90.00%	Penalty Kill Percentage	86.96%

San Diego Gulls
7-4-0, 14pts

FINAL

Chicago Wolves
6-4-0, 12pts

Team Stats
L1	Streak	W1
3-2-0	Home Record	4-2-0
4-2-0	Home Record	2-2-0
6-4-0	Last 10 Games	6-3-1
3.55	Goals Per Game	3.20
2.73	Goals Against Per Game	3.00
15.79%	Power Play Percentage	13.64%
86.96%	Penalty Kill Percentage	90.00%

Chicago Wolves
6-4-0, 12pts

2024-05-14

San Diego Gulls
7-4-0, 14pts

Team Stats
W1	Streak	L1
4-2-0	Home Record	3-2-0
2-2-0	Away Record	4-2-0
6-3-1	Last 10 Games	6-4-0
3.20	Goals Per Game	3.55
3.00	Goals Against Per Game	3.55
13.64%	Power Play Percentage	15.79%
90.00%	Penalty Kill Percentage	86.96%

San Diego Gulls
7-4-0, 14pts

2024-05-16

Chicago Wolves
6-4-0, 12pts

Team Stats
L1	Streak	W1
3-2-0	Home Record	4-2-0
4-2-0	Away Record	2-2-0
6-4-0	Last 10 Games	6-3-1
3.55	Goals Per Game	3.20
2.73	Goals Against Per Game	3.20
15.79%	Power Play Percentage	13.64%
86.96%	Penalty Kill Percentage	90.00%

Team Leaders
	Goals Jason Dickinson 7	Assists Urho Vaakanainen 8

	Points Jason Dickinson 12	Plus/Minus Brayden Pachal 5

	Wins Akira Schmid 6	Save Percentage Akira Schmid 0.912

Team Stats
Goals For 32 3.20 GFG	Shots For 316 31.60 Avg	Power Play Percentage 13.6% 3 GF	Offensive Zone Start 40.0%
Goals Against 30 3.00 GAA	Shots Against 322 32.20 Avg	Penalty Kill Percentage 90.0%% 2 GA	Defensive Zone Start 39.6%

Team Info
	General Manager	Chris Floresco
	Coach	Dave Lowry
	Division	Central
	Conference	Western Conference
	Captain	Alex Wennberg
	Assistant #1
	Assistant #2
Arena Info
	Capacity	8,000
	Attendance	7,992
	Season Tickets	0
Roster Info
	Pro Team	24
	Farm Team	19
	Contract Limit	43 / 45
	Prospects	2

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	Jack McBain	86	X	X			100.00	87	60	77	68	76	69	75	60	65	71	65	73	54	56	58	0	60	69	0	24	1	883,750$
2	Jason Dickinson	81	X	X			100.00	72	48	85	70	77	71	88	62	71	73	62	76	54	65	59	0	60	69	0	28	2	2,650,000$
3	Mason Appleton	29			X		100.00	72	40	88	66	74	73	81	62	53	74	63	73	54	62	58	0	60	68	0	28	3	2,166,667$
4	Quinton Byfield	49	X				100.00	68	43	77	64	81	65	67	61	68	72	60	68	64	56	57	0	60	66	0	21	2	894,167$
5	Axel Jonsson-Fjallby	73		X			100.00	67	47	81	64	69	61	65	60	53	67	62	70	55	58	57	0	60	64	0	26	1	750,000$
6	Jansen Harkins	20	X	X	X		100.00	63	57	77	63	66	61	69	60	66	64	63	68	54	60	57	0	60	63	0	26	2	850,000$
7	Saku Maenalanen	74			X		100.00	72	38	82	61	78	57	68	55	50	67	58	70	51	54	55	0	60	63	0	29	1	750,000$
8	Anders Bjork	27		X	X		100.00	57	48	78	61	69	64	68	59	53	64	59	65	60	62	58	0	60	62	0	27	1	1,600,000$
9	Nathan Smith	58	X		X		100.00	61	52	70	61	65	67	62	57	57	61	60	59	56	58	57	0	60	61	0	25	1	883,750$
10	Pontus Holmberg	80		X	X		100.00	59	49	73	61	69	59	59	59	68	62	59	63	54	57	55	0	60	61	0	25	1	827,500$
11	Oskar Steen	48	X	X	X		100.00	55	57	66	61	60	65	63	59	69	57	59	62	54	60	56	0	60	60	0	26	2	800,000$
12	Stephen Halliday (R)	82	X	X			100.00	45	45	45	45	45	45	45	45	45	45	45	45	45	45	45	0	60	45	0	21	4	750,000$
13	Braden Schneider	6				X	100.00	70	66	92	70	79	73	79	60	40	71	60	81	54	57	58	0	60	69	0	22	2	925,000$
14	Jacob Bryson	5				X	100.00	66	40	90	73	71	73	83	58	40	71	58	84	54	59	58	0	60	69	0	26	2	1,850,000$
15	Urho Vaakanainen	57				X	100.00	65	41	91	59	76	76	60	56	40	70	55	78	54	56	58	0	60	66	0	25	2	850,000$
16	Darren Raddysh	77				X	100.00	57	53	69	61	67	66	62	60	40	62	58	67	54	62	58	0	60	62	0	28	2	762,500$
17	Brayden Pachal	78				X	100.00	59	53	62	60	66	66	64	56	40	58	55	63	54	58	55	0	60	59	0	24	1	750,000$
18	Marcus Bjork	72				X	100.00	59	47	63	57	69	66	58	55	38	60	55	64	51	56	54	0	60	59	0	26	1	925,000$
19	Simon Edvinsson	56				X	100.00	54	50	60	56	69	63	59	55	38	56	55	58	51	49	49	0	60	57	0	21	3	925,000$
Scratches
1	Topi Ronni (R)	70	X	X			100.00	45	45	45	45	45	45	45	45	45	45	45	45	45	45	45	0	60	45	0	20	4	750,000$
2	Elias Salomonsson (R)	71				X	100.00	45	45	45	45	45	45	45	45	45	45	45	45	45	45	45	0	60	45	0	19	4	750,000$

