Stanley Cup Winners Hockey League

ST-LOUIS Blues
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Career Team Stat
Web Lines Editor

Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Player Name	#	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP
1	Justin Auger	0			X		100.00	74	43	70	60	95	59	84	56	51	56	57	60	57	50	50	60	86	64	0
2	Dominic Turgeon (R)	0	X				100.00	65	42	74	61	76	60	91	58	66	57	58	68	58	50	50	68	86	64	0
3	Alexander Volkov (R)	0		X			100.00	62	45	64	60	72	61	92	60	50	58	61	60	61	50	50	44	83	63	0
4	Kyle Baun	0			X		100.00	69	43	71	60	80	58	88	55	50	57	53	60	53	50	50	42	86	62	0
5	Shane Prince	0	X	X			100.00	59	41	82	69	67	77	35	58	30	59	59	59	64	55	53	64	86	60	0
6	Taylor Beck	0		X	X		100.00	51	35	76	71	76	48	42	62	30	58	57	56	57	64	61	55	86	59	0
7	Alexander Khokhlachev	0	X	X			100.00	45	35	80	75	61	49	41	62	42	59	58	59	65	58	49	79	86	57	0
8	Rourke Chartier	0	X				100.00	52	40	79	60	45	71	45	60	67	61	58	60	58	50	50	44	86	57	0
9	Jake Dotchin (R)	0				X	100.00	79	43	80	66	81	79	65	70	30	59	63	67	68	53	54	48	84	67	0
10	Ian McCoshen (R)	0				X	100.00	75	42	83	65	83	75	57	67	31	52	63	65	74	52	51	78	86	64	0
11	Anthony DeAngelo	0				X	100.00	54	40	73	80	64	54	52	57	30	37	39	60	76	56	52	87	86	55	0
Scratches

	TEAM AVERAGE					100.00	62	41	76	66	73	63	63	60	43	56	57	61	63	53	52	61	86	61

Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Jordan Binnington	100.00	69	64	70	67	69	69	70	69	69	69	66	50	60	61	77	60	0
Scratches

	TEAM AVERAGE	100.00	69	64	70	67	69	69	70	69	69	69	66	50	60	61	77	60

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Ralph Krueger	66	66	66	66	77	77	52	CAN	59	4	81,800$

Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Player Name	Team Name	POS	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	S1	S2	S3
1	Ian McCoshen	CHICAGO Wolves (STL)	D	41	14	36	50	-2	18	88	61	133	30	81	10.53%	73	1072	26.15	7	7	14	43	88	0	46	3	0	50.00%	10	1	0.93	0	0	1	2	4
2	Rourke Chartier	CHICAGO Wolves (STL)	C	41	19	28	47	15	2	21	116	137	44	118	13.87%	24	872	21.29	0	9	9	7	48	0	19	2	0	62.75%	808	1	1.08	0	0	4	3	1
3	Alexander Volkov	CHICAGO Wolves (STL)	LW	40	15	30	45	12	14	53	60	152	53	117	9.87%	18	942	23.57	2	5	7	16	77	2	38	3	0	53.57%	140	0	0.95	0	1	4	0	3
4	Dominic Turgeon	CHICAGO Wolves (STL)	C	41	18	21	39	8	8	42	87	154	35	104	11.69%	20	980	23.91	1	5	6	11	77	2	50	0	1	62.28%	745	0	0.80	1	1	3	0	4
5	Shane Prince	CHICAGO Wolves (STL)	C/LW	41	8	26	34	-24	10	47	90	111	31	87	7.21%	39	1020	24.90	2	7	9	20	86	0	41	2	0	43.39%	620	0	0.67	0	1	2	1	0
6	Kyle Baun	CHICAGO Wolves (STL)	RW	41	13	20	33	-1	14	91	48	110	28	87	11.82%	8	938	22.90	2	4	6	16	75	0	33	2	0	57.78%	90	0	0.70	0	1	0	3	2
7	Justin Auger	CHICAGO Wolves (STL)	RW	41	12	19	31	5	30	83	67	110	35	78	10.91%	20	982	23.96	3	2	5	17	76	0	40	0	0	62.03%	187	0	0.63	0	1	0	3	2
8	Taylor Beck	CHICAGO Wolves (STL)	LW/RW	41	14	17	31	-22	2	22	55	140	44	72	10.00%	8	830	20.25	4	6	10	16	82	0	13	2	0	48.31%	118	1	0.75	0	0	0	2	0
9	Alexander Khokhlachev	CHICAGO Wolves (STL)	C/LW	41	8	19	27	-9	10	16	62	86	31	69	9.30%	30	833	20.32	3	5	8	20	67	0	3	3	0	58.07%	322	0	0.65	0	0	0	0	1
10	Jake Dotchin	CHICAGO Wolves (STL)	D	22	7	15	22	-11	12	43	35	64	17	50	10.94%	50	601	27.36	4	2	6	20	57	0	28	2	0	27.27%	22	0	0.73	0	0	2	2	1
11	Anthony DeAngelo	CHICAGO Wolves (STL)	D	41	7	11	18	-5	28	47	31	51	19	38	13.73%	49	928	22.65	4	5	9	21	71	0	43	0	0	53.85%	13	0	0.39	0	0	0	1	1
Team Total or Average				431	135	242	377	-34	148	553	712	1248	367	901	10.82%	339	10003	23.21	32	57	89	207	809	4	360	19	1	56.75%	3075	3	0.75	1	5	16	17	19

Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Goalie Name	Team Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Jordan Binnington	CHICAGO Wolves (STL)	41	19	21	1	0.908	3.01	2433	4	4	122	1323	0	4	1	0.600	5	41	0	3	0	0
Team Total or Average			41	19	21	1	0.908	3.01	2433	4	4	122	1323	0	4	1	0.600	5	41	0	3	0	0

Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

Player Name	Team Name	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Contract	Status	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Link
Alexander Khokhlachev	CHICAGO Wolves (STL)	C/LW	24	9/9/1993	No	82 Kg	178 CM	No	No	No	1	RFA	Pro & Farm	750,000$	0$	0$	No
Alexander Volkov	CHICAGO Wolves (STL)	LW	21	8/2/1997	Yes	87 Kg	185 CM	No	No	No	2	RFA	Pro & Farm	735,000$	0$	0$	No	Link
Anthony DeAngelo	CHICAGO Wolves (STL)	D	22	10/24/1995	No	83 Kg	180 CM	No	No	No	1	RFA	Pro & Farm	865,000$	0$	0$	No
Dominic Turgeon	CHICAGO Wolves (STL)	C	22	2/25/1996	Yes	91 Kg	188 CM	No	No	No	2	RFA	Pro & Farm	750,000$	0$	0$	No	Link
Ian McCoshen	CHICAGO Wolves (STL)	D	23	8/5/1995	Yes	99 Kg	191 CM	No	No	No	2	RFA	Pro & Farm	735,000$	0$	0$	No	Link
Jake Dotchin	CHICAGO Wolves (STL)	D	24	3/24/1994	Yes	95 Kg	191 CM	No	No	No	2	RFA	Pro & Farm	750,000$	0$	0$	No	Link
Jordan Binnington	CHICAGO Wolves (STL)	G	25	7/11/1993	No	76 Kg	185 CM	No	No	No	3	RFA	Pro & Farm	750,000$	0$	0$	No	Link
Justin Auger	CHICAGO Wolves (STL)	RW	24	5/14/1994	No	105 Kg	198 CM	No	No	No	1	RFA	Pro & Farm	750,000$	0$	0$	No	Link
Kyle Baun	CHICAGO Wolves (STL)	RW	26	5/4/1992	No	95 Kg	188 CM	No	No	No	1	RFA	Pro & Farm	750,000$	0$	0$	No	Link
Rourke Chartier	CHICAGO Wolves (STL)	C	22	4/3/1996	No	86 Kg	155 CM	No	No	No	2	RFA	Pro & Farm	735,000$	0$	0$	No	Link
Shane Prince	CHICAGO Wolves (STL)	C/LW	25	11/16/1992	No	88 Kg	180 CM	No	No	No	2	RFA	Pro & Farm	700,000$	0$	0$	No	Link
Taylor Beck	CHICAGO Wolves (STL)	LW/RW	27	5/13/1991	No	92 Kg	188 CM	No	No	No	1	RFA	Pro & Farm	500,000$	0$	0$	No

