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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	Magnus Paajarvi		X			100.00	71	35	93	84	70	76	93	54	50	47	60	84	57	66	77	0	38	74	0
2	Patrik Berglund	X				100.00	76	36	91	83	75	69	54	56	65	48	63	71	62	75	81	0	50	71	0
3	Michael Rasmussen (R) (A)	X				100.00	81	40	83	83	81	70	75	57	48	53	60	63	57	42	64	0	50	71	0
4	Marcus Kruger		X			100.00	64	34	85	85	57	70	84	52	65	49	54	82	53	69	76	0	50	70	0
5	Filip Chytil (R) (A)	X				100.00	55	35	92	83	69	76	91	57	50	52	61	59	58	42	59	0	50	69	0
6	Dale Weise			X		100.00	78	39	91	83	68	68	66	52	51	47	56	68	54	75	79	0	50	69	0
7	Zach Sanford		X			100.00	58	34	92	84	72	55	60	53	47	50	56	75	55	57	68	0	50	68	0
8	Jaromir Jagr		X			100.00	48	34	93	80	78	61	44	54	46	52	56	63	54	99	86	0	50	68	0
9	Alexandre Burrows (C)			X		100.00	57	35	91	84	63	63	57	51	46	46	55	65	55	96	85	0	50	67	0
10	Joey Anderson (R)			X		100.00	73	34	92	84	57	64	41	52	48	46	58	76	56	45	65	0	50	66	0
11	Chris Stewart			X		100.00	57	44	92	77	80	60	52	50	45	45	56	57	61	78	75	0	50	65	0
12	Maxim Mamin	X				100.00	55	38	95	82	68	58	28	46	48	41	52	58	51	54	61	0	50	60	0
13	Korbinian Holzer				X	100.00	89	34	92	82	71	66	30	54	51	52	55	87	53	75	85	0	31	74	0
14	Christian Jaros (R)				X	100.00	89	37	85	85	65	68	74	54	48	56	52	68	51	51	70	0	50	71	0
15	Dalton Prout				X	100.00	58	36	93	82	70	63	28	50	49	48	52	63	51	69	71	0	50	64	0
16	Jakub Jerabek				X	100.00	52	31	95	83	57	60	34	51	49	48	52	58	52	66	68	0	50	62	0
Rayé
1	Cody McLeod		X			100.00	96	75	73	83	67	62	59	47	51	42	52	58	51	87	85	0	50	67	0
2	Clarke MacArthur		X			100.00	52	31	95	84	59	54	28	51	43	47	54	55	53	84	77	0	50	62	0
3	Justin Kloos (R)	X				100.00	46	33	95	85	48	55	25	45	48	40	50	60	50	60	63	0	50	57	0
4	Brandon Gignac (R)	X				100.00	45	33	95	87	50	55	25	45	47	40	50	59	50	48	56	0	50	57	0
5	Bogdan Kiselevich				X	100.00	59	32	94	83	60	58	36	50	49	52	50	60	50	69	71	0	50	63	0
6	Taylor Chorney				X	100.00	49	32	95	84	57	57	27	48	50	47	51	60	51	78	73	0	50	62	0
7	Petter Granberg				X	100.00	47	32	95	84	65	51	25	46	49	45	50	56	50	63	65	0	50	59	0

	MOYENNE D'ÉQUIPE					100.00	63	37	91	83	66	63	49	51	50	48	55	65	54	67	72	0	49	66

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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	Richard Bachman	100.00	70	25	58	68	76	66	67	70	73	65	57	81	67	0	45	66	0
Rayé
1	Antti Niemi	100.00	79	39	81	78	76	77	79	79	78	69	65	97	83	0	50	76	0
2	Andrew Hammond	100.00	68	27	79	79	64	69	71	68	67	65	57	77	65	0	50	65	0
3	Eric Comrie (R)	100.00	70	25	61	68	77	63	67	71	74	65	57	49	51	0	50	65	0

