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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	Anthony Duclair		X	X		100.00	63	41	93	87	69	64	82	70	29	65	77	56	84	66	66	72	50	67	0
2	Andre Burakovsky		X	X		100.00	65	42	93	86	76	59	83	60	35	63	70	63	41	67	69	69	50	66	0
3	Kyle Rau	X	X			100.00	69	61	88	71	61	84	89	64	80	59	65	63	62	48	49	66	50	64	0
4	Jack Roslovic	X		X		100.00	63	41	98	79	69	55	76	63	49	65	62	53	25	59	60	64	50	62	0
5	Brad Malone	X	X			100.00	87	46	91	66	76	53	77	60	58	50	55	79	25	65	66	63	50	62	0
6	Christoffer Ehn (R)	X	X	X		100.00	77	44	96	64	70	57	79	55	53	60	57	78	25	50	50	65	50	61	0
7	Taro Hirose (R)		X			100.00	57	39	93	63	59	70	48	62	25	90	59	62	25	44	44	65	50	61	0
8	Vinni Lettieri	X		X		100.00	80	44	84	72	70	56	90	62	52	57	56	61	25	47	47	62	50	61	0
9	Matthew Highmore		X	X		100.00	71	66	84	70	66	62	62	65	50	65	62	63	59	44	44	65	50	61	0
10	Tyrell Goulbourne (R)		X	X		100.00	63	70	46	64	70	77	84	53	50	47	54	56	51	44	44	56	50	56	0
11	Jordan Subban				X	100.00	69	62	84	68	62	63	66	51	25	46	41	59	39	46	46	54	50	57	0
12	Blake Hillman (R)				X	100.00	74	68	89	59	68	61	66	43	25	32	39	59	37	44	44	50	50	55	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	70	52	87	71	68	63	75	59	44	58	58	63	42	52	52	63	50	61

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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	Oscar Dansk	100.00	60	61	76	79	60	62	59	66	62	61	30	44	44	61	50	61	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	60	61	76	79	60	62	59	66	62	61	30	44	44	61	50	61

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Troy Mann	38	56	61	38	57	47	50	CAN	47	3	700,000$

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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	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	S1	S2	S3
1	Anthony Duclair	Heat (CGY)	LW/RW	8	12	14	26	22	2	1	14	51	16	40	23.53%	3	175	21.89	0	2	2	2	20	0	0	0	0	9	2	1	78.95%	38	3	2.97	2	0	3
2	Andre Burakovsky	Heat (CGY)	LW/RW	8	11	12	23	22	2	11	11	54	17	43	20.37%	3	167	20.97	1	0	1	3	22	0	0	0	0	0	2	0	60.00%	15	1	2.74	1	2	1
3	Jack Roslovic	Heat (CGY)	C/RW	8	4	16	20	18	0	7	12	29	3	21	13.79%	1	146	18.29	0	0	0	2	21	0	0	0	0	1	1	0	69.14%	162	0	2.73	0	2	0
4	Taro Hirose	Heat (CGY)	LW	8	2	17	19	17	2	1	10	41	9	27	4.88%	1	154	19.32	0	2	2	2	19	0	0	0	0	3	1	0	54.55%	11	0	2.46	0	1	0
5	Christoffer Ehn	Heat (CGY)	C/LW/RW	8	7	11	18	16	2	9	22	45	14	27	15.56%	7	155	19.48	2	0	2	4	18	1	0	1	2	15	0	1	79.74%	153	0	2.31	0	1	0
6	Kyle Rau	Heat (CGY)	C/LW	8	8	9	17	18	0	7	11	55	16	47	14.55%	0	169	21.18	1	2	3	9	20	0	0	0	1	14	0	0	75.00%	44	0	2.01	2	0	0
7	Brad Malone	Heat (CGY)	C/LW	8	9	7	16	18	2	13	12	67	15	26	13.43%	0	140	17.59	0	0	0	1	4	0	0	0	3	5	2	0	76.86%	121	1	2.27	0	1	3
8	Matthew Highmore	Heat (CGY)	LW/RW	8	9	6	15	15	0	4	9	45	10	29	20.00%	1	125	15.75	1	0	1	1	2	0	0	0	0	0	0	0	88.89%	9	1	2.38	2	0	0
9	Vinni Lettieri	Heat (CGY)	C/RW	8	5	7	12	16	8	12	10	38	16	27	13.16%	0	143	17.92	0	0	0	0	7	0	1	1	3	13	0	0	36.36%	11	1	1.67	1	1	0
10	Jordan Subban	Heat (CGY)	D	8	1	7	8	15	10	14	4	10	2	3	10.00%	6	168	21.00	0	3	3	5	21	0	0	0	0	10	0	0	50.00%	2	0	0.95	0	0	0
11	Tyrell Goulbourne	Heat (CGY)	LW/RW	8	2	4	6	16	6	17	2	9	5	8	22.22%	3	146	18.34	1	0	1	2	21	0	0	0	0	0	0	0	100.00%	1	0	0.82	0	0	1
12	Steven Santini	Flames	D	3	0	4	4	9	0	6	2	7	1	3	0.00%	2	62	20.71	0	0	0	4	7	0	0	0	0	3	0	0	0.00%	0	0	1.29	0	0	0
13	Blake Hillman	Heat (CGY)	D	8	0	3	3	11	2	9	0	6	1	7	0.00%	5	139	17.45	0	1	1	1	22	0	0	0	0	10	0	0	0.00%	0	0	0.43	0	0	0
Stats d'équipe Total ou en Moyenne				99	70	117	187	213	36	111	119	457	125	308	15.32%	32	1895	19.15	6	10	16	36	210	1	1	2	9	86	8	2	73.90%	567	7	1.97	8	8	8

