Association Québécoise de Hockey Simulé

Association Québécoise de Hockey Simulé - Husky

Husky

GP: 21 | W: 8 | L: 11 | OTL: 2 | P: 18
GF: 69 | GA: 77 | PP%: 28.81% | PK%: 73.97%
DG: René-Karl Poirier | Morale : 90 | Moyenne d'Équipe : 59
Prochain matchs #328 vs Vandals

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Montreal Canadiens
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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	Yakov Trenin		X			100.00	96	48	78	81	70	76	93	58	30	53	63	80	67	86	78	0	90	68	0
2	Kasperi Kapanen			X		100.00	80	45	88	86	65	68	80	66	30	64	69	64	70	86	77	0	90	68	0
3	Christian Fischer			X		100.00	82	45	88	79	76	74	97	59	30	54	63	79	69	86	78	0	90	68	0
4	Yegor Sharangovich		X			100.00	52	45	89	83	68	72	91	61	30	59	64	84	84	84	75	0	90	67	0
5	Parker Kelly (R)			X		100.00	99	54	77	77	61	53	66	48	30	44	51	79	50	83	71	0	90	61	0
6	Jansen Harkins (R)		X			100.00	62	45	80	79	68	54	40	54	30	47	61	65	66	85	73	0	90	59	0
7	Sean Couturier (C)	X				100.00	50	45	95	74	78	50	40	45	30	40	50	55	50	91	78	0	90	54	0
8	Dmitrij Jaskin			X		100.00	50	45	95	73	78	50	40	45	30	40	50	55	50	90	77	0	90	54	0
9	Alexandre Texier (A)		X			100.00	50	45	95	78	61	50	40	45	30	40	50	55	50	84	71	0	90	53	0
10	Michael Sgarbossa	X				100.00	50	45	95	78	56	50	40	45	30	40	50	55	50	91	78	0	90	53	0
11	Nikolay Goldobin			X		100.00	50	45	95	76	62	50	40	45	30	40	50	55	50	88	75	0	90	53	0
12	Oliver Ekman-Larsson				X	100.00	70	45	84	86	65	84	65	62	30	70	55	64	69	91	85	0	90	69	0
13	Austin Strand (R)				X	100.00	51	45	92	74	74	52	40	45	30	40	50	58	50	86	74	0	90	58	0
14	Jake Gardiner				X	100.00	50	45	95	75	66	50	40	45	30	40	50	55	50	93	80	0	90	57	0
15	Xavier Ouellet				X	100.00	50	45	95	76	60	50	40	45	30	40	50	55	50	90	77	0	90	57	0
16	Ryan Murphy				X	100.00	50	45	95	78	51	50	40	45	30	40	50	55	50	90	77	0	90	56	0
17	Robbie Russo				X	100.00	50	45	95	77	55	50	40	45	30	40	50	55	50	90	77	0	90	56	0
Rayé
1	Filip Chlapik	X				100.00	50	45	95	77	67	50	40	45	30	40	50	55	50	86	73	0	90	53	0
2	MacKenzie MacEachern		X			100.00	50	45	95	77	67	50	40	45	30	40	50	55	50	89	76	0	90	53	0
3	Jayden Halbgewachs (R)		X			100.00	50	45	95	80	40	50	40	45	30	40	50	55	50	86	73	0	90	52	0
4	Semyon Der-Arguchintsev (R)	X				100.00	50	45	95	79	48	50	40	45	30	40	50	55	50	82	69	0	90	52	0

	MOYENNE D'ÉQUIPE					100.00	59	46	91	78	64	56	52	49	30	45	54	61	56	87	76	0	90	58

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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	Kaapo Kahkonen	100.00	72	71	88	91	70	75	72	75	70	70	70	86	81	0	90	66	0
2	Mads Sogaard (R)	100.00	71	53	76	89	73	70	71	70	73	70	70	82	73	0	90	63	0
Rayé
1	Chris Driedger	100.00	70	40	40	90	65	65	65	65	65	65	65	87	80	0	90	59	0

	MOYENNE D'ÉQUIPE	100.00	71	55	68	90	69	70	69	70	69	68	68	85	78	0	90	63

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Jeff Blashill	65	65	65	65	84	78	72	CAN	50	2	1,000,000$

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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

