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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	Bobby Ryan			X		100.00	82	94	74	72	79	65	68	79	56	61	72	54	63	82	84	68	50	67	0
2	Casey Mittelstadt	X	X			100.00	63	42	97	83	74	63	89	70	66	63	64	58	79	57	57	67	50	66	0
3	Kaapo Kakko (R)			X		100.00	61	41	92	82	72	67	78	74	34	65	68	61	75	52	52	68	50	66	0
4	Mathieu Perreault	X	X	X		100.00	79	43	93	79	66	64	83	72	57	63	66	62	41	70	72	68	50	66	0
5	Jack Hughes (R)	X	X			100.00	59	40	94	82	62	72	75	84	69	68	60	55	48	51	51	65	50	65	0
6	Drake Caggiula		X	X		100.00	85	77	77	82	68	61	65	59	34	62	74	67	25	64	64	71	50	65	0
7	Alexander Volkov (R)		X	X		100.00	75	71	83	64	71	81	86	63	50	62	58	67	55	55	55	65	50	63	0
8	Aleksi Saarela	X				100.00	76	70	91	66	70	76	80	63	79	61	61	66	58	47	48	65	50	63	0
9	Mitchell Stephens (R)	X				100.00	73	43	91	65	70	57	64	60	86	58	58	74	25	47	47	63	50	61	0
10	Marcus Pettersson				X	100.00	80	54	85	81	71	78	94	67	25	63	48	75	25	60	61	65	50	70	0
11	Rasmus Andersson				X	100.00	69	43	77	86	77	79	92	70	25	58	49	70	25	60	61	63	50	69	0
12	Brennan Menell				X	100.00	71	66	82	64	66	78	83	61	25	63	42	65	40	55	55	59	50	63	0
13	Oliver Kylington				X	100.00	71	42	89	79	67	62	71	58	25	49	48	62	25	55	55	58	50	62	0
14	Christian Jaros				X	100.00	76	44	86	63	76	55	72	53	25	57	47	65	25	54	54	59	50	61	0
Rayé
1	Jesse Puljujarvi			X		100.00	75	44	89	81	80	62	80	61	44	60	59	65	25	57	57	64	50	64	0
2	Rem Pitlick (R)	X	X	X		100.00	74	70	82	65	70	66	68	60	75	53	62	63	59	44	44	62	50	60	0
3	Egor Sharangovich	X				100.00	80	73	97	64	73	74	79	57	71	53	57	65	54	44	44	63	50	60	0
4	Michael Mersch		X			100.00	77	78	76	63	78	75	80	57	50	50	59	64	56	44	44	62	50	60	0
5	German Rubtsov (R)	X				100.00	72	67	85	71	67	56	58	52	65	53	46	60	44	44	44	55	50	56	0
6	Nelson Nogier				X	100.00	74	72	78	65	72	76	84	46	25	37	39	60	37	44	44	52	50	59	0

	MOYENNE D'ÉQUIPE					100.00	74	59	86	73	71	68	77	63	49	58	57	64	44	54	55	63	50	63

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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	Martin Jones	100.00	56	85	82	81	51	62	77	60	62	57	89	67	70	64	50	66	0
2	Cayden Primeau (R)	100.00	60	65	81	73	58	65	59	65	63	62	30	44	44	62	50	61	0
Rayé
1	Kevin Boyle	100.00	59	64	80	78	60	61	52	62	57	56	30	44	44	59	50	59	0
2	Matiss Kivlenieks	100.00	53	54	68	69	54	56	50	57	54	53	30	44	44	54	50	55	0

	MOYENNE D'ÉQUIPE	100.00	57	67	78	75	56	61	60	61	59	57	45	50	51	60	50	60

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Mark Dennehy	53	73	73	61	49	33	57	USA	52	2	600,000$

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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	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSS	FL	S1	S2	S3
1	Casey Mittelstadt	Checkers (CAR)	C/LW	30	39	15	54	11	14	0	110	70	180	56	144	21.67%	40	611	20.39	3	3	6	19	59	1	1	4	29	2	1	83.78%	37	6	1.77	3	0	5	2	1
2	Christian Jaros	Checkers (CAR)	D	30	7	14	21	-5	27	5	99	38	30	14	23	23.33%	53	455	15.19	0	0	0	1	6	0	0	0	0	1	0	0.00%	0	0	0.92	0	1	0	1	4
3	Nick Paul	Hurricanes	C/LW	2	0	0	0	-1	2	0	4	5	5	1	8	0.00%	0	26	13.45	0	0	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				62	46	29	75	5	43	5	213	113	215	71	175	21.40%	93	1094	17.65	3	3	6	20	65	1	1	4	30	3	1	83.78%	37	6	1.37	3	1	5	3	5

