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

Game Center
	Philadelphia Phantoms 0-1-0, 0pts	3	FINAL	8	Chicago Wolves 1-0-0, 2pts

Team Stats
L1	Streak	W1
0-0-0	Home Record	1-0-0
0-1-0	Home Record	0-0-0
0-1-0	Last 10 Games	1-0-0
3.00	Goals Per Game	8.00
8.00	Goals Against Per Game	3.00
50.00%	Power Play Percentage	0.00%
100.00%	Penalty Kill Percentage	50.00%

Philadelphia Phantoms
0-1-0, 0pts

2024-05-06

Chicago Wolves
1-0-0, 2pts

Team Stats
L1	Streak	W1
0-0-0	Home Record	1-0-0
0-1-0	Away Record	0-0-0
0-1-0	Last 10 Games	1-0-0
3.00	Goals Per Game	8.00
8.00	Goals Against Per Game	8.00
50.00%	Power Play Percentage	0.00%
100.00%	Penalty Kill Percentage	50.00%

Chicago Wolves
1-0-0, 2pts

2024-05-07

Philadelphia Phantoms
0-1-0, 0pts

Team Stats
W1	Streak	L1
1-0-0	Home Record	0-0-0
0-0-0	Away Record	0-1-0
1-0-0	Last 10 Games	0-1-0
8.00	Goals Per Game	3.00
3.00	Goals Against Per Game	3.00
0.00%	Power Play Percentage	50.00%
50.00%	Penalty Kill Percentage	100.00%

Chicago Wolves
1-0-0, 2pts

2024-05-08

Philadelphia Phantoms
0-1-0, 0pts

Team Stats
W1	Streak	L1
1-0-0	Home Record	0-0-0
0-0-0	Away Record	0-1-0
1-0-0	Last 10 Games	0-1-0
8.00	Goals Per Game	3.00
3.00	Goals Against Per Game	3.00
0.00%	Power Play Percentage	50.00%
50.00%	Penalty Kill Percentage	100.00%

Team Leaders
	Goals Pavol Regenda 2	Assists Valtteri Puustinen 2

	Points Valtteri Puustinen 3	Plus/Minus Valtteri Puustinen 3

	Wins Daniel Vladar 1	Save Percentage Daniel Vladar 0.939

Team Stats
Goals For 8 8.00 GFG	Shots For 42 42.00 Avg	Power Play Percentage 0.0% 0 GF	Offensive Zone Start 27.3%
Goals Against 3 3.00 GAA	Shots Against 49 49.00 Avg	Penalty Kill Percentage 50.0%% 1 GA	Defensive Zone Start 36.4%

