Stanley Cup Winners Hockey League

ST-LOUIS Blues
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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	#	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	Tom Kuhnhackl	0		X	X		100.00	67	42	83	64	81	74	67	63	59	62	61	69	64	75	66	53	99	68	0
2	Alexander Volkov	0		X			100.00	61	46	88	63	76	82	79	62	56	61	59	60	62	65	63	72	99	67	0
3	Dominic Turgeon	0	X				100.00	63	47	88	62	80	83	76	60	74	59	57	63	61	67	64	66	99	67	0
4	Otto Koivula (R)	0		X			100.00	68	47	88	61	90	78	72	62	65	60	60	64	61	63	62	59	99	67	0
5	Tommy Novak (R)	0	X				100.00	57	44	84	63	73	86	87	61	75	62	56	57	63	65	62	64	99	66	0
6	Blaine Byron	0	X				100.00	53	45	87	59	69	88	86	57	60	58	58	56	57	69	65	60	99	65	0
7	Connor Bunnaman (R)	0	X				100.00	63	43	87	60	79	78	77	58	56	57	59	61	58	63	62	61	99	65	0
8	Fabian Zetterlund (R)	0	X				100.00	53	39	89	59	60	82	81	57	62	55	56	58	59	61	63	68	99	63	0
9	Lucas Carlsson (R)	0				X	100.00	59	43	89	63	72	82	79	62	30	60	58	65	52	65	67	63	99	67	0
10	Jake Dotchin	0				X	100.00	69	53	70	59	85	73	71	58	30	57	57	60	52	71	66	59	99	65	0
11	Joe Hicketts	0				X	100.00	55	45	88	63	64	85	74	62	30	61	57	65	53	67	63	54	99	65	0
12	Mark Friedman	0				X	100.00	58	45	87	60	69	81	79	58	30	59	57	61	50	69	64	58	99	65	0
13	Sean Day (R)	0				X	100.00	75	46	87	58	88	77	76	56	30	55	53	57	45	63	62	67	99	65	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	62	45	86	61	76	81	77	60	51	59	58	61	57	66	64	62	99	66

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Stephon Williams (R)	100.00	45	52	65	77	60	47	48	49	49	50	55	60	49	71	99	49	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	45	52	65	77	60	47	48	49	49	50	55	60	49	71	99	49

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Ralph Krueger	69	69	69	74	79	79	48	CAN	61	3	400,000$

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	P/20	PSS	FT	S1	S2	S3
1	Otto Koivula	CHICAGO Wolves (STL)	LW	38	16	18	34	-6	12	0	55	67	169	49	122	9.47%	22	1001	26.36	0	2	2	21	92	0	0	0	2	51	0	1	51.83%	164	0.68	1	0	2	3	2
2	Connor Bunnaman	CHICAGO Wolves (STL)	C	38	14	15	29	-6	12	0	50	60	126	31	86	11.11%	11	947	24.94	2	0	2	16	92	0	0	0	0	0	2	1	46.34%	464	0.61	0	0	1	1	1
3	Lucas Carlsson	CHICAGO Wolves (STL)	D	38	4	25	29	-10	41	5	73	55	107	24	59	3.74%	82	954	25.13	1	4	5	54	107	0	0	0	0	52	0	1	100.00%	1	0.61	0	0	2	1	0
4	Dominic Turgeon	CHICAGO Wolves (STL)	C	38	8	20	28	-8	2	0	59	77	147	32	96	5.44%	11	1026	27.01	0	3	3	23	93	0	1	1	0	58	2	0	55.86%	759	0.55	1	0	1	1	2
5	Alexander Volkov	CHICAGO Wolves (STL)	LW	38	7	16	23	-18	12	0	57	60	154	36	112	4.55%	16	981	25.84	0	2	2	10	98	0	0	0	0	39	0	0	48.70%	115	0.47	1	0	1	1	1
6	Blaine Byron	CHICAGO Wolves (STL)	C	38	13	9	22	-12	8	0	40	56	117	37	92	11.11%	10	998	26.26	2	1	3	16	101	1	0	1	1	29	4	0	56.21%	507	0.44	0	0	2	1	1
7	Tommy Novak	CHICAGO Wolves (STL)	C	38	9	11	20	-20	12	0	42	76	123	31	87	7.32%	4	999	26.29	2	2	4	18	98	0	0	0	0	37	2	0	56.80%	743	0.40	1	0	1	2	1
8	Mark Friedman	CHICAGO Wolves (STL)	D	38	4	15	19	-9	10	0	14	42	63	16	45	6.35%	34	715	18.84	1	1	2	28	86	0	1	1	2	41	0	0	0.00%	0	0.53	0	0	0	1	2
9	Joe Hicketts	CHICAGO Wolves (STL)	D	38	3	12	15	-4	8	0	11	39	46	5	27	6.52%	34	655	17.24	0	0	0	3	14	0	0	0	0	4	0	0	0.00%	7	0.46	0	0	0	0	1
10	Sean Day	CHICAGO Wolves (STL)	D	38	2	11	13	-8	24	0	67	16	43	5	29	4.65%	33	776	20.45	0	1	1	15	90	0	0	0	1	43	1	0	100.00%	2	0.33	0	0	1	0	0
11	Jake Dotchin	CHICAGO Wolves (STL)	D	38	3	8	11	-3	27	5	56	26	29	7	13	10.34%	33	595	15.68	0	0	0	3	5	0	0	0	0	22	1	0	0.00%	0	0.37	0	1	0	1	1
12	Fabian Zetterlund	CHICAGO Wolves (STL)	C	38	8	3	11	1	2	0	16	34	53	20	38	15.09%	4	369	9.72	0	0	0	3	21	0	0	0	0	8	1	0	49.03%	155	0.60	0	0	1	1	1
13	Xavier Ouellet	ST-LOUIS Blues	D	1	0	1	1	1	0	0	2	1	1	1	2	0.00%	4	25	25.50	0	0	0	1	1	0	0	0	0	1	0	0	0.00%	0	0.78	0	0	0	0	1
Stats d'équipe Total ou en Moyenne				457	91	164	255	-102	170	10	542	609	1178	294	808	7.72%	298	10048	21.99	8	16	24	211	902	1	2	3	6	390	13	3	53.69%	2917	0.51	4	1	12	13	14