	TEAM AVERAGE						100.00	62	49	72	61	67	63	65	57	52	63	57	66	53	56	55	0	60	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	Akira Schmid	39	100.00	81	65	65	85	77	72	81	72	75	70	77	56	54	0	60	74	0	24	2	850,833$
2	Chris Driedger	1	100.00	64	62	62	77	67	60	70	60	62	60	67	66	59	0	60	64	0	29	2	3,500,000$
Scratches
1	Matt Tomkins	36	100.00	51	51	51	51	51	51	51	51	51	51	51	51	51	0	60	51	0	29	1	750,000$

	TEAM AVERAGE		100.00	65	59	59	71	65	61	67	61	63	60	65	58	55	0	60	63

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Dave Lowry	75	75	75	75	75	75	75	CAN	58	3	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	Team Name	POS	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKS	PKM	GW	GT	FO%	FOT	P/20	S1	S2	S3
1	Jason Dickinson	Chicago Wolves (CAR)	C/LW	10	7	5	12	4	0	7	46	36	15	18	19.44%	1	223	22.39	1	2	3	4	19	0	20	2	0	54.33%	289	1.07	1	1	0
2	Mason Appleton	Chicago Wolves (CAR)	RW	10	3	6	9	2	0	2	16	39	11	22	7.69%	6	212	21.25	0	1	1	6	19	1	11	2	0	42.31%	26	0.85	0	2	0
3	Urho Vaakanainen	Chicago Wolves (CAR)	D	10	0	8	8	0	6	27	18	4	3	8	0%	14	237	23.75	0	1	1	2	19	0	14	0	0	0%	0	0.67	0	0	0
4	Jack McBain	Chicago Wolves (CAR)	C/LW	10	4	4	8	4	2	31	17	30	7	20	13.33%	3	222	22.22	2	0	2	4	19	0	19	0	1	68.75%	16	0.72	1	0	1
5	Jacob Bryson	Chicago Wolves (CAR)	D	10	0	7	7	-4	2	14	17	9	6	8	0%	16	263	26.34	0	1	1	6	19	0	17	0	0	0%	0	0.53	1	0	1
6	Brayden Pachal	Chicago Wolves (CAR)	D	10	0	6	6	5	6	11	3	5	1	3	0%	7	188	18.86	0	0	0	1	0	0	0	0	0	0%	0	0.64	0	0	0
7	Jansen Harkins	Chicago Wolves (CAR)	C/LW/RW	10	2	3	5	-3	4	12	15	22	5	25	9.09%	0	156	15.69	0	0	0	0	0	0	0	0	0	55.69%	167	0.64	1	0	0
8	Pontus Holmberg	Chicago Wolves (CAR)	LW/RW	10	4	1	5	-1	4	13	9	19	0	10	21.05%	2	100	10.10	0	0	0	0	0	0	0	0	0	57.14%	7	0.99	1	0	0
9	Anders Bjork	Chicago Wolves (CAR)	LW/RW	10	4	1	5	-3	2	7	12	27	6	16	14.81%	0	161	16.14	0	0	0	0	0	0	5	0	0	50.00%	12	0.62	0	0	0
10	Marcus Bjork	Chicago Wolves (CAR)	D	10	0	5	5	5	0	14	4	9	4	6	0%	13	194	19.40	0	0	0	0	0	0	7	0	0	0%	0	0.52	0	0	1
11	Quinton Byfield	Chicago Wolves (CAR)	C	10	2	2	4	1	2	18	19	28	4	26	7.14%	3	207	20.73	0	0	0	6	19	0	11	1	0	58.97%	195	0.39	0	1	0
12	Axel Jonsson-Fjallby	Chicago Wolves (CAR)	LW	10	2	2	4	2	2	10	9	28	8	21	7.14%	0	193	19.33	0	0	0	7	19	0	0	0	0	53.85%	13	0.41	0	0	0
13	Oskar Steen	Chicago Wolves (CAR)	C/LW/RW	10	2	2	4	-1	2	10	12	13	9	11	15.38%	2	101	10.10	0	0	0	0	0	0	0	1	0	47.32%	112	0.79	0	0	0
14	Darren Raddysh	Chicago Wolves (CAR)	D	10	0	3	3	0	6	28	3	4	2	2	0%	14	238	23.82	0	0	0	1	19	0	15	0	0	0%	0	0.25	0	0	0
15	Nathan Smith	Chicago Wolves (CAR)	C/RW	10	0	3	3	-3	0	12	8	11	1	12	0%	2	154	15.48	0	0	0	0	0	0	0	0	0	27.27%	11	0.39	0	0	0
16	Saku Maenalanen	Chicago Wolves (CAR)	RW	10	2	1	3	2	2	10	16	18	7	18	11.11%	0	193	19.31	0	0	0	1	19	0	0	0	0	53.85%	13	0.31	0	1	0
17	Braden Schneider	Chicago Wolves (CAR)	D	10	0	1	1	-4	8	20	17	13	2	6	0%	10	261	26.12	0	0	0	4	19	0	14	0	0	0%	0	0.08	0	0	0
18	Simon Edvinsson	Chicago Wolves (CAR)	D	10	0	0	0	0	0	0	0	0	0	0	0%	0	4	0.44	0	0	0	0	0	0	2	0	0	0%	0	0	0	0	0
19	Stephen Halliday	Chicago Wolves (CAR)	C/LW	10	0	0	0	-1	4	9	2	1	0	1	0%	0	106	10.63	0	0	0	0	0	0	3	0	0	50.00%	10	0	0	0	0
Team Total or Average				190	32	60	92	5	52	255	243	316	91	233	10.13%	93	3420	18.00	3	5	8	42	200	1	144	6	1	54.19%	871	0.54	5	5	3