Team Info
	General Manager	Camil Costandi
	Coach	Alex Tanguay
	Division	Marcel Dionne
	Conference	Conference 1
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	3,000
	Season Tickets	300
Roster Info
	Pro Team	28
	Farm Team	21
	Contract Limit	49 / 53
	Prospects	23
Team History
	This Season	1-0
	History	229-251-46 (0.435%)
	Playoff Appearances	1
	Playoff Record (W-L)	1-0
	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	SP	Age	Contract	Salary
1	Hudson Fasching	0			X		100.00	73	44	93	65	76	63	82	63	31	63	68	66	75	59	59	69	83	63	0	27	2	760,000$
2	Tyson Foerster (R)	0	X				100.00	73	72	76	70	72	76	70	70	79	65	63	64	59	45	45	63	86	63	0	21	1	775,000$
3	Vasily Podkolzin	0			X		100.00	78	58	92	78	71	62	90	62	39	58	59	61	25	58	58	64	87	62	0	22	3	1,775,000$
4	Saku Maenalanen	0		X	X		100.00	83	45	91	65	78	57	73	58	36	59	57	80	25	56	57	65	88	62	0	29	1	775,000$
5	Valtteri Puustinen (R)	0		X	X		100.00	70	62	90	65	62	84	89	64	50	62	63	62	60	44	44	66	90	61	0	24	1	775,000$
6	Buddy Robinson	0			X		100.00	82	88	67	71	88	72	77	57	50	50	57	70	54	59	59	62	84	61	0	31	1	775,000$
7	Mitchell Stephens	0	X				100.00	72	69	79	72	69	63	64	61	76	56	61	65	58	53	53	64	89	60	0	26	1	775,000$
8	Lukas Rousek (R)	0		X	X		100.00	72	64	89	70	64	63	63	63	50	65	58	63	55	44	44	63	87	60	0	24	2	775,000$
9	William Lockwood (R)	0			X		100.00	93	45	99	67	63	57	74	57	25	58	55	73	25	45	45	64	88	59	0	25	2	775,000$
10	Pavol Regenda (R)	0		X	X		100.00	67	43	90	65	80	57	71	62	44	62	58	67	25	45	45	63	83	59	0	23	1	925,000$
11	Jackson Cates (R)	0	X	X			100.00	76	70	90	67	70	71	76	55	69	49	56	63	53	44	44	60	88	58	0	25	1	775,000$
12	Vitali Kravtsov (R)	0		X	X		100.00	67	42	92	74	70	59	54	62	25	59	59	60	25	48	48	61	48	58	0	23	1	775,000$
13	Jordan Harris (R)	0				X	100.00	71	42	87	67	67	76	58	63	25	53	48	80	25	53	53	62	88	63	0	22	2	1,400,000$
14	Lawrence Pilut	0				X	100.00	63	41	99	63	66	60	68	58	25	50	48	76	25	51	51	60	88	60	0	27	1	775,000$
15	Dean Stewart	0				X	100.00	78	75	86	70	75	64	68	50	25	46	39	63	37	44	44	53	85	59	0	25	1	775,000$
16	Cavan Fitzgerald	0				X	100.00	75	72	82	65	72	58	61	51	25	44	41	64	39	54	54	53	86	58	0	26	1	775,000$
17	Santtu Kinnunen (R)	0				X	100.00	68	60	88	67	60	73	78	54	25	49	45	59	43	44	44	57	88	58	0	24	1	925,000$
18	Jakub Galvas (R)	0				X	100.00	69	61	88	64	61	74	80	53	25	50	41	59	39	44	44	54	85	57	0	24	1	775,000$
Scratches
1	C.J. Smith	0	X	X	X		100.00	73	66	90	68	66	58	60	54	50	45	57	64	54	55	55	59	81	56	0	28	1	760,000$
2	C.J. Suess (R)	0		X			100.00	71	69	74	66	69	62	64	58	50	54	57	61	54	44	44	60	25	56	0	29	1	775,000$
3	Daniil Chayka (R)	0				X	100.00	79	71	96	64	71	65	71	45	25	34	40	62	38	44	44	52	23	56	0	20	2	902,500$
4	Oskari Laaksonen (R)	0				X	100.00	71	65	84	63	65	66	71	48	25	40	40	59	38	44	44	52	24	55	0	23	1	775,000$
5	Linus Hogberg (R)	0				X	100.00	72	66	87	65	66	57	60	47	25	37	41	59	39	44	44	51	24	54	0	24	1	775,000$

	TEAM AVERAGE						100.00	74	60	87	67	70	65	71	57	39	53	53	65	42	49	49	60	74	59

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	Daniel Vladar	0	98.00	62	55	54	80	65	58	79	63	65	62	75	48	49	64	87	63	0	25	2	2,200,000$
2	Devin Cooley (R)	0	100.00	56	47	59	83	61	58	52	60	57	56	30	44	44	56	87	57	0	26	1	775,000$
Scratches
1	Pavel Francouz	0	100.00	70	52	51	65	77	64	77	72	77	70	95	47	48	70	34	65	0	33	1	2,000,000$
2	Kyle Keyser (R)	0	100.00	56	50	63	73	58	58	56	61	59	58	30	44	44	57	27	56	0	24	1	775,000$
3	Nico Daws	0	100.00	53	54	68	84	52	55	53	58	54	54	30	46	46	54	24	56	0	22	1	925,000$

