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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	Âge	Contrat	Salaire
1	Sebastian Aho	X	X			100.00	71	42	88	92	63	81	92	83	81	80	95	71	48	64	65	86	50	77	0	23	4	8,450,000$
2	Max Comtois (R)		X			100.00	86	90	77	78	78	66	67	73	32	66	70	61	75	46	46	70	50	65	0	22	2	925,000$
3	Dominik Simon		X	X		100.00	73	43	93	77	69	67	85	62	37	68	60	56	25	63	64	65	50	63	0	26	3	895,000$
4	Glenn Gawdin	X				100.00	75	71	84	64	71	74	76	66	80	67	61	65	58	44	44	65	50	63	0	24	3	925,000$
5	Joseph Blandisi	X	X			100.00	77	43	85	73	66	51	74	56	53	62	59	71	25	58	58	65	50	62	0	26	2	650,000$
6	Kiefer Sherwood		X	X		100.00	89	45	82	72	66	65	80	58	57	60	55	72	25	50	50	64	50	62	0	26	1	925,000$
7	Logan Brown (C)	X				100.00	67	44	94	62	85	59	69	72	62	75	56	57	25	46	46	63	50	62	0	23	2	1,573,333$
8	Jesper Boqvist (R)		X			100.00	76	43	92	86	63	57	70	66	35	50	60	61	75	47	47	64	50	61	0	22	3	925,000$
9	Zac Dalpe	X		X		100.00	79	73	92	54	73	58	58	61	76	50	65	69	62	58	59	64	50	60	0	31	1	850,000$
10	Valentin Zykov		X	X		100.00	78	45	87	71	80	62	59	64	25	65	58	56	25	48	48	62	50	60	0	26	1	775,000$
11	Carsen Twarynski (R)		X			100.00	92	46	91	66	73	57	75	56	26	50	58	64	25	45	45	62	50	59	0	23	3	775,833$
12	Skyler McKenzie		X			100.00	70	61	90	66	61	71	75	56	50	52	56	60	53	44	44	60	50	58	0	23	3	741,666$
13	Griffen Molino	X	X			100.00	71	63	88	60	63	52	52	54	68	54	47	62	45	51	51	55	50	55	0	27	1	800,000$
14	Cameron Schilling				X	100.00	74	69	86	64	69	72	78	54	25	48	42	64	40	55	55	56	50	61	0	32	1	700,000$
15	Matt Bartkowski				X	100.00	72	72	72	65	72	58	61	52	25	46	40	65	38	64	65	53	50	60	0	32	1	675,000$
16	Mitch Vande Sompel (R)				X	100.00	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	50	54	1	24	3	935,833$
17	Ville Pokka (R)				X	100.00	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	50	54	1	26	3	750,000$
Rayé
1	Max Veronneau			X		100.00	76	67	96	64	67	59	61	53	50	47	53	63	50	45	45	58	50	56	0	25	1	925,000$

	MOYENNE D'ÉQUIPE					100.00	74	57	84	68	68	62	69	61	50	58	58	63	45	52	52	62	50	61

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

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
Rayé
1	Igor Shesterkin (R)	100.00	69	63	60	71	77	62	88	69	77	69	75	45	45	71	50	68	0