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	SO	GA	SA	EG	PS %	ST	BG	S1	S2
1	Akira Schmid	Chicago Wolves (CAR)	10	6	3	1	0.912	2.48	678	1	28	318	1	0	10	0	2	1
2	Chris Driedger	Chicago Wolves (CAR)	1	0	0	0	0.667	4.29	14	0	1	3	0	0	0	10	0	0
Team Total or Average			11	6	3	1	0.910	2.51	693	1	29	321	1		10	10	2	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	Acquired By	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	Salary Year 6	Salary Year 7	Salary Year 8	Salary Year 9	Salary Year 10	No Trade Year 2	No Trade Year 3	No Trade Year 4	No Trade Year 5	No Trade Year 6	No Trade Year 7	No Trade Year 8	No Trade Year 9	No Trade Year 10	Link
Akira Schmid	Chicago Wolves (CAR)	G	24	05/12/00	No	205 Lbs	6 ft5	No	No	N/A	No	No	2	Pro & Farm	850,833$	0$	0$	No	850,833$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Anders Bjork (1 Way Contract)	Chicago Wolves (CAR)	LW/RW	27	08/05/96	No	190 Lbs	6 ft0	No	No	N/A	No	No	1	Pro & Farm	1,600,000$	1,600,000$	171,429$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Axel Jonsson-Fjallby	Chicago Wolves (CAR)	LW	26	02/10/98	No	189 Lbs	6 ft1	No	No	N/A	No	No	1	Pro & Farm	750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Braden Schneider	Chicago Wolves (CAR)	D	22	09/20/01	No	208 Lbs	6 ft3	No	No	N/A	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Brayden Pachal	Chicago Wolves (CAR)	D	24	08/23/99	No	203 Lbs	6 ft2	No	No	N/A	No	No	1	Pro & Farm	750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Chris Driedger	Chicago Wolves (CAR)	G	29	05/18/94	No	207 Lbs	6 ft4	No	No	N/A	No	No	2	Pro & Farm	3,500,000$	0$	0$	No	3,500,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Darren Raddysh	Chicago Wolves (CAR)	D	28	02/28/96	No	200 Lbs	6 ft1	No	No	N/A	No	No	2	Pro & Farm	762,500$	0$	0$	No	762,500$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Elias Salomonsson	Chicago Wolves (CAR)	D	19	08/31/04 4:46:17 AM	Yes	183 Lbs	6 ft1	No	No	N/A	No	No	4	Pro & Farm	750,000$	0$	0$	No	750,000$	750,000$	750,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-
Jack McBain	Chicago Wolves (CAR)	C/LW	24	01/06/00	No	201 Lbs	6 ft3	No	No	N/A	No	No	1	Pro & Farm	883,750$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jacob Bryson (1 Way Contract)	Chicago Wolves (CAR)	D	26	11/18/97	No	176 Lbs	5 ft9	No	No	N/A	No	No	2	Pro & Farm	1,850,000$	1,850,000$	198,214$	No	1,850,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Jansen Harkins	Chicago Wolves (CAR)	C/LW/RW	26	05/23/97	No	197 Lbs	6 ft2	No	No	N/A	No	No	2	Pro & Farm	850,000$	0$	0$	No	850,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Jason Dickinson (1 Way Contract)	Chicago Wolves (CAR)	C/LW	28	07/04/95	No	200 Lbs	6 ft2	No	No	N/A	No	No	2	Pro & Farm	2,650,000$	2,650,000$	283,929$	No	2,650,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Marcus Bjork	Chicago Wolves (CAR)	D	26	11/24/97	No	211 Lbs	6 ft4	No	No	N/A	No	No	1	Pro & Farm	925,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Mason