	TEAM AVERAGE		99.60	59	52	59	77	63	59	63	63	62	60	52	46	46	60	52	59

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Alex Tanguay	62	62	62	62	55	55	1	CAN	43	1	60,000$

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	PPM	PKS	PKM	GW	FO%	FOT	GA	TA	P/20	FW	FT	S1	S2	S3
1	Valtteri Puustinen	Chicago Wolves (QUE)	LW/RW	1	1	2	3	3	0	0	1	2	4	1	3	25.00%	0	17	17.20	0	1	1	0	100.00%	1	0	0	3.49	0	0	1	0	0
2	Hudson Fasching	Chicago Wolves (QUE)	RW	1	1	1	2	1	0	0	2	0	3	1	1	33.33%	1	14	14.57	1	0	0	0	0%	0	0	0	2.75	0	0	0	0	0
3	Mitchell Stephens	Chicago Wolves (QUE)	C	1	1	1	2	3	5	5	1	2	3	0	0	33.33%	0	14	14.25	0	0	0	0	50.00%	12	0	1	2.81	1	0	0	0	0
4	Tyson Foerster	Chicago Wolves (QUE)	C	1	1	1	2	1	0	0	2	2	1	1	1	100.00%	0	17	17.68	1	0	2	0	55.88%	34	0	1	2.26	0	0	0	0	0
5	Vasily Podkolzin	Chicago Wolves (QUE)	RW	1	1	1	2	3	0	0	0	1	2	0	1	50.00%	0	14	14.37	0	0	0	1	0%	1	1	0	2.78	0	0	0	0	1
6	William Lockwood	Chicago Wolves (QUE)	RW	1	0	2	2	1	0	0	2	1	4	2	2	0%	1	13	13.48	0	0	0	0	0%	0	2	0	2.97	0	0	0	0	0
7	Saku Maenalanen	Chicago Wolves (QUE)	LW/RW	1	0	2	2	1	0	0	3	0	0	0	1	0%	1	16	16.00	1	0	1	0	100.00%	1	0	0	2.50	0	0	0	0	0
8	Pavol Regenda	Chicago Wolves (QUE)	LW/RW	1	2	0	2	1	0	0	2	0	11	1	4	18.18%	0	13	13.48	0	0	0	0	0%	2	0	0	2.97	0	0	0	1	0
9	Jordan Harris	Chicago Wolves (QUE)	D	1	0	1	1	1	0	0	0	3	3	1	2	0%	1	23	23.03	1	0	2	0	0%	0	0	2	0.87	0	0	0	0	0
10	Jackson Cates	Chicago Wolves (QUE)	C/LW	1	1	0	1	1	5	5	3	4	2	0	1	50.00%	0	13	13.40	0	0	0	0	54.55%	11	0	2	1.49	0	1	0	0	0
11	Lukas Rousek	Chicago Wolves (QUE)	LW/RW	1	0	1	1	1	0	0	1	0	1	0	2	0%	0	14	14.05	0	0	0	0	100.00%	1	1	0	1.42	0	0	0	0	0
12	Buddy Robinson	Chicago Wolves (QUE)	RW	1	0	1	1	1	2	0	3	2	3	1	1	0%	0	14	14.20	0	0	0	0	0%	0	1	1	1.41	0	0	0	0	0
13	Cavan Fitzgerald	Chicago Wolves (QUE)	D	1	0	1	1	3	0	0	2	2	2	3	1	0%	0	21	21.23	0	0	1	0	0%	0	0	0	0.94	0	0	0	0	0
14	Santtu Kinnunen	Chicago Wolves (QUE)	D	1	0	1	1	2	5	5	1	0	1	2	0	0%	3	20	20.93	0	0	0	0	0%	0	0	0	0.96	0	1	0	0	0
15	Jakub Galvas	Chicago Wolves (QUE)	D	1	0	0	0	3	5	5	2	2	1	0	0	0%	2	18	18.10	0	0	0	0	0%	0	0	1	0	1	0	0	0	0
16	Dean Stewart	Chicago Wolves (QUE)	D	1	0	0	0	2	17	5	1	0	0	0	0	0%	0	14	14.62	0	0	0	0	0%	0	0	0	0	0	1	0	0	0
17	Lawrence Pilut	Chicago Wolves (QUE)	D	1	0	0	0	1	0	0	1	3	1	1	1	0%	0	22	22.03	1	0	2	0	0%	0	0	0	0	0	0	0	0	0
Team Total or Average				17	8	15	23	29	39	25	27	24	42	14	21	19.05%	9	282	16.63	9	1	14	1	53.97%	63	5	8	1.63	2	3	1	1	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