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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	Oscar Dansk	Heat (CGY)	8	8	0	0	0.919	1.25	480	0	2	10	123	0	2	0	0.000	0	8	0	0	0	0
Stats d'équipe Total ou en Moyenne			8	8	0	0	0.919	1.25	480	0	2	10	123	0	2	0	0.000	0	8	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	Status	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Link
Andre Burakovsky	Heat (CGY)	LW/RW	24	1995-02-09	No	201 Lbs	6 ft3	No	No	No	1	Avec Restriction	Pro & Farm	800,000$	0$	0$	No	Lien
Anthony Duclair	Heat (CGY)	LW/RW	24	1995-08-25	No	191 Lbs	5 ft11	No	No	No	1	Avec Restriction	Pro & Farm	1,650,000$	0$	0$	No	Lien
Blake Hillman	Heat (CGY)	D	23	1996-01-26	Yes	181 Lbs	6 ft1	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Brad Malone	Heat (CGY)	C/LW	30	1989-05-20	No	207 Lbs	6 ft2	No	No	No	3	Sans Restriction	Pro & Farm	650,000$	0$	0$	No	Lien
Christoffer Ehn	Heat (CGY)	C/LW/RW	23	1996-04-05	Yes	181 Lbs	6 ft3	No	No	No	3	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Jack Roslovic	Heat (CGY)	C/RW	22	1997-01-28	No	187 Lbs	6 ft1	No	No	No	2	Avec Restriction	Pro & Farm	1,106,666$	0$	0$	No	Lien
Jordan Subban	Heat (CGY)	D	24	1995-03-03	No	175 Lbs	5 ft9	No	No	No	2	Avec Restriction	Pro & Farm	755,000$	0$	0$	No	Lien
Kyle Rau	Heat (CGY)	C/LW	27	1992-10-24	No	176 Lbs	5 ft8	No	No	No	1	Avec Restriction	Pro & Farm	700,000$	0$	0$	No	Lien
Matthew Highmore	Heat (CGY)	LW/RW	23	1996-02-27	No	181 Lbs	5 ft11	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Oscar Dansk	Heat (CGY)	G	25	1994-02-28	No	195 Lbs	6 ft3	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Taro Hirose	Heat (CGY)	LW	23	1996-06-30	Yes	161 Lbs	5 ft10	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Tyrell Goulbourne	Heat (CGY)	LW/RW	25	1994-01-25	Yes	195 Lbs	5 ft11	No	No	No	3	Avec Restriction	Pro & Farm	773,333$	0$	0$	No	Lien
Vinni Lettieri	Heat (CGY)	C/RW	24	1995-02-06	No	195 Lbs	5 ft11	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
13	24.38	187 Lbs	6 ft0	2.00	921,923$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Anthony Duclair	Jack Roslovic	Andre Burakovsky	48	0	0	5
2	Taro Hirose	Brad Malone	Kyle Rau	22	0	0	5
3	Matthew Highmore	Christoffer Ehn	Vinni Lettieri	20	0	0	5
4	Tyrell Goulbourne	Anthony Duclair	Andre Burakovsky	10	0	0	5

