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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	Joshua Ho-Sang			X		100.00	55	40	82	81	64	68	75	82	25	60	59	67	25	48	48	65	50	64	0
2	Austin Czarnik	X	X	X		100.00	59	40	94	80	58	59	81	68	44	67	60	62	25	62	62	65	50	63	0
3	Logan Brown (R)	X				100.00	85	85	86	62	85	62	62	64	80	65	60	70	57	44	44	65	50	63	0
4	Kiefer Sherwood (R)		X	X		100.00	81	44	94	72	66	59	79	59	38	61	62	65	25	48	48	66	50	62	0
5	Peter Cehlarik		X	X		100.00	61	42	90	67	75	63	73	69	26	60	72	52	25	46	46	67	50	61	0
6	Valentin Zykov		X	X		100.00	73	44	97	71	80	55	71	69	32	60	58	52	25	46	46	62	50	60	0
7	Max Veronneau (R)			X		100.00	55	40	99	64	67	63	49	63	25	63	70	56	25	45	45	65	50	59	0
8	Griffen Molino	X	X			100.00	72	63	92	64	63	57	60	47	59	42	45	61	43	51	51	52	50	53	0
9	Matt Tennyson				X	100.00	75	76	74	70	76	69	74	54	25	49	42	65	40	57	57	56	50	62	0
10	Matt Bartkowski				X	100.00	74	72	79	69	72	59	62	50	25	39	41	66	39	64	65	54	50	60	0
11	Justin Holl				X	100.00	71	42	93	63	66	60	57	57	25	49	47	54	25	46	46	56	50	56	0
Rayé
1	Joseph Blandisi	X	X			100.00	64	66	58	73	66	71	73	66	80	63	62	63	59	56	56	64	50	63	0

	MOYENNE D'ÉQUIPE					100.00	69	55	87	70	70	62	68	62	40	57	57	61	34	51	51	61	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	Al Montoya	100.00	46	47	59	78	44	45	50	52	46	47	30	61	61	48	50	52	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	46	47	59	78	44	45	50	52	46	47	30	61	61	48	50	52

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Mike Van Ryn	50	68	59	66	45	46	56	CAN	39	2	1,340,000$

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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	PKS	PKM	GW	FO%	FOT	HT	P/20	FL	S1	S2	S3
1	Joshua Ho-Sang	IceHogs (CHI)	RW	5	5	8	13	13	0	0	2	15	27	9	19	18.52%	1	110	22.12	0	0	0	3	12	1	17	2	41.67%	36	0	2.35	0	1	0	2
2	Austin Czarnik	IceHogs (CHI)	C/LW/RW	5	2	9	11	12	0	0	0	8	20	5	15	10.00%	2	103	20.66	0	1	1	0	12	0	11	0	50.00%	16	0	2.13	0	0	1	0
3	Logan Brown	IceHogs (CHI)	C	5	6	5	11	12	2	0	15	8	16	6	14	37.50%	1	105	21.14	1	0	1	3	12	0	15	0	70.30%	101	0	2.08	0	2	0	1
4	Griffen Molino	IceHogs (CHI)	C/LW	5	2	8	10	5	7	5	5	7	15	3	5	13.33%	0	80	16.13	2	0	2	3	7	0	0	0	68.06%	72	0	2.48	1	0	0	0
5	Max Veronneau	IceHogs (CHI)	RW	5	5	5	10	8	0	0	3	4	31	9	35	16.13%	3	85	17.11	0	0	0	0	1	0	0	2	0.00%	1	0	2.34	0	0	1	0
6	Valentin Zykov	IceHogs (CHI)	LW/RW	5	5	5	10	8	4	0	14	11	14	11	9	35.71%	2	87	17.45	0	0	0	0	2	0	4	0	20.00%	5	0	2.29	0	0	2	1
7	Kiefer Sherwood	IceHogs (CHI)	LW/RW	5	4	5	9	4	4	0	11	8	23	6	21	17.39%	1	90	18.19	0	2	2	1	8	0	7	0	100.00%	2	1	1.98	0	2	0	0
8	Peter Cehlarik	IceHogs (CHI)	LW/RW	5	4	4	8	4	2	0	2	7	27	12	11	14.81%	5	93	18.72	0	2	2	3	9	0	7	1	66.67%	6	0	1.71	0	0	1	0
9	Justin Holl	IceHogs (CHI)	D	5	1	5	6	4	10	0	10	0	6	0	1	16.67%	5	88	17.73	1	1	2	2	9	0	11	0	0.00%	0	0	1.35	0	0	0	1
10	Matt Tennyson	IceHogs (CHI)	D	5	0	3	3	8	8	0	20	2	5	5	4	0.00%	6	101	20.32	0	1	1	2	11	0	11	0	0.00%	0	0	0.59	0	0	0	0
11	Matt Bartkowski	IceHogs (CHI)	D	1	0	1	1	0	0	0	1	0	0	1	0	0.00%	1	17	17.72	0	0	0	0	1	0	3	0	0.00%	0	0	1.13	0	0	0	0
12	Cameron Schilling	Blackhawks	D	1	0	1	1	1	4	0	0	0	1	0	1	0.00%	1	17	17.70	0	0	0	0	0	0	2	0	0.00%	0	0	1.13	0	0	0	0
Stats d'équipe Total ou en Moyenne				52	34	59	93	79	41	5	83	70	185	67	135	18.38%	28	983	18.91	4	7	11	17	90	1	94	5	62.76%	239	1	1.89	1	5	5	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

