Profi League - Wolves

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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	Chris Neil (C)			X		100.00	84	99	57	70	79	71	69	67	61	41	41	60	50	85	90	0	52	64	0
2	Jonathan Drouin		X	X		100.00	61	43	96	90	61	79	69	70	62	46	44	58	50	55	56	0	51	63	0
3	Alexander Semin (A)			X		100.00	57	42	65	74	58	58	68	61	62	42	40	57	61	76	79	0	45	58	0
4	Jason Akeson			X		100.00	72	68	75	66	53	70	67	60	74	40	35	58	50	45	46	0	49	56	0
5	Bill Arnold	X				100.00	78	77	76	59	59	65	63	57	73	32	35	64	50	44	44	0	51	55	0
6	Andrew Agozzino	X	X			100.00	61	66	60	73	51	67	64	62	71	42	38	57	56	44	44	0	51	55	0
7	Tyler Graovac (R)	X				100.00	65	78	63	70	59	65	63	57	74	33	33	60	50	44	44	0	55	54	0
8	Zac Dalpe	X		X		100.00	58	50	67	66	55	52	65	62	58	40	37	58	50	58	58	0	51	54	0
9	Nikita Scherbak (R)		X	X		100.00	64	66	71	72	51	51	60	56	68	35	33	60	50	44	44	0	51	53	0
10	Cory Kane	X	X			100.00	84	74	78	62	56	61	61	53	64	27	32	60	50	44	44	0	52	53	0
11	Travis Boyd (R)	X		X		100.00	65	66	71	63	51	59	61	60	69	37	38	58	50	44	44	0	51	53	0
12	Morgan Klimchuk (R)		X			100.00	67	67	72	68	52	51	60	53	59	28	30	58	50	44	44	0	51	51	0
13	Nate Schmidt				X	100.00	72	57	88	83	78	83	79	74	25	44	39	76	50	69	67	0	51	68	0
14	Trevor van Riemsdyk (R) (A)				X	100.00	68	52	84	80	68	79	78	78	25	42	39	77	50	57	62	0	51	66	0
15	Shea Theodore (R)				X	100.00	62	41	89	85	67	78	64	77	25	44	44	72	50	60	58	0	51	65	0
16	Brett Pesce (R)				X	100.00	67	42	88	77	80	89	65	71	25	44	40	76	50	52	52	0	51	65	0
17	Ryan Pulock (R)				X	100.00	66	89	85	82	78	71	76	74	25	41	43	70	50	59	59	0	54	65	0
18	David Rundblad				X	100.00	63	50	87	81	68	61	65	74	25	43	38	70	50	67	68	0	51	63	0
19	Brandon Carlo (R)				X	100.00	74	45	75	69	69	65	61	64	25	37	35	69	50	51	51	0	51	58	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	68	62	76	73	63	67	66	65	51	39	38	64	51	55	55	0	51	59

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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	Jason LaBarbera	100.00	57	71	81	69	64	67	61	65	60	61	64	62	63	0	47	64	0
2	Steve Michalek (R)	100.00	65	70	69	75	65	71	65	69	68	63	46	55	55	0	51	64	0
Rayé
1	Zane McIntyre (R)	100.00	59	71	76	69	65	71	63	66	61	62	47	58	58	0	48	63	0

	MOYENNE D'ÉQUIPE	100.00	60	71	75	71	65	70	63	67	63	62	52	58	59	0	49	64

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Mike Stothers	67	76	64	68	60	58	62	CAN	54	5	100,000$