	MOYENNE D'ÉQUIPE	100.00	72	29	70	73	73	69	71	72	73	66	59	76	67	0	49	68

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Scott Gordon	74	78	79	82	75	74	69	USA	56	1	500,000$

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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	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	P/20	PSS	S1	S2	S3
1	Patrik Berglund	Sea Dogs (MTL)	C	13	5	11	16	-2	8	15	23	50	11	36	10.00%	10	288	22.17	2	6	8	6	30	0	1	1	2	28	2	0	60.32%	373	18	8	1.11	0	2	1	1
2	Magnus Paajarvi	Sea Dogs (MTL)	LW	13	8	4	12	-2	0	29	16	62	11	34	12.90%	13	294	22.65	4	1	5	7	30	1	0	1	2	32	0	1	42.11%	76	17	4	0.82	0	3	1	0
3	Zach Sanford	Sea Dogs (MTL)	LW	13	3	5	8	5	4	12	5	23	10	18	13.04%	7	178	13.72	1	1	2	1	3	0	0	0	1	5	1	0	25.00%	4	14	5	0.90	0	0	1	1
4	Dale Weise	Sea Dogs (MTL)	RW	13	1	5	6	0	10	15	7	43	10	21	2.33%	2	254	19.59	0	3	3	4	30	0	0	0	0	0	0	0	40.00%	15	7	10	0.47	0	0	0	1
5	Christian Jaros	Sea Dogs (MTL)	D	13	2	4	6	-1	18	29	25	22	9	13	9.09%	15	353	27.17	2	3	5	4	35	0	0	0	0	22	1	0	0.00%	0	9	16	0.34	0	0	0	1
6	Filip Chytil	Sea Dogs (MTL)	C	13	2	3	5	5	2	8	20	29	13	14	6.90%	1	182	14.06	0	0	0	0	3	0	0	0	0	6	0	0	46.91%	81	8	3	0.55	0	0	0	0
7	Joey Anderson	Sea Dogs (MTL)	RW	13	2	2	4	5	2	15	16	21	8	10	9.52%	5	167	12.92	0	1	1	0	2	0	0	0	0	0	0	0	40.00%	5	5	7	0.48	0	0	1	0
8	Korbinian Holzer	Sea Dogs (MTL)	D	13	2	2	4	-1	4	22	25	28	14	17	7.14%	21	361	27.82	2	0	2	2	35	0	0	0	2	31	1	0	0.00%	0	13	20	0.22	0	0	0	1
9	Jakub Jerabek	Sea Dogs (MTL)	D	13	0	3	3	-1	2	6	11	6	2	4	0.00%	7	183	14.09	0	0	0	0	0	0	0	0	0	1	0	0	0.00%	0	2	8	0.33	0	0	0	0
10	Jaromir Jagr	Sea Dogs (MTL)	LW	13	1	1	2	-2	0	6	11	29	12	14	3.45%	2	136	10.53	0	0	0	0	1	0	0	0	1	13	0	0	33.33%	3	10	7	0.29	0	0	0	0
11	Michael Rasmussen	Sea Dogs (MTL)	C	13	0	2	2	-1	6	24	29	45	10	25	0.00%	2	231	17.82	0	1	1	1	27	0	0	0	3	12	0	0	40.43%	141	21	3	0.17	0	0	0	0
12	Marcus Kruger	Sea Dogs (MTL)	LW	13	2	0	2	-1	8	15	18	35	12	23	5.71%	5	231	17.82	1	0	1	2	27	0	0	0	1	11	1	0	57.14%	7	14	6	0.17	0	0	1	0
13	Alexandre Burrows	Sea Dogs (MTL)	RW	13	0	1	1	-2	2	12	12	12	5	20	0.00%	3	218	16.78	0	1	1	0	27	0	0	0	0	0	0	0	50.00%	10	9	5	0.09	0	0	0	0
14	Chris Stewart	Sea Dogs (MTL)	RW	13	0	1	1	-3	0	10	5	17	4	6	0.00%	2	114	8.80	0	0	0	0	0	0	0	0	0	0	0	0	100.00%	2	3	4	0.17	0	0	0	0
15	Bogdan Kiselevich	Sea Dogs (MTL)	D	8	0	1	1	2	2	4	9	4	4	1	0.00%	7	155	19.43	0	0	0	0	11	0	0	0	1	10	0	0	0.00%	0	2	1	0.13	0	0	0	0