#	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	Al Montoya	IceHogs (CHI)	5	5	0	0	0.886	2.00	300	0	0	10	88	0	0	0	0.000	0	5	0	0	0	0
Stats d'équipe Total ou en Moyenne			5	5	0	0	0.886	2.00	300	0	0	10	88	0	0	0	0.000	0	5	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
Al Montoya	IceHogs (CHI)	G	34	1985-02-12	No	201 Lbs	6 ft2	Yes	No	No	1	Sans Restriction	Pro & Farm	675,000$	0$	0$	No	Lien
Austin Czarnik	IceHogs (CHI)	C/LW/RW	26	1992-12-12	No	160 Lbs	5 ft9	No	No	No	1	Avec Restriction	Pro & Farm	975,000$	0$	0$	No	Lien
Griffen Molino	IceHogs (CHI)	C/LW	25	1994-01-21	No	171 Lbs	5 ft11	No	No	No	1	Avec Restriction	Pro & Farm	1,000,000$	0$	0$	No	Lien
Joseph Blandisi	IceHogs (CHI)	C/LW	25	1994-07-18	No	182 Lbs	5 ft11	No	No	No	3	Avec Restriction	Pro & Farm	650,000$	0$	0$	No	Lien
Joshua Ho-Sang	IceHogs (CHI)	RW	23	1996-01-22	No	173 Lbs	6 ft0	No	No	No	1	Avec Restriction	Pro & Farm	1,075,833$	0$	0$	No	Lien
Justin Holl	IceHogs (CHI)	D	27	1992-01-30	No	170 Lbs	6 ft2	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Kiefer Sherwood	IceHogs (CHI)	LW/RW	24	1995-03-31	Yes	180 Lbs	5 ft11	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Logan Brown	IceHogs (CHI)	C	21	1998-03-04	Yes	220 Lbs	6 ft6	No	No	No	3	Avec Restriction	Pro & Farm	1,573,333$	0$	0$	No	Lien
Matt Bartkowski	IceHogs (CHI)	D	31	1988-06-04	No	196 Lbs	6 ft1	Yes	No	No	2	Sans Restriction	Pro & Farm	675,000$	0$	0$	No	Lien
Matt Tennyson	IceHogs (CHI)	D	29	1990-04-23	No	205 Lbs	6 ft2	Yes	No	No	2	Sans Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Max Veronneau	IceHogs (CHI)	RW	23	1995-12-11	Yes	181 Lbs	6 ft0	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Peter Cehlarik	IceHogs (CHI)	LW/RW	24	1995-05-12	No	202 Lbs	6 ft2	No	No	No	2	Avec Restriction	Pro & Farm	925,000$	0$	0$	No	Lien
Valentin Zykov	IceHogs (CHI)	LW/RW	24	1995-05-14	No	224 Lbs	6 ft1	No	No	No	1	Avec Restriction	Pro & Farm	894,167$	0$	0$	No	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
13	25.85	190 Lbs	6 ft1	1.77	920,641$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Austin Czarnik	Logan Brown	Joshua Ho-Sang	40	1	2	2
2	Kiefer Sherwood	Griffen Molino	Peter Cehlarik	30	1	2	2
3	Valentin Zykov		Max Veronneau	20	1	2	2
4	Logan Brown	Joshua Ho-Sang	Austin Czarnik	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Matt Tennyson	40	1	2	2
2		Justin Holl	30	1	2	2
3			20	1	2	2
4		Matt Tennyson	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Austin Czarnik	Logan Brown	Joshua Ho-Sang	60	1	2	2
2	Kiefer Sherwood	Griffen Molino	Peter Cehlarik	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Matt Tennyson	60	1	2	2
2		Justin Holl	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Joshua Ho-Sang	Logan Brown	60	1	2	2
2	Austin Czarnik	Kiefer Sherwood	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Matt Tennyson	60	1	2	2
2		Justin Holl	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Joshua Ho-Sang	60	1	2	2		Matt Tennyson	60	1	2	2
2	Logan Brown	40	1	2	2		Justin Holl	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Joshua Ho-Sang	Logan Brown	60	1	2	2
2	Austin Czarnik	Kiefer Sherwood	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Matt Tennyson	60	1	2	2
2		Justin Holl	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Austin Czarnik	Logan Brown	Joshua Ho-Sang		Matt Tennyson