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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	GP	G	A	P	+/-	PIM	PIM5	HIT	SHT	OSB	OSM	SHT%	SB	MP	PPG	PPA	PPP	PKG	PKM	GW	GT	FO%	FOT	GA	TA	EG	HT	P/20	PSG	PSS
1	Jonathan Drouin	Wolves (STL)		LW/RW	4	4	6	10	5	0	0	1	18	6	12	22.22%	2	23.:.21	0	0	0	0	5	0	0	50.00%	6	33	0	0	1	2.14	0	0
2	Trevor van Riemsdyk	Wolves (STL)		D	4	5	5	10	2	0	0	6	12	5	4	41.67%	6	24.:.22	0	0	0	0	5	0	0	0.00%	0	4	3	0	1	2.05	0	0
3	Nate Schmidt	Wolves (STL)		D	4	3	4	7	3	0	0	4	11	4	10	27.27%	5	25.:.30	0	1	1	0	8	1	0	0.00%	0	4	10	0	0	1.37	0	0
4	Shea Theodore	Wolves (STL)		D	4	5	2	7	7	0	0	1	21	12	12	23.81%	5	16.:.14	1	0	1	0	3	0	0	0.00%	0	7	2	0	0	2.16	0	0
5	David Rundblad	Wolves (STL)		D	4	0	6	6	12	0	0	3	7	9	3	0.00%	3	19.:.49	0	0	0	0	1	0	0	0.00%	0	5	2	0	0	1.51	0	0
6	Tyler Graovac	Wolves (STL)		C	4	0	5	5	8	10	10	5	3	2	3	0.00%	2	15.:.05	0	0	0	0	3	0	0	48.57%	35	0	0	0	0	1.66	0	0
7	Chris Neil	Wolves (STL)		RW	4	1	4	5	4	22	10	18	10	4	10	10.00%	3	19.:.02	0	0	0	0	3	0	0	57.14%	35	6	1	0	0	1.31	0	0
8	Andrew Agozzino	Wolves (STL)		C/LW	4	1	3	4	6	0	0	5	4	1	3	25.00%	2	21.:.22	0	0	0	0	0	1	0	43.86%	57	3	0	0	0	0.94	0	0
9	Brett Pesce	Wolves (STL)		D	4	1	2	3	4	0	0	3	5	7	8	20.00%	3	17.:.54	0	0	0	0	2	0	0	0.00%	0	3	3	0	0	0.84	0	0
10	Jason Akeson	Wolves (STL)		RW	3	1	2	3	5	0	0	6	5	0	1	20.00%	0	15.:.05	0	0	0	0	0	0	0	70.59%	17	1	1	0	0	1.32	0	0
11	Nikita Scherbak	Wolves (STL)		LW/RW	4	0	2	2	-1	2	0	8	2	1	2	0.00%	0	09.:.33	0	0	0	0	0	0	0	0.00%	1	1	1	0	0	1.05	0	0
12	Travis Boyd	Wolves (STL)		C/RW	4	0	2	2	3	0	0	2	2	0	1	0.00%	0	05.:.40	0	0	0	0	1	0	0	0.00%	1	2	0	0	0	1.76	0	0
13	Ryan Pulock	Wolves (STL)		D	4	1	1	2	1	5	5	3	10	1	8	10.00%	3	14.:.10	0	0	0	0	2	0	0	0.00%	0	5	2	0	0	0.71	0	0
14	Bill Arnold	Wolves (STL)		C	4	0	1	1	3	0	0	11	4	1	1	0.00%	1	16.:.59	0	0	0	0	0	0	0	33.33%	18	0	1	0	0	0.29	0	0
15	Cory Kane	Wolves (STL)		C/LW	3	1	0	1	-1	2	0	5	1	1	2	100.00%	0	10.:.37	0	0	0	0	0	0	0	33.33%	3	0	0	0	0	0.63	0	0
16	Zac Dalpe	Wolves (STL)		C/RW	4	0	1	1	5	4	0	2	2	5	2	0.00%	2	13.:.00	0	0	0	0	0	0	0	24.24%	33	0	0	0	0	0.38	0	0
17	Morgan Klimchuk	Wolves (STL)		LW	4	0	0	0	5	0	0	4	0	0	0	0.00%	0	07.:.52	0	0	0	0	2	0	0	0.00%	1	0	1	0	0	0.00	0	0
18	Brandon Carlo	Wolves (STL)		D	4	0	0	0	1	0	0	2	1	0	0	0.00%	0	01.:.58	0	0	0	0	0	0	0	0.00%	0	0	1	0	0	0.00	0	0
19	Alexander Semin	Wolves (STL)		RW	3	0	0	0	-4	4	0	5	6	3	6	0.00%	0	17.:.35	0	0	0	0	0	0	0	0.00%	3	7	0	0	0	0.00	0	0
Stats d'équipe Total ou en Moyenne		73	23	46	69	68	49	25	94	84	124	62	88	18.55%	37	1137	15.59	1	1	2	7	46	0	2	41	2	0	43.81%	210	81	28	0	2	1.21	0	0	3	0	2	2	0	3