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
12	23.75	90 Kg	183 CM	1.67	730,833$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Alexander Volkov	Dominic Turgeon	Justin Auger	31	1	2	2
2	Taylor Beck	Shane Prince	Kyle Baun	26	1	2	2
3	Justin Auger	Alexander Khokhlachev	Dominic Turgeon	23	1	2	2
4	Alexander Volkov	Rourke Chartier	Kyle Baun	20	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Ian McCoshen	31	1	2	2
2	Anthony DeAngelo	Alexander Khokhlachev	26	1	2	2
3		Ian McCoshen	23	1	2	2
4	Anthony DeAngelo	Shane Prince	20	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Alexander Volkov	Dominic Turgeon	Justin Auger	55	1	2	2
2	Taylor Beck	Shane Prince	Kyle Baun	45	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Ian McCoshen	55	1	2	2
2	Anthony DeAngelo	Alexander Khokhlachev	45	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Justin Auger	Dominic Turgeon	55	1	2	2
2	Alexander Volkov	Kyle Baun	45	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Ian McCoshen	55	1	2	2
2	Anthony DeAngelo		45	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Justin Auger	55	1	2	2		Ian McCoshen	55	1	2	2
2	Dominic Turgeon	45	1	2	2	Anthony DeAngelo		45	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Justin Auger	Dominic Turgeon	55	1	2	2
2	Alexander Volkov	Kyle Baun	45	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Ian McCoshen	55	1	2	2
2	Anthony DeAngelo		45	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Alexander Volkov	Dominic Turgeon	Justin Auger		Ian McCoshen

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Alexander Volkov	Dominic Turgeon	Justin Auger		Ian McCoshen

Extra Forwards
Normal	PowerPlay	Penalty Kill
Rourke Chartier, Taylor Beck, Shane Prince	Rourke Chartier, Taylor Beck	Shane Prince

Extra Defensemen
Normal	PowerPlay	Penalty Kill
, Ian McCoshen, Anthony DeAngelo		Ian McCoshen, Anthony DeAngelo

Penalty Shots
Justin Auger, Dominic Turgeon, Alexander Volkov, Kyle Baun, Shane Prince

Goalie
#1 : Jordan Binnington, #2 :

Custom OT Lines Forwards
Justin Auger, Dominic Turgeon, Alexander Volkov, Kyle Baun, Shane Prince, Taylor Beck, Taylor Beck, Alexander Khokhlachev, Rourke Chartier, ,