#	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	GA	TA	EG	HT	P/20	FW	FT	S1	S2	S3
1	Yakov Trenin	Husky (MTL)	LW	21	12	15	27	-3	24	0	47	37	75	27	43	16.00%	11	459	21.87	4	3	7	18	48	1	0	1	3	48	1	0	35.59%	59	21	22	2	0	1.18	0	0	3	1	2
2	Kasperi Kapanen	Husky (MTL)	RW	21	7	15	22	-10	31	5	30	32	68	19	45	10.29%	10	418	19.92	4	5	9	12	42	1	1	2	2	50	0	2	37.36%	91	15	12	0	0	1.05	0	0	1	1	1
3	Yegor Sharangovich	Husky (MTL)	LW	21	12	8	20	-1	12	0	25	38	75	18	31	16.00%	11	397	18.95	2	2	4	9	35	2	0	2	6	42	0	2	29.63%	27	13	17	0	0	1.01	0	0	1	4	2
4	Christian Fischer	Husky (MTL)	RW	12	8	12	20	5	8	0	20	27	30	12	28	26.67%	11	255	21.26	1	5	6	3	26	0	0	0	0	29	0	0	31.08%	74	8	12	0	0	1.57	0	0	3	2	1
5	Michael Sgarbossa	Husky (MTL)	C	21	6	11	17	-9	6	0	25	30	28	2	11	21.43%	10	391	18.66	2	3	5	10	45	0	0	0	2	22	2	0	35.99%	339	4	11	0	0	0.87	0	0	0	1	3
6	Jansen Harkins	Husky (MTL)	LW	21	9	5	14	-1	15	5	26	25	56	16	39	16.07%	11	340	16.24	0	0	0	0	4	0	0	0	3	23	2	1	45.71%	35	23	12	0	1	0.82	0	1	2	1	0
7	Oliver Ekman-Larsson	Husky (MTL)	D	13	1	10	11	3	16	0	31	20	28	14	9	3.57%	14	323	24.85	1	3	4	6	33	0	2	2	2	33	0	0	0.00%	2	8	19	0	0	0.68	0	0	0	1	1
8	Parker Kelly	Husky (MTL)	RW	21	2	4	6	-3	20	0	34	31	40	12	19	5.00%	9	344	16.42	0	1	1	1	16	0	0	0	1	16	0	0	18.18%	11	6	12	0	0	0.35	0	0	0	0	0
9	Alexandre Texier	Husky (MTL)	LW	21	4	2	6	4	2	0	11	13	21	6	5	19.05%	8	264	12.59	0	0	0	0	0	0	0	0	1	3	2	0	30.30%	66	4	11	0	0	0.45	0	0	0	2	0
10	Dmitrij Jaskin	Husky (MTL)	RW	21	1	5	6	-1	5	5	21	22	13	3	8	7.69%	3	334	15.95	0	0	0	1	13	0	0	0	0	1	0	0	40.87%	115	5	6	0	0	0.36	0	1	0	0	0
11	Nikolay Goldobin	Husky (MTL)	RW	21	0	5	5	-1	2	0	14	20	12	4	11	0.00%	5	281	13.38	0	1	1	0	15	0	0	0	0	9	0	0	41.10%	146	1	7	0	0	0.36	0	0	0	0	1
12	Jake Gardiner	Husky (MTL)	D	21	0	4	4	-6	7	5	8	19	23	4	6	0.00%	26	449	21.42	0	0	0	2	37	0	1	1	0	55	0	0	0.00%	0	0	16	0	0	0.18	1	0	0	0	0
13	Robbie Russo	Husky (MTL)	D	21	1	3	4	-11	7	5	14	12	14	5	6	7.14%	27	406	19.37	0	1	1	1	26	0	0	0	0	43	0	0	0.00%	2	3	19	0	0	0.20	0	1	0	0	0
14	Ryan Murphy	Husky (MTL)	D	21	0	3	3	-3	0	0	3	17	10	6	5	0.00%	17	370	17.64	0	0	0	0	18	0	0	0	0	31	0	0	0.00%	1	1	20	0	0	0.16	0	0	0	0	0
15	Sean Couturier	Husky (MTL)	C	6	1	1	2	-2	2	0	5	9	7	2	7	14.29%	2	123	20.52	1	0	1	4	9	0	0	0	0	3	0	0	31.40%	121	1	4	0	0	0.32	0	0	0	0	0
16	Austin Strand	Husky (MTL)	D	7	0	1	1	1	4	0	5	10	11	2	5	0.00%	7	171	24.48	0	1	1	0	14	0	0	0	0	12	0	0	0.00%	0	0	6	0	0	0.12	0	0	0	0	0
17	Xavier Ouellet	Husky (MTL)	D	11	0	1	1	-5	0	0	10	19	11	4	6	0.00%	11	248	22.63	0	0	0	0	26	0	0	0	0	37	0	0	100.00%	1	1	18	0	0	0.08	0	0	0	0	0
18	Filip Chlapik	Husky (MTL)	C	1	1	0	1	1	0	0	1	3	2	0	0	50.00%	0	19	19.45	0	0	0	0	2	0	0	0	0	0	0	0	44.44%	27	0	0	0	0	1.03	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				302	65	105	170	-42	161	25	330	384	524	156	284	12.40%	193	5601	18.55	15	25	40	67	416	4	4	8	20	463	7	5	36.17%	1117	114	224	2	1	0.61	1	3	10	13	11