	MOYENNE D'ÉQUIPE	100.00	69	63	60	71	77	62	88	69	77	69	75	45	45	71	50	68

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	40	1	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	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Kiefer Sherwood	IceHogs (CHI)	LW/RW	82	41	53	94	50	80	0	195	122	311	72	197	13.18%	34	1534	18.72	7	18	25	53	275	1	0	1	7	101	5	1	68.13%	91	2	1.22	1	1	0	0	0	10	3	9
2	Jesper Boqvist	IceHogs (CHI)	LW	82	36	53	89	82	44	0	62	131	294	93	191	12.24%	10	1273	15.53	1	5	6	13	65	1	0	1	8	68	6	1	50.63%	79	1	1.40	0	0	0	0	0	4	7	7
3	Joseph Blandisi	IceHogs (CHI)	C/LW	82	32	54	86	45	46	0	87	128	240	79	162	13.33%	12	1398	17.05	4	16	20	26	235	0	0	0	0	41	5	2	63.70%	708	1	1.23	0	1	0	0	0	8	4	3
4	Logan Brown	IceHogs (CHI)	C	82	32	51	83	66	16	0	35	123	195	57	137	16.41%	9	1468	17.91	7	8	15	27	216	0	0	0	4	128	7	2	63.04%	1361	1	1.13	0	2	0	0	0	4	5	7
5	Zac Dalpe	IceHogs (CHI)	C/RW	79	40	40	80	76	40	10	78	99	225	58	175	17.78%	15	1156	14.64	3	0	3	18	53	0	2	2	2	64	8	0	69.99%	753	3	1.38	0	0	2	0	0	5	7	4
6	Valentin Zykov	IceHogs (CHI)	LW/RW	82	24	51	75	42	34	0	83	79	213	59	149	11.27%	7	1373	16.75	8	15	23	38	240	0	0	0	0	13	7	1	48.81%	84	1	1.09	0	0	0	0	0	1	2	6
7	Dominik Simon	IceHogs (CHI)	LW/RW	62	32	39	71	45	14	0	42	96	235	61	149	13.62%	17	1299	20.95	7	9	16	49	212	1	0	1	10	193	2	1	53.13%	96	2	1.09	1	3	0	0	0	4	6	5
8	Max Veronneau	IceHogs (CHI)	RW	81	25	36	61	72	32	0	63	56	156	39	109	16.03%	12	1054	13.02	1	2	3	8	27	0	1	1	1	41	3	0	50.65%	77	0	1.16	0	0	0	0	0	4	4	6
9	Glenn Gawdin	IceHogs (CHI)	C	40	16	36	52	31	20	0	25	84	121	30	79	13.22%	10	870	21.75	3	9	12	26	126	0	0	0	5	152	1	0	68.27%	851	1	1.20	0	1	0	0	0	3	6	4
10	Carsen Twarynski	IceHogs (CHI)	LW	77	14	35	49	66	82	0	137	53	114	30	105	12.28%	12	1039	13.50	0	0	0	0	6	0	0	0	1	45	0	0	56.00%	25	0	0.94	2	3	0	0	0	1	3	0
11	Skyler McKenzie	IceHogs (CHI)	LW	82	4	33	37	76	39	5	59	34	54	18	47	7.41%	29	1238	15.10	0	1	1	2	26	0	0	0	1	73	2	0	65.79%	38	0	0.60	0	0	0	0	1	2	1	2
12	Griffen Molino	IceHogs (CHI)	C/LW	82	16	17	33	33	29	5	24	56	83	18	53	19.28%	5	505	6.16	0	0	0	0	2	0	0	0	0	3	3	2	68.22%	428	0	1.31	0	0	0	0	1	2	2	3
13	Cameron Schilling	IceHogs (CHI)	D	59	6	27	33	52	40	10	72	26	46	9	41	13.04%	47	1346	22.82	3	4	7	20	188	0	2	2	3	193	2	0	0.00%	0	0	0.49	0	0	0	1	1	0	2	2
14	Mitch Vande Sompel	IceHogs (CHI)	D	82	6	25	31	64	52	0	79	26	34	15	26	17.65%	38	1398	17.06	2	2	4	11	110	0	1	1	2	135	2	0	0.00%	0	0	0.44	0	0	0	0	0	1	1	0
15	Max Comtois	IceHogs (CHI)	LW	20	15	14	29	18	19	5	47	43	85	28	72	17.65%	4	442	22.14	3	3	6	11	52	0	0	0	1	71	1	0	59.59%	146	2	1.31	0	0	1	0	0	3	0	1
16	Ville Pokka	IceHogs (CHI)	D	79	9	19	28	63	35	5	104	21	33	14	23	27.27%	35	1475	18.68	5	0	5	9	171	0	0	0	0	176	1	2	0.00%	0	0	0.38	0	0	0	1	0	1	1	1
17	Matt Bartkowski	IceHogs (CHI)	D	61	4	22	26	51	62	10	121	25	44	13	22	9.09%	45	1262	20.70	0	2	2	20	182	0	1	1	0	168	2	0	0.00%	0	0	0.41	0	0	0	0	2	3	1	0
18	Sebastian Aho	IceHogs (CHI)	C/LW	1	0	2	2	2	0	0	0	1	10	1	12	0.00%	1	24	24.47	0	0	0	3	1	0	0	0	0	3	0	0	90.63%	32	0	1.63	0	0	0	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				1215	352	607	959	934	684	50	1313	1203	2493	694	1749	14.12%	342	20161	16.59	54	94	148	334	2196	3	7	10	45	1675	57	12	64.94%	4769	14	0.95	4	11	3	2	5	56	55	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	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	Igor Shesterkin	IceHogs (CHI)	76	58	14	4	0.918	1.39	4490	4	24	104	1274	0	5	0	0.636	11	76	0	4	5	0
Stats d'équipe Total ou en Moyenne			76	58	14	4	0.918	1.39	4490	4	24	104	1274	0	5	0	0.636	11	76	0	4	5	0