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Stephon Williams	CHICAGO Wolves (STL)	38	13	21	4	0.890	3.12	2232	2	1	116	1058	0	1	0	0.750	4	38	0	2	2	0
Stats d'équipe Total ou en Moyenne			38	13	21	4	0.890	3.12	2232	2	1	116	1058	0	1	0	0.750	4	38	0	2	2	0

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Salaire Moyen	Salaire Moyen Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Alexander Volkov	CHICAGO Wolves (STL)	LW	23	1997-08-02	No	87 Kg	185 CM	No	No	No	3	Pro & Farm	850,000$	444,495$	850,000$	444,495$	0$	0$	No	850,000$	850,000$		Lien
Blaine Byron	CHICAGO Wolves (STL)	C	25	1995-02-21	No	78 Kg	183 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$	Lien
Connor Bunnaman	CHICAGO Wolves (STL)	C	22	1998-04-16	Yes	94 Kg	185 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Dominic Turgeon	CHICAGO Wolves (STL)	C	24	1996-02-25	No	90 Kg	188 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$		Lien
Fabian Zetterlund	CHICAGO Wolves (STL)	C	21	1999-08-25	Yes	89 Kg	155 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No
Jake Dotchin	CHICAGO Wolves (STL)	D	26	1994-03-24	No	95 Kg	191 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$		Lien
Joe Hicketts	CHICAGO Wolves (STL)	D	24	1996-05-04	No	82 Kg	173 CM	No	No	No	2	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$			Lien
Lucas Carlsson	CHICAGO Wolves (STL)	D	23	1997-07-05	Yes	86 Kg	183 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Mark Friedman	CHICAGO Wolves (STL)	D	24	1995-12-25	No	84 Kg	180 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No				Lien
Otto Koivula	CHICAGO Wolves (STL)	LW	22	1998-09-01	Yes	100 Kg	193 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Sean Day	CHICAGO Wolves (STL)	D	22	1998-01-09	Yes	102 Kg	191 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Stephon Williams	CHICAGO Wolves (STL)	G	27	1993-04-28	Yes	89 Kg	191 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No
Tom Kuhnhackl	CHICAGO Wolves (STL)	LW/RW	28	1992-01-21	No	89 Kg	188 CM	No	No	No	3	Pro & Farm	850,000$	444,495$	850,000$	444,495$	0$	0$	No	850,000$	850,000$		Lien
Tommy Novak	CHICAGO Wolves (STL)	C	23	1997-04-28	Yes	81 Kg	185 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
14	23.86	89 Kg	183 CM	2.93	764,286$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Otto Koivula	Dominic Turgeon	Connor Bunnaman	31	1	2	2
2	Alexander Volkov	Tommy Novak	Blaine Byron	26	1	2	2
3	Otto Koivula	Connor Bunnaman	Dominic Turgeon	23	1	2	2
4	Alexander Volkov	Blaine Byron	Tommy Novak	20	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Lucas Carlsson	31	1	2	2
2	Sean Day	Mark Friedman	26	1	2	2
3	Joe Hicketts	Jake Dotchin	23	1	2	2
4		Lucas Carlsson	20	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Otto Koivula	Dominic Turgeon	Connor Bunnaman	55	1	2	2
2	Alexander Volkov	Tommy Novak	Blaine Byron	45	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Lucas Carlsson	55	1	2	2
2	Sean Day	Mark Friedman	45	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Otto Koivula	Dominic Turgeon	55	1	2	2
2	Alexander Volkov	Tommy Novak	45	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Lucas Carlsson	55	1	2	2
2	Sean Day	Mark Friedman	45	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Otto Koivula	55	1	2	2		Lucas Carlsson	55	1	2	2
2	Dominic Turgeon	45	1	2	2	Sean Day	Mark Friedman	45	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Otto Koivula	Dominic Turgeon	55	1	2	2
2	Alexander Volkov	Tommy Novak	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Lucas Carlsson	55	1	2	2
2	Sean Day	Mark Friedman	45	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Otto Koivula	Dominic Turgeon	Connor Bunnaman		Lucas Carlsson