16	Dalton Prout	Sea Dogs (MTL)	D	13	0	1	1	2	2	13	17	13	6	5	0.00%	12	276	21.27	0	0	0	0	25	0	0	0	0	22	0	0	0.00%	0	8	12	0.07	1	0	0	0
17	Maxim Mamin	Sea Dogs (MTL)	C	13	0	0	0	-2	0	8	4	9	3	5	0.00%	4	113	8.70	0	0	0	0	0	0	0	0	0	0	0	0	46.34%	41	0	2	0.00	0	0	0	0
18	Taylor Chorney	Sea Dogs (MTL)	D	8	0	0	0	-2	0	5	6	6	2	2	0.00%	5	111	13.90	0	0	0	0	0	0	0	0	0	4	0	0	0.00%	0	1	5	0.00	0	0	0	0
Stats d'équipe Total ou en Moyenne				224	28	46	74	-1	70	248	259	454	146	268	6.17%	123	3853	17.20	12	17	29	27	291	1	1	2	13	204	6	1	51.72%	758	161	126	0.38	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	GA	SA	SAR	A	ST	BG	S1	S2	S3
1	Antti Niemi	Sea Dogs (MTL)	12	5	3	3	0.920	2.23	699	2	26	325	156	2	12	0	1	1	1
2	Richard Bachman	Sea Dogs (MTL)	2	1	0	1	0.952	1.51	119	0	3	63	34	0	1	13	1	0	0
Stats d'équipe Total ou en Moyenne			14	6	3	4	0.925	2.13	818	2	29	388	190	2	13	13	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	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Alexandre Burrows	Sea Dogs (MTL)	RW	39	1981-01-01	No	197 Lbs	6 ft1	No	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$
Andrew Hammond	Sea Dogs (MTL)	G	32	1988-01-01	No	215 Lbs	6 ft2	No	No	No	3	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$		Lien NHL
Antti Niemi (Contrat à 1 Volet)	Sea Dogs (MTL)	G	37	1983-01-01	No	209 Lbs	6 ft2	No	No	No	1	Farm Only	1,800,000$	180,000$	0$	No				Lien NHL
Bogdan Kiselevich	Sea Dogs (MTL)	D	30	1990-01-01	No	202 Lbs	6 ft0	No	No	No	2	Pro & Farm	800,000$	0$	0$	No	800,000$			Lien
Brandon Gignac	Sea Dogs (MTL)	C	23	1997-01-01	Yes	170 Lbs	5 ft11	No	No	No	1	Pro & Farm	650,000$	0$	0$	No
Chris Stewart	Sea Dogs (MTL)	RW	33	1987-01-01	No	242 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,500,000$	0$	0$	No				Lien NHL
Christian Jaros	Sea Dogs (MTL)	D	24	1996-01-01	Yes	201 Lbs	6 ft3	No	No	No	2	Pro & Farm	850,000$	0$	0$	No	850,000$			Lien / Lien NHL
Clarke MacArthur	Sea Dogs (MTL)	LW	35	1985-01-01	No	192 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,900,000$	0$	0$	No
Cody McLeod	Sea Dogs (MTL)	LW	36	1984-01-01	No	204 Lbs	6 ft2	No	No	No	3	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$		Lien NHL
Dale Weise	Sea Dogs (MTL)	RW	32	1988-01-01	No	206 Lbs	6 ft2	No	No	No	4	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$	1,000,000$	1,000,000$	Lien NHL