Appleton (1 Way Contract)	Chicago Wolves (CAR)	RW	28	01/15/96	No	197 Lbs	6 ft3	No	No	N/A	No	No	3	Pro & Farm	2,166,667$	2,166,667$	232,143$	No	2,166,667$	2,166,667$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Matt Tomkins	Chicago Wolves (CAR)	G	29	06/19/94	No	194 Lbs	6 ft3	No	No	N/A	No	No	1	Pro & Farm	750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Nathan Smith	Chicago Wolves (CAR)	C/RW	25	10/19/98	No	177 Lbs	6 ft0	No	No	N/A	No	No	1	Pro & Farm	883,750$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Oskar Steen	Chicago Wolves (CAR)	C/LW/RW	26	03/09/98	No	193 Lbs	5 ft10	No	No	N/A	No	No	2	Pro & Farm	800,000$	0$	0$	No	800,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Pontus Holmberg	Chicago Wolves (CAR)	LW/RW	25	03/09/99	No	202 Lbs	6 ft0	No	No	N/A	No	No	1	Pro & Farm	827,500$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Quinton Byfield	Chicago Wolves (CAR)	C	21	08/19/02	No	220 Lbs	6 ft5	No	No	N/A	No	No	2	Pro & Farm	894,167$	0$	0$	No	894,167$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Saku Maenalanen	Chicago Wolves (CAR)	RW	29	05/29/94	No	214 Lbs	6 ft4	No	No	N/A	No	No	1	Pro & Farm	750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Simon Edvinsson	Chicago Wolves (CAR)	D	21	02/05/03	No	209 Lbs	6 ft6	No	No	N/A	No	No	3	Pro & Farm	925,000$	0$	0$	No	925,000$	925,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Stephen Halliday	Chicago Wolves (CAR)	C/LW	21	07/02/02 2:32:31 AM	Yes	214 Lbs	6 ft4	No	No	N/A	No	No	4	Pro & Farm	750,000$	0$	0$	No	750,000$	750,000$	750,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-
Topi Ronni	Chicago Wolves (CAR)	C/LW	20	05/05/04 3:39:10 AM	Yes	181 Lbs	6 ft2	No	No	N/A	No	No	4	Pro & Farm	750,000$	0$	0$	No	750,000$	750,000$	750,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-
Urho Vaakanainen	Chicago Wolves (CAR)	D	25	01/01/99	No	200 Lbs	6 ft2	No	No	N/A	No	No	2	Pro & Farm	850,000$	0$	0$	No	850,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
24	24.96	199 Lbs	6 ft2	1.96	1,141,424$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Jack McBain	Jason Dickinson	Mason Appleton	30	1	2	2
2	Axel Jonsson-Fjallby	Quinton Byfield	Saku Maenalanen	30	1	2	2
3	Anders Bjork	Jansen Harkins	Nathan Smith	25	1	2	2
4	Stephen Halliday	Oskar Steen	Pontus Holmberg	15	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Braden Schneider	Jacob Bryson	30	1	2	2
2	Urho Vaakanainen	Darren Raddysh	30	1	2	2
3	Brayden Pachal	Marcus Bjork	25	1	2	2
4	Braden Schneider	Jacob Bryson	15	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Jack McBain	Jason Dickinson	Mason Appleton	60	1	1	3
2	Axel Jonsson-Fjallby	Quinton Byfield	Saku Maenalanen	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Braden Schneider	Jacob Bryson	60	1	1	3
2	Urho Vaakanainen	Darren Raddysh	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Jason Dickinson	Jack McBain	60	1	3	1
2	Mason Appleton	Quinton Byfield	40	1	3	1