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Otto Koivula	Dominic Turgeon	Connor Bunnaman		Lucas Carlsson

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Fabian Zetterlund, Connor Bunnaman, Blaine Byron	Fabian Zetterlund, Connor Bunnaman	Blaine Byron

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Joe Hicketts, Jake Dotchin, Sean Day	Joe Hicketts	Jake Dotchin, Sean Day

Tirs de Pénalité
Otto Koivula, Dominic Turgeon, Alexander Volkov, Tommy Novak, Connor Bunnaman

Gardien
#1 : Stephon Williams, #2 :

Lignes d'Attaque Perso. en Prol.
Otto Koivula, Dominic Turgeon, Alexander Volkov, Tommy Novak, Connor Bunnaman, Blaine Byron, Blaine Byron, Fabian Zetterlund, , ,

Lignes de Défense Perso. en Prol.
, Lucas Carlsson, Sean Day, Mark Friedman, Joe Hicketts

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

	Ligue												Domicile											Visiteur
#	VS Équipe	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	BELLEVILLE Senators	1	0	1	0	0	0	0	0	0	3	-3	1	0	1	0	0	0	0	0	0	3	-3	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	31	28	28	4	25	365	386	413	21	24	11	6	18	4	0	0.00%	2	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
2	BRIDGEPORT Sound Tigers	2	0	1	0	1	0	0	0	4	5	-1	1	0	0	0	1	0	0	0	3	2	1	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	4	7	11	0	0	31	28	28	4	51	365	386	413	21	55	9	14	28	10	1	10.00%	6	1	83.33%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
3	BROOKLYN Wolfpack	3	2	0	0	1	0	0	0	13	5	8	2	1	0	0	1	0	0	0	6	2	4	1	1	0	0	0	0	0	0	7	3	4	6	1.000	13	22	35	0	1	31	28	28	4	96	365	386	413	21	73	25	10	36	6	1	16.67%	5	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
4	COLORADO Eagles	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	0	0	31	28	28	4	26	365	386	413	21	28	9	6	12	3	0	0.00%	3	2	33.33%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
5	HERSEY Bears	2	1	1	0	0	0	0	0	6	5	1	1	1	0	0	0	0	0	0	3	1	2	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	6	11	17	0	0	31	28	28	4	68	365	386	413	21	48	19	4	27	7	0	0.00%	2	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
6	HOLLYWOOD Oscar	1	0	1	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	4	-1	0	0.000	3	4	7	0	0	31	28	28	4	32	365	386	413	21	30	4	10	18	3	0	0.00%	5	2	60.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
7	IOWA Wild	2	1	1	0	0	0	0	0	5	8	-3	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	5	8	-3	2	0.500	5	10	15	0	0	31	28	28	4	66	365	386	413	21	52	17	8	21	8	1	12.50%	2	0	100.00%	1	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
8	LEHIGH VALLEY Phantoms	2	1	1	0	0	0	0	0	9	14	-5	2	1	1	0	0	0	0	0	9	14	-5	0	0	0	0	0	0	0	0	0	0	0	2	0.500	9	16	25	0	0	31	28	28	4	79	365	386	413	21	75	19	8	43	6	2	33.33%	4	1	75.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
9	MONT-LAURIER Sommet	1	0	1	0	0	0	0	0	0	4	-4	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	4	-4	0	0.000	0	0	0	0	0	31	28	28	4	35	365	386	413	21	22	5	0	25	1	0	0.00%	0	0	0.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
10	Manitoba Moose	3	0	2	0	0	1	0	0	5	13	-8	1	0	0	0	0	1	0	0	2	3	-1	2	0	2	0	0	0	0	0	3	10	-7	1	0.167	5	10	15	0	0	31	28	28	4	84	365	386	413	21	116	31	22	34	12	0	0.00%	11	3	72.73%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