#	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	Daniel Vladar	Chicago Wolves (QUE)	1	1	0	0	0.939	3.00	60	0	0	3	49	29	0	0	0	0	1	0	0	0	0
Team Total or Average			1	1	0	0	0.939	3.00	60	0	0	3	49	29	0	0		0	1	0	0	0	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	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Acquired By	Force Waivers	Waiver Possible	Contract	Contract Signature Date	Force UFA	Emergency Recall	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
Buddy Robinson	Chicago Wolves (QUE)	RW	31	1991-09-29	No	236 Lbs	6 ft5	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
C.J. Smith	Chicago Wolves (QUE)	C/LW/RW	28	1994-11-30	No	181 Lbs	5 ft11	No	No	N/A	No	No	1		False	False	Pro & Farm	760,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
C.J. Suess	Chicago Wolves (QUE)	LW	29	1994-03-16	Yes	190 Lbs	5 ft11	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Cavan Fitzgerald	Chicago Wolves (QUE)	D	26	1996-08-23	No	196 Lbs	6 ft1	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Daniel Vladar	Chicago Wolves (QUE)	G	25	1997-08-20	No	185 Lbs	6 ft5	No	No	N/A	No	No	2		False	False	Pro & Farm	2,200,000$	0$	0$	No	2,200,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Daniil Chayka	Chicago Wolves (QUE)	D	20	2002-10-22	Yes	187 Lbs	6 ft3	No	No	N/A	No	No	2		False	False	Pro & Farm	902,500$	0$	0$	No	902,500$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Dean Stewart	Chicago Wolves (QUE)	D	25	1998-06-12	No	201 Lbs	6 ft2	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Devin Cooley	Chicago Wolves (QUE)	G	26	1997-05-24	Yes	190 Lbs	6 ft5	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Hudson Fasching	Chicago Wolves (QUE)	RW	27	1995-07-28	No	209 Lbs	6 ft2	No	No	N/A	No	No	2		False	False	Pro & Farm	760,000$	0$	0$	No	760,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Jackson Cates	Chicago Wolves (QUE)	C/LW	25	1997-09-26	Yes	190 Lbs	6 ft0	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jakub Galvas	Chicago Wolves (QUE)	D	24	1999-06-15	Yes	165 Lbs	5 ft11	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jordan Harris	Chicago Wolves (QUE)	D	22	2000-07-07	Yes	189 Lbs	5 ft11	No	No	N/A	No	No	2		False	False	Pro & Farm	1,400,000$	0$	0$	No	1,400,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Kyle Keyser	Chicago Wolves (QUE)	G	24	1999-03-08	Yes	185 Lbs	6 ft2	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Lawrence Pilut	Chicago Wolves (QUE)	D	27	1995-12-30	No	180 Lbs	5 ft11	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Linus Hogberg	Chicago Wolves (QUE)	D	24	1998-09-04	Yes	175 Lbs	6 ft1	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Lukas Rousek	Chicago Wolves (QUE)	LW/RW	24	1999-04-20	Yes	175 Lbs	5 ft11	No	No	N/A	No	No	2		False	False	Pro & Farm	775,000$	0$	0$	No	775,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Mitchell Stephens	Chicago Wolves (QUE)	C	26	1997-02-05	No	193 Lbs	5 ft11	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Nico Daws	Chicago Wolves (QUE)	G	22	2000-12-22	No	205 Lbs	6 ft4	No	No	N/A	No	No	1		False	False	Pro & Farm	925,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Oskari Laaksonen	Chicago Wolves (QUE)	D	23	1999-07-02	Yes	172 Lbs	6 ft1	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Pavel Francouz	Nordiques	G	33	1990-06-03	No	179 Lbs	6 ft0	No	No	N/A	No	No	1		False	False	Pro & Farm	2,000,000$	2,000,000$	1,714,286$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Pavol Regenda	Chicago Wolves (QUE)	LW/RW	23	1999-07-12	Yes	219 Lbs	6 ft3	No	No	N/A	No	No	1		False	False	Pro & Farm	925,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Saku Maenalanen	Chicago Wolves (QUE)	LW/RW	29	1994-05-29	No	207 Lbs	6 ft4	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Santtu Kinnunen	Chicago Wolves (QUE)	D	24	1999-03-25	Yes	154 Lbs	6 ft2	No	No	N/A	No	No	1		False	False	Pro & Farm	925,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Tyson Foerster	Chicago Wolves (QUE)	C	21	2002-01-18	Yes	194 Lbs	6 ft2	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Valtteri Puustinen	Chicago Wolves (QUE)	LW/RW	24	1999-06-04	Yes	179 Lbs	5 ft9	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Vasily Podkolzin	Chicago Wolves (QUE)	RW	22	2001-06-24	No	190 Lbs	6 ft1	No	No	N/A	No	No	3	2024-05-01	False	False	Pro & Farm	1,775,000$	0$	0$	No	1,000,000$	1,000,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Vitali Kravtsov	Chicago Wolves (QUE)	LW/RW	23	1999-12-23	Yes	183 Lbs	6 ft3	No	No	N/A	No	No	1		False	False	Pro & Farm	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
William Lockwood	Chicago Wolves (QUE)	RW	25	1998-06-20	Yes	172 Lbs	5 ft11	No	No	N/A	No	No	2		False	False	Pro & Farm	775,000$	0$	0$	No	775,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
28	25.07	189 Lbs	6 ft1	1.29	947,232$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Saku Maenalanen	Tyson Foerster	Hudson Fasching	25	0	2	3
2	Valtteri Puustinen	Mitchell Stephens	Vasily Podkolzin	25	0	2	3
3	Lukas Rousek	Jackson Cates	Buddy Robinson	25	0	2	3
4	Pavol Regenda	Tyson Foerster	William Lockwood	25	0	2	3