Dalton Prout	Sea Dogs (MTL)	D	30	1990-01-01	No	215 Lbs	6 ft3	No	No	No	3	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$		Lien / Lien NHL
Eric Comrie	Sea Dogs (MTL)	G	25	1995-01-01	Yes	175 Lbs	6 ft1	No	No	No	2	Pro & Farm	800,000$	0$	0$	No	800,000$			Lien NHL
Filip Chytil	Sea Dogs (MTL)	C	21	1999-01-01	Yes	208 Lbs	6 ft2	No	No	No	3	Pro & Farm	1,100,000$	0$	0$	No	1,100,000$	1,100,000$		Lien NHL
Jakub Jerabek	Sea Dogs (MTL)	D	29	1991-01-01	No	199 Lbs	5 ft11	No	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$			Lien / Lien NHL
Jaromir Jagr	Sea Dogs (MTL)	LW	48	1972-01-01	No	230 Lbs	6 ft3	No	No	No	4	Pro & Farm	1,100,000$	0$	0$	No	1,100,000$	1,100,000$	1,100,000$	Lien NHL
Joey Anderson	Sea Dogs (MTL)	RW	22	1998-01-01	Yes	190 Lbs	5 ft11	No	No	No	1	Pro & Farm	700,000$	0$	0$	No
Justin Kloos	Sea Dogs (MTL)	C	27	1993-01-01	Yes	175 Lbs	5 ft9	No	No	No	3	Pro & Farm	600,000$	0$	0$	No	600,000$	600,000$		Lien NHL
Korbinian Holzer	Sea Dogs (MTL)	D	32	1988-01-01	No	218 Lbs	6 ft3	No	No	No	1	Pro & Farm	1,000,000$	0$	0$	No				Lien / Lien NHL
Magnus Paajarvi	Sea Dogs (MTL)	LW	29	1991-01-01	No	206 Lbs	6 ft3	No	No	No	4	Pro & Farm	800,000$	0$	0$	No	800,000$	800,000$	800,000$	Lien NHL
Marcus Kruger	Sea Dogs (MTL)	LW	30	1990-01-01	No	186 Lbs	6 ft0	No	No	No	4	Pro & Farm	1,400,000$	0$	0$	No	1,400,000$	1,400,000$	1,400,000$	Lien NHL
Maxim Mamin	Sea Dogs (MTL)	C	25	1995-01-01	No	206 Lbs	6 ft2	No	No	No	3	Pro & Farm	550,000$	0$	0$	No	550,000$	550,000$		Lien NHL
Michael Rasmussen	Sea Dogs (MTL)	C	21	1999-01-01	Yes	221 Lbs	6 ft6	No	No	No	2	Pro & Farm	850,000$	0$	0$	No	850,000$
Patrik Berglund	Sea Dogs (MTL)	C	32	1988-01-01	No	215 Lbs	6 ft4	No	No	No	4	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$	1,000,000$	1,000,000$	Lien NHL
Petter Granberg	Sea Dogs (MTL)	D	28	1992-01-01	No	200 Lbs	6 ft3	No	No	No	4	Pro & Farm	800,000$	0$	0$	No	800,000$	800,000$	800,000$	Lien
Richard Bachman	Sea Dogs (MTL)	G	33	1987-01-01	No	183 Lbs	5 ft10	No	No	No	3	Pro & Farm	800,000$	0$	0$	No	800,000$	800,000$
Taylor Chorney	Sea Dogs (MTL)	D	33	1987-01-01	No	191 Lbs	6 ft0	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$			Lien / Lien NHL
Zach Sanford	Sea Dogs (MTL)	LW	26	1994-01-01	No	207 Lbs	6 ft4	No	No	No	2	Pro & Farm	900,000$	0$	0$	No	900,000$