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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 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	Kevin Boyle	Checkers (CAR)	2	0	1	1	0.857	6.08	79	0	0	8	56	0	0	0	0.000	0	2	0	0	0	0
Stats d'équipe Total ou en Moyenne			2	0	1	1	0.857	6.08	79	0	0	8	56	0	0	0	0.000	0	2	0	0	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	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	Link
Aleksi Saarela	Checkers (CAR)	C	23	1997-01-07	No	198 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$			Lien / Lien NHL
Alexander Volkov	Checkers (CAR)	LW/RW	23	1997-08-02	Yes	191 Lbs	6 ft1	No	No	No	3	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$	925,000$		Lien / Lien NHL
Bobby Ryan	Checkers (CAR)	RW	33	1987-03-17	No	209 Lbs	6 ft2	No	No	No	2	Pro & Farm	7,000,000$	7,000,000$	0$	0$	No	7,000,000$			Lien / Lien NHL
Brennan Menell	Checkers (CAR)	D	23	1997-05-24	No	183 Lbs	5 ft11	No	No	No	1	Pro & Farm	716,666$	716,666$	0$	0$	No				Lien / Lien NHL
Casey Mittelstadt	Checkers (CAR)	C/LW	21	1998-11-22	No	203 Lbs	6 ft1	No	No	No	2	Pro & Farm	1,491,666$	1,491,666$	0$	0$	No	1,491,666$			Lien / Lien NHL
Cayden Primeau	Checkers (CAR)	G	21	1999-08-11	Yes	181 Lbs	6 ft3	No	No	No	3	Pro & Farm	966,666$	966,666$	0$	0$	No	966,666$	966,666$		Lien / Lien NHL
Christian Jaros	Checkers (CAR)	D	24	1996-04-02	No	201 Lbs	6 ft3	No	No	No	1	Pro & Farm	801,667$	801,667$	0$	0$	No				Lien / Lien NHL
Drake Caggiula	Checkers (CAR)	LW/RW	26	1994-06-20	No	185 Lbs	5 ft10	No	No	No	1	Pro & Farm	1,250,000$	1,250,000$	0$	0$	No				Lien / Lien NHL
Egor Sharangovich	Checkers (CAR)	C	22	1998-06-06	No	196 Lbs	6 ft2	No	No	No	3	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$	925,000$		Lien / Lien NHL
German Rubtsov	Checkers (CAR)	C	22	1998-06-27	Yes	178 Lbs	6 ft2	No	No	No	3	Pro & Farm	1,106,666$	1,106,666$	0$	0$	No	1,106,666$	1,106,666$		Lien / Lien NHL
Jack Hughes	Checkers (CAR)	C/LW	19	2001-05-13	Yes	171 Lbs	5 ft10	No	No	No	3	Pro & Farm	3,775,000$	3,775,000$	0$	0$	No	3,775,000$	3,775,000$		Lien / Lien NHL
Jesse Puljujarvi	Checkers (CAR)	RW	22	1998-05-07	No	211 Lbs	6 ft4	No	No	No	0	Pro & Farm			0$	0$	No				Lien
Kaapo Kakko	Checkers (CAR)	RW	19	2001-02-13	Yes	190 Lbs	6 ft2	No	No	No	3	Pro & Farm	3,575,000$	3,575,000$	0$	0$	No	3,575,000$	3,575,000$		Lien / Lien NHL
Kevin Boyle	Checkers (CAR)	G	28	1992-05-29	No	200 Lbs	6 ft2	No	No	No	3	Pro & Farm	975,000$	975,000$	0$	0$	No	975,000$	975,000$		Lien / Lien NHL