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jordan Harris	Lawrence Pilut	34	0	2	3
2	Dean Stewart	Cavan Fitzgerald	33	0	2	3
3	Santtu Kinnunen	Jakub Galvas	33	0	2	3
4	Jordan Harris	Lawrence Pilut	0	0	2	3

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Saku Maenalanen	Tyson Foerster	Hudson Fasching	50	0	2	3
2	Valtteri Puustinen	Mitchell Stephens	Vasily Podkolzin	50	0	2	3

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jordan Harris	Lawrence Pilut	50	0	2	3
2	Dean Stewart	Cavan Fitzgerald	50	0	2	3

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Tyson Foerster	Saku Maenalanen	50	0	2	3
2	Mitchell Stephens	Valtteri Puustinen	50	0	2	3

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jordan Harris	Lawrence Pilut	50	0	2	3
2	Dean Stewart	Cavan Fitzgerald	50	0	2	3

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Tyson Foerster	50	0	2	3	Jordan Harris	Lawrence Pilut	50	0	2	3
2	Mitchell Stephens	50	0	2	3	Dean Stewart	Cavan Fitzgerald	50	0	2	3

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Tyson Foerster	Saku Maenalanen	50	0	2	3
2	Mitchell Stephens	Valtteri Puustinen	50	0	2	3

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jordan Harris	Lawrence Pilut	50	0	2	3
2	Dean Stewart	Cavan Fitzgerald	50	0	2	3

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Saku Maenalanen	Tyson Foerster	Hudson Fasching	Jordan Harris	Lawrence Pilut

