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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	Robert Thomas	X		X		100.00	59	41	91	83	68	68	85	69	59	84	68	55	25	59	61	70	50	68	0
2	Warren Foegele		X	X		100.00	73	43	85	67	72	65	90	59	41	70	71	71	25	59	60	71	50	66	0
3	Zack MacEwen	X		X		100.00	89	99	68	66	80	54	72	58	44	56	80	61	25	45	45	71	50	62	0
4	Joseph LaBate	X	X			100.00	77	81	69	61	81	72	76	60	75	58	58	65	55	45	45	62	50	61	0
5	Kieffer Bellows		X			100.00	72	71	74	69	71	77	82	59	50	47	67	62	64	44	44	64	50	61	0
6	Dylan Sikura	X	X	X		100.00	66	58	86	61	58	80	84	64	80	61	62	61	59	47	47	64	50	61	0
7	Brendan Leipsic		X	X		100.00	73	52	92	71	65	53	79	64	38	61	57	63	25	63	63	62	50	61	0
8	Matt Lorito		X	X		100.00	69	61	88	63	61	61	62	59	50	57	56	60	53	44	44	60	50	57	0
9	Cole Ully		X	X		100.00	70	66	79	64	66	56	58	49	50	49	44	58	42	44	44	53	50	53	0
10	Gustav Forsling				X	100.00	73	68	84	68	68	67	70	56	25	47	48	65	46	59	59	59	50	61	0
11	Jimmy Schuldt (R)				X	100.00	76	75	77	61	75	61	64	53	25	46	45	62	43	44	44	55	50	58	1
12	Zac Leslie				X	100.00	68	64	77	63	64	70	74	54	25	52	42	59	40	44	44	55	50	58	0
13	Michael Downing				X	100.00	77	77	77	66	77	42	41	46	25	37	40	61	38	44	44	50	50	55	0
Rayé
1	J.T. Brown	X		X		100.00	65	60	75	66	60	74	79	57	71	50	54	64	51	68	71	59	50	60	0
2	Danick Martel		X			100.00	62	57	73	62	57	62	63	63	50	57	63	61	60	55	55	62	50	59	0
3	Robbie Russo				X	100.00	72	68	81	63	68	71	77	51	25	47	40	60	38	45	45	54	50	58	0

	MOYENNE D'ÉQUIPE					100.00	71	65	80	66	68	65	72	58	46	55	56	62	43	51	51	61	50	60

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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	Laurent Brossoit	100.00	59	53	53	81	65	54	53	57	62	59	45	53	53	58	50	59	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	59	53	53	81	65	54	53	57	62	59	45	53	53	58	50	59

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Martin Gelinas	41	65	73	55	45	46	54	CAN	46	1	675,000$

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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