Marcus Pettersson	Checkers (CAR)	D	24	1996-05-08	No	180 Lbs	6 ft4	No	No	No	1	Pro & Farm	894,167$	894,167$	0$	0$	No				Lien / Lien NHL
Martin Jones	Checkers (CAR)	G	30	1990-01-10	No	195 Lbs	6 ft4	No	No	No	3	Pro & Farm	6,000,000$	6,000,000$	0$	0$	No	6,000,000$	6,000,000$		Lien / Lien NHL
Mathieu Perreault	Checkers (CAR)	C/LW/RW	32	1988-01-04	No	188 Lbs	5 ft10	No	No	No	1	Pro & Farm	4,500,000$	4,500,000$	0$	0$	No				Lien / Lien NHL
Matiss Kivlenieks	Checkers (CAR)	G	24	1996-08-25	No	178 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,066,667$	1,066,667$	0$	0$	No				Lien / Lien NHL
Michael Mersch	Checkers (CAR)	LW	28	1992-10-02	No	213 Lbs	6 ft2	No	No	No	0	Pro & Farm			0$	0$	No				Lien / Lien NHL
Mitchell Stephens	Checkers (CAR)	C	23	1997-02-05	Yes	191 Lbs	6 ft0	No	No	No	3	Pro & Farm	919,166$	919,166$	0$	0$	No	919,166$	919,166$		Lien / Lien NHL
Nelson Nogier	Checkers (CAR)	D	24	1996-05-26	No	191 Lbs	6 ft2	Yes	No	No	4	Pro & Farm	975,000$	975,000$	0$	0$	No	975,000$	975,000$	975,000$	Lien / Lien NHL
Oliver Kylington	Checkers (CAR)	D	23	1997-05-19	No	183 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,350,000$	1,350,000$	0$	0$	No				Lien / Lien NHL
Rasmus Andersson	Checkers (CAR)	D	24	1996-10-26	No	214 Lbs	6 ft1	No	No	No	4	Pro & Farm	3,900,000$	3,900,000$	0$	0$	No	3,900,000$	3,900,000$	3,900,000$	Lien / Lien NHL
Rem Pitlick	Checkers (CAR)	C/LW/RW	23	1997-04-01	Yes	196 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$			Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
24	24.21	193 Lbs	6 ft1	2.08	1,873,472$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Casey Mittelstadt			40	1	2	2
2				30	1	2	2
3				20	1	2	2
4			Casey Mittelstadt	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			40	1	2	2
2		Christian Jaros	30	1	2	2
3			20	1	2	2
4			10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Casey Mittelstadt			60	1	2	2
2				40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2		Christian Jaros	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Casey Mittelstadt	60	1	2	2
2			40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2		Christian Jaros	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		60	1	2	2			60	1	2	2
2	Casey Mittelstadt	40	1	2	2		Christian Jaros	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Casey Mittelstadt	60	1	2	2
2			40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2		Christian Jaros	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Casey Mittelstadt