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Saku Maenalanen	Tyson Foerster	Hudson Fasching	Jordan Harris	Lawrence Pilut

Extra Forwards
Normal	PowerPlay	Penalty Kill
Valtteri Puustinen, Buddy Robinson, Lukas Rousek	Valtteri Puustinen, Buddy Robinson	Valtteri Puustinen

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Cavan Fitzgerald, Santtu Kinnunen, Jakub Galvas	Cavan Fitzgerald	Cavan Fitzgerald, Santtu Kinnunen

Penalty Shots
Hudson Fasching, Tyson Foerster, Saku Maenalanen, Vasily Podkolzin, Valtteri Puustinen

Goalie
#1 : Devin Cooley, #2 : Daniel Vladar, #3 : 0

Custom OT Lines Forwards
, , , , , , , , , ,

Custom OT Lines Defensemen
, , , ,

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	Philadelphia Phantoms	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	2	1.000	8	15	23	0	0	3	3	2	0	42	20	6	16	0	49	9	39	27	1	0	0.00%	2	1	50.00%	0	9	18	50.00%	13	24	54.17%	13	24	54.17%	22	14	18	8	18	9
	Total	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	2	1.000	8	15	23	0	0	3	3	2	0	42	20	6	16	0	49	9	39	27	1	0	0.00%	2	1	50.00%	0	9	18	50.00%	13	24	54.17%	13	24	54.17%	22	14	18	8	18	9
	_Since Last GM Reset	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	2	1.000	8	15	23	0	0	3	3	2	0	42	20	6	16	0	49	9	39	27	1	0	0.00%	2	1	50.00%	0	9	18	50.00%	13	24	54.17%	13	24	54.17%	22	14	18	8	18	9
	_Vs Conference	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	2	1.000	8	15	23	0	0	3	3	2	0	42	20	6	16	0	49	9	39	27	1	0	0.00%	2	1	50.00%	0	9	18	50.00%	13	24	54.17%	13	24	54.17%	22	14	18	8	18	9

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
1	2	W1	8	15	23	42	49	9	39	27	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
1	1	0	0	0	0	0	8	3

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
1	1	0	0	0	0	0	8	3

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

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

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
1	0	0.00%	2	1	50.00%	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
20	6	16	0	3	3	2	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
9	18	50.00%	13	24	54.17%	13	24	54.17%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
22	14	18	8	18	9

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	Link
1 - 2024-05-05	1	Philadelphia Phantoms	3	Chicago Wolves	8	W	BoxScore
2 - 2024-05-06	9	Philadelphia Phantoms	-	Chicago Wolves	-
3 - 2024-05-07	17	Chicago Wolves	-	Philadelphia Phantoms	-
4 - 2024-05-08	25	Chicago Wolves	-	Philadelphia Phantoms	-
5 - 2024-05-09	33	Philadelphia Phantoms	-	Chicago Wolves	-
6 - 2024-05-10	41	Chicago Wolves	-	Philadelphia Phantoms	-
7 - 2024-05-11	49	Philadelphia Phantoms	-	Chicago Wolves	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	25	10
Attendance	2,000	1,000
Attendance PCT	100.00%	100.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
40	3000 - 100.00%	89,400$	89,400$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
0$	2,529,750$	2,295,208$	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$	6	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
1	Mitchell Stephens	373	239	348	587	21	325	507	780	1421	16.82%	234	7207	19.32	44	87	131	118	1	5	6	9	28	5	58.05%	16	1.63	6	18
2	Hudson Fasching	448	241	241	482	25	270	576	573	1245	19.36%	267	8175	18.25	49	56	105	120	5	3	8	12	33	10	47.44%	13	1.18	4	9
3	Patrick Brown	377	148	193	341	-111	396	505	580	1013	14.61%	206	6844	18.16	48	49	97	102	3	1	4	9	8	4	52.49%	9	1.00	2	3
4	Sean Collins	311	159	181	340	-128	211	407	539	998	15.93%	171	6241	20.07	33	47	80	89	1	1	2	14	13	10	54.06%	3	1.09	2	9
5	C.J. Suess	211	132	173	305	9	328	276	311	695	18.99%	144	4158	19.71	27	48	75	88	8	4	12	16	9	6	50.35%	11	1.47	2	8