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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	GA	SA	SAR	A	ST	BG	S1	S2	S3
1	Kaapo Kahkonen	Husky (MTL)	18	6	10	2	0.875	3.76	1037	65	518	270	2	18	3	1	0	0
2	Mads Sogaard	Husky (MTL)	5	2	1	0	0.905	3.14	229	12	126	62	0	3	18	1	1	1
Stats d'équipe Total ou en Moyenne			23	8	11	2	0.880	3.65	1266	77	644	332	2	21	21	2	1	1

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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
Alexandre Texier	Husky (MTL)	LW	24	1999-09-13	No	186 Lbs	6 ft1	No	No	No	4	Avec Restriction	Pro & Farm	1,200,000$	0$	0$	No
Austin Strand	Husky (MTL)	D	26	1997-02-17	Yes	215 Lbs	6 ft3	No	No	No	3	Avec Restriction	Pro & Farm	850,000$	0$	0$	No
Chris Driedger	Husky (MTL)	G	29	1994-05-18	No	208 Lbs	6 ft4	No	No	No	4	Sans Restriction	Pro & Farm	850,000$	0$	0$	No
Christian Fischer	Husky (MTL)	RW	26	1997-04-15	No	212 Lbs	6 ft2	No	No	No	3	Avec Restriction	Pro & Farm	1,025,000$	0$	0$	No
Dmitrij Jaskin	Husky (MTL)	RW	30	1993-03-23	No	216 Lbs	6 ft2	No	No	No	3	Sans Restriction	Pro & Farm	800,000$	0$	0$	No
Filip Chlapik	Husky (MTL)	C	26	1997-06-03	No	194 Lbs	6 ft2	No	No	No	3	Avec Restriction	Pro & Farm	750,000$	0$	0$	No
Jake Gardiner	Husky (MTL)	D	33	1990-07-04	No	203 Lbs	6 ft2	No	No	No	2	Sans Restriction	Pro & Farm	900,000$	0$	0$	No
Jansen Harkins	Husky (MTL)	LW	26	1997-05-23	Yes	197 Lbs	6 ft2	No	No	No	1	Avec Restriction	Pro & Farm	925,000$	0$	0$	No
Jayden Halbgewachs	Husky (MTL)	LW	26	1997-03-22	Yes	160 Lbs	5 ft8	No	No	No	2	Avec Restriction	Pro & Farm	750,000$	0$	0$	No
Kaapo Kahkonen	Husky (MTL)	G	27	1996-08-16	No	217 Lbs	6 ft2	No	No	No	1	Avec Restriction	Pro & Farm	1,500,000$	0$	0$	No
Kasperi Kapanen	Husky (MTL)	RW	27	1996-07-23	No	194 Lbs	6 ft1	No	No	No	1	Avec Restriction	Pro & Farm	1,750,000$	0$	0$	No
MacKenzie MacEachern	Husky (MTL)	LW	29	1994-03-09	No	193 Lbs	6 ft2	No	No	No	4	Sans Restriction	Pro & Farm	800,000$	0$	0$	No
Mads Sogaard	Husky (MTL)	G	22	2000-12-13	Yes	196 Lbs	6 ft7	No	No	No	3	Avec Restriction	Pro & Farm	1,025,000$	0$	0$	No
Michael Sgarbossa	Husky (MTL)	C	31	1992-07-25	No	179 Lbs	6 ft0	No	No	No	2	Sans Restriction	Pro & Farm	800,000$	0$	0$	No
Nikolay Goldobin	Husky (MTL)	RW	27	1995-10-07	No	196 Lbs	5 ft11	No	No	No	1	Avec Restriction	Pro & Farm	800,000$	0$	0$	No
Oliver Ekman-Larsson	Husky (MTL)	D	32	1991-07-17	No	200 Lbs	6 ft2	No	No	No	4	Sans Restriction	Pro & Farm	2,500,000$	0$	0$	No
Parker Kelly	Husky (MTL)	RW	24	1999-05-14	Yes	190 Lbs	6 ft0	No	No	No	3	Avec Restriction	Pro & Farm	900,000$	0$	0$	No
Robbie Russo	Husky (MTL)	D	29	1993-10-12	No	189 Lbs	6 ft0	No	No	No	2	Sans Restriction	Pro & Farm	800,000$	0$	0$	No
Ryan Murphy	Husky (MTL)	D	29	1993-10-12	No	185 Lbs	5 ft11	No	No	No	2	Sans Restriction	Pro & Farm	800,000$	0$	0$	No
Sean Couturier	Husky (MTL)	C	30	1992-12-07	No	211 Lbs	6 ft3	No	No	No	1	Sans Restriction	Pro & Farm	7,000,000$	0$	0$	No
Semyon Der-Arguchintsev	Husky (MTL)	C	23	2000-09-15	Yes	173 Lbs	5 ft10	No	No	No	4	Avec Restriction	Pro & Farm	800,000$	0$	0$	No
Xavier Ouellet	Husky (MTL)	D	30	1993-07-29	No	199 Lbs	6 ft0	No	No	No	2	Sans Restriction	Pro & Farm	900,000$	0$	0$	No
Yakov Trenin	Husky (MTL)	LW	26	1997-01-13	No	201 Lbs	6 ft2	No	No	No	3	Avec Restriction	Pro & Farm	1,500,000$	0$	0$	No
Yegor Sharangovich	Husky (MTL)	LW	25	1998-06-06	No	196 Lbs	6 ft2	No	No	No	1	Avec Restriction	Pro & Farm	2,000,000$	0$	0$	No