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#	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	HT	P/20	PSG	PSS	FW	S1	S2	S3
1	Warren Foegele	Barracudas (SJS)	LW/RW	28	23	32	55	39	10	0	18	41	156	44	120	14.74%	13	615	22.00	2	3	5	14	74	1	0	1	4	106	4	0	59.46%	37	1	1.79	0	1	0	3	5	2
2	Robert Thomas	Barracudas (SJS)	C/RW	28	14	40	54	41	2	0	2	68	112	36	73	12.50%	6	626	22.39	0	2	2	13	76	0	1	1	6	117	1	0	67.19%	826	1	1.72	1	1	0	3	2	3
3	Joseph LaBate	Barracudas (SJS)	C/LW	30	22	29	51	44	28	0	69	55	167	63	115	13.17%	18	522	17.43	0	2	2	6	22	1	0	1	1	36	4	0	73.53%	408	2	1.95	0	0	0	3	1	1
4	Kieffer Bellows	Barracudas (SJS)	LW	28	25	20	45	37	20	0	25	42	166	45	134	15.06%	12	502	17.93	1	1	2	5	31	0	0	0	0	38	1	0	68.42%	38	3	1.79	0	0	0	4	4	0
5	Dylan Sikura	Barracudas (SJS)	C/LW/RW	28	19	19	38	33	18	0	25	44	126	31	80	15.08%	8	514	18.36	1	2	3	17	75	0	0	0	1	10	2	2	74.00%	400	1	1.48	0	0	0	4	1	4
6	Brendan Leipsic	Barracudas (SJS)	LW/RW	28	12	25	37	30	22	0	31	49	100	27	73	12.00%	6	542	19.37	1	2	3	6	61	0	1	1	1	56	2	0	68.97%	29	0	1.36	0	0	0	1	1	2
7	Zack MacEwen	Barracudas (SJS)	C/RW	28	20	14	34	37	45	5	89	24	90	30	98	22.22%	12	543	19.42	1	1	2	12	71	1	0	1	3	57	1	0	68.25%	63	2	1.25	1	1	1	2	2	2
8	Matt Lorito	Barracudas (SJS)	LW/RW	28	7	25	32	27	24	0	23	35	94	12	65	7.45%	6	490	17.53	1	2	3	4	63	0	0	0	2	10	3	0	63.64%	33	0	1.30	0	0	0	0	1	2
9	Cole Ully	Barracudas (SJS)	LW/RW	28	2	25	27	42	8	0	26	26	59	21	38	3.39%	6	493	17.64	0	0	0	1	16	0	0	0	0	58	1	0	56.76%	37	0	1.09	0	0	0	0	1	1
10	Gustav Forsling	Barracudas (SJS)	D	28	4	15	19	34	30	0	40	14	33	14	22	12.12%	26	606	21.66	2	2	4	21	79	0	1	1	0	103	0	0	0.00%	0	0	0.63	0	0	0	0	0	1
11	Jimmy Schuldt	Barracudas (SJS)	D	30	3	13	16	30	28	0	50	11	24	8	25	12.50%	30	534	17.80	1	2	3	11	74	0	1	1	0	94	0	0	0.00%	0	0	0.60	0	0	0	0	1	0
12	Zac Leslie	Barracudas (SJS)	D	28	4	6	10	33	24	0	39	7	11	7	15	36.36%	20	606	21.66	1	2	3	4	79	0	0	0	0	107	2	0	0.00%	0	0	0.33	0	0	0	0	0	0
13	Michael Downing	Barracudas (SJS)	D	28	2	8	10	27	24	0	41	9	23	9	16	8.70%	17	486	17.36	1	1	2	8	67	0	0	0	1	89	0	1	0.00%	0	0	0.41	0	0	0	0	0	1
14	Nic Petan	Sharks	C/LW/RW	4	2	5	7	7	2	0	3	8	14	6	16	14.29%	2	81	20.40	0	1	1	0	11	0	0	0	1	4	0	0	59.09%	66	0	1.72	0	0	0	1	0	1
Stats d'équipe Total ou en Moyenne				372	159	276	435	461	285	5	481	433	1175	353	890	13.53%	182	7167	19.27	12	23	35	122	806	3	4	7	20	890	21	3	69.33%	1937	10	1.21	2	3	1	21	19	20