Custom OT Lines Defensemen
, Ian McCoshen, Anthony DeAngelo, ,

Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

	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	BRIDGEPORT Sound Tigers	4	4	0	0	0	0	0	0	30	9	21	1	1	0	0	0	0	0	0	7	2	5	3	3	0	0	0	0	0	0	23	7	16	8	1.000	30	56	86	0	0	61	45	26	4	120	417	456	371	25	127	29	14	53	10	6	60.00%	6	1	83.33%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
2	BROOKLYN Wolfpack	2	2	0	0	0	0	0	0	21	0	21	1	1	0	0	0	0	0	0	12	0	12	1	1	0	0	0	0	0	0	9	0	9	4	1.000	21	38	59	0	2	61	45	26	4	223	417	456	371	25	16	8	0	22	0	0	0.00%	0	0	0.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
3	CLEVELAND Monsters	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	2	4	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	2	4	6	1	0	61	45	26	4	21	417	456	371	25	40	7	2	22	1	1	100.00%	1	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
4	COLORADO Eagles	4	1	3	0	0	0	0	0	7	14	-7	2	1	1	0	0	0	0	0	5	5	0	2	0	2	0	0	0	0	0	2	9	-7	2	0.250	7	13	20	0	1	61	45	26	4	82	417	456	371	25	130	34	22	58	10	3	30.00%	11	3	72.73%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
5	IOWA Wild	3	2	0	0	1	0	0	0	13	6	7	2	2	0	0	0	0	0	0	10	4	6	1	0	0	0	1	0	0	0	3	2	1	6	1.000	13	25	38	0	0	61	45	26	4	85	417	456	371	25	81	19	12	39	5	2	40.00%	6	1	83.33%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
6	LAVAL Rockets	2	0	2	0	0	0	0	0	4	12	-8	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	1	7	-6	0	0.000	4	7	11	0	0	61	45	26	4	53	417	456	371	25	93	16	10	33	11	2	18.18%	5	1	80.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
7	MILWAUKEE Admirals	5	2	2	0	1	0	0	0	14	13	1	3	2	1	0	0	0	0	0	12	8	4	2	0	1	0	1	0	0	0	2	5	-3	6	0.600	14	25	39	0	1	61	45	26	4	122	417	456	371	25	166	61	18	76	14	1	7.14%	9	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
8	MONT-LAURIER Sommet	2	1	1	0	0	0	0	0	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	4	5	-1	2	0.500	4	6	10	0	0	61	45	26	4	75	417	456	371	25	60	10	6	28	4	1	25.00%	2	1	50.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
9	PROVIDENCE Bruins	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	2	3	5	0	0	61	45	26	4	23	417	456	371	25	46	6	14	13	7	2	28.57%	5	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
10	PV Sharapovas	2	1	1	0	0	0	0	0	3	6	-3	1	0	1	0	0	0	0	0	1	5	-4	1	1	0	0	0	0	0	0	2	1	1	2	0.500	3	6	9	0	0	61	45	26	4	52	417	456	371	25	70	15	8	27	6	3	50.00%	3	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
11	ROCKFORD IceHogs	4	1	3	0	0	0	0	0	11	17	-6	1	1	0	0	0	0	0	0	4	1	3	3	0	3	0	0	0	0	0	7	16	-9	2	0.250	11	21	32	0	0	61	45	26	4	98	417	456	371	25	121	30	10	50	13	2	15.38%	4	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
12	SAN DIEGO Gulls	2	1	0	0	0	0	0	1	7	7	0	1	0	0	0	0	0	0	1	3	4	-1	1	1	0	0	0	0	0	0	4	3	1	3	0.750	7	11	18	0	0	61	45	26	4	84	417	456	371	25	69	12	6	26	8	3	37.50%	2	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
13	SYRACUSE Crunch	2	0	1	0	1	0	0	0	7	8	-1	1	0	0	0	1	0	0	0	4	3	1	1	0	1	0	0	0	0	0	3	5	-2	2	0.500	7	11	18	0	0	61	45	26	4	63	417	456	371	25	82	23	4	32	7	2	28.57%	1	0	100.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
14	TORONTO Marlies	1	0	1	0	0	0	0	0	1	2	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	2	-1	0	0.000	1	2	3	0	0	61	45	26	4	28	417	456	371	25	27	18	8	15	0	0	0.00%	3	1	66.67%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
15	TUSCON Roadrunners	1	0	1	0	0	0	0	0	0	4	-4	1	0	1	0	0	0	0	0	0	4	-4	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	61	45	26	4	17	417	456	371	25	37	8	0	16	3	0	0.00%	0	0	0.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
	Total	41	16	21	0	3	0	0	1	135	126	9	20	9	9	0	1	0	0	1	72	57	15	21	7	12	0	2	0	0	0	63	69	-6	39	0.476	135	244	379	1	4	61	45	26	4	1264	417	456	371	25	1337	345	152	567	120	32	26.67%	67	11	83.58%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
17	UTICA Comets	3	1	2	0	0	0	0	0	7	7	0	2	1	1	0	0	0	0	0	6	5	1	1	0	1	0	0	0	0	0	1	2	-1	2	0.333	7	13	20	0	0	61	45	26	4	65	417	456	371	25	100	35	10	29	12	4	33.33%	5	1	80.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
18	VICTORIAVILLE Tigres	2	0	2	0	0	0	0	0	2	9	-7	1	0	1	0	0	0	0	0	1	4	-3	1	0	1	0	0	0	0	0	1	5	-4	0	0.000	2	3	5	0	0	61	45	26	4	53	417	456	371	25	72	14	8	28	9	0	0.00%	4	2	50.00%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
	_Since Last GM Reset	41	16	21	0	3	0	0	1	135	126	9	20	9	9	0	1	0	0	1	72	57	15	21	7	12	0	2	0	0	0	63	69	-6	39	0.476	135	244	379	1	4	61	45	26	4	1264	417	456	371	25	1337	345	152	567	120	32	26.67%	67	11	83.58%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
	_Vs Conference	28	12	14	0	1	0	0	1	105	86	19	14	7	6	0	0	0	0	1	56	37	19	14	5	8	0	1	0	0	0	49	49	0	27	0.482	105	191	296	1	4	61	45	26	4	891	417	456	371	25	908	238	106	382	86	24	27.91%	48	6	87.50%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
	_Vs Division	18	9	10	0	1	0	0	1	47	59	-12	9	5	3	0	0	0	0	1	32	22	10	9	4	7	0	1	0	0	0	15	37	-22	21	0.583	47	87	134	0	2	61	45	26	4	440	417	456	371	25	570	158	70	251	51	8	15.69%	34	6	82.35%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
41	39	L1	135	244	379	1264	1337	345	152	567	1	4