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	Dan Vladar	137	62	59	6	0.890	4.09	7563	4	1	515	4662	2567	8	2	0.563	16
2	Jeff Zatkoff	114	48	44	16	0.885	3.25	6307	4	3	342	2962	1606	2	2	0.654	26
3	Nico Daws	86	44	20	12	0.886	4.14	4841	16	0	334	2940	1714	8	0	0.579	38
4	David LeNeveu	76	37	29	6	0.863	3.72	4294	2	2	266	1936	1150	5	2	0.852	27
5	Kyle Keyser	107	33	49	8	0.864	4.92	5383	4	0	441	3231	1899	6	2	0.750	32

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
2012	82	25	38	0	3	8	3	5	238	297	-59	41	10	21	0	2	6	0	2	109	144	-35	41	15	17	0	1	2	3	3	129	153	-24	64	238	387	625	2	2	43	88	101	8	1901	449	626	799	51	2244	741	581	1272	132	35	26.52%	189	58	69.31%	3	644	1268	50.79%	702	1474	47.63%	561	1143	49.08%	1511	677	1650	817	1812	934
2013	82	22	45	0	4	3	3	5	311	371	-60	41	16	16	0	2	3	2	2	168	161	7	41	6	29	0	2	0	1	3	143	210	-67	66	311	497	808	4	1	41	114	149	10	2159	455	801	880	46	2440	814	664	1493	217	76	35.02%	212	75	64.62%	8	794	1422	55.84%	813	1474	55.16%	735	1323	55.56%	1543	732	1731	807	1693	869
2014	82	30	33	0	2	8	8	1	299	317	-18	41	16	12	0	2	5	5	1	166	158	8	41	14	21	0	0	3	3	0	133	159	-26	89	299	473	772	3	2	57	104	128	16	2144	486	861	773	54	2297	814	583	1488	210	77	36.67%	207	81	60.87%	7	777	1418	54.80%	735	1381	53.22%	727	1271	57.20%	1617	789	1634	789	1730	884
2015	82	16	51	0	3	5	3	4	274	398	-124	41	4	25	0	3	4	2	3	134	210	-76	41	12	26	0	0	1	1	1	140	188	-48	53	274	436	710	2	2	44	116	108	7	2296	565	822	867	64	2876	950	1721	1489	201	64	31.84%	220	72	67.27%	5	688	1423	48.35%	763	1651	46.21%	621	1409	44.07%	1510	716	1775	801	1684	851
2016	82	24	44	0	5	6	1	2	349	428	-79	41	11	22	0	4	3	1	0	177	215	-38	41	13	22	0	1	3	0	2	172	213	-41	68	349	590	939	5	0	83	131	129	6	2658	703	961	960	46	3050	1020	1271	1487	199	95	47.74%	183	67	63.39%	4	825	1682	49.05%	796	1610	49.44%	754	1511	49.90%	1566	791	1729	793	1661	833
2017	78	21	48	0	2	3	2	2	235	306	-71	40	10	25	0	1	1	2	1	130	167	-37	38	11	23	0	1	2	0	1	105	139	-34	55	235	377	612	2	1	58	79	94	8	2033	581	689	749	28	2856	963	690	1444	169	58	34.32%	190	50	73.68%	6	627	1266	49.53%	784	1744	44.95%	556	1184	46.96%	1428	646	1665	744	1615	852
2018	82	47	20	0	8	4	1	2	378	311	67	41	25	8	0	4	2	0	2	195	147	48	41	22	12	0	4	2	1	0	183	164	19	118	378	595	973	0	0	71	172	126	10	2900	827	1080	956	47	2862	977	2092	1787	232	80	34.48%	270	72	73.33%	6	864	1679	51.46%	1012	1934	52.33%	735	1474	49.86%	1650	863	1711	790	1596	795