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Austin Czarnik	Logan Brown	Joshua Ho-Sang		Matt Tennyson

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Valentin Zykov, Max Veronneau, Peter Cehlarik	Valentin Zykov, Max Veronneau	Peter Cehlarik

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, Justin Holl,		Justin Holl,

Tirs de Pénalité
Joshua Ho-Sang, Logan Brown, Austin Czarnik, Kiefer Sherwood, Peter Cehlarik

Gardien
#1 : Al Montoya, #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	Barracudas	1	1	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	7	2	5	0	0	0	0	0	0	0	0	0	0	0	2	1.000	7	12	19	0	0	13	11	11	0	44	61	71	59	0	16	4	6	26	3	1	33.33%	3	0	100.00%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
2	Condors	1	1	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	9	2	7	0	0	0	0	0	0	0	0	0	0	0	2	1.000	9	17	26	0	0	13	11	11	0	32	61	71	59	0	22	7	12	15	6	3	50.00%	6	1	83.33%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
3	Monsters	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	5	2	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	5	7	12	0	0	13	11	11	0	29	61	71	59	0	16	7	11	11	2	0	0.00%	3	1	66.67%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
4	Moose	1	1	0	0	0	0	0	0	7	3	4	1	1	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	2	1.000	7	14	21	0	0	13	11	11	0	45	61	71	59	0	19	6	8	16	2	0	0.00%	3	2	33.33%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
5	Phantoms	1	1	0	0	0	0	0	0	7	1	6	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	7	1	6	2	1.000	7	12	19	0	0	13	11	11	0	41	61	71	59	0	15	7	8	24	1	0	0.00%	4	0	100.00%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
	Total	5	5	0	0	0	0	0	0	35	10	25	4	4	0	0	0	0	0	0	28	9	19	1	1	0	0	0	0	0	0	7	1	6	10	1.000	35	62	97	0	0	13	11	11	0	191	61	71	59	0	88	31	45	92	14	4	28.57%	19	4	78.95%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
	_Since Last GM Reset	5	5	0	0	0	0	0	0	35	10	25	4	4	0	0	0	0	0	0	28	9	19	1	1	0	0	0	0	0	0	7	1	6	10	1.000	35	62	97	0	0	13	11	11	0	191	61	71	59	0	88	31	45	92	14	4	28.57%	19	4	78.95%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
	_Vs Conference	3	3	0	0	0	0	0	0	23	7	16	3	3	0	0	0	0	0	0	23	7	16	0	0	0	0	0	0	0	0	0	0	0	6	1.000	23	43	66	0	0	13	11	11	0	121	61	71	59	0	57	17	26	57	11	4	36.36%	12	3	75.00%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
	_Vs Division	1	1	0	0	0	0	0	0	7	3	4	1	1	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	2	1.000	7	14	21	0	0	13	11	11	0	45	61	71	59	0	19	6	8	16	2	0	0.00%	3	2	33.33%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29