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	16	21	3	0	0	1	135	126

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
20	9	9	1	0	0	1	72	57

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
21	7	12	2	0	0	0	63	69

Last 10 Games
W	L	OTW	OTL	SOW	SOL
6	4	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
120	32	26.67%	67	11	83.58%	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
417	456	371	25	61	45	26	4

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
705	1203	58.60%	684	1360	50.29%	384	664	57.83%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
970	695	994	280	507	253

Last Played Games Show or Hide Column

Filter Tips

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

Day	Game	Visitor Team	Score	Home Team	Score	ST	OT	SO	RI	Link
1 - 2019-01-23	6	MILWAUKEE Admirals	4	CHICAGO Wolves	5	W				BoxScore
2 - 2019-01-24	13	CHICAGO Wolves	0	COLORADO Eagles	5	L				BoxScore
4 - 2019-01-26	35	BRIDGEPORT Sound Tigers	2	CHICAGO Wolves	7	W				BoxScore
5 - 2019-01-27	39	CHICAGO Wolves	1	LAVAL Rockets	7	L				BoxScore
8 - 2019-01-30	62	COLORADO Eagles	5	CHICAGO Wolves	2	L				BoxScore
9 - 2019-01-31	76	CHICAGO Wolves	3	ROCKFORD IceHogs	5	L				BoxScore
10 - 2019-02-01	85	CHICAGO Wolves	6	BRIDGEPORT Sound Tigers	1	W				BoxScore
11 - 2019-02-02	96	SAN DIEGO Gulls	4	CHICAGO Wolves	3	L	X	X		BoxScore
13 - 2019-02-04	106	CHICAGO Wolves	4	SAN DIEGO Gulls	3	W				BoxScore
14 - 2019-02-05	123	MILWAUKEE Admirals	3	CHICAGO Wolves	1	L				BoxScore
16 - 2019-02-07	136	ROCKFORD IceHogs	1	CHICAGO Wolves	4	W				BoxScore
17 - 2019-02-08	147	CHICAGO Wolves	1	MONT-LAURIER Sommet	3	L				BoxScore
18 - 2019-02-09	157	CHICAGO Wolves	2	COLORADO Eagles	4	L				BoxScore
20 - 2019-02-11	170	IOWA Wild	2	CHICAGO Wolves	7	W				BoxScore
22 - 2019-02-13	187	LAVAL Rockets	5	CHICAGO Wolves	3	L				BoxScore
23 - 2019-02-14	197	CHICAGO Wolves	1	ROCKFORD IceHogs	4	L				BoxScore
24 - 2019-02-15	215	CHICAGO Wolves	1	TORONTO Marlies	2	L				BoxScore
25 - 2019-02-16	224	CHICAGO Wolves	3	SYRACUSE Crunch	5	L				BoxScore
27 - 2019-02-18	235	COLORADO Eagles	0	CHICAGO Wolves	3	W				BoxScore
29 - 2019-02-20	250	CHICAGO Wolves	3	IOWA Wild	2	W	X			BoxScore
30 - 2019-02-21	260	PROVIDENCE Bruins	3	CHICAGO Wolves	2	L			R3	BoxScore
32 - 2019-02-23	279	PV Sharapovas	5	CHICAGO Wolves	1	L				BoxScore
33 - 2019-02-24	290	CHICAGO Wolves	1	VICTORIAVILLE Tigres	5	L				BoxScore
34 - 2019-02-25	301	IOWA Wild	2	CHICAGO Wolves	3	W				BoxScore
36 - 2019-02-27	313	CHICAGO Wolves	1	UTICA Comets	2	L				BoxScore
37 - 2019-02-28	327	MILWAUKEE Admirals	1	CHICAGO Wolves	6	W				BoxScore
39 - 2019-03-02	340	CHICAGO Wolves	1	MILWAUKEE Admirals	0	W	X			BoxScore
41 - 2019-03-04	357	UTICA Comets	3	CHICAGO Wolves	2	L				BoxScore
42 - 2019-03-05	368	CHICAGO Wolves	1	MILWAUKEE Admirals	5	L				BoxScore
43 - 2019-03-06	378	UTICA Comets	2	CHICAGO Wolves	4	W				BoxScore
45 - 2019-03-08	392	CHICAGO Wolves	8	BRIDGEPORT Sound Tigers	3	W				BoxScore
46 - 2019-03-09	406	CHICAGO Wolves	3	MONT-LAURIER Sommet	2	W				BoxScore