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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	Laurent Brossoit	Barracudas (SJS)	28	22	3	3	0.902	1.69	1672	6	6	47	479	0	1	0	0.667	3	28	0	1	1	0
Stats d'équipe Total ou en Moyenne			28	22	3	3	0.902	1.69	1672	6	6	47	479	0	1	0	0.667	3	28	0	1	1	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	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Salaire Année 5	Link
Brendan Leipsic	Barracudas (SJS)	LW/RW	26	1994-05-19	No	180 Lbs	5 ft10	No	No	No	1	Pro & Farm	895,000$	895,000$	0$	0$	No					Lien / Lien NHL
Cole Ully	Barracudas (SJS)	LW/RW	25	1995-02-20	No	170 Lbs	6 ft0	No	No	No	3	Pro & Farm	646,667$	646,667$	0$	0$	No	646,667$	646,667$			Lien
Danick Martel	Barracudas (SJS)	LW	25	1994-12-12	No	162 Lbs	5 ft8	No	No	No	1	Pro & Farm	700,000$	700,000$	0$	0$	No					Lien / Lien NHL
Dylan Sikura	Barracudas (SJS)	C/LW/RW	25	1995-06-01	No	158 Lbs	5 ft11	No	No	No	1	Pro & Farm	1,387,500$	1,387,500$	0$	0$	No					Lien / Lien NHL
Gustav Forsling	Barracudas (SJS)	D	24	1996-06-12	No	186 Lbs	6 ft0	No	No	No	1	Pro & Farm	875,000$	875,000$	0$	0$	No					Lien / Lien NHL
J.T. Brown	Barracudas (SJS)	C/RW	30	1990-07-02	No	169 Lbs	5 ft10	No	No	No	1	Pro & Farm	1,250,000$	1,250,000$	0$	0$	No					Lien / Lien NHL
Jimmy Schuldt	Barracudas (SJS)	D	25	1995-05-11	Yes	205 Lbs	6 ft1	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$				Lien / Lien NHL
Joseph LaBate	Barracudas (SJS)	C/LW	27	1993-04-16	No	210 Lbs	6 ft5	No	No	No	2	Pro & Farm	681,000$	681,000$	0$	0$	No	681,000$				Lien / Lien NHL
Kieffer Bellows	Barracudas (SJS)	LW	22	1998-06-10	No	196 Lbs	6 ft0	No	No	No	3	Pro & Farm	1,106,666$	1,106,666$	0$	0$	No	1,106,666$	1,106,666$			Lien / Lien NHL
Laurent Brossoit	Barracudas (SJS)	G	27	1993-03-22	No	204 Lbs	6 ft3	No	No	No	5	Pro & Farm	2,300,000$	2,300,000$	0$	0$	No	2,300,000$	2,300,000$	2,300,000$	2,300,000$	Lien / Lien NHL
Matt Lorito	Barracudas (SJS)	LW/RW	30	1990-07-03	No	170 Lbs	5 ft9	No	No	No	1	Pro & Farm	800,000$	800,000$	0$	0$	No					Lien / Lien NHL
Michael Downing	Barracudas (SJS)	D	25	1995-05-19	No	205 Lbs	6 ft3	No	No	No	3	Pro & Farm	820,000$	820,000$	0$	0$	No	820,000$	820,000$			Lien
Robbie Russo	Barracudas (SJS)	D	27	1993-02-15	No	189 Lbs	6 ft0	No	No	No	0	Pro & Farm			0$	0$	No					Lien / Lien NHL
Robert Thomas	Barracudas (SJS)	C/RW	21	1999-07-02	No	188 Lbs	6 ft0	No	No	No	2	Pro & Farm	1,177,500$	1,177,500$	0$	0$	No	1,177,500$				Lien / Lien NHL
Warren Foegele	Barracudas (SJS)	LW/RW	24	1996-04-01	No	190 Lbs	6 ft2	No	No	No	1	Pro & Farm	800,000$	800,000$	0$	0$	No					Lien / Lien NHL
Zac Leslie	Barracudas (SJS)	D	26	1994-01-31	No	174 Lbs	6 ft0	No	No	No	3	Pro & Farm	616,667$	616,667$	0$	0$	No	616,667$	616,667$			Lien
Zack MacEwen	Barracudas (SJS)	C/RW	24	1996-07-08	No	205 Lbs	6 ft3	No	No	No	1	Pro & Farm	996,000$	996,000$	0$	0$	No					Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
17	25.47	186 Lbs	6 ft0	1.82	939,824$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Warren Foegele	Robert Thomas	Zack MacEwen	40	1	2	2
2	Brendan Leipsic	Dylan Sikura	Matt Lorito	30	1	2	2
3	Kieffer Bellows	Joseph LaBate	Cole Ully	20	1	2	2
4	Warren Foegele	Robert Thomas	Zack MacEwen	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Gustav Forsling	Zac Leslie	40	1	2	2
2	Jimmy Schuldt	Michael Downing	30	1	2	2
3			20	1	2	2
4	Gustav Forsling	Zac Leslie	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Warren Foegele	Robert Thomas	Zack MacEwen	60	1	2	2
2	Brendan Leipsic	Dylan Sikura	Matt Lorito	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Gustav Forsling	Zac Leslie	60	1	2	2
2	Jimmy Schuldt	Michael Downing	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Robert Thomas	Warren Foegele	60	1	2	2
2	Zack MacEwen	Brendan Leipsic	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Gustav Forsling	Zac Leslie	60	1	2	2
2	Jimmy Schuldt	Michael Downing	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Robert Thomas	60	1	2	2	Gustav Forsling	Zac Leslie	60	1	2	2
2	Warren Foegele	40	1	2	2	Jimmy Schuldt	Michael Downing	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Robert Thomas	Warren Foegele	60	1	2	2
2	Zack MacEwen	Brendan Leipsic	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Gustav Forsling	Zac Leslie	60	1	2	2
2	Jimmy Schuldt	Michael Downing	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Warren Foegele	Robert Thomas	Zack MacEwen	Gustav Forsling	Zac Leslie