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

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	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Cameron Schilling	IceHogs (CHI)	D	32	1988-10-07	No	182 Lbs	6 ft2	No	No	No	1	Pro & Farm	700,000$	0$	0$	No				Lien / Lien NHL
Carsen Twarynski	IceHogs (CHI)	LW	23	1997-11-24	Yes	198 Lbs	6 ft2	No	No	No	3	Pro & Farm	775,833$	0$	0$	No	775,833$	775,833$		Lien / Lien NHL
Dominik Simon	IceHogs (CHI)	LW/RW	26	1994-08-08	No	190 Lbs	5 ft11	No	No	No	3	Pro & Farm	895,000$	0$	0$	No	895,000$	895,000$		Lien / Lien NHL
Glenn Gawdin	IceHogs (CHI)	C	24	1997-03-25	No	191 Lbs	6 ft1	No	No	No	3	Pro & Farm	925,000$	0$	0$	No	925,000$	925,000$		Lien / Lien NHL
Griffen Molino	IceHogs (CHI)	C/LW	27	1994-01-21	No	171 Lbs	5 ft11	No	No	No	1	Pro & Farm	800,000$	0$	0$	No				Lien / Lien NHL
Igor Shesterkin	IceHogs (CHI)	G	25	1995-12-30	Yes	187 Lbs	6 ft1	No	No	No	3	Pro & Farm	3,775,000$	0$	0$	No	3,775,000$	3,775,000$		Lien / Lien NHL
Jesper Boqvist	IceHogs (CHI)	LW	22	1998-10-30	Yes	174 Lbs	5 ft11	No	No	No	3	Pro & Farm	925,000$	0$	0$	No	925,000$	925,000$		Lien / Lien NHL
Joseph Blandisi	IceHogs (CHI)	C/LW	26	1994-07-18	No	182 Lbs	5 ft11	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$			Lien / Lien NHL
Kiefer Sherwood	IceHogs (CHI)	LW/RW	26	1995-03-31	No	180 Lbs	5 ft11	No	No	No	1	Pro & Farm	925,000$	0$	0$	No				Lien / Lien NHL
Logan Brown	IceHogs (CHI)	C	23	1998-03-04	No	220 Lbs	6 ft6	No	No	No	2	Pro & Farm	1,573,333$	0$	0$	No	1,573,333$			Lien / Lien NHL
Matt Bartkowski	IceHogs (CHI)	D	32	1988-06-04	No	196 Lbs	6 ft1	No	No	No	1	Pro & Farm	675,000$	0$	0$	No				Lien / Lien NHL
Max Comtois	IceHogs (CHI)	LW	22	1999-01-08	Yes	207 Lbs	6 ft2	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$			Lien / Lien NHL
Max Veronneau	IceHogs (CHI)	RW	25	1995-12-11	No	181 Lbs	6 ft0	No	No	No	1	Pro & Farm	925,000$	0$	0$	No				Lien / Lien NHL
Mitch Vande Sompel	IceHogs (CHI)	D	24	1997-02-11 10:37:52 AM	Yes	198 Lbs	5 ft11	No	No	No	3	Pro & Farm	935,833$	0$	0$	No	935,833$	935,833$
Sebastian Aho	IceHogs (CHI)	C/LW	23	1997-07-26	No	172 Lbs	5 ft11	No	No	No	4	Pro & Farm	8,450,000$	0$	0$	No	8,450,000$	8,450,000$	8,450,000$	Lien / Lien NHL
Skyler McKenzie	IceHogs (CHI)	LW	23	1998-01-20	No	170 Lbs	5 ft9	No	No	No	3	Pro & Farm	741,666$	0$	0$	No	741,666$	741,666$		Lien / Lien NHL
Valentin Zykov	IceHogs (CHI)	LW/RW	26	1995-05-14	No	224 Lbs	6 ft1	No	No	No	1	Pro & Farm	775,000$	0$	0$	No				Lien / Lien NHL
Ville Pokka	IceHogs (CHI)	D	26	1994-06-03 10:45:39 AM	Yes	214 Lbs	6 ft0	No	No	No	3	Pro & Farm	750,000$	0$	0$	No	750,000$	750,000$
Zac Dalpe	IceHogs (CHI)	C/RW	31	1989-10-31	No	199 Lbs	6 ft2	No	No	No	1	Pro & Farm	850,000$	0$	0$	No				Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
19	25.58	191 Lbs	6 ft0	2.16	1,419,561$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Max Comtois	Sebastian Aho	Dominik Simon	40	1	2	2
2	Joseph Blandisi	Glenn Gawdin	Kiefer Sherwood	30	1	2	2
3	Jesper Boqvist	Logan Brown	Valentin Zykov	20	1	2	2
4	Carsen Twarynski	Zac Dalpe	Sebastian Aho	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Cameron Schilling	Matt Bartkowski	40	1	2	2
2	Ville Pokka	Mitch Vande Sompel	30	1	2	2
3	Zac Dalpe	Carsen Twarynski	20	1	2	2
4	Cameron Schilling	Matt Bartkowski	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Max Comtois	Sebastian Aho	Dominik Simon	60	1	2	2
2	Joseph Blandisi	Glenn Gawdin	Kiefer Sherwood	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Cameron Schilling	Matt Bartkowski	60	1	2	2
2	Ville Pokka	Mitch Vande Sompel	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Sebastian Aho	Max Comtois	60	1	2	2
2	Dominik Simon	Glenn Gawdin	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Cameron Schilling	Matt Bartkowski	60	1	2	2
2	Ville Pokka	Mitch Vande Sompel	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Sebastian Aho	60	1	2	2	Cameron Schilling	Matt Bartkowski	60	1	2	2
2	Max Comtois	40	1	2	2	Ville Pokka	Mitch Vande Sompel	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Sebastian Aho	Max Comtois	60	1	2	2
2	Dominik Simon	Glenn Gawdin	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Cameron Schilling	Matt Bartkowski	60	1	2	2
2	Ville Pokka	Mitch Vande Sompel	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Max Comtois	Sebastian Aho	Dominik Simon	Cameron Schilling	Matt Bartkowski