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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	PS %	PSA	ST	BG	S2	S3
1	Jason LaBarbera	Wolves (STL)	3	1	2	0	83.1%	5.69	137	0	0	13	77	44	0	0.0%	0	3	0	0	0
2	Steve Michalek	Wolves (STL)	2	1	0	0	93.0%	1.77	102	0	0	3	43	28	1	0.0%	0	1	3	0	0
Stats d'équipe Total ou en Moyenne	5	2	2	0	0.867	4.00	240	0	0	16	120	72	1	0	0.000	4	3	0	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	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Salaire Année 2	Salaire Année 3	Salaire Année 4	Salaire Année 5	Salaire Année 6	Salaire Année 7	Salaire Année 8	Salaire Année 9	Salaire Année 10	Link
Alexander Semin	RW	32	03.03.1984	No	209 Lbs	6 ft2	No	No	1	Pro & Farm	600,000$	0$										Lien
Andrew Agozzino	C/LW	24	03.01.1992 18:52:37	No	187 Lbs	5 ft10	No	No	1	Pro & Farm	1,000,000$	0$										Lien
Bill Arnold	C	24	01.07.1992	No	218 Lbs	6 ft0	No	No	9	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Brandon Carlo	D	19	26.11.1996 0:59:04	Yes	203 Lbs	6 ft5	No	No	10	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$
Brett Pesce	D	21	15.11.1994 14:30:28	Yes	195 Lbs	6 ft3	No	No	3	Pro & Farm	1,100,000$	0$	1,100,000$	1,100,000$								Lien
Chris Neil	RW	37	18.06.1979	No	215 Lbs	6 ft1	No	No	1	Pro & Farm	1,200,000$	0$										Lien
Cory Kane	C/LW	26	15.09.1990	No	201 Lbs	6 ft1	No	No	1	Pro & Farm	600,000$	0$										Lien
David Rundblad	D	26	08.10.1990 14:40:01	No	187 Lbs	6 ft2	No	No	1	Pro & Farm	1,400,000$	0$										Lien
Jason Akeson	RW	26	03.06.1990	No	190 Lbs	5 ft10	No	No	1	Pro & Farm	600,000$	0$										Lien
Jason LaBarbera	G	36	18.02.1980	No	232 Lbs	6 ft3	No	No	1	Pro & Farm	600,000$	0$										Lien
Jonathan Drouin	LW/RW	21	27.03.1995 16:22:57	No	186 Lbs	5 ft11	No	No	2	Pro & Farm	1,400,000$	0$	1,400,000$									Lien
Morgan Klimchuk	LW	21	01.03.1995	Yes	180 Lbs	6 ft0	No	No	10	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Nate Schmidt	D	25	16.07.1991 1:54:20	No	185 Lbs	6 ft0	No	No	1	Pro & Farm	1,400,000$	0$										Lien
Nikita Scherbak	LW/RW	20	29.12.1995	Yes	175 Lbs	6 ft1	No	No	10	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Ryan Pulock	D	22	06.10.1994	Yes	212 Lbs	6 ft2	No	No	3	Pro & Farm	1,300,000$	0$	1,300,000$	1,300,000$								Lien
Shea Theodore	D	21	03.08.1995 16:27:32	Yes	182 Lbs	6 ft2	No	No	3	Pro & Farm	1,300,000$	0$	1,300,000$	1,300,000$								Lien
Steve Michalek	G	23	05.08.1993	Yes	197 Lbs	6 ft3	No	No	10	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Travis Boyd	C/RW	23	14.09.1993	Yes	185 Lbs	5 ft10	No	No	10	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Trevor van Riemsdyk	D	25	24.07.1991 16:29:39	Yes	187 Lbs	6 ft2	No	No	1	Pro & Farm	1,100,000$	0$										Lien
Tyler Graovac	C	23	27.04.1993	Yes	200 Lbs	6 ft5	No	No	8	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$			Lien
Zac Dalpe	C/RW	26	01.11.1989	No	195 Lbs	6 ft1	No	No	2	Pro & Farm	800,000$	0$	800,000$									Lien
Zane McIntyre	G	24	20.08.1992	Yes	207 Lbs	6 ft2	No	No	10	Farm Only	100,000$	0$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
22	24.77	197 Lbs	6 ft1	4.5	690,909$

Attaque à 5 contre 5
Ligne #	Centre	Ailier Gauche	Ailier Droit	% Temps	PHY	DF	OF
1	Andrew Agozzino	Jonathan Drouin	Chris Neil	40	1	2	2
2	Bill Arnold	Cory Kane	Alexander Semin	30	1	2	2
3	Zac Dalpe	Nikita Scherbak	Jason Akeson	20	1	2	2
4	Tyler Graovac	Morgan Klimchuk	Travis Boyd	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nate Schmidt	Trevor van Riemsdyk	40	1	2	2
2	Brett Pesce	Ryan Pulock	30	1	2	2
3	Shea Theodore	David Rundblad	20	1	2	2
4	Brandon Carlo	Nate Schmidt	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Centre	Ailier Gauche	Ailier Droit	% Temps	PHY	DF	OF
1	Andrew Agozzino	Jonathan Drouin	Chris Neil	60	1	2	2
2	Bill Arnold	Cory Kane	Alexander Semin	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nate Schmidt	Trevor van Riemsdyk	60	1	2	2
2	Brett Pesce	Ryan Pulock	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Chris Neil	Jonathan Drouin	60	1	2	2
2	Alexander Semin	Jason Akeson	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nate Schmidt	Trevor van Riemsdyk	60	1	2	2
2	Brett Pesce	Ryan Pulock	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Chris Neil	60	1	2	2	Nate Schmidt	Trevor van Riemsdyk	60	1	2	2
2	Jonathan Drouin	40	1	2	2	Brett Pesce	Ryan Pulock	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Chris Neil	Jonathan Drouin	60	1	2	2
2	Alexander Semin	Jason Akeson	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nate Schmidt	Trevor van Riemsdyk	60	1	2	2
2	Brett Pesce	Ryan Pulock	40	1	2	2