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Jordan Subban	40	1	2	2
2	Blake Hillman	Tyrell Goulbourne	30	1	2	2
3			20	1	2	2
4		Jordan Subban	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Anthony Duclair	Jack Roslovic	Andre Burakovsky	60	0	0	5
2	Taro Hirose	Kyle Rau	Christoffer Ehn	40	0	0	5

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Jordan Subban	60	0	0	5
2	Blake Hillman	Tyrell Goulbourne	40	0	0	5

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Christoffer Ehn	Vinni Lettieri	60	0	5	0
2	Kyle Rau	Brad Malone	40	0	5	0

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Jordan Subban	60	0	5	0
2	Blake Hillman		40	0	5	0

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Christoffer Ehn	60	1	2	2		Jordan Subban	60	1	2	2
2	Kyle Rau	40	1	2	2	Blake Hillman		40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Anthony Duclair	Andre Burakovsky	60	1	2	2
2	Kyle Rau	Brad Malone	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Jordan Subban	60	1	2	2
2	Blake Hillman		40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Anthony Duclair	Jack Roslovic	Andre Burakovsky		Jordan Subban

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Anthony Duclair	Kyle Rau	Andre Burakovsky		Jordan Subban

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Christoffer Ehn, Taro Hirose, Vinni Lettieri	Christoffer Ehn, Taro Hirose	Vinni Lettieri

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Blake Hillman, , Jordan Subban	Blake Hillman	, Jordan Subban

Tirs de Pénalité
Anthony Duclair, Andre Burakovsky, Kyle Rau, Brad Malone, Jack Roslovic

Gardien
#1 : Oscar Dansk, #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	Barracudas	1	1	0	0	0	0	0	0	8	2	6	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	8	2	6	2	1.000	8	12	20	0	0	33	22	15	0	49	141	168	150	0	18	6	8	24	7	0	0.00%	4	2	50.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
2	Comets	1	1	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	5	1	4	0	0	0	0	0	0	0	0	0	0	0	2	1.000	5	9	14	0	0	33	22	15	0	30	141	168	150	0	22	6	4	13	7	1	14.29%	2	0	100.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
3	Eagles	1	1	0	0	0	0	0	0	16	0	16	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	16	0	16	2	1.000	16	26	42	0	1	33	22	15	0	76	141	168	150	0	15	3	0	17	1	0	0.00%	0	0	0.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
4	Griffins	1	1	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	6	2	4	0	0	0	0	0	0	0	0	0	0	0	2	1.000	6	10	16	0	0	33	22	15	0	38	141	168	150	0	17	4	6	8	4	1	25.00%	3	0	100.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
5	Phantoms	1	1	0	0	0	0	0	0	11	1	10	1	1	0	0	0	0	0	0	11	1	10	0	0	0	0	0	0	0	0	0	0	0	2	1.000	11	19	30	0	0	33	22	15	0	72	141	168	150	0	12	1	6	20	2	1	50.00%	3	0	100.00%	1	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
6	Reign	1	1	0	0	0	0	0	0	11	0	11	1	1	0	0	0	0	0	0	11	0	11	0	0	0	0	0	0	0	0	0	0	0	2	1.000	11	18	29	0	1	33	22	15	0	87	141	168	150	0	4	3	2	17	2	0	0.00%	1	0	100.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
7	Stars	1	1	0	0	0	0	0	0	9	3	6	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	9	3	6	2	1.000	9	17	26	0	0	33	22	15	0	74	141	168	150	0	19	5	8	25	2	1	50.00%	3	0	100.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
	Total	8	8	0	0	0	0	0	0	70	10	60	4	4	0	0	0	0	0	0	33	4	29	4	4	0	0	0	0	0	0	37	6	31	16	1.000	70	117	187	0	2	33	22	15	0	459	141	168	150	0	123	34	38	132	28	6	21.43%	18	2	88.89%	1	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
9	Wolves	1	1	0	0	0	0	0	0	4	1	3	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	1	3	2	1.000	4	6	10	0	0	33	22	15	0	33	141	168	150	0	16	6	4	8	3	2	66.67%	2	0	100.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
	_Since Last GM Reset	8	8	0	0	0	0	0	0	70	10	60	4	4	0	0	0	0	0	0	33	4	29	4	4	0	0	0	0	0	0	37	6	31	16	1.000	70	117	187	0	2	33	22	15	0	459	141	168	150	0	123	34	38	132	28	6	21.43%	18	2	88.89%	1	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
	_Vs Conference	5	5	0	0	0	0	0	0	48	6	42	1	1	0	0	0	0	0	0	11	0	11	4	4	0	0	0	0	0	0	37	6	31	10	1.000	48	79	127	0	2	33	22	15	0	319	141	168	150	0	72	23	22	91	15	3	20.00%	10	2	80.00%	0	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47