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Braden Schneider	Jacob Bryson	60	1	4	0
2	Urho Vaakanainen	Darren Raddysh	40	1	4	0

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Jason Dickinson	60	1	4	0	Braden Schneider	Jacob Bryson	60	1	4	0
2	Jack McBain	40	1	4	0	Urho Vaakanainen	Darren Raddysh	40	1	4	0

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Jason Dickinson	Jack McBain	60	1	2	2
2	Mason Appleton	Quinton Byfield	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Braden Schneider	Jacob Bryson	60	1	2	2
2	Urho Vaakanainen	Darren Raddysh	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Jack McBain	Jason Dickinson	Mason Appleton	Braden Schneider	Jacob Bryson

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Jack McBain	Jason Dickinson	Mason Appleton	Braden Schneider	Jacob Bryson

Extra Forwards
Normal	PowerPlay	Penalty Kill
Stephen Halliday, Jansen Harkins, Anders Bjork	Stephen Halliday, Jansen Harkins	Anders Bjork

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Brayden Pachal, Marcus Bjork, Simon Edvinsson	Brayden Pachal	Marcus Bjork, Simon Edvinsson

Penalty Shots
Jason Dickinson, Jack McBain, Mason Appleton, Quinton Byfield, Axel Jonsson-Fjallby

Goalie
#1 : Akira Schmid, #2 : Chris Driedger

Custom OT Lines Forwards
Jason Dickinson, Jack McBain, Mason Appleton, Quinton Byfield, Axel Jonsson-Fjallby, Saku Maenalanen, Saku Maenalanen, Jansen Harkins, Anders Bjork, Nathan Smith, Pontus Holmberg

Custom OT Lines Defensemen
Braden Schneider, Jacob Bryson, Urho Vaakanainen, Darren Raddysh, Brayden Pachal