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Warren Foegele	Robert Thomas	Zack MacEwen	Gustav Forsling	Zac Leslie

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Kieffer Bellows, Joseph LaBate, Cole Ully	Kieffer Bellows, Joseph LaBate	Cole Ully

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Jimmy Schuldt, Michael Downing, Gustav Forsling	Jimmy Schuldt	Michael Downing, Gustav Forsling

Tirs de Pénalité
Robert Thomas, Warren Foegele, Zack MacEwen, Brendan Leipsic, Dylan Sikura

Gardien
#1 : Laurent Brossoit, #2 :

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

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

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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

	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	Americans	3	1	1	0	0	1	0	0	12	12	0	1	0	1	0	0	0	0	0	2	4	-2	2	1	0	0	0	1	0	0	10	8	2	3	0.500	12	21	33	0	0	71	59	35	2	73	397	425	438	8	77	38	40	69	19	4	21.05%	16	2	87.50%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
2	Admirals	2	2	0	0	0	0	0	0	12	0	12	1	1	0	0	0	0	0	0	7	0	7	1	1	0	0	0	0	0	0	5	0	5	4	1.000	12	21	33	0	2	71	59	35	2	121	397	425	438	8	19	9	16	43	3	1	33.33%	7	0	100.00%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
3	Condors	2	2	0	0	0	0	0	0	7	3	4	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	2	1	1	4	1.000	7	12	19	0	0	71	59	35	2	38	397	425	438	8	56	29	36	28	14	1	7.14%	17	2	88.24%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
4	Eagles	2	2	0	0	0	0	0	0	17	4	13	1	1	0	0	0	0	0	0	11	2	9	1	1	0	0	0	0	0	0	6	2	4	4	1.000	17	26	43	0	0	71	59	35	2	114	397	425	438	8	25	13	18	40	4	0	0.00%	8	2	75.00%	1	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
5	Gulls	2	2	0	0	0	0	0	0	11	2	9	1	1	0	0	0	0	0	0	8	2	6	1	1	0	0	0	0	0	0	3	0	3	4	1.000	11	20	31	0	1	71	59	35	2	101	397	425	438	8	24	4	18	34	5	1	20.00%	8	0	100.00%	1	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
6	Heat	2	1	0	0	0	0	1	0	5	3	2	1	1	0	0	0	0	0	0	2	1	1	1	0	0	0	0	0	1	0	3	2	1	4	1.000	5	8	13	0	0	71	59	35	2	43	397	425	438	8	44	15	28	31	11	1	9.09%	14	1	92.86%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
7	IceHogs	2	0	1	0	0	1	0	0	2	9	-7	1	0	0	0	0	1	0	0	2	3	-1	1	0	1	0	0	0	0	0	0	6	-6	1	0.250	2	3	5	0	0	71	59	35	2	59	397	425	438	8	31	14	34	36	10	1	10.00%	14	5	64.29%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
8	Moose	2	1	0	0	0	1	0	0	7	6	1	1	0	0	0	0	1	0	0	2	3	-1	1	1	0	0	0	0	0	0	5	3	2	3	0.750	7	12	19	0	0	71	59	35	2	60	397	425	438	8	56	19	24	39	9	1	11.11%	12	2	83.33%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