			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
8	16	W8	70	117	187	459	123	34	38	132	0	2

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	8	0	0	0	0	0	70	10

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	4	0	0	0	0	0	33	4

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	4	0	0	0	0	0	37	6

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
8	0	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
28	6	21.43%	18	2	88.89%	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
141	168	150	0	33	22	15	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
217	292	74.32%	98	140	70.00%	105	137	76.64%

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
282	233	119	35	77	47

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	Lien
2 - 2019-10-05	7	Heat	16	Eagles	0	W	Sommaire du Match
4 - 2019-10-07	21	Comets	1	Heat	5	W	Sommaire du Match
7 - 2019-10-10	36	Reign	0	Heat	11	W	Sommaire du Match
9 - 2019-10-12	50	Heat	9	Stars	3	W	Sommaire du Match
11 - 2019-10-14	74	Heat	4	Wolves	1	W	Sommaire du Match
12 - 2019-10-15	76	Heat	8	Barracudas	2	W	Sommaire du Match
14 - 2019-10-17	85	Phantoms	1	Heat	11	W	Sommaire du Match
16 - 2019-10-19	100	Griffins	2	Heat	6	W	Sommaire du Match
18 - 2019-10-21	115	Heat	-	Reign	-
19 - 2019-10-22	124	Heat	-	Gulls	-
21 - 2019-10-24	135	Bears	-	Heat	-
23 - 2019-10-26	146	Thunderbirds	-	Heat	-
25 - 2019-10-28	169	Heat	-	Moose	-
28 - 2019-10-31	181	Heat	-	Checkers	-
30 - 2019-11-02	194	Heat	-	Admirals	-
32 - 2019-11-04	208	Heat	-	Monsters	-
33 - 2019-11-05	219	Heat	-	Bears	-
35 - 2019-11-07	226	Roadrunners	-	Heat	-
37 - 2019-11-09	239	Devils	-	Heat	-
39 - 2019-11-11	253	Rampage	-	Heat	-
43 - 2019-11-15	281	Stars	-	Heat	-
46 - 2019-11-18	299	Heat	-	Roadrunners	-
47 - 2019-11-19	315	Heat	-	Wolves	-
49 - 2019-11-21	320	Eagles	-	Heat	-
51 - 2019-11-23	344	Heat	-	Rampage	-
53 - 2019-11-25	355	Heat	-	Phantoms	-
55 - 2019-11-27	372	Heat	-	Penguins	-
57 - 2019-11-29	378	Heat	-	Americans	-
60 - 2019-12-02	404	Senators	-	Heat	-
65 - 2019-12-07	441	Americans	-	Heat	-
67 - 2019-12-09	454	Reign	-	Heat	-
69 - 2019-12-11	468	Heat	-	Eagles	-
70 - 2019-12-12	472	Heat	-	Roadrunners	-
72 - 2019-12-14	490	Marlies	-	Heat	-
74 - 2019-12-16	503	Checkers	-	Heat	-
77 - 2019-12-19	525	Penguins	-	Heat	-
79 - 2019-12-21	541	Rocket	-	Heat	-
82 - 2019-12-24	565	Heat	-	Stars	-
83 - 2019-12-25	572	Heat	-	Wild	-
87 - 2019-12-29	586	Heat	-	Condors	-
89 - 2019-12-31	606	Comets	-	Heat	-
91 - 2020-01-02	617	IceHogs	-	Heat	-