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
27	30.07	202 Lbs	6 ft1	2.48	951,852$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Magnus Paajarvi	Patrik Berglund	Dale Weise	40	1	2	2
2	Marcus Kruger	Michael Rasmussen	Alexandre Burrows	30	1	2	2
3	Zach Sanford	Filip Chytil	Joey Anderson	20	1	2	2
4	Jaromir Jagr	Maxim Mamin	Chris Stewart	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Korbinian Holzer	Christian Jaros	40	1	2	2
2	Dalton Prout		30	1	2	2
3	Jakub Jerabek		20	1	2	2
4	Korbinian Holzer	Christian Jaros	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Magnus Paajarvi	Patrik Berglund	Dale Weise	60	1	2	2
2	Marcus Kruger	Michael Rasmussen	Alexandre Burrows	40	1	2	2

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

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Magnus Paajarvi	Patrik Berglund	60	1	2	2
2	Michael Rasmussen	Marcus Kruger	40	1	2	2

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

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Magnus Paajarvi	60	1	2	2	Korbinian Holzer	Christian Jaros	60	1	2	2
2	Patrik Berglund	40	1	2	2	Dalton Prout		40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Magnus Paajarvi	Patrik Berglund	60	1	2	2
2	Michael Rasmussen	Marcus Kruger	40	1	2	2

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

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Magnus Paajarvi	Patrik Berglund	Dale Weise	Korbinian Holzer	Christian Jaros

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Magnus Paajarvi	Patrik Berglund	Dale Weise	Korbinian Holzer	Christian Jaros

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Filip Chytil, Zach Sanford, Jaromir Jagr	Filip Chytil, Zach Sanford	Jaromir Jagr

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Jakub Jerabek, , Dalton Prout	Jakub Jerabek	, Dalton Prout

Tirs de Pénalité
Magnus Paajarvi, Patrik Berglund, Michael Rasmussen, Marcus Kruger, Dale Weise

Gardien
#1 : , #2 : Richard Bachman

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	National	7	4	3	0	0	0	0	0	19	16	3	4	3	1	0	0	0	0	0	11	8	3	3	1	2	0	0	0	0	0	8	8	0	8	0.571	19	31	50	0	0	5	10	11	2	256	117	166	148	25	182	68	42	145	20	9	45.00%	22	8	63.64%	1	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124
	Total	13	6	7	0	0	0	0	0	28	29	-1	7	4	3	0	0	0	0	0	15	13	2	6	2	4	0	0	0	0	0	13	16	-3	12	0.462	28	47	75	0	0	5	10	11	2	456	117	166	148	25	388	129	78	249	39	12	30.77%	40	13	67.50%	1	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124
3	Wolf Pack	6	2	4	0	0	0	0	0	9	13	-4	3	1	2	0	0	0	0	0	4	5	-1	3	1	2	0	0	0	0	0	5	8	-3	4	0.333	9	16	25	0	0	5	10	11	2	200	117	166	148	25	206	61	36	104	19	3	15.79%	18	5	72.22%	0	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124
	_Since Last GM Reset	13	6	7	0	0	0	0	0	28	29	-1	7	4	3	0	0	0	0	0	15	13	2	6	2	4	0	0	0	0	0	13	16	-3	12	0.462	28	47	75	0	0	5	10	11	2	456	117	166	148	25	388	129	78	249	39	12	30.77%	40	13	67.50%	1	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124
	_Vs Conference	13	6	7	0	0	0	0	0	28	29	-1	7	4	3	0	0	0	0	0	15	13	2	6	2	4	0	0	0	0	0	13	16	-3	12	0.462	28	47	75	0	0	5	10	11	2	456	117	166	148	25	388	129	78	249	39	12	30.77%	40	13	67.50%	1	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124
	_Vs Division	13	2	4	0	0	0	0	0	28	29	-1	7	1	2	0	0	0	0	0	15	13	2	6	1	2	0	0	0	0	0	13	16	-3	4	0.154	28	47	75	0	0	5	10	11	2	456	117	166	148	25	388	129	78	249	39	12	30.77%	40	13	67.50%	1	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124