11	Marlies de Toronto	1	0	1	0	0	0	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	2	3	5	0	0	31	28	28	4	33	365	386	413	21	35	17	6	16	2	0	0.00%	3	1	66.67%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
12	PROVIDENCE Bruins	1	0	0	0	0	1	0	0	1	2	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	1	2	-1	1	0.500	1	2	3	0	0	31	28	28	4	31	365	386	413	21	41	6	10	27	7	1	14.29%	5	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
13	PV Sharapovas	2	0	1	0	1	0	0	0	6	10	-4	2	0	1	0	1	0	0	0	6	10	-4	0	0	0	0	0	0	0	0	0	0	0	2	0.500	6	10	16	0	0	31	28	28	4	45	365	386	413	21	63	7	10	29	7	2	28.57%	5	2	60.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
14	Quebec Nordiques	2	1	0	0	1	0	0	0	5	3	2	2	1	0	0	1	0	0	0	5	3	2	0	0	0	0	0	0	0	0	0	0	0	4	1.000	5	9	14	0	0	31	28	28	4	105	365	386	413	21	40	8	6	36	1	0	0.00%	3	1	66.67%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
15	ROCKFORD IceHogs	2	0	1	0	0	1	0	0	3	8	-5	1	0	1	0	0	0	0	0	1	5	-4	1	0	0	0	0	1	0	0	2	3	-1	1	0.250	3	5	8	0	0	31	28	28	4	67	365	386	413	21	63	19	0	24	5	0	0.00%	0	0	0.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
16	SAN DIEGO Gulls	2	2	0	0	0	0	0	0	15	3	12	2	2	0	0	0	0	0	0	15	3	12	0	0	0	0	0	0	0	0	0	0	0	4	1.000	15	29	44	0	0	31	28	28	4	101	365	386	413	21	29	7	4	22	3	0	0.00%	2	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
17	STOCKTON Flames	4	1	2	0	0	0	0	1	4	8	-4	2	1	1	0	0	0	0	0	3	4	-1	2	0	1	0	0	0	0	1	1	4	-3	3	0.375	4	8	12	0	0	31	28	28	4	92	365	386	413	21	123	45	23	55	12	0	0.00%	5	1	80.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
18	TUSCON Roadrunners	1	0	1	0	0	0	0	0	0	2	-2	1	0	1	0	0	0	0	0	0	2	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	31	28	28	4	27	365	386	413	21	23	6	2	9	2	0	0.00%	1	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
	Total	38	9	21	0	4	3	0	1	91	122	-31	20	7	8	0	4	1	0	0	56	59	-3	18	2	13	0	0	2	0	1	35	63	-28	30	0.395	91	164	255	0	1	31	28	28	4	1181	365	386	413	21	1088	298	172	542	112	8	7.14%	73	16	78.08%	1	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
20	UTICA Comets	1	0	1	0	0	0	0	0	1	4	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	4	-3	0	0.000	1	1	2	0	0	31	28	28	4	20	365	386	413	21	32	2	4	10	2	0	0.00%	2	0	100.00%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
21	VICTORIAVILLE Tigres	4	0	4	0	0	0	0	0	7	14	-7	1	0	1	0	0	0	0	0	1	3	-2	3	0	3	0	0	0	0	0	6	11	-5	0	0.000	7	13	20	0	0	31	28	28	4	98	365	386	413	21	116	32	19	52	11	0	0.00%	7	2	71.43%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
	_Since Last GM Reset	38	9	21	0	4	3	0	1	91	122	-31	20	7	8	0	4	1	0	0	56	59	-3	18	2	13	0	0	2	0	1	35	63	-28	30	0.395	91	164	255	0	1	31	28	28	4	1181	365	386	413	21	1088	298	172	542	112	8	7.14%	73	16	78.08%	1	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
	_Vs Conference	16	5	7	0	2	2	0	0	47	41	6	9	4	3	0	2	0	0	0	30	19	11	7	1	4	0	0	2	0	0	17	22	-5	16	0.500	47	84	131	0	1	31	28	28	4	520	365	386	413	21	427	119	60	211	47	3	6.38%	29	4	86.21%	0	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
	_Vs Division	12	2	3	0	1	1	0	0	22	47	-25	4	2	2	0	1	0	0	0	6	15	-9	8	0	1	0	0	1	0	0	16	32	-16	7	0.292	22	42	64	0	0	31	28	28	4	341	365	386	413	21	375	108	55	143	39	1	2.56%	23	7	69.57%	1	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241