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Max Comtois	Sebastian Aho	Dominik Simon	Cameron Schilling	Matt Bartkowski

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Skyler McKenzie, Griffen Molino, Logan Brown	Skyler McKenzie, Griffen Molino	Logan Brown

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Ville Pokka, Mitch Vande Sompel, Cameron Schilling	Ville Pokka	Mitch Vande Sompel, Cameron Schilling

Tirs de Pénalité
Sebastian Aho, Max Comtois, Dominik Simon, Glenn Gawdin, Joseph Blandisi

Gardien
#1 : , #2 :

Lignes d'Attaque Perso. en Prol.
Sebastian Aho, Max Comtois, Dominik Simon, Glenn Gawdin, Joseph Blandisi, Logan Brown, Logan Brown, Kiefer Sherwood, Jesper Boqvist, Valentin Zykov, Zac Dalpe

Lignes de Défense Perso. en Prol.
Cameron Schilling, Matt Bartkowski, Ville Pokka, Mitch Vande Sompel,

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	Americans	2	2	0	0	0	0	0	0	13	3	10	1	1	0	0	0	0	0	0	8	2	6	1	1	0	0	0	0	0	0	5	1	4	4	1.000	13	23	36	0	0	155	126	87	5	39	853	898	850	24	44	12	30	33	17	5	29.41%	10	2	80.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
2	Admirals	5	2	3	0	0	0	0	0	13	14	-1	3	2	1	0	0	0	0	0	9	6	3	2	0	2	0	0	0	0	0	4	8	-4	4	0.400	13	22	35	0	0	155	126	87	5	124	853	898	850	24	122	35	54	106	26	5	19.23%	27	1	96.30%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
3	Barracudas	3	1	1	0	0	0	0	1	9	11	-2	2	1	1	0	0	0	0	0	7	8	-1	1	0	0	0	0	0	0	1	2	3	-1	3	0.500	9	16	25	0	0	155	126	87	5	75	853	898	850	24	73	23	30	65	9	3	33.33%	15	1	93.33%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
4	Bears	2	2	0	0	0	0	0	0	19	1	18	1	1	0	0	0	0	0	0	11	0	11	1	1	0	0	0	0	0	0	8	1	7	4	1.000	19	35	54	0	1	155	126	87	5	107	853	898	850	24	14	6	10	44	6	2	33.33%	4	1	75.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
5	Bruins	2	0	1	0	0	1	0	0	5	9	-4	1	0	1	0	0	0	0	0	3	6	-3	1	0	0	0	0	1	0	0	2	3	-1	1	0.250	5	8	13	0	0	155	126	87	5	49	853	898	850	24	44	10	16	41	15	3	20.00%	7	2	71.43%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
6	Checkers	2	2	0	0	0	0	0	0	12	1	11	1	1	0	0	0	0	0	0	9	0	9	1	1	0	0	0	0	0	0	3	1	2	4	1.000	12	21	33	0	1	155	126	87	5	55	853	898	850	24	26	8	16	28	6	0	0.00%	7	0	100.00%	1	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
7	Comets	3	2	1	0	0	0	0	0	4	4	0	1	1	0	0	0	0	0	0	1	0	1	2	1	1	0	0	0	0	0	3	4	-1	4	0.667	4	7	11	0	1	155	126	87	5	54	853	898	850	24	54	19	35	55	13	1	7.69%	14	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
8	Condors	3	0	1	0	1	1	0	0	6	8	-2	2	0	1	0	0	1	0	0	2	5	-3	1	0	0	0	1	0	0	0	4	3	1	3	0.500	6	11	17	0	0	155	126	87	5	65	853	898	850	24	65	14	28	64	24	2	8.33%	13	1	92.31%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
9	Crunch	2	1	1	0	0	0	0	0	10	8	2	1	1	0	0	0	0	0	0	8	5	3	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	10	17	27	0	0	155	126	87	5	72	853	898	850	24	32	10	14	40	8	2	25.00%	6	2	66.67%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