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
24	27.38	196 Lbs	6 ft1	2.46	1,330,208$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Yakov Trenin	Michael Sgarbossa	Kasperi Kapanen	35	1	2	2
2	Yegor Sharangovich	Nikolay Goldobin	Parker Kelly	30	1	2	2
3	Jansen Harkins	Alexandre Texier	Dmitrij Jaskin	25	1	2	2
4	Alexandre Texier	Kasperi Kapanen	Nikolay Goldobin	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jake Gardiner	Robbie Russo	35	1	2	2
2	Ryan Murphy		30	1	2	2
3			25	1	2	2
4	Jake Gardiner	Robbie Russo	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Yakov Trenin	Michael Sgarbossa	Kasperi Kapanen	55	1	2	2
2	Yegor Sharangovich	Nikolay Goldobin	Parker Kelly	45	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jake Gardiner	Robbie Russo	55	1	2	2
2	Ryan Murphy		45	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Kasperi Kapanen	Yakov Trenin	55	1	2	2
2	Yegor Sharangovich	Parker Kelly	45	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jake Gardiner	Robbie Russo	55	1	2	2
2	Ryan Murphy		45	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Kasperi Kapanen	55	1	2	2	Jake Gardiner	Robbie Russo	55	1	2	2
2	Yakov Trenin	45	1	2	2	Ryan Murphy		45	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Kasperi Kapanen	Yakov Trenin	55	1	2	2
2	Yegor Sharangovich	Parker Kelly	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jake Gardiner	Robbie Russo	55	1	2	2
2	Ryan Murphy		45	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Yakov Trenin	Michael Sgarbossa	Kasperi Kapanen	Jake Gardiner	Robbie Russo

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Yakov Trenin	Michael Sgarbossa	Kasperi Kapanen	Jake Gardiner	Robbie Russo

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Jansen Harkins, Dmitrij Jaskin, Michael Sgarbossa	Jansen Harkins, Dmitrij Jaskin	Michael Sgarbossa

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Ryan Murphy, Jake Gardiner, Robbie Russo	Ryan Murphy	Jake Gardiner, Robbie Russo

Tirs de Pénalité
Kasperi Kapanen, Yakov Trenin, Yegor Sharangovich, Parker Kelly, Jansen Harkins