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Casey Mittelstadt

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,	,

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,		,

Tirs de Pénalité
, Casey Mittelstadt, , ,

Gardien
#1 : , #2 :

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

	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	Bears	2	0	2	0	0	0	0	0	11	15	-4	1	0	1	0	0	0	0	0	7	8	-1	1	0	1	0	0	0	0	0	4	7	-3	0	0.000	11	18	29	0	0	48	30	21	0	97	295	335	308	15	89	30	6	55	3	0	0.00%	3	1	66.67%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
2	Bruins	2	0	1	0	0	1	0	0	5	12	-7	1	0	1	0	0	0	0	0	2	8	-6	1	0	0	0	0	1	0	0	3	4	-1	1	0.250	5	10	15	0	0	48	30	21	0	34	295	335	308	15	87	22	6	32	6	1	16.67%	3	2	33.33%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
3	Comets	2	0	2	0	0	0	0	0	2	15	-13	1	0	1	0	0	0	0	0	1	8	-7	1	0	1	0	0	0	0	0	1	7	-6	0	0.000	2	4	6	0	0	48	30	21	0	37	295	335	308	15	78	17	0	32	7	1	14.29%	0	0	0.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
4	Crunch	2	0	2	0	0	0	0	0	2	11	-9	1	0	1	0	0	0	0	0	2	5	-3	1	0	1	0	0	0	0	0	0	6	-6	0	0.000	2	4	6	0	0	48	30	21	0	55	295	335	308	15	66	26	4	25	9	0	0.00%	2	1	50.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
5	Devils	2	2	0	0	0	0	0	0	11	9	2	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	7	6	1	4	1.000	11	21	32	0	0	48	30	21	0	91	295	335	308	15	72	22	8	41	5	2	40.00%	4	2	50.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
6	Griffins	2	0	1	0	0	0	0	1	12	16	-4	1	0	1	0	0	0	0	0	6	9	-3	1	0	0	0	0	0	0	1	6	7	-1	1	0.250	12	22	34	0	0	48	30	21	0	65	295	335	308	15	110	29	10	53	3	0	0.00%	5	1	80.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
7	Marlies	2	0	2	0	0	0	0	0	8	15	-7	1	0	1	0	0	0	0	0	5	7	-2	1	0	1	0	0	0	0	0	3	8	-5	0	0.000	8	16	24	1	0	48	30	21	0	83	295	335	308	15	81	14	10	40	5	0	0.00%	5	2	60.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
8	Monsters	2	0	2	0	0	0	0	0	3	15	-12	1	0	1	0	0	0	0	0	1	6	-5	1	0	1	0	0	0	0	0	2	9	-7	0	0.000	3	5	8	0	0	48	30	21	0	29	295	335	308	15	81	24	8	24	9	1	11.11%	4	1	75.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
9	Penguins	2	0	2	0	0	0	0	0	2	12	-10	1	0	1	0	0	0	0	0	0	8	-8	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	2	4	6	0	0	48	30	21	0	40	295	335	308	15	61	28	12	38	4	1	25.00%	5	1	80.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
10	Phantoms	2	0	2	0	0	0	0	0	10	14	-4	1	0	1	0	0	0	0	0	6	8	-2	1	0	1	0	0	0	0	0	4	6	-2	0	0.000	10	17	27	0	0	48	30	21	0	76	295	335	308	15	85	18	6	36	6	0	0.00%	2	1	50.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
11	Rocket	2	0	2	0	0	0	0	0	2	15	-13	1	0	1	0	0	0	0	0	2	6	-4	1	0	1	0	0	0	0	0	0	9	-9	0	0.000	2	4	6	0	0	48	30	21	0	27	295	335	308	15	80	22	11	23	7	0	0.00%	2	0	100.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
12	Senators	2	2	0	0	0	0	0	0	11	7	4	1	1	0	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	4	3	1	4	1.000	11	21	32	0	0	48	30	21	0	90	295	335	308	15	78	23	4	31	5	2	40.00%	2	1	50.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
13	Sound Tigers	2	1	0	0	0	0	0	1	9	9	0	1	1	0	0	0	0	0	0	5	4	1	1	0	0	0	0	0	0	1	4	5	-1	3	0.750	9	16	25	0	0	48	30	21	0	97	295	335	308	15	61	22	12	40	6	1	16.67%	6	1	83.33%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
14	Thunderbirds	2	0	2	0	0	0	0	0	2	10	-8	1	0	1	0	0	0	0	0	2	5	-3	1	0	1	0	0	0	0	0	0	5	-5	0	0.000	2	3	5	0	0	48	30	21	0	31	295	335	308	15	49	15	8	20	10	2	20.00%	4	0	100.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
	Total	30	6	20	0	0	1	0	3	99	183	-84	15	3	11	0	0	0	0	1	53	93	-40	15	3	9	0	0	1	0	2	46	90	-44	16	0.267	99	181	280	1	0	48	30	21	0	944	295	335	308	15	1162	345	113	542	87	11	12.64%	51	15	70.59%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
16	Wolf Pack	2	1	0	0	0	0	0	1	9	8	1	1	0	0	0	0	0	0	1	3	4	-1	1	1	0	0	0	0	0	0	6	4	2	3	0.750	9	16	25	0	0	48	30	21	0	92	295	335	308	15	84	33	8	52	2	0	0.00%	4	1	75.00%	0	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
	_Since Last GM Reset	30	6	20	0	0	1	0	3	99	183	-84	15	3	11	0	0	0	0	1	53	93	-40	15	3	9	0	0	1	0	2	46	90	-44	16	0.267	99	181	280	1	0	48	30	21	0	944	295	335	308	15	1162	345	113	542	87	11	12.64%	51	15	70.59%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
	_Vs Conference	30	6	20	0	0	1	0	3	99	183	-84	15	3	11	0	0	0	0	1	53	93	-40	15	3	9	0	0	1	0	2	46	90	-44	16	0.267	99	181	280	1	0	48	30	21	0	944	295	335	308	15	1162	345	113	542	87	11	12.64%	51	15	70.59%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
	_Vs Division	14	2	9	0	0	1	0	2	55	82	-27	7	1	5	0	0	0	0	1	26	41	-15	7	1	4	0	0	1	0	1	29	41	-12	7	0.250	55	97	152	0	0	48	30	21	0	522	295	335	308	15	533	177	60	286	35	5	14.29%	28	8	71.43%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150