			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
13	12	OTL1	28	47	75	456	388	129	78	249	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
13	6	7	0	0	0	0	28	29

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
7	4	3	0	0	0	0	15	13

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
6	2	4	0	0	0	0	13	16

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	3	0	3	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
39	12	30.77%	40	13	67.50%	1

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
117	166	148	25	5	10	11	2

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
152	299	50.84%	155	297	52.19%	85	162	52.47%

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
289	162	272	135	259	124

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 - 2020-07-20	1	National	2	Sea Dogs	1	L	X	Sommaire du Match
2 - 2020-07-21	9	National	2	Sea Dogs	3	W	X	Sommaire du Match
3 - 2020-07-22	17	Sea Dogs	3	National	1	W		Sommaire du Match
4 - 2020-07-23	25	Sea Dogs	2	National	3	L	X	Sommaire du Match
5 - 2020-07-24	33	National	1	Sea Dogs	3	W		Sommaire du Match
6 - 2020-07-25	41	Sea Dogs	3	National	4	L		Sommaire du Match
7 - 2020-07-26	49	National	3	Sea Dogs	4	W	X	Sommaire du Match
8 - 2020-07-27	57	Wolf Pack	2	Sea Dogs	1	L		Sommaire du Match
9 - 2020-07-28	61	Wolf Pack	2	Sea Dogs	1	L	X	Sommaire du Match
10 - 2020-07-29	65	Sea Dogs	1	Wolf Pack	5	L		Sommaire du Match
11 - 2020-07-30	69	Sea Dogs	3	Wolf Pack	1	W		Sommaire du Match
12 - 2020-07-31	73	Wolf Pack	1	Sea Dogs	2	W		Sommaire du Match
13 - 2020-08-01	77	Sea Dogs	1	Wolf Pack	2	L	X	Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	50	30
Assistance	10,937	4,505
Assistance PCT	78.12%	64.36%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
32	2206 - 73.53%	112,043$	784,299$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
0$	2,390,000$	2,390,000$	0$