Gardien
#1 : Kaapo Kahkonen, #2 : Mads Sogaard

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	Bandits	3	1	2	0	0	0	0	0	11	15	-4	2	0	2	0	0	0	0	0	6	14	-8	1	1	0	0	0	0	0	0	5	1	4	2	0.333	11	18	29	0	0	14	25	27	3	83	140	199	185	17	94	30	39	65	7	1	14.29%	12	6	50.00%	1	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
2	Barracudas	1	0	1	0	0	0	0	0	1	4	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	4	-3	0	0.000	1	1	2	0	0	14	25	27	3	24	140	199	185	17	26	6	2	9	5	1	20.00%	1	1	0.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
3	CoolFm	1	0	1	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	4	-1	0	0.000	3	6	9	0	0	14	25	27	3	36	140	199	185	17	28	9	4	22	5	2	40.00%	2	1	50.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
4	Farmers	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	3	5	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	5	8	0	0	14	25	27	3	25	140	199	185	17	32	11	8	18	3	1	33.33%	4	1	75.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
5	Goons	2	1	0	0	1	0	0	0	12	5	7	1	1	0	0	0	0	0	0	8	2	6	1	0	0	0	1	0	0	0	4	3	1	4	1.000	12	19	31	0	0	14	25	27	3	56	140	199	185	17	76	30	18	41	6	3	50.00%	9	2	77.78%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
6	Hunters	2	0	1	0	0	1	0	0	4	6	-2	1	0	0	0	0	1	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	3	-1	1	0.250	4	6	10	0	0	14	25	27	3	49	140	199	185	17	44	14	22	21	11	1	9.09%	11	1	90.91%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
7	Igloos	3	1	1	0	1	0	0	0	9	8	1	1	1	0	0	0	0	0	0	3	1	2	2	0	1	0	1	0	0	0	6	7	-1	4	0.667	9	13	22	0	0	14	25	27	3	57	140	199	185	17	91	15	15	48	4	1	25.00%	5	0	100.00%	2	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
8	Marmots	1	0	1	0	0	0	0	0	2	4	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	2	3	5	0	0	14	25	27	3	20	140	199	185	17	27	17	12	13	0	0	0.00%	6	2	66.67%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
9	Smirnoff Ice	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	3	1	2	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	6	9	0	0	14	25	27	3	19	140	199	185	17	26	12	4	18	4	1	25.00%	2	0	100.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
10	Snowbirds	1	0	1	0	0	0	0	0	4	6	-2	1	0	1	0	0	0	0	0	4	6	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	4	8	12	0	0	14	25	27	3	29	140	199	185	17	40	11	17	17	1	1	100.00%	6	3	50.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
11	TigersCats	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	14	25	27	3	32	140	199	185	17	31	8	8	15	4	2	50.00%	4	0	100.00%	1	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
	Total	21	5	11	0	3	2	0	0	69	77	-8	10	4	4	0	0	2	0	0	37	37	0	11	1	7	0	3	0	0	0	32	40	-8	18	0.429	69	113	182	0	0	14	25	27	3	541	140	199	185	17	644	208	182	359	59	17	28.81%	73	19	73.97%	4	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
13	Vipers	1	0	1	0	0	0	0	0	2	6	-4	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	6	-4	0	0.000	2	4	6	0	0	14	25	27	3	19	140	199	185	17	34	12	21	16	1	0	0.00%	5	2	60.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
14	Wolves	1	0	0	0	0	1	0	0	3	4	-1	1	0	0	0	0	1	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	3	5	8	0	0	14	25	27	3	27	140	199	185	17	28	11	6	10	4	1	25.00%	3	0	100.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
15	Xpress	2	0	1	0	1	0	0	0	7	8	-1	0	0	0	0	0	0	0	0	0	0	0	2	0	1	0	1	0	0	0	7	8	-1	2	0.500	7	10	17	0	0	14	25	27	3	65	140	199	185	17	67	22	6	46	4	2	50.00%	3	0	100.00%	0	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
	_Since Last GM Reset	21	5	11	0	3	2	0	0	69	77	-8	10	4	4	0	0	2	0	0	37	37	0	11	1	7	0	3	0	0	0	32	40	-8	18	0.429	69	113	182	0	0	14	25	27	3	541	140	199	185	17	644	208	182	359	59	17	28.81%	73	19	73.97%	4	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
	_Vs Conference	17	5	8	0	3	1	0	0	59	57	2	8	4	3	0	0	1	0	0	30	27	3	9	1	5	0	3	0	0	0	29	30	-1	17	0.500	59	95	154	0	0	14	25	27	3	442	140	199	185	17	516	168	136	307	48	14	29.17%	58	13	77.59%	4	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
	_Vs Division	13	3	6	0	3	1	0	0	46	46	0	5	2	2	0	0	1	0	0	19	20	-1	8	1	4	0	3	0	0	0	27	26	1	13	0.500	46	72	118	0	0	14	25	27	3	346	140	199	185	17	400	120	104	243	37	10	27.03%	42	10	76.19%	3	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197