93 - 2020-01-04	630	Wolf Pack	-	Heat	-
96 - 2020-01-07	656	Heat	-	Wild	-
98 - 2020-01-09	665	Heat	-	IceHogs	-
100 - 2020-01-11	680	Wild	-	Heat	-
102 - 2020-01-13	694	Condors	-	Heat	-
104 - 2020-01-15	709	Heat	-	Rocket	-
107 - 2020-01-18	737	Heat	-	Marlies	-
109 - 2020-01-20	751	Heat	-	Senators	-
119 - 2020-01-30	775	Rampage	-	Heat	-
120 - 2020-01-31	778	Heat	-	Condors	-
123 - 2020-02-03	794	Condors	-	Heat	-
126 - 2020-02-06	816	Barracudas	-	Heat	-
128 - 2020-02-08	829	Admirals	-	Heat	-
130 - 2020-02-10	851	Heat	-	Comets	-
132 - 2020-02-12	863	Heat	-	Barracudas	-
134 - 2020-02-14	878	Heat	-	Reign	-
135 - 2020-02-15	880	Heat	-	Gulls	-
137 - 2020-02-17	897	IceHogs	-	Heat	-
139 - 2020-02-19	916	Gulls	-	Heat	-
143 - 2020-02-23	943	Bruins	-	Heat	-
145 - 2020-02-25	962	Heat	-	Griffins	-
147 - 2020-02-27	970	Heat	-	Bruins	-
149 - 2020-02-29	988	Heat	-	Admirals	-
151 - 2020-03-02	1009	Heat	-	Crunch	-
152 - 2020-03-03	1013	Heat	-	Thunderbirds	-
155 - 2020-03-06	1029	Monsters	-	Heat	-
157 - 2020-03-08	1044	Roadrunners	-	Heat	-
159 - 2020-03-10	1060	Wolves	-	Heat	-
163 - 2020-03-14	1085	Sound Tigers	-	Heat	-
165 - 2020-03-16	1102	Moose	-	Heat	-
167 - 2020-03-18	1121	Heat	-	Wolf Pack	-
168 - 2020-03-19	1127	Heat	-	Sound Tigers	-
170 - 2020-03-21	1142	Heat	-	Devils	-
172 - 2020-03-23	1155	Crunch	-	Heat	-
174 - 2020-03-25	1170	Barracudas	-	Heat	-
176 - 2020-03-27	1185	Gulls	-	Heat	-
178 - 2020-03-29	1203	Heat	-	Comets	-
182 - 2020-04-02	1229	Moose	-	Heat	-
184 - 2020-04-04	1244	Wolves	-	Heat	-
186 - 2020-04-06	1260	Condors	-	Heat	-

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	6,150	2,902
Assistance PCT	76.88%	72.55%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
37	2263 - 75.43%	55,021$	220,084$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
177,495$	1,198,500$	1,198,500$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
6,444$	113,524$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
2,035,777$	169	10,207$	1,724,983$

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
2019	8	8	0	0	0	0	0	0	70	10	60	4	4	0	0	0	0	0	0	33	4	29	4	4	0	0	0	0	0	0	37	6	31	16	70	117	187	0	2	33	22	15	0	459	141	168	150	0	123	34	38	132	28	6	21.43%	18	2	88.89%	1	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47
Total Saison Régulière	8	8	0	0	0	0	0	0	70	10	60	4	4	0	0	0	0	0	0	33	4	29	4	4	0	0	0	0	0	0	37	6	31	16	70	117	187	0	2	33	22	15	0	459	141	168	150	0	123	34	38	132	28	6	21.43%	18	2	88.89%	1	217	292	74.32%	98	140	70.00%	105	137	76.64%	282	233	119	35	77	47