10	Devils	2	2	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	1	3	4	1.000	8	12	20	0	0	155	126	87	5	44	853	898	850	24	45	7	22	39	8	1	12.50%	11	1	90.91%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
11	Eagles	4	4	0	0	0	0	0	0	30	4	26	2	2	0	0	0	0	0	0	15	1	14	2	2	0	0	0	0	0	0	15	3	12	8	1.000	30	56	86	0	1	155	126	87	5	193	853	898	850	24	43	14	17	78	6	2	33.33%	6	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
12	Griffins	2	2	0	0	0	0	0	0	8	0	8	1	1	0	0	0	0	0	0	5	0	5	1	1	0	0	0	0	0	0	3	0	3	4	1.000	8	12	20	0	2	155	126	87	5	64	853	898	850	24	22	8	22	44	5	0	0.00%	11	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
13	Gulls	3	3	0	0	0	0	0	0	14	4	10	2	2	0	0	0	0	0	0	9	1	8	1	1	0	0	0	0	0	0	5	3	2	6	1.000	14	27	41	0	1	155	126	87	5	108	853	898	850	24	49	11	39	75	8	0	0.00%	17	2	88.24%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
14	Heat	3	2	1	0	0	0	0	0	10	4	6	1	1	0	0	0	0	0	0	4	0	4	2	1	1	0	0	0	0	0	6	4	2	4	0.667	10	18	28	0	1	155	126	87	5	57	853	898	850	24	48	15	20	53	19	2	10.53%	9	2	77.78%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
15	Marlies	2	2	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	2	0	2	1	1	0	0	0	0	0	0	3	1	2	4	1.000	5	9	14	0	1	155	126	87	5	36	853	898	850	24	36	10	14	45	18	1	5.56%	7	1	85.71%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
16	Monsters	2	1	0	0	0	0	1	0	7	4	3	1	0	0	0	0	0	1	0	3	2	1	1	1	0	0	0	0	0	0	4	2	2	4	1.000	7	10	17	0	0	155	126	87	5	46	853	898	850	24	45	12	18	39	9	1	11.11%	8	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
17	Moose	5	3	2	0	0	0	0	0	11	8	3	2	1	1	0	0	0	0	0	4	2	2	3	2	1	0	0	0	0	0	7	6	1	6	0.600	11	18	29	0	2	155	126	87	5	93	853	898	850	24	110	27	77	77	27	3	11.11%	30	3	90.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
18	Penguins	2	0	2	0	0	0	0	0	5	7	-2	1	0	1	0	0	0	0	0	4	5	-1	1	0	1	0	0	0	0	0	1	2	-1	0	0.000	5	10	15	0	0	155	126	87	5	57	853	898	850	24	47	12	20	31	4	1	25.00%	10	1	90.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
19	Phantoms	2	1	1	0	0	0	0	0	3	1	2	1	0	1	0	0	0	0	0	0	1	-1	1	1	0	0	0	0	0	0	3	0	3	2	0.500	3	5	8	0	1	155	126	87	5	45	853	898	850	24	29	12	16	23	15	0	0.00%	8	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
20	Rampage	4	4	0	0	0	0	0	0	37	1	36	2	2	0	0	0	0	0	0	19	0	19	2	2	0	0	0	0	0	0	18	1	17	8	1.000	37	67	104	0	3	155	126	87	5	213	853	898	850	24	41	22	20	72	8	2	25.00%	10	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
21	Reign	3	2	1	0	0	0	0	0	11	7	4	2	1	1	0	0	0	0	0	5	4	1	1	1	0	0	0	0	0	0	6	3	3	4	0.667	11	18	29	0	0	155	126	87	5	100	853	898	850	24	40	8	22	54	13	3	23.08%	11	1	90.91%	1	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
22	Roadrunners	3	3	0	0	0	0	0	0	18	3	15	1	1	0	0	0	0	0	0	4	1	3	2	2	0	0	0	0	0	0	14	2	12	6	1.000	18	30	48	0	1	155	126	87	5	130	853	898	850	24	51	16	28	51	7	2	28.57%	14	1	92.86%	2	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