			Total Pour les Joueurs
Matchs Joués	Points	Séquence	Buts	Passes	Points	Tirs Pour	Tirs Contre	Tirs Bloqués	Minutes de Pénalité	Mises en Échec	Buts en Filet Désert	Blanchissage
38	30	W1	91	164	255	1181	1088	298	172	542	0	1

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
38	9	21	4	3	0	1	91	122

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
20	7	8	4	1	0	0	56	59

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
18	2	13	0	2	0	1	35	63

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
3	6	0	1	0	0

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
112	8	7.14%	73	16	78.08%	1

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
365	386	413	21	31	28	28	4

Mises en Jeu
Gagnées en Zone Offensive	Total en Zone Offensive	% Gagnées en Zone Offensive	Gagnées en Zone Défensive	Total en Zone Défensive	% Gagnées en Zone Défensive	Gagnées en Zone Neutre	Total en Zone Neutre	% Gagnées en Zone Neutre
677	1252	54.07%	540	1059	50.99%	350	596	58.72%

Temps Avec la Rondelle
En Zone Offensive	Contrôle en Zone Offensive	En Zone Défensive	Contrôle en Zone Défensive	En Zone Neutre	Contrôle en Zone Neutre
935	665	890	260	476	241

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	RI	Lien
1 - 2020-08-02	9	BROOKLYN Wolfpack	2	CHICAGO Wolves	3	W	X			Sommaire du Match
3 - 2020-08-04	28	SAN DIEGO Gulls	2	CHICAGO Wolves	6	W				Sommaire du Match
5 - 2020-08-06	47	CHICAGO Wolves	1	STOCKTON Flames	2	L	X	X		Sommaire du Match
6 - 2020-08-07	54	SAN DIEGO Gulls	1	CHICAGO Wolves	9	W				Sommaire du Match
7 - 2020-08-08	72	CHICAGO Wolves	3	HOLLYWOOD Oscar	4	L				Sommaire du Match
9 - 2020-08-10	81	PV Sharapovas	4	CHICAGO Wolves	5	W	X			Sommaire du Match
11 - 2020-08-12	103	CHICAGO Wolves	3	HERSEY Bears	4	L				Sommaire du Match
12 - 2020-08-13	113	ROCKFORD IceHogs	5	CHICAGO Wolves	1	L				Sommaire du Match
13 - 2020-08-14	126	CHICAGO Wolves	2	IOWA Wild	6	L				Sommaire du Match
14 - 2020-08-15	139	BRIDGEPORT Sound Tigers	2	CHICAGO Wolves	3	W	X			Sommaire du Match
16 - 2020-08-17	152	CHICAGO Wolves	1	Manitoba Moose	4	L				Sommaire du Match
17 - 2020-08-18	163	STOCKTON Flames	1	CHICAGO Wolves	2	W				Sommaire du Match
19 - 2020-08-20	179	CHICAGO Wolves	4	VICTORIAVILLE Tigres	5	L				Sommaire du Match
20 - 2020-08-21	191	Quebec Nordiques	2	CHICAGO Wolves	3	W				Sommaire du Match
21 - 2020-08-22	206	CHICAGO Wolves	1	VICTORIAVILLE Tigres	3	L				Sommaire du Match
22 - 2020-08-23	213	COLORADO Eagles	4	CHICAGO Wolves	2	L				Sommaire du Match
24 - 2020-08-25	236	TUSCON Roadrunners	2	CHICAGO Wolves	0	L				Sommaire du Match
25 - 2020-08-26	251	CHICAGO Wolves	2	Manitoba Moose	6	L				Sommaire du Match
26 - 2020-08-27	260	Manitoba Moose	3	CHICAGO Wolves	2	L	X			Sommaire du Match
27 - 2020-08-28	270	CHICAGO Wolves	7	BROOKLYN Wolfpack	3	W				Sommaire du Match
29 - 2020-08-30	286	CHICAGO Wolves	1	VICTORIAVILLE Tigres	3	L				Sommaire du Match
30 - 2020-08-31	293	STOCKTON Flames	3	CHICAGO Wolves	1	L				Sommaire du Match
32 - 2020-09-02	313	CHICAGO Wolves	2	Marlies de Toronto	3	L				Sommaire du Match
33 - 2020-09-03	319	VICTORIAVILLE Tigres	3	CHICAGO Wolves	1	L				Sommaire du Match
34 - 2020-09-04	335	CHICAGO Wolves	2	ROCKFORD IceHogs	3	L	X			Sommaire du Match
35 - 2020-09-05	344	Quebec Nordiques	1	CHICAGO Wolves	2	W	X			Sommaire du Match
37 - 2020-09-07	361	CHICAGO Wolves	0	STOCKTON Flames	2	L				Sommaire du Match
38 - 2020-09-08	370	LEHIGH VALLEY Phantoms	4	CHICAGO Wolves	6	W				Sommaire du Match
40 - 2020-09-10	388	PV Sharapovas	6	CHICAGO Wolves	1	L				Sommaire du Match