48 - 2019-03-11	415	TUSCON Roadrunners	4	CHICAGO Wolves	0	L				BoxScore
49 - 2019-03-12	431	BROOKLYN Wolfpack	0	CHICAGO Wolves	12	W				BoxScore
51 - 2019-03-14	449	CHICAGO Wolves	9	BROOKLYN Wolfpack	0	W				BoxScore
52 - 2019-03-15	457	VICTORIAVILLE Tigres	4	CHICAGO Wolves	1	L				BoxScore
53 - 2019-03-16	472	CHICAGO Wolves	2	PV Sharapovas	1	W				BoxScore
54 - 2019-03-17	483	SYRACUSE Crunch	3	CHICAGO Wolves	4	W	X			BoxScore
56 - 2019-03-19	501	CHICAGO Wolves	3	ROCKFORD IceHogs	7	L				BoxScore
57 - 2019-03-20	510	CHICAGO Wolves	9	BRIDGEPORT Sound Tigers	3	W				BoxScore
58 - 2019-03-21	518	CLEVELAND Monsters	4	CHICAGO Wolves	2	L				BoxScore
61 - 2019-03-24	537	TORONTO Marlies	-	CHICAGO Wolves	-
63 - 2019-03-26	553	COLORADO Eagles	-	CHICAGO Wolves	-
64 - 2019-03-27	564	CHICAGO Wolves	-	ROCKFORD IceHogs	-
65 - 2019-03-28	575	CHICAGO Wolves	-	BELLEVILLE Senators	-
66 - 2019-03-29	588	ROCKFORD IceHogs	-	CHICAGO Wolves	-
68 - 2019-03-31	605	WILKIES-BARRIE Penguins	-	CHICAGO Wolves	-
69 - 2019-04-01	618	CHICAGO Wolves	-	CLEVELAND Monsters	-
71 - 2019-04-03	632	HOLLYWOOD Oscar	-	CHICAGO Wolves	-
72 - 2019-04-04	643	CHICAGO Wolves	-	TUSCON Roadrunners	-
73 - 2019-04-05	656	LEHIGH VALLEY Phantoms	-	CHICAGO Wolves	-
74 - 2019-04-06	667	CHICAGO Wolves	-	MILWAUKEE Admirals	-
76 - 2019-04-08	684	MANITOBA Moose	-	CHICAGO Wolves	-
77 - 2019-04-09	690	CHICAGO Wolves	-	SAN DIEGO Gulls	-
78 - 2019-04-10	703	CHICAGO Wolves	-	HOLLYWOOD Oscar	-
80 - 2019-04-12	718	BRIDGEPORT Sound Tigers	-	CHICAGO Wolves	-
81 - 2019-04-13	735	LAVAL Rockets	-	CHICAGO Wolves	-
83 - 2019-04-15	750	CHICAGO Wolves	-	LAVAL Rockets	-
84 - 2019-04-16	760	ROCKFORD IceHogs	-	CHICAGO Wolves	-
86 - 2019-04-18	774	CHICAGO Wolves	-	WILKIES-BARRIE Penguins	-
87 - 2019-04-19	783	CHICAGO Wolves	-	HERSEY Bears	-
88 - 2019-04-20	795	BELLEVILLE Senators	-	CHICAGO Wolves	-
89 - 2019-04-21	803	CHICAGO Wolves	-	WILKIES-BARRIE Penguins	-
91 - 2019-04-23	820	CORNWALL Aces	-	CHICAGO Wolves	-
94 - 2019-04-26	840	LAVAL Rockets	-	CHICAGO Wolves	-
95 - 2019-04-27	851	CHICAGO Wolves	-	COLORADO Eagles	-
96 - 2019-04-28	863	CHICAGO Wolves	-	SAN DIEGO Gulls	-
97 - 2019-04-29	878	STOCKTON Flames	-	CHICAGO Wolves	-
99 - 2019-05-01	894	CHICAGO Wolves	-	COLORADO Eagles	-
100 - 2019-05-02	904	TUSCON Roadrunners	-	CHICAGO Wolves	-
102 - 2019-05-04	920	BRIDGEPORT Sound Tigers	-	CHICAGO Wolves	-
103 - 2019-05-05	933	CHICAGO Wolves	-	STOCKTON Flames	-
105 - 2019-05-07	948	HERSEY Bears	-	CHICAGO Wolves	-
106 - 2019-05-08	959	CHICAGO Wolves	-	PROVIDENCE Bruins	-
108 - 2019-05-10	970	CHICAGO Wolves	-	MANITOBA Moose	-
109 - 2019-05-11	984	SAN DIEGO Gulls	-	CHICAGO Wolves	-
110 - 2019-05-12	991	CHICAGO Wolves	-	LEHIGH VALLEY Phantoms	-
Trade Deadline --- Trades can’t be done after this day is simulated!
112 - 2019-05-14	1006	MONT-LAURIER Sommet	-	CHICAGO Wolves	-
114 - 2019-05-16	1022	MONT-LAURIER Sommet	-	CHICAGO Wolves	-
116 - 2019-05-18	1041	CHICAGO Wolves	-	LAVAL Rockets	-
117 - 2019-05-19	1043	CHICAGO Wolves	-	CORNWALL Aces	-
119 - 2019-05-21	1066	SAN DIEGO Gulls	-	CHICAGO Wolves	-