			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
5	10	W5	35	62	97	191	88	31	45	92	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
5	5	0	0	0	0	0	35	10

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	4	0	0	0	0	0	28	9

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
1	1	0	0	0	0	0	7	1

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
5	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
14	4	28.57%	19	4	78.95%	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
61	71	59	0	13	11	11	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
75	129	58.14%	45	85	52.94%	49	88	55.68%

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
160	129	87	23	51	29

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
3 - 2019-10-06	16	IceHogs	7	Phantoms	1	W	Sommaire du Match
9 - 2019-10-12	48	Barracudas	2	IceHogs	7	W	Sommaire du Match
11 - 2019-10-14	63	Moose	3	IceHogs	7	W	Sommaire du Match
13 - 2019-10-16	80	Condors	2	IceHogs	9	W	Sommaire du Match
17 - 2019-10-20	108	Monsters	2	IceHogs	5	W	Sommaire du Match
19 - 2019-10-22	125	Bears	-	IceHogs	-
21 - 2019-10-24	136	Wolves	-	IceHogs	-
23 - 2019-10-26	147	Phantoms	-	IceHogs	-
25 - 2019-10-28	162	IceHogs	-	Checkers	-
26 - 2019-10-29	170	Reign	-	IceHogs	-
28 - 2019-10-31	184	IceHogs	-	Admirals	-
32 - 2019-11-04	211	IceHogs	-	Reign	-
33 - 2019-11-05	218	IceHogs	-	Gulls	-
35 - 2019-11-07	231	IceHogs	-	Barracudas	-
37 - 2019-11-09	240	Comets	-	IceHogs	-
39 - 2019-11-11	258	IceHogs	-	Penguins	-
40 - 2019-11-12	265	Marlies	-	IceHogs	-
43 - 2019-11-15	285	IceHogs	-	Wolves	-
46 - 2019-11-18	307	IceHogs	-	Admirals	-
47 - 2019-11-19	314	Americans	-	IceHogs	-
49 - 2019-11-21	321	Checkers	-	IceHogs	-
51 - 2019-11-23	337	Crunch	-	IceHogs	-
53 - 2019-11-25	351	IceHogs	-	Stars	-
56 - 2019-11-28	374	Stars	-	IceHogs	-
59 - 2019-12-01	396	Eagles	-	IceHogs	-
60 - 2019-12-02	405	IceHogs	-	Eagles	-
62 - 2019-12-04	421	Rampage	-	IceHogs	-
65 - 2019-12-07	438	IceHogs	-	Bruins	-
66 - 2019-12-08	449	IceHogs	-	Devils	-
68 - 2019-12-10	463	Roadrunners	-	IceHogs	-
70 - 2019-12-12	482	IceHogs	-	Wolves	-
72 - 2019-12-14	488	IceHogs	-	Roadrunners	-
74 - 2019-12-16	513	IceHogs	-	Rampage	-
75 - 2019-12-17	515	Wild	-	IceHogs	-
78 - 2019-12-20	535	Eagles	-	IceHogs	-
79 - 2019-12-21	547	IceHogs	-	Moose	-
81 - 2019-12-23	556	IceHogs	-	Eagles	-
83 - 2019-12-25	570	Devils	-	IceHogs	-
87 - 2019-12-29	584	Sound Tigers	-	IceHogs	-
89 - 2019-12-31	605	IceHogs	-	Monsters	-
91 - 2020-01-02	617	IceHogs	-	Heat	-
93 - 2020-01-04	636	IceHogs	-	Comets	-
96 - 2020-01-07	655	Griffins	-	IceHogs	-