9	Rampage	2	2	0	0	0	0	0	0	17	0	17	1	1	0	0	0	0	0	0	9	0	9	1	1	0	0	0	0	0	0	8	0	8	4	1.000	17	32	49	0	2	71	59	35	2	112	397	425	438	8	17	3	16	45	4	1	25.00%	7	0	100.00%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
10	Reign	3	2	0	0	0	1	0	0	23	8	15	2	2	0	0	0	0	0	0	19	3	16	1	0	0	0	0	1	0	0	4	5	-1	5	0.833	23	40	63	0	0	71	59	35	2	170	397	425	438	8	98	34	10	81	2	0	0.00%	5	1	80.00%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
11	Roadrunners	2	2	0	0	0	0	0	0	15	1	14	1	1	0	0	0	0	0	0	9	1	8	1	1	0	0	0	0	0	0	6	0	6	4	1.000	15	27	42	0	1	71	59	35	2	90	397	425	438	8	39	2	18	39	8	0	0.00%	7	1	85.71%	1	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
12	Stars	2	2	0	0	0	0	0	0	17	2	15	1	1	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	10	1	9	4	1.000	17	33	50	0	0	71	59	35	2	127	397	425	438	8	25	5	10	29	2	0	0.00%	4	0	100.00%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
	Total	30	22	3	0	0	4	1	0	166	56	110	15	12	1	0	0	2	0	0	94	25	69	15	10	2	0	0	2	1	0	72	31	41	50	0.833	166	294	460	0	6	71	59	35	2	1265	397	425	438	8	569	202	317	579	101	12	11.88%	140	19	86.43%	3	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
14	Wild	2	2	0	0	0	0	0	0	17	2	15	1	1	0	0	0	0	0	0	8	1	7	1	1	0	0	0	0	0	0	9	1	8	4	1.000	17	31	48	0	0	71	59	35	2	116	397	425	438	8	28	5	14	35	1	0	0.00%	7	0	100.00%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
15	Wolves	2	1	1	0	0	0	0	0	4	4	0	1	1	0	0	0	0	0	0	3	2	1	1	0	1	0	0	0	0	0	1	2	-1	2	0.500	4	8	12	0	0	71	59	35	2	41	397	425	438	8	30	12	35	30	9	1	11.11%	14	3	78.57%	0	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
	_Since Last GM Reset	30	22	3	0	0	4	1	0	166	56	110	15	12	1	0	0	2	0	0	94	25	69	15	10	2	0	0	2	1	0	72	31	41	50	0.833	166	294	460	0	6	71	59	35	2	1265	397	425	438	8	569	202	317	579	101	12	11.88%	140	19	86.43%	3	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
	_Vs Conference	30	22	3	0	0	4	1	0	166	56	110	15	12	1	0	0	2	0	0	94	25	69	15	10	2	0	0	2	1	0	72	31	41	50	0.833	166	294	460	0	6	71	59	35	2	1265	397	425	438	8	569	202	317	579	101	12	11.88%	140	19	86.43%	3	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181

			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
30	50	W1	166	294	460	1265	569	202	317	579	0	6

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
30	22	3	0	4	1	0	166	56

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	12	1	0	2	0	0	94	25

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	10	2	0	2	1	0	72	31

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
7	1	0	1	1	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
101	12	11.88%	140	19	86.43%	3