Attaque Dernière Minute
Centre	Ailier Gauche	Ailier Droit	Défense	Défense
Andrew Agozzino	Jonathan Drouin	Chris Neil	Nate Schmidt	Trevor van Riemsdyk

Défense Dernière Minute
Centre	Ailier Gauche	Ailier Droit	Défense	Défense
Andrew Agozzino	Jonathan Drouin	Chris Neil	Nate Schmidt	Trevor van Riemsdyk

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Zac Dalpe, Tyler Graovac, Travis Boyd	Zac Dalpe, Tyler Graovac	Travis Boyd

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Shea Theodore, David Rundblad, Brandon Carlo	Shea Theodore	David Rundblad, Brandon Carlo

Tirs de Pénalité
Chris Neil, Jonathan Drouin, Alexander Semin, Jason Akeson, Andrew Agozzino

Gardien
#1 : Jason LaBarbera, #2 : Steve Michalek

Lignes d'Attaque Perso. en Prol.
, , , , , , , , , ,

Lignes de Défense Perso. en Prol.
, , , ,

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

#	VS Équipe	GP	W	L	GF	GA	Diff	P	PCT	G	A	TP	GP1	GP2	GP3	SHF	SH1	SP2	SP3	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	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	Bears	1	0	1	3	6	-3	0	0.000	3	6	9	13	13	4	33	50	40	47	30	13	4	22	2	0	0.00%	2	1	50.00%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
2	Marlies	1	1	0	5	3	2	2	1.000	5	10	15	13	13	4	40	50	40	47	28	11	14	31	1	0	0.00%	2	0	100.00%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
3	Admirals	1	0	1	0	6	-6	0	0.000	0	0	0	13	13	4	26	50	40	47	36	6	24	18	2	0	0.00%	2	1	50.00%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
4	Heat	1	1	0	22	1	21	2	1.000	22	32	54	13	13	4	38	50	40	47	26	7	7	25	1	1	100.00%	1	0	100.00%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
	Vs Division	1	1	0	22	1	21	2	1.000	22	32	54	13	13	4	38	50	40	47	26	7	7	25	1	1	100.00%	1	0	100.00%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
	Vs Conference	1	1	0	22	1	21	2	1.000	22	32	54	13	13	4	38	50	40	47	26	7	7	25	1	1	100.00%	1	0	100.00%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
	Since Last GM Reset	4	2	2	30	16	14	4	0.500	30	48	78	13	13	4	137	50	40	47	120	37	49	96	6	1	16.67%	7	2	71.43%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34
	Total	4	2	2	30	16	14	4	0.500	30	48	78	13	13	4	137	50	40	47	120	37	49	96	6	1	16.67%	7	2	71.43%	32	68	47.06%	26	64	40.63%	38	83	45.78%	92	58	80	31	66	34

			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
4	4	L1	30	48	78	137	120	37	49	96	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	2	0	0	0	0	30	16

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
2	1	1	0	0	0	0	22	7

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
2	1	1	0	0	0	0	8	9

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
2	2	0	0	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
6	1	16.67%	7	2	71.43%	0

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
50	40	47	0	13	13	4	0

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
32	68	47.06%	26	64	40.63%	38	83	45.78%

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
92	58	80	31	66	34

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

Date		Result	Detail
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
Mon, Apr 21	@	Admirals6,Wolves0	RECAP
Tue, Apr 22	@	Heat1,Wolves22	RECAP
Fri, Apr 25	@	Wolves5,Marlies3	RECAP
Sat, Apr 26	@	Wolves3,Bears6	RECAP

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
0	0 - 0.00%	0$	0$	3000	100

Dépenses
Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,520,000$	670,000$	0$

Dépenses Annuelles à Ce Jour	Cap Salarial Par Jour	Cap salarial à ce jour
0$	0$	0$

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	0$	0$

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