23	Rocket	2	2	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	3	1	2	4	1.000	6	12	18	0	0	155	126	87	5	52	853	898	850	24	26	9	16	36	9	2	22.22%	8	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
24	Senators	2	2	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	4	0	4	1	1	0	0	0	0	0	0	3	1	2	4	1.000	7	13	20	0	1	155	126	87	5	64	853	898	850	24	41	8	18	43	6	2	33.33%	9	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
25	Sound Tigers	2	2	0	0	0	0	0	0	23	3	20	1	1	0	0	0	0	0	0	14	0	14	1	1	0	0	0	0	0	0	9	3	6	4	1.000	23	46	69	0	1	155	126	87	5	95	853	898	850	24	20	6	18	31	3	1	33.33%	8	1	87.50%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
26	Stars	4	3	0	0	1	0	0	0	16	8	8	2	2	0	0	0	0	0	0	10	6	4	2	1	0	0	1	0	0	0	6	2	4	8	1.000	16	28	44	0	1	155	126	87	5	133	853	898	850	24	73	20	26	101	13	4	30.77%	13	1	92.31%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
27	Thunderbirds	2	2	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	3	1	2	4	1.000	6	11	17	0	0	155	126	87	5	39	853	898	850	24	42	14	29	26	10	2	20.00%	11	2	81.82%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
28	Wild	4	4	0	0	0	0	0	0	32	1	31	2	2	0	0	0	0	0	0	16	0	16	2	2	0	0	0	0	0	0	16	1	15	8	1.000	32	59	91	0	3	155	126	87	5	227	853	898	850	24	40	11	25	55	7	3	42.86%	10	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
29	Wolf Pack	2	2	0	0	0	0	0	0	18	1	17	1	1	0	0	0	0	0	0	11	0	11	1	1	0	0	0	0	0	0	7	1	6	4	1.000	18	30	48	0	1	155	126	87	5	110	853	898	850	24	20	4	17	31	4	0	0.00%	6	0	100.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
30	Wolves	3	1	1	0	0	0	0	1	5	4	1	1	1	0	0	0	0	0	0	3	0	3	2	0	1	0	0	0	0	1	2	4	-2	3	0.500	5	8	13	0	1	155	126	87	5	68	853	898	850	24	43	11	25	54	21	3	14.29%	10	2	80.00%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
	Total	82	59	16	0	2	2	1	2	371	128	243	41	31	8	0	0	1	1	0	200	59	141	41	28	8	0	2	1	0	2	171	69	102	128	0.780	371	659	1030	0	24	155	126	87	5	2614	853	898	850	24	1385	394	742	1534	344	58	16.86%	330	28	91.52%	4	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
	_Since Last GM Reset	82	59	16	0	2	2	1	2	371	128	243	41	31	8	0	0	1	1	0	200	59	141	41	28	8	0	2	1	0	2	171	69	102	128	0.780	371	659	1030	0	24	155	126	87	5	2614	853	898	850	24	1385	394	742	1534	344	58	16.86%	330	28	91.52%	4	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
	_Vs Conference	49	34	10	0	2	1	0	2	225	80	145	25	19	5	0	0	1	0	0	115	36	79	24	15	5	0	2	0	0	2	110	44	66	75	0.765	225	401	626	0	14	155	126	87	5	1625	853	898	850	24	842	239	441	938	205	39	19.02%	195	17	91.28%	3	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518
	_Vs Division	26	19	6	0	1	0	0	1	139	36	103	13	11	2	0	0	0	0	0	73	15	58	13	8	4	0	1	0	0	1	66	21	45	41	0.788	139	250	389	0	10	155	126	87	5	983	853	898	850	24	429	129	219	489	87	19	21.84%	96	5	94.79%	0	1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%	2547	1962	1481	472	912	518