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
397	425	438	8	71	59	35	2

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
674	962	70.06%	404	659	61.31%	338	475	71.16%

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
990	782	507	164	320	181

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	Lien
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
1 - 2020-10-24	10	Reign	2	Barracudas	8	W			Sommaire du Match
2 - 2020-10-25	27	Barracudas	4	Reign	5	L	X		Sommaire du Match
3 - 2020-10-26	40	Barracudas	3	Americans	4	L	X		Sommaire du Match
4 - 2020-10-27	55	Rampage	0	Barracudas	9	W			Sommaire du Match
5 - 2020-10-28	67	Barracudas	8	Rampage	0	W			Sommaire du Match
6 - 2020-10-29	80	Barracudas	6	Roadrunners	0	W			Sommaire du Match
7 - 2020-10-30	95	IceHogs	3	Barracudas	2	L	X		Sommaire du Match
9 - 2020-11-01	110	Barracudas	7	Americans	4	W			Sommaire du Match
10 - 2020-11-02	125	Heat	1	Barracudas	2	W			Sommaire du Match
11 - 2020-11-03	143	Condors	2	Barracudas	5	W			Sommaire du Match
12 - 2020-11-04	161	Admirals	0	Barracudas	7	W			Sommaire du Match
14 - 2020-11-06	182	Barracudas	9	Wild	1	W			Sommaire du Match
15 - 2020-11-07	196	Americans	4	Barracudas	2	L			Sommaire du Match
16 - 2020-11-08	209	Barracudas	5	Moose	3	W			Sommaire du Match
17 - 2020-11-09	226	Gulls	2	Barracudas	8	W			Sommaire du Match
18 - 2020-11-10	233	Barracudas	5	Admirals	0	W			Sommaire du Match
19 - 2020-11-11	247	Barracudas	2	Condors	1	W			Sommaire du Match
20 - 2020-11-12	268	Stars	1	Barracudas	7	W			Sommaire du Match
22 - 2020-11-14	282	Barracudas	6	Eagles	2	W			Sommaire du Match
23 - 2020-11-15	293	Barracudas	0	IceHogs	6	L			Sommaire du Match
24 - 2020-11-16	306	Wolves	2	Barracudas	3	W			Sommaire du Match
25 - 2020-11-17	326	Moose	3	Barracudas	2	L	X		Sommaire du Match
26 - 2020-11-18	340	Barracudas	3	Gulls	0	W			Sommaire du Match
27 - 2020-11-19	352	Barracudas	3	Heat	2	W	X	X	Sommaire du Match
28 - 2020-11-20	369	Roadrunners	1	Barracudas	9	W			Sommaire du Match
31 - 2020-11-23	397	Reign	1	Barracudas	11	W			Sommaire du Match
32 - 2020-11-24	412	Wild	1	Barracudas	8	W			Sommaire du Match
33 - 2020-11-25	416	Barracudas	10	Stars	1	W			Sommaire du Match
34 - 2020-11-26	430	Barracudas	1	Wolves	2	L			Sommaire du Match
38 - 2020-11-30	458	Eagles	2	Barracudas	11	W			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	99	0
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
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
0$	1,597,701$	1,597,701$	0$

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

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	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
2020	30	22	3	0	0	4	1	0	166	56	110	15	12	1	0	0	2	0	0	94	25	69	15	10	2	0	0	2	1	0	72	31	41	50	166	294	460	0	6	71	59	35	2	1265	397	425	438	8	569	202	317	579	101	12	11.88%	140	19	86.43%	3	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181
Total Saison Régulière	30	22	3	0	0	4	1	0	166	56	110	15	12	1	0	0	2	0	0	94	25	69	15	10	2	0	0	2	1	0	72	31	41	50	166	294	460	0	6	71	59	35	2	1265	397	425	438	8	569	202	317	579	101	12	11.88%	140	19	86.43%	3	674	962	70.06%	404	659	61.31%	338	475	71.16%	990	782	507	164	320	181