			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	16	W1	99	181	280	944	1162	345	113	542	1	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
30	6	20	0	1	0	3	99	183

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	3	11	0	0	0	1	53	93

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	3	9	0	1	0	2	46	90

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

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
87	11	12.64%	51	15	70.59%	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
295	335	308	15	48	30	21	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
283	715	39.58%	274	758	36.15%	198	535	37.01%

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
635	471	851	192	329	150

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	5	Checkers	1	Comets	7	L			Sommaire du Match
2 - 2020-10-25	24	Checkers	3	Bruins	4	L	X		Sommaire du Match
3 - 2020-10-26	31	Penguins	8	Checkers	0	L			Sommaire du Match
4 - 2020-10-27	57	Bears	8	Checkers	7	L			Sommaire du Match
5 - 2020-10-28	74	Rocket	6	Checkers	2	L			Sommaire du Match
6 - 2020-10-29	85	Checkers	6	Griffins	7	L	X	X	Sommaire du Match
7 - 2020-10-30	99	Comets	8	Checkers	1	L			Sommaire du Match
9 - 2020-11-01	116	Checkers	0	Crunch	6	L			Sommaire du Match
10 - 2020-11-02	129	Monsters	6	Checkers	1	L			Sommaire du Match
11 - 2020-11-03	147	Checkers	6	Wolf Pack	4	W			Sommaire du Match
12 - 2020-11-04	159	Bruins	8	Checkers	2	L			Sommaire du Match
14 - 2020-11-06	188	Marlies	7	Checkers	5	L			Sommaire du Match
15 - 2020-11-07	198	Checkers	4	Bears	7	L			Sommaire du Match
17 - 2020-11-09	221	Griffins	9	Checkers	6	L			Sommaire du Match
18 - 2020-11-10	231	Checkers	4	Phantoms	6	L			Sommaire du Match
19 - 2020-11-11	253	Devils	3	Checkers	4	W			Sommaire du Match
20 - 2020-11-12	267	Checkers	4	Sound Tigers	5	L	X	X	Sommaire du Match
22 - 2020-11-14	280	Checkers	2	Monsters	9	L			Sommaire du Match
23 - 2020-11-15	292	Senators	4	Checkers	7	W			Sommaire du Match
24 - 2020-11-16	310	Checkers	2	Penguins	4	L			Sommaire du Match
25 - 2020-11-17	320	Wolf Pack	4	Checkers	3	L	X	X	Sommaire du Match
26 - 2020-11-18	342	Phantoms	8	Checkers	6	L			Sommaire du Match
28 - 2020-11-20	361	Checkers	0	Rocket	9	L			Sommaire du Match
29 - 2020-11-21	376	Thunderbirds	5	Checkers	2	L			Sommaire du Match
30 - 2020-11-22	386	Checkers	3	Marlies	8	L			Sommaire du Match
32 - 2020-11-24	406	Sound Tigers	4	Checkers	5	W			Sommaire du Match
34 - 2020-11-26	433	Checkers	0	Thunderbirds	5	L			Sommaire du Match
36 - 2020-11-28	439	Checkers	7	Devils	6	W			Sommaire du Match
37 - 2020-11-29	445	Crunch	5	Checkers	2	L			Sommaire du Match
38 - 2020-11-30	451	Checkers	4	Senators	3	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	50	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$	4,496,336$	3,869,968$	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	6	20	0	0	1	0	3	99	183	-84	15	3	11	0	0	0	0	1	53	93	-40	15	3	9	0	0	1	0	2	46	90	-44	16	99	181	280	1	0	48	30	21	0	944	295	335	308	15	1162	345	113	542	87	11	12.64%	51	15	70.59%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150
Total Saison Régulière	30	6	20	0	0	1	0	3	99	183	-84	15	3	11	0	0	0	0	1	53	93	-40	15	3	9	0	0	1	0	2	46	90	-44	16	99	181	280	1	0	48	30	21	0	944	295	335	308	15	1162	345	113	542	87	11	12.64%	51	15	70.59%	1	283	715	39.58%	274	758	36.15%	198	535	37.01%	635	471	851	192	329	150