98 - 2020-01-09	665	Heat	-	IceHogs	-
100 - 2020-01-11	681	Admirals	-	IceHogs	-
102 - 2020-01-13	695	Gulls	-	IceHogs	-
105 - 2020-01-16	720	IceHogs	-	Senators	-
106 - 2020-01-17	725	IceHogs	-	Rocket	-
109 - 2020-01-20	753	IceHogs	-	Marlies	-
110 - 2020-01-21	756	Moose	-	IceHogs	-
112 - 2020-01-23	763	Thunderbirds	-	IceHogs	-
123 - 2020-02-03	792	IceHogs	-	Roadrunners	-
126 - 2020-02-06	817	IceHogs	-	Wild	-
127 - 2020-02-07	825	Bruins	-	IceHogs	-
131 - 2020-02-11	859	IceHogs	-	Moose	-
133 - 2020-02-13	870	IceHogs	-	Condors	-
134 - 2020-02-14	879	IceHogs	-	Comets	-
137 - 2020-02-17	897	IceHogs	-	Heat	-
138 - 2020-02-18	914	IceHogs	-	Moose	-
141 - 2020-02-21	928	Wolf Pack	-	IceHogs	-
143 - 2020-02-23	944	Admirals	-	IceHogs	-
145 - 2020-02-25	961	IceHogs	-	Stars	-
147 - 2020-02-27	978	IceHogs	-	Rampage	-
149 - 2020-02-29	992	IceHogs	-	Crunch	-
151 - 2020-03-02	1001	IceHogs	-	Thunderbirds	-
154 - 2020-03-05	1019	Gulls	-	IceHogs	-
156 - 2020-03-07	1034	Condors	-	IceHogs	-
157 - 2020-03-08	1045	IceHogs	-	Griffins	-
159 - 2020-03-10	1061	Rampage	-	IceHogs	-
162 - 2020-03-13	1079	Barracudas	-	IceHogs	-
164 - 2020-03-15	1094	Senators	-	IceHogs	-
165 - 2020-03-16	1108	IceHogs	-	Bears	-
168 - 2020-03-19	1126	IceHogs	-	Wild	-
170 - 2020-03-21	1139	Wild	-	IceHogs	-
172 - 2020-03-23	1153	IceHogs	-	Americans	-
173 - 2020-03-24	1165	Admirals	-	IceHogs	-
176 - 2020-03-27	1186	Penguins	-	IceHogs	-
178 - 2020-03-29	1199	Stars	-	IceHogs	-
180 - 2020-03-31	1216	Reign	-	IceHogs	-
182 - 2020-04-02	1230	Rocket	-	IceHogs	-
184 - 2020-04-04	1248	IceHogs	-	Sound Tigers	-
186 - 2020-04-06	1267	IceHogs	-	Wolf Pack	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	0
Assistance	5,751	2,963
Assistance PCT	71.89%	74.08%

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	2179 - 72.62%	50,321$	201,285$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
228,531$	1,196,833$	1,196,833$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
6,435$	106,051$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
1,861,886$	169	13,639$	2,304,991$

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	5	5	0	0	0	0	0	0	35	10	25	4	4	0	0	0	0	0	0	28	9	19	1	1	0	0	0	0	0	0	7	1	6	10	35	62	97	0	0	13	11	11	0	191	61	71	59	0	88	31	45	92	14	4	28.57%	19	4	78.95%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29
Total Saison Régulière	5	5	0	0	0	0	0	0	35	10	25	4	4	0	0	0	0	0	0	28	9	19	1	1	0	0	0	0	0	0	7	1	6	10	35	62	97	0	0	13	11	11	0	191	61	71	59	0	88	31	45	92	14	4	28.57%	19	4	78.95%	0	75	129	58.14%	45	85	52.94%	49	88	55.68%	160	129	87	23	51	29