			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
21	18	L5	69	113	182	541	644	208	182	359	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
21	5	11	3	2	0	0	69	77

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
10	4	4	0	2	0	0	37	37

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
11	1	7	3	0	0	0	32	40

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	6	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
59	17	28.81%	73	19	73.97%	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
140	199	185	17	14	25	27	3

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
139	378	36.77%	165	433	38.11%	101	300	33.67%

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
350	161	531	190	387	197

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	Lien
1 - 2024-04-06	7	Bandits	10	Husky	3	L		Sommaire du Match
2 - 2024-04-07	13	Husky	4	Xpress	3	W	X	Sommaire du Match
4 - 2024-04-09	36	Husky	4	Igloos	3	W	X	Sommaire du Match
5 - 2024-04-10	44	Husky	3	Xpress	5	L		Sommaire du Match
7 - 2024-04-12	60	Wolves	4	Husky	3	L	X	Sommaire du Match
8 - 2024-04-13	76	Husky	3	CoolFm	4	L		Sommaire du Match
9 - 2024-04-14	88	Bandits	4	Husky	3	L		Sommaire du Match
11 - 2024-04-16	114	Smirnoff Ice	1	Husky	3	W		Sommaire du Match
13 - 2024-04-18	131	Hunters	3	Husky	2	L	X	Sommaire du Match
15 - 2024-04-20	152	Husky	2	Hunters	3	L		Sommaire du Match
16 - 2024-04-21	162	Husky	4	Goons	3	W	X	Sommaire du Match
17 - 2024-04-22	171	Goons	2	Husky	8	W		Sommaire du Match
18 - 2024-04-23	189	Husky	2	Igloos	4	L		Sommaire du Match
20 - 2024-04-25	207	Igloos	1	Husky	3	W		Sommaire du Match
21 - 2024-04-26	224	TigersCats	1	Husky	5	W		Sommaire du Match
22 - 2024-04-27	232	Husky	5	Bandits	1	W		Sommaire du Match
24 - 2024-04-29	254	Husky	2	Marmots	4	L		Sommaire du Match
25 - 2024-04-30	265	Husky	2	Vipers	6	L		Sommaire du Match
26 - 2024-05-01	276	Snowbirds	6	Husky	4	L		Sommaire du Match
28 - 2024-05-03	294	Husky	1	Barracudas	4	L		Sommaire du Match
29 - 2024-05-04	306	Farmers	5	Husky	3	L		Sommaire du Match
31 - 2024-05-06	328	Vandals	-	Husky	-
32 - 2024-05-07	341	Husky	-	Xpress	-
34 - 2024-05-09	359	Marmots	-	Husky	-
35 - 2024-05-10	377	Chiwawa	-	Husky	-
37 - 2024-05-12	394	Husky	-	Predateurs	-
38 - 2024-05-13	404	Husky	-	CoolFm	-
40 - 2024-05-15	418	Vipers	-	Husky	-
42 - 2024-05-17	441	Husky	-	Snowbirds	-
43 - 2024-05-18	449	Hunters	-	Husky	-
44 - 2024-05-19	469	Husky	-	Igloos	-
45 - 2024-05-20	482	Wolves	-	Husky	-
46 - 2024-05-21	492	Husky	-	Chiwawa	-
47 - 2024-05-22	505	Husky	-	Farmers	-
49 - 2024-05-24	525	Spartans	-	Husky	-
51 - 2024-05-26	544	Husky	-	Twins	-
52 - 2024-05-27	554	Scorpions	-	Husky	-
54 - 2024-05-29	579	Bandits	-	Husky	-
55 - 2024-05-30	589	Husky	-	Thugs	-
57 - 2024-06-01	602	Husky	-	Bandits	-
58 - 2024-06-02	616	Predateurs	-	Husky	-
60 - 2024-06-04	639	Supreme	-	Husky	-
62 - 2024-06-06	653	Husky	-	Outlaws	-
63 - 2024-06-07	666	Husky	-	Warriors	-
64 - 2024-06-08	678	Marlies	-	Husky	-
66 - 2024-06-10	697	Twins	-	Husky	-
67 - 2024-06-11	712	Husky	-	Marlies	-
68 - 2024-06-12	731	CoolFm	-	Husky	-
69 - 2024-06-13	741	Husky	-	Bayou	-
70 - 2024-06-14	752	Husky	-	Spartans	-
72 - 2024-06-16	770	Thugs	-	Husky	-
73 - 2024-06-17	793	Grizzlies	-	Husky	-
74 - 2024-06-18	804	Husky	-	Smirnoff Ice	-
75 - 2024-06-19	819	Husky	-	Wolves	-
77 - 2024-06-21	831	Xpress	-	Husky	-
78 - 2024-06-22	854	Husky	-	Grizzlies	-
79 - 2024-06-23	865	Husky	-	TigersCats	-
81 - 2024-06-25	875	Raptors	-	Husky	-
83 - 2024-06-27	894	Saguenéens	-	Husky	-
85 - 2024-06-29	917	Rockets	-	Husky	-
87 - 2024-07-01	938	CoolFm	-	Husky	-
88 - 2024-07-02	953	Husky	-	Scorpions	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
90 - 2024-07-04	968	Husky	-	Supreme	-
91 - 2024-07-05	983	Barracudas	-	Husky	-
93 - 2024-07-07	1002	Husky	-	Raptors	-
94 - 2024-07-08	1013	Outlaws	-	Husky	-
95 - 2024-07-09	1029	Husky	-	Hunters	-
96 - 2024-07-10	1041	Husky	-	Goons	-
98 - 2024-07-12	1052	Xpress	-	Husky	-
100 - 2024-07-14	1074	Husky	-	Rockets	-
101 - 2024-07-15	1080	Warriors	-	Husky	-
103 - 2024-07-17	1098	Husky	-	Vandals	-
104 - 2024-07-18	1103	Husky	-	Saguenéens	-
105 - 2024-07-19	1116	Igloos	-	Husky	-
108 - 2024-07-22	1145	Bayou	-	Husky	-
111 - 2024-07-25	1168	Goons	-	Husky	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	15
Assistance	19,830	9,987
Assistance PCT	99.15%	99.87%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
28	2982 - 99.39%	101,263$	1,012,626$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,113,060$	3,192,500$	3,192,500$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	847,560$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
2,835,353$	83	37,102$	3,079,466$