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

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

Afficher ou Masquer des Colonnes

	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
11	78	43	20	0	4	2	4	5	255	175	80	39	22	11	0	2	0	1	3	122	84	38	39	21	9	0	2	2	3	2	133	91	42	86	255	416	671	6	4	26	100	121	14	2582	699	943	917	60	2351	804	593	1660	321	132	41.12%	272	83	69.49%	5	893	1730	51.62%	836	1731	48.30%	592	1120	52.86%	1712	964	1629	691	1395	728
12	82	32	35	0	1	0	8	6	251	246	5	41	16	18	0	0	0	5	2	122	126	-4	41	16	17	0	1	0	3	4	129	120	9	64	251	401	652	2	3	39	104	99	17	2715	769	1043	884	65	2695	860	607	1718	283	127	44.88%	285	118	58.60%	5	909	1844	49.30%	841	1695	49.62%	606	1213	49.96%	1775	1014	1749	744	1466	732
13	82	32	34	0	2	2	6	6	267	245	22	41	15	19	0	1	1	2	3	126	126	0	41	17	15	0	1	1	4	3	141	119	22	64	267	431	698	2	4	38	114	107	18	2722	769	980	943	78	2783	935	525	1676	291	150	51.55%	262	112	57.25%	1	967	1679	57.59%	937	1751	53.51%	739	1292	57.20%	1756	989	1752	739	1482	749
14	82	41	17	0	1	4	8	11	241	173	68	41	24	5	0	1	2	5	4	144	89	55	41	17	12	0	0	2	3	7	97	84	13	82	241	355	596	0	5	41	93	98	15	2702	707	984	970	121	2568	884	584	1803	290	128	44.14%	277	88	68.23%	4	944	1849	51.05%	921	1914	48.12%	614	1129	54.38%	1759	971	1773	759	1495	761
15	82	35	18	0	10	4	6	9	264	182	82	41	18	8	0	4	0	5	6	126	87	39	41	17	10	0	6	4	1	3	138	95	43	70	264	418	682	0	10	37	115	96	23	2721	750	980	928	113	2487	895	496	1625	307	124	40.39%	229	94	58.95%	1	927	1697	54.63%	959	1725	55.59%	652	1125	57.96%	1809	1034	1747	731	1476	765
16	82	48	18	0	2	2	3	9	273	179	94	41	21	9	0	2	2	1	6	123	88	35	41	27	9	0	0	0	2	3	150	91	59	96	273	433	706	1	3	46	104	118	12	2676	743	1017	868	92	2502	754	572	1679	344	151	43.90%	255	75	70.59%	4	1036	1772	58.47%	954	1708	55.85%	708	1188	59.60%	1829	1036	1721	695	1435	775
17	82	36	23	0	4	4	9	6	285	229	56	41	18	12	0	2	3	3	3	136	113	23	41	18	11	0	2	1	6	3	149	116	33	72	285	470	755	0	0	35	125	112	19	2818	769	1012	989	102	2591	923	557	1591	334	178	53.29%	263	104	60.46%	5	913	1806	50.55%	870	1705	51.03%	658	1281	51.37%	1840	1081	1737	727	1440	733
18	82	52	16	0	1	4	6	3	283	181	102	41	27	8	0	0	2	3	1	139	92	47	41	25	8	0	1	2	3	2	144	89	55	104	283	444	727	1	5	39	112	125	14	2707	758	940	986	52	2525	835	557	1654	341	145	42.52%	264	91	65.53%	2	963	1688	57.05%	957	1703	56.19%	673	1205	55.85%	1824	1038	1705	703	1450	769
19	82	37	24	0	2	5	6	8	242	197	45	41	19	12	0	1	3	3	3	124	97	27	41	18	12	0	1	2	3	5	118	100	18	74	242	366	608	1	4	29	100	105	20	2637	707	962	936	84	2392	820	604	1711	303	120	39.60%	277	88	68.23%	10	919	1678	54.77%	909	1634	55.63%	694	1170	59.32%	1799	1006	1704	735	1500	780
20	78	44	14	0	3	2	10	5	251	162	89	39	25	4	0	2	0	6	2	134	72	62	39	19	10	0	1	2	4	3	117	90	27	88	251	377	628	1	4	37	93	108	20	2583	707	938	911	86	2371	809	541	1646	306	132	43.14%	264	85	67.80%	5	864	1681	51.40%	843	1696	49.71%	562	1064	52.82%	1697	955	1670	715	1399	714