2019	82	40	33	0	4	2	0	3	468	440	28	41	26	12	0	2	1	0	0	252	202	50	41	14	21	0	2	1	0	3	216	238	-22	93	468	769	1237	1	0	111	200	153	4	3245	1011	1219	998	26	3218	1023	919	1413	218	89	40.83%	208	78	62.50%	6	779	1683	46.29%	778	1668	46.64%	756	1663	45.46%	1722	997	1720	748	1504	742
2020	40	13	20	0	2	3	1	1	197	193	4	20	7	11	0	0	1	0	1	100	99	1	20	6	9	0	2	2	1	0	97	94	3	36	197	310	507	4	0	40	84	70	4	1366	392	535	430	14	1600	568	470	684	107	49	45.79%	100	41	59.00%	2	376	757	49.67%	400	871	45.92%	359	760	47.24%	766	398	887	376	768	388
2021	78	15	50	0	3	4	1	5	335	467	-132	39	5	26	0	1	3	1	3	155	239	-84	39	10	24	0	2	1	0	2	180	228	-48	47	335	544	879	2	0	69	131	131	6	2870	775	1013	1057	41	2930	991	1563	1551	188	82	43.62%	244	91	62.70%	3	798	1657	48.16%	700	1493	46.89%	748	1538	48.63%	1544	822	1668	749	1510	734
2022	82	31	36	0	4	4	4	3	487	446	41	41	14	18	0	4	2	2	1	223	198	25	41	17	18	0	0	2	2	2	264	248	16	85	487	755	1242	1	1	137	186	156	15	2657	735	919	974	59	2993	991	1214	1504	211	79	37.44%	188	57	69.68%	11	797	1478	53.92%	846	1558	54.30%	935	1682	55.59%	1646	831	1677	752	1646	882
Total Regular Season	524	191	251	0	28	26	10	18	2449	2591	-142	263	98	122	0	16	13	6	8	1232	1267	-35	261	93	129	0	12	13	4	10	1217	1324	-107	502	2449	3940	6389	15	2	569	983	859	53	17729	5024	6416	6124	261	19509	6533	8219	9870	1324	532	40.18%	1383	456	67.03%	38	5066	10202	49.66%	5316	10878	48.87%	4843	9812	49.36%	10324	5351	11059	4954	10303	5228
Playoff
2023	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	2	8	15	23	0	0	3	3	2	0	42	20	6	16	0	49	9	39	27	1	0	0.00%	2	1	50.00%	0	9	18	50.00%	13	24	54.17%	13	24	54.17%	22	14	18	8	18	9
Total Playoff	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	2	8	15	23	0	0	3	3	2	0	42	20	6	16	0	49	9	39	27	1	0	0.00%	2	1	50.00%	0	9	18	50.00%	13	24	54.17%	13	24	54.17%	22	14	18	8	18	9

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	PKS	FO%	P/20
1	Valtteri Puustinen	1	1	2	3	3	0	1	2	4	25.00%	0	17	17.20	1	100.00%	3.49
2	Hudson Fasching	1	1	1	2	1	0	2	0	3	33.33%	1	14	14.57	0	0%	2.75
3	Mitchell Stephens	1	1	1	2	3	5	1	2	3	33.33%	0	14	14.25	0	50.00%	2.81
4	Tyson Foerster	1	1	1	2	1	0	2	2	1	100.00%	0	17	17.68	0	55.88%	2.26
5	William Lockwood	1	0	2	2	1	0	2	1	4	0%	1	13	13.48	0	0%	2.97

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
1	Daniel Vladar	1	1	0	0	0.939	3.00	60	0	0	3	49	29	0	0	0	0

	Chicago Wolves
	GP: 1 \| W: 1 \| L: 0 GF: 8 \| GA: 3 \| PP%: 0.00% \| PK%: 50.00% GM : Camil Costandi \| Morale : 94 \| Team Overall : 59 Next Games #9 vs Philadelphia Phantoms