			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
82	128	W3	371	659	1030	2614	1385	394	742	1534	0	24

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	59	16	2	2	1	2	371	128

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	31	8	0	1	1	0	200	59

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	28	8	2	1	0	2	171	69

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
8	2	0	0	0	0

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
344	58	16.86%	330	28	91.52%	4

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
853	898	850	24	155	126	87	5

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
1507	2368	63.64%	1018	1836	55.45%	788	1202	65.56%

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
2547	1962	1481	472	912	518

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
3 - 2020-10-26	16	IceHogs	3	Phantoms	0	W			Sommaire du Match
9 - 2020-11-01	48	Barracudas	2	IceHogs	6	W			Sommaire du Match
11 - 2020-11-03	63	Moose	2	IceHogs	1	L			Sommaire du Match
13 - 2020-11-05	80	Condors	3	IceHogs	1	L			Sommaire du Match
17 - 2020-11-09	108	Monsters	2	IceHogs	3	W	X	X	Sommaire du Match
19 - 2020-11-11	125	Bears	0	IceHogs	11	W			Sommaire du Match
21 - 2020-11-13	136	Wolves	0	IceHogs	3	W			Sommaire du Match
23 - 2020-11-15	147	Phantoms	1	IceHogs	0	L			Sommaire du Match
25 - 2020-11-17	162	IceHogs	3	Checkers	1	W			Sommaire du Match
26 - 2020-11-18	170	Reign	1	IceHogs	3	W			Sommaire du Match
28 - 2020-11-20	184	IceHogs	2	Admirals	5	L			Sommaire du Match
32 - 2020-11-24	211	IceHogs	6	Reign	3	W			Sommaire du Match
33 - 2020-11-25	218	IceHogs	5	Gulls	3	W			Sommaire du Match
35 - 2020-11-27	231	IceHogs	2	Barracudas	3	L	X	X	Sommaire du Match
37 - 2020-11-29	240	Comets	0	IceHogs	1	W			Sommaire du Match
39 - 2020-12-01	258	IceHogs	1	Penguins	2	L			Sommaire du Match
40 - 2020-12-02	265	Marlies	0	IceHogs	2	W			Sommaire du Match
43 - 2020-12-05	285	IceHogs	2	Wolves	3	L	X	X	Sommaire du Match
46 - 2020-12-08	307	IceHogs	2	Admirals	3	L			Sommaire du Match
47 - 2020-12-09	314	Americans	2	IceHogs	8	W			Sommaire du Match
49 - 2020-12-11	321	Checkers	0	IceHogs	9	W			Sommaire du Match
51 - 2020-12-13	337	Crunch	5	IceHogs	8	W			Sommaire du Match
53 - 2020-12-15	351	IceHogs	3	Stars	2	W	X		Sommaire du Match
56 - 2020-12-18	374	Stars	2	IceHogs	3	W			Sommaire du Match
59 - 2020-12-21	396	Eagles	0	IceHogs	9	W			Sommaire du Match
60 - 2020-12-22	405	IceHogs	9	Eagles	1	W			Sommaire du Match
62 - 2020-12-24	421	Rampage	0	IceHogs	10	W			Sommaire du Match
65 - 2020-12-27	438	IceHogs	2	Bruins	3	L	X		Sommaire du Match
66 - 2020-12-28	449	IceHogs	4	Devils	1	W			Sommaire du Match
68 - 2020-12-30	463	Roadrunners	1	IceHogs	4	W			Sommaire du Match
70 - 2021-01-01	482	IceHogs	0	Wolves	1	L			Sommaire du Match
72 - 2021-01-03	488	IceHogs	6	Roadrunners	0	W			Sommaire du Match
74 - 2021-01-05	513	IceHogs	9	Rampage	0	W			Sommaire du Match
75 - 2021-01-06	515	Wild	0	IceHogs	7	W			Sommaire du Match
78 - 2021-01-09	535	Eagles	1	IceHogs	6	W			Sommaire du Match
79 - 2021-01-10	547	IceHogs	3	Moose	2	W			Sommaire du Match
81 - 2021-01-12	556	IceHogs	6	Eagles	2	W			Sommaire du Match
83 - 2021-01-14	570	Devils	2	IceHogs	4	W			Sommaire du Match
87 - 2021-01-18	584	Sound Tigers	0	IceHogs	14	W			Sommaire du Match
89 - 2021-01-20	605	IceHogs	4	Monsters	2	W			Sommaire du Match