Total Saison Régulière	812	400	219	0	30	29	66	68	2612	1969	643	406	205	106	0	15	13	34	33	1296	974	322	406	195	113	0	15	16	32	35	1316	995	321	800	2612	4111	6723	14	42	367	1060	1089	172	26863	7378	9799	9332	853	25265	8519	5636	16763	3120	1387	44.46%	2648	938	64.58%	42	9335	17424	53.58%	9027	17262	52.29%	6498	11787	55.13%	17805	10094	17193	7244	14542	7513
Séries
11	14	7	7	0	0	0	0	0	28	29	-1	8	3	5	0	0	0	0	0	16	19	-3	6	4	2	0	0	0	0	0	12	10	2	14	28	46	74	0	0	5	8	10	5	418	115	136	140	27	418	136	124	302	41	13	31.71%	62	19	69.35%	0	166	306	54.25%	187	358	52.23%	83	176	47.16%	300	156	312	147	290	145
12	11	6	5	0	0	0	0	0	26	25	1	5	1	4	0	0	0	0	0	10	14	-4	6	5	1	0	0	0	0	0	16	11	5	12	26	43	69	0	1	2	12	10	2	358	71	139	130	18	355	131	52	252	37	14	37.84%	27	6	77.78%	1	107	263	40.68%	97	237	40.93%	58	144	40.28%	230	124	255	113	213	102
13	5	1	4	0	0	0	0	0	11	13	-2	2	0	2	0	0	0	0	0	3	5	-2	3	1	2	0	0	0	0	0	8	8	0	2	11	17	28	0	0	4	3	4	0	203	41	77	82	3	181	51	38	94	18	4	22.22%	19	6	68.42%	0	54	139	38.85%	43	115	37.39%	31	66	46.97%	106	59	114	50	94	44
14	24	16	8	0	0	0	0	0	69	56	13	12	8	4	0	0	0	0	0	33	28	5	12	8	4	0	0	0	0	0	36	28	8	32	69	109	178	0	0	7	25	33	4	778	170	278	291	39	807	284	160	549	67	24	35.82%	80	26	67.50%	0	253	526	48.10%	311	610	50.98%	182	333	54.65%	509	278	531	230	452	226
15	17	8	9	0	0	0	0	0	51	49	2	9	6	3	0	0	0	0	0	32	24	8	8	2	6	0	0	0	0	0	19	25	-6	16	51	80	131	1	0	9	16	23	3	521	140	179	189	13	564	187	111	334	51	24	47.06%	55	20	63.64%	1	162	304	53.29%	184	355	51.83%	111	239	46.44%	363	202	377	154	306	158
16	17	8	9	0	0	0	0	0	51	49	2	9	6	3	0	0	0	0	0	32	24	8	8	2	6	0	0	0	0	0	19	25	-6	16	51	80	131	1	0	9	16	23	3	521	140	179	189	13	564	187	111	334	51	24	47.06%	55	20	63.64%	1	162	304	53.29%	184	355	51.83%	111	239	46.44%	363	202	377	154	306	158
17	16	9	7	0	0	0	0	0	35	34	1	9	4	5	0	0	0	0	0	19	21	-2	7	5	2	0	0	0	0	0	16	13	3	18	35	62	97	1	1	6	12	12	5	430	123	167	124	16	423	165	96	343	58	19	32.76%	48	18	62.50%	0	166	343	48.40%	141	318	44.34%	117	222	52.70%	340	180	322	156	322	161
18	26	16	10	0	0	0	0	0	65	61	4	14	11	3	0	0	0	0	0	41	28	13	12	5	7	0	0	0	0	0	24	33	-9	32	65	105	170	0	3	8	29	26	2	849	254	267	320	8	737	238	146	499	105	32	30.48%	75	23	69.33%	0	312	578	53.98%	290	528	54.92%	216	365	59.18%	580	333	536	230	454	237
19	11	5	6	0	0	0	0	0	31	31	0	6	1	5	0	0	0	0	0	13	19	-6	5	4	1	0	0	0	0	0	18	12	6	10	31	52	83	0	0	2	14	14	1	336	98	112	121	5	384	172	88	177	46	15	32.61%	44	19	56.82%	0	133	222	59.91%	157	284	55.28%	120	182	65.93%	226	130	257	96	182	93
20	13	6	7	0	0	0	0	0	28	29	-1	7	4	3	0	0	0	0	0	15	13	2	6	2	4	0	0	0	0	0	13	16	-3	12	28	47	75	0	0	5	10	11	2	456	117	166	148	25	388	129	78	249	39	12	30.77%	40	13	67.50%	1	152	299	50.84%	155	297	52.19%	85	162	52.47%	289	162	272	135	259	124
Total Séries	154	82	72	0	0	0	0	0	395	376	19	81	44	37	0	0	0	0	0	214	195	19	73	38	35	0	0	0	0	0	181	181	0	164	395	641	1036	3	5	57	145	166	27	4870	1269	1700	1734	167	4821	1680	1004	3133	513	181	35.28%	505	170	66.34%	4	1667	3284	50.76%	1749	3457	50.59%	1114	2128	52.35%	3311	1833	3357	1469	2881	1452