41 - 2020-09-11	403	CHICAGO Wolves	3	IOWA Wild	2	W				Sommaire du Match
42 - 2020-09-12	415	BELLEVILLE Senators	3	CHICAGO Wolves	0	L				Sommaire du Match
44 - 2020-09-14	426	CHICAGO Wolves	1	UTICA Comets	4	L				Sommaire du Match
46 - 2020-09-16	443	LEHIGH VALLEY Phantoms	10	CHICAGO Wolves	3	L				Sommaire du Match
47 - 2020-09-17	456	CHICAGO Wolves	1	BRIDGEPORT Sound Tigers	3	L				Sommaire du Match
48 - 2020-09-18	467	CHICAGO Wolves	1	PROVIDENCE Bruins	2	L	X		R3	Sommaire du Match
49 - 2020-09-19	476	HERSEY Bears	1	CHICAGO Wolves	3	W				Sommaire du Match
50 - 2020-09-20	492	CHICAGO Wolves	0	MONT-LAURIER Sommet	4	L				Sommaire du Match
52 - 2020-09-22	502	BROOKLYN Wolfpack	0	CHICAGO Wolves	3	W				Sommaire du Match
54 - 2020-09-24	522	UTICA Comets	-	CHICAGO Wolves	-
55 - 2020-09-25	534	CHICAGO Wolves	-	TUSCON Roadrunners	-
56 - 2020-09-26	544	CHICAGO Wolves	-	MONT-LAURIER Sommet	-
57 - 2020-09-27	553	SAN DIEGO Gulls	-	CHICAGO Wolves	-
58 - 2020-09-28	571	BRIDGEPORT Sound Tigers	-	CHICAGO Wolves	-
60 - 2020-09-30	584	CHICAGO Wolves	-	Quebec Nordiques	-
61 - 2020-10-01	597	CHICAGO Wolves	-	PV Sharapovas	-
62 - 2020-10-02	606	HOLLYWOOD Oscar	-	CHICAGO Wolves	-
64 - 2020-10-04	626	MONT-LAURIER Sommet	-	CHICAGO Wolves	-
65 - 2020-10-05	638	CHICAGO Wolves	-	TUSCON Roadrunners	-
66 - 2020-10-06	647	CHICAGO Wolves	-	HOLLYWOOD Oscar	-
67 - 2020-10-07	655	CHICAGO Wolves	-	MILWAUKEE Admirals	-
68 - 2020-10-08	666	ROCKFORD IceHogs	-	CHICAGO Wolves	-
69 - 2020-10-09	681	CHICAGO Wolves	-	LAVAL Rockets	-
70 - 2020-10-10	690	WILKIES-BARRIE Penguins	-	CHICAGO Wolves	-
72 - 2020-10-12	707	SAN DIEGO Gulls	-	CHICAGO Wolves	-
73 - 2020-10-13	726	CHICAGO Wolves	-	MILWAUKEE Admirals	-
74 - 2020-10-14	733	IOWA Wild	-	CHICAGO Wolves	-
76 - 2020-10-16	748	CHICAGO Wolves	-	HERSEY Bears	-
77 - 2020-10-17	756	CHICAGO Wolves	-	WILKIES-BARRIE Penguins	-
78 - 2020-10-18	766	Marlies de Toronto	-	CHICAGO Wolves	-
79 - 2020-10-19	780	CHICAGO Wolves	-	Binghampton Devils	-
80 - 2020-10-20	791	Binghampton Devils	-	CHICAGO Wolves	-
82 - 2020-10-22	811	CHICAGO Wolves	-	Marlies de Toronto	-
83 - 2020-10-23	816	MILWAUKEE Admirals	-	CHICAGO Wolves	-
85 - 2020-10-25	839	UTICA Comets	-	CHICAGO Wolves	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
87 - 2020-10-27	861	MONT-LAURIER Sommet	-	CHICAGO Wolves	-
88 - 2020-10-28	869	CHICAGO Wolves	-	Syracruse Crunch	-
89 - 2020-10-29	884	CHICAGO Wolves	-	COLORADO Eagles	-
90 - 2020-10-30	892	CHICAGO Wolves	-	LEHIGH VALLEY Phantoms	-
91 - 2020-10-31	897	PROVIDENCE Bruins	-	CHICAGO Wolves	-
92 - 2020-11-01	914	CHICAGO Wolves	-	COLORADO Eagles	-
93 - 2020-11-02	921	LAVAL Rockets	-	CHICAGO Wolves	-
94 - 2020-11-03	936	CHICAGO Wolves	-	LAVAL Rockets	-
96 - 2020-11-05	948	PROVIDENCE Bruins	-	CHICAGO Wolves	-
97 - 2020-11-06	965	CHICAGO Wolves	-	BELLEVILLE Senators	-
99 - 2020-11-08	975	Syracruse Crunch	-	CHICAGO Wolves	-
101 - 2020-11-10	993	CHICAGO Wolves	-	WILKIES-BARRIE Penguins	-
102 - 2020-11-11	996	Syracruse Crunch	-	CHICAGO Wolves	-
103 - 2020-11-12	1001	CHICAGO Wolves	-	BELLEVILLE Senators	-
105 - 2020-11-14	1020	Binghampton Devils	-	CHICAGO Wolves	-
108 - 2020-11-17	1040	CHICAGO Wolves	-	SAN DIEGO Gulls	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	15
Assistance	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
20	0 - 0.00%	0$	0$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
514,061$	1,070,000$	1,070,000$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	510,392$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	57	9,817$	559,569$