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

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
21	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
461,558$	877,000$	877,000$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	422,002$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	62	7,990$	495,380$

Show or Hide Column

	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
28	78	51	18	0	2	3	1	3	421	150	271	39	26	5	0	2	3	1	2	216	63	153	39	25	13	0	0	0	0	1	205	87	118	102	421	710	1131	0	19	218	102	98	3	3671	1148	1240	1272	23	1779	517	263	1017	123	22	17.89%	92	16	82.61%	3	1419	2232	63.58%	836	1713	48.80%	789	1239	63.68%	2466	1962	1396	404	848	474
30	82	29	40	0	1	4	2	6	275	228	47	41	17	17	0	0	3	2	2	146	114	32	41	12	23	0	1	1	0	4	129	114	15	58	275	446	721	1	6	137	90	45	7	2535	844	826	832	66	2675	808	165	935	160	34	21.25%	62	8	87.10%	1	1009	1959	51.51%	1036	2493	41.56%	661	1278	51.72%	1929	1451	2107	519	938	457
31	82	35	37	0	3	5	1	1	283	201	82	41	18	18	0	1	3	0	1	143	102	41	41	17	19	0	2	2	1	0	140	99	41	70	283	505	788	6	12	115	103	61	4	2523	801	862	836	30	2389	680	387	1188	213	35	16.43%	141	17	87.94%	3	1231	2324	52.97%	1048	2377	44.09%	674	1282	52.57%	2031	1476	1929	536	984	504
32	41	16	21	0	3	0	0	1	135	126	9	20	9	9	0	1	0	0	1	72	57	15	21	7	12	0	2	0	0	0	63	69	-6	39	135	244	379	1	4	61	45	26	4	1264	417	456	371	25	1337	345	152	567	120	32	26.67%	67	11	83.58%	0	705	1203	58.60%	684	1360	50.29%	384	664	57.83%	970	695	994	280	507	253
Total Regular Season	283	131	116	0	9	12	4	11	1114	705	409	141	70	49	0	4	9	3	6	577	336	241	142	61	67	0	5	3	1	5	537	369	168	269	1114	1905	3019	8	41	531	340	230	18	9993	3210	3384	3311	144	8180	2350	967	3707	616	123	19.97%	362	52	85.64%	7	4364	7718	56.54%	3604	7943	45.37%	2508	4463	56.20%	7398	5586	6428	1741	3278	1688
Playoff
28	5	1	4	0	0	0	0	0	10	21	-11	3	0	3	0	0	0	0	0	4	12	-8	2	1	1	0	0	0	0	0	6	9	-3	2	10	19	29	0	0	5	3	2	0	111	32	39	40	0	226	66	14	64	15	0	0.00%	6	0	100.00%	0	41	123	33.33%	55	201	27.36%	25	80	31.25%	89	60	147	35	62	27
Total Playoff	5	1	4	0	0	0	0	0	10	21	-11	3	0	3	0	0	0	0	0	4	12	-8	2	1	1	0	0	0	0	0	6	9	-3	2	10	19	29	0	0	5	3	2	0	111	32	39	40	0	226	66	14	64	15	0	0.00%	6	0	100.00%	0	41	123	33.33%	55	201	27.36%	25	80	31.25%	89	60	147	35	62	27