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	Milwaukee Admirals	5	4	1	0	0	0	0	0	20	13	7	3	3	0	0	0	0	0	0	11	7	4	2	1	1	0	0	0	0	0	9	6	3	8	0.800	20	38	58	0	0	16	9	6	1	203	114	73	82	47	205	48	32	159	12	2	16.67%	15	0	100.00%	0	186	348	53.45%	186	345	53.91%	100	178	56.18%	311	222	231	77	150	80
2	San Diego Gulls	5	2	3	0	0	0	0	0	12	17	-5	3	1	2	0	0	0	0	0	8	11	-3	2	1	1	0	0	0	0	0	4	6	-2	4	0.400	12	22	34	1	1	16	9	6	1	113	114	73	82	47	117	45	20	96	10	1	10.00%	5	2	60.00%	0	186	348	53.45%	186	345	53.91%	100	178	56.18%	311	222	231	77	150	80
	Total	10	6	4	0	0	0	0	0	32	30	2	6	4	2	0	0	0	0	0	19	18	1	4	2	2	0	0	0	0	0	13	12	1	12	0.600	32	60	92	1	1	16	9	6	1	316	114	73	82	47	322	93	52	255	22	3	13.64%	20	2	90.00%	0	186	348	53.45%	186	345	53.91%	100	178	56.18%	311	222	231	77	150	80
	_Since Last GM Reset	10	6	4	0	0	0	0	0	32	30	2	6	4	2	0	0	0	0	0	19	18	1	4	2	2	0	0	0	0	0	13	12	1	12	0.600	32	60	92	1	1	16	9	6	1	316	114	73	82	47	322	93	52	255	22	3	13.64%	20	2	90.00%	0	186	348	53.45%	186	345	53.91%	100	178	56.18%	311	222	231	77	150	80
	_Vs Conference	10	6	4	0	0	0	0	0	32	30	2	6	4	2	0	0	0	0	0	19	18	1	4	2	2	0	0	0	0	0	13	12	1	12	0.600	32	60	92	1	1	16	9	6	1	316	114	73	82	47	322	93	52	255	22	3	13.64%	20	2	90.00%	0	186	348	53.45%	186	345	53.91%	100	178	56.18%	311	222	231	77	150	80
	_Vs Division	10	4	1	0	0	0	0	0	32	30	2	6	3	0	0	0	0	0	0	19	18	1	4	1	1	0	0	0	0	0	13	12	1	8	0.400	32	60	92	1	1	16	9	6	1	316	114	73	82	47	322	93	52	255	22	3	13.64%	20	2	90.00%	0	186	348	53.45%	186	345	53.91%	100	178	56.18%	311	222	231	77	150	80

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
10	12	W1	32	60	92	316	322	93	52	255	1	1

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
10	6	4	0	0	0	0	32	30

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
6	4	2	0	0	0	0	19	18

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	2	0	0	0	0	13	12

Last 10 Games
W	L	OTW	OTL	SOW	SOL
6	3	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
22	3	13.64%	20	2	90.00%	0

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
114	73	82	47	16	9	6	1

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
186	348	53.45%	186	345	53.91%	100	178	56.18%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
311	222	231	77	150	80

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 - 2024-04-20	5	Milwaukee Admirals	3	Chicago Wolves	4	BW		BoxScore
4 - 2024-04-22	13	Milwaukee Admirals	2	Chicago Wolves	3	BW	X	BoxScore
6 - 2024-04-24	21	Chicago Wolves	4	Milwaukee Admirals	5	AL	X	BoxScore
8 - 2024-04-26	29	Chicago Wolves	5	Milwaukee Admirals	1	AW		BoxScore
10 - 2024-04-28	37	Milwaukee Admirals	2	Chicago Wolves	4	BW		BoxScore
16 - 2024-05-04	59	San Diego Gulls	5	Chicago Wolves	3	BL		BoxScore
18 - 2024-05-06	63	San Diego Gulls	4	Chicago Wolves	2	BL		BoxScore
20 - 2024-05-08	67	Chicago Wolves	1	San Diego Gulls	0	AW		BoxScore
22 - 2024-05-10	71	Chicago Wolves	3	San Diego Gulls	6	AL		BoxScore
24 - 2024-05-12	75	San Diego Gulls	2	Chicago Wolves	3	BW		BoxScore
26 - 2024-05-14	79	Chicago Wolves	-	San Diego Gulls	-
28 - 2024-05-16	83	San Diego Gulls	-	Chicago Wolves	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	4500	3500
Ticket Price	35	0
Attendance	27,000	20,953
Attendance PCT	100.00%	99.78%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
28	7992 - 99.90%	157,500$	945,000$	8000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
0$	1,912,750$	1,687,750$	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$	3	0$	0$

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: 10 \| W: 6 \| L: 4 GF: 32 \| GA: 30 \| PP%: 13.64% \| PK%: 90.00% GM : Chris Floresco \| Morale : 60 \| Team Overall : 61 Next Games #79 vs San Diego Gulls