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	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
Saison Régulière
33	82	28	47	0	2	2	1	2	315	323	-8	41	14	23	0	0	2	1	1	152	157	-5	41	14	24	0	2	0	0	1	163	166	-3	56	315	542	857	1	9	144	94	74	4	3047	989	1012	1028	28	2849	836	265	1169	221	37	16.74%	107	35	67.29%	1	1385	2373	58.36%	918	2121	43.28%	712	1359	52.39%	2096	1605	1955	479	893	452
34	38	9	21	0	4	3	0	1	91	122	-31	20	7	8	0	4	1	0	0	56	59	-3	18	2	13	0	0	2	0	1	35	63	-28	30	91	164	255	0	1	31	28	28	4	1181	365	386	413	21	1088	298	172	542	112	8	7.14%	73	16	78.08%	1	677	1252	54.07%	540	1059	50.99%	350	596	58.72%	935	665	890	260	476	241
Total Saison Régulière	120	37	68	0	6	5	1	3	406	445	-39	61	21	31	0	4	3	1	1	208	216	-8	59	16	37	0	2	2	0	2	198	229	-31	86	406	706	1112	1	10	175	122	102	8	4228	1354	1398	1441	49	3937	1134	437	1711	333	45	13.51%	180	51	71.67%	2	2062	3625	56.88%	1458	3180	45.85%	1062	1955	54.32%	3032	2271	2846	740	1369	693