91 - 2021-01-22	617	IceHogs	4	Heat	1	W			Sommaire du Match
93 - 2021-01-24	636	IceHogs	1	Comets	3	L			Sommaire du Match
96 - 2021-01-27	655	Griffins	0	IceHogs	5	W			Sommaire du Match
98 - 2021-01-29	665	Heat	0	IceHogs	4	W			Sommaire du Match
100 - 2021-01-31	681	Admirals	3	IceHogs	5	W			Sommaire du Match
102 - 2021-02-02	695	Gulls	0	IceHogs	6	W			Sommaire du Match
105 - 2021-02-05	720	IceHogs	3	Senators	1	W			Sommaire du Match
106 - 2021-02-06	725	IceHogs	3	Rocket	1	W			Sommaire du Match
109 - 2021-02-09	753	IceHogs	3	Marlies	1	W			Sommaire du Match
110 - 2021-02-10	756	Moose	0	IceHogs	3	W			Sommaire du Match
112 - 2021-02-12	763	Thunderbirds	1	IceHogs	3	W			Sommaire du Match
123 - 2021-02-23	792	IceHogs	8	Roadrunners	2	W			Sommaire du Match
126 - 2021-02-26	817	IceHogs	7	Wild	0	W			Sommaire du Match
127 - 2021-02-27	825	Bruins	6	IceHogs	3	L			Sommaire du Match
131 - 2021-03-03	859	IceHogs	1	Moose	4	L			Sommaire du Match
133 - 2021-03-05	870	IceHogs	4	Condors	3	W	X		Sommaire du Match
134 - 2021-03-06	879	IceHogs	2	Comets	1	W			Sommaire du Match
137 - 2021-03-09	897	IceHogs	2	Heat	3	L			Sommaire du Match
138 - 2021-03-10	914	IceHogs	3	Moose	0	W			Sommaire du Match
141 - 2021-03-13	928	Wolf Pack	0	IceHogs	11	W			Sommaire du Match
143 - 2021-03-15	944	Admirals	2	IceHogs	1	L			Sommaire du Match
145 - 2021-03-17	961	IceHogs	3	Stars	0	W			Sommaire du Match
147 - 2021-03-19	978	IceHogs	9	Rampage	1	W			Sommaire du Match
149 - 2021-03-21	992	IceHogs	2	Crunch	3	L			Sommaire du Match
151 - 2021-03-23	1001	IceHogs	3	Thunderbirds	1	W			Sommaire du Match
154 - 2021-03-26	1019	Gulls	1	IceHogs	3	W			Sommaire du Match
156 - 2021-03-28	1034	Condors	2	IceHogs	1	L	X		Sommaire du Match
157 - 2021-03-29	1045	IceHogs	3	Griffins	0	W			Sommaire du Match
159 - 2021-03-31	1061	Rampage	0	IceHogs	9	W			Sommaire du Match
162 - 2021-04-03	1079	Barracudas	6	IceHogs	1	L			Sommaire du Match
164 - 2021-04-05	1094	Senators	0	IceHogs	4	W			Sommaire du Match
165 - 2021-04-06	1108	IceHogs	8	Bears	1	W			Sommaire du Match
168 - 2021-04-09	1126	IceHogs	9	Wild	1	W			Sommaire du Match
170 - 2021-04-11	1139	Wild	0	IceHogs	9	W			Sommaire du Match
172 - 2021-04-13	1153	IceHogs	5	Americans	1	W			Sommaire du Match
173 - 2021-04-14	1165	Admirals	1	IceHogs	3	W			Sommaire du Match
176 - 2021-04-17	1186	Penguins	5	IceHogs	4	L			Sommaire du Match
178 - 2021-04-19	1199	Stars	4	IceHogs	7	W			Sommaire du Match
180 - 2021-04-21	1216	Reign	3	IceHogs	2	L			Sommaire du Match
182 - 2021-04-23	1230	Rocket	1	IceHogs	3	W			Sommaire du Match
184 - 2021-04-25	1248	IceHogs	9	Sound Tigers	3	W			Sommaire du Match
186 - 2021-04-27	1267	IceHogs	7	Wolf Pack	1	W			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité	2000	1000
Prix des Billets	50	0
Assistance	79,286	39,111
Assistance PCT	96.69%	95.39%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité	Popularité de l'Équipe
0	2888 - 96.26%	90,837$	3,724,300$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
3,279,651$	2,697,166$	2,686,666$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
14,501$	1,939,648$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	21,705$	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

	IceHogs
	GP: 82 \| W: 62 \| L: 16 \| OTL: 4 \| P: 128 GF: 371 \| GA: 128 \| PP%: 16.86% \| PK%: 91.52% DG: Mark \| Morale : 50 \| Moyenne d'Équipe : 61