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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
Saison Régulière
45	82	30	38	0	3	3	3	5	272	306	-34	41	16	19	0	1	2	2	1	138	152	-14	41	14	19	0	2	1	1	4	134	154	-20	80	272	460	732	1	2	51	100	115	9	2268	565	777	911	44	2340	771	598	1240	238	42	17.65%	252	77	69.44%	4	638	1477	43.20%	697	1511	46.13%	582	1338	43.50%	1826	1202	1905	642	1233	607
46	82	40	32	0	4	5	1	0	297	281	16	41	18	18	0	2	2	1	0	150	150	0	41	22	14	0	2	3	0	0	147	131	16	95	297	518	815	2	2	52	120	120	5	2141	492	799	831	22	2234	755	762	1168	238	64	26.89%	260	63	75.77%	8	715	1373	52.08%	748	1522	49.15%	674	1293	52.13%	1913	1292	1804	631	1225	607
47	21	5	11	0	3	2	0	0	69	77	-8	10	4	4	0	0	2	0	0	37	37	0	11	1	7	0	3	0	0	0	32	40	-8	18	69	113	182	0	0	14	25	27	3	541	140	199	185	17	644	208	182	359	59	17	28.81%	73	19	73.97%	4	139	378	36.77%	165	433	38.11%	101	300	33.67%	350	161	531	190	387	197
Total Saison Régulière	185	75	81	0	10	10	4	5	638	664	-26	92	38	41	0	3	6	3	1	325	339	-14	93	37	40	0	7	4	1	4	313	325	-12	193	638	1091	1729	3	4	117	245	262	17	4950	1197	1775	1927	83	5218	1734	1542	2767	535	123	22.99%	585	159	72.82%	16	1492	3228	46.22%	1610	3466	46.45%	1357	2931	46.30%	4090	2657	4241	1464	2846	1412

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