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	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	Alex Dostie (R)	X				100.00	71	60	96	63	60	59	60	57	71	45	63	64	60	55	55	62	50	0	0	23	3	925,000$
2	Justin Dowling	X	X	X		100.00	73	43	90	70	66	57	70	68	52	60	59	69	25	51	52	64	50	0	0	29	1	650,000$
3	Warren Foegele		X	X		100.00	73	43	85	67	72	65	90	59	41	70	71	71	25	59	60	71	47	0	0	24	1	746,667$
4	Adam Erne		X	X		100.00	90	46	85	72	77	61	78	65	57	55	56	66	75	60	61	64	50	0	0	25	1	894,167$
5	Tom Kuhnhackl		X	X		100.00	76	44	97	77	73	59	56	58	25	60	60	74	25	60	63	65	50	0	0	28	2	700,000$
6	Tomas Nosek	X	X			100.00	79	45	90	69	79	59	85	59	74	60	62	72	25	60	62	67	50	0	0	27	2	962,500$
7	Victor Rask	X	X	X		100.00	67	43	95	72	75	56	64	62	66	65	62	58	36	67	67	64	46	0	0	27	1	1,000,000$
8	Dominik Simon (A)		X	X		100.00	73	43	93	77	69	67	85	62	37	68	60	56	25	63	64	65	50	0	0	25	4	750,000$
9	Matthew Strome (R)		X			100.00	82	78	92	55	78	61	64	49	50	45	48	64	46	44	44	56	50	0	0	21	3	879,167$
10	Remi Elie		X			100.00	81	77	90	60	77	57	58	56	50	47	60	66	57	47	47	61	50	0	0	25	3	450,000$
11	Alex Petrovic (R)				X	100.00	83	81	86	62	81	60	63	51	25	47	40	65	38	44	44	54	50	0	0	28	1	1,050,000$
12	Connor Hobbs				X	100.00	72	69	80	59	69	55	58	45	25	32	41	61	39	53	53	50	50	0	0	23	1	720,000$
13	Joseph Cecconi (R)				X	100.00	81	76	91	61	76	52	54	44	25	34	39	63	37	44	44	51	50	0	0	23	3	1,293,750$
Rayé
1	Jan Rutta				X	100.00	67	43	87	77	76	67	65	61	25	53	48	72	25	57	58	60	26	0	0	29	1	2,300,000$

	MOYENNE D'ÉQUIPE					100.00	76	57	90	67	73	60	68	57	45	53	55	66	38	55	55	61	48	0

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Michael Hutchinson	100.00	53	61	61	81	57	48	50	55	58	53	78	59	59	55	50	0	0
2	Joey Daccord (R)	100.00	60	60	75	77	62	64	55	64	61	60	30	44	44	60	50	0	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	57	61	68	79	60	56	53	60	60	57	54	52	52	58	50	0

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Derek Lalonde	83	80	79	85	73	58	84	USA	46	3	1,250,000$

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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	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Dominik Simon	Stars (DAL)	LW/RW	43	8	32	40	-7	22	0	40	88	101	18	54	7.92%	8	808	18.80	4	15	19	28	190	0	0	0	1	40	0	0	35.00%	40	0	0	0.99	0	1	0	0	0	0	3	4
2	Tomas Nosek	Stars (DAL)	C/LW	43	19	20	39	12	16	0	58	100	122	28	75	15.57%	16	892	20.76	2	8	10	26	177	0	1	1	4	139	3	1	60.45%	976	0	0	0.87	0	1	0	0	0	3	3	5
3	Adam Erne	Stars (DAL)	LW/RW	43	15	22	37	9	50	0	105	84	109	28	76	13.76%	4	773	17.99	4	6	10	29	180	1	0	1	2	37	5	2	55.17%	29	0	0	0.96	2	4	0	0	0	4	3	3
4	Tom Kuhnhackl	Stars (DAL)	LW/RW	43	8	27	35	7	6	0	43	66	82	34	55	9.76%	12	914	21.26	2	9	11	23	183	0	1	1	0	129	1	0	32.76%	58	0	0	0.77	0	0	0	0	0	3	1	6
5	Warren Foegele	Stars (DAL)	LW/RW	43	17	13	30	-1	12	0	59	74	89	22	80	19.10%	12	888	20.66	8	4	12	26	206	2	0	2	4	139	2	3	45.32%	139	1	0	0.68	0	1	0	0	0	2	3	2
6	Victor Rask	Stars (DAL)	C/LW/RW	41	8	18	26	1	4	0	20	87	76	22	47	10.53%	6	832	20.31	4	6	10	24	170	0	1	1	2	76	1	2	59.26%	837	0	0	0.62	1	1	0	0	0	5	0	1
7	Matthew Strome	Stars (DAL)	LW	43	6	16	22	1	49	15	39	27	36	13	29	16.67%	11	725	16.88	2	3	5	14	155	0	0	0	0	7	1	1	45.00%	20	0	0	0.61	0	1	1	0	2	2	1	1
8	Alex Dostie	Stars (DAL)	C	43	11	10	21	-11	21	5	23	80	93	29	65	11.83%	11	760	17.69	1	2	3	20	150	0	0	0	0	29	3	2	56.11%	442	0	1	0.55	2	4	0	1	0	3	1	0
9	Remi Elie	Stars (DAL)	LW	43	7	8	15	-14	43	5	60	59	70	20	49	10.00%	12	658	15.32	1	0	1	4	52	0	0	0	0	27	1	1	52.94%	34	0	0	0.46	0	3	1	0	0	2	1	1
10	Jan Rutta	Stars (DAL)	D	27	4	7	11	-2	14	0	17	36	39	15	22	10.26%	25	535	19.83	4	5	9	30	127	0	1	1	0	105	1	0	25.00%	4	0	0	0.41	0	0	0	0	0	0	1	0
11	Alex Petrovic	Stars (DAL)	D	43	4	6	10	-5	49	5	66	24	16	3	15	25.00%	23	831	19.34	3	3	6	12	182	0	0	0	0	146	1	0	75.00%	4	0	0	0.24	0	0	0	0	1	0	0	2
12	Justin Dowling	Stars (DAL)	C/LW/RW	43	3	7	10	-2	10	0	31	53	62	13	32	4.84%	5	541	12.59	0	1	1	16	78	0	1	1	2	147	0	1	45.80%	345	0	0	0.37	0	0	0	0	0	0	2	0
13	Christian Folin	Stars	D	12	1	4	5	-3	18	0	42	7	13	3	9	7.69%	12	255	21.28	1	2	3	9	53	0	0	0	1	38	0	0	0.00%	1	0	0	0.39	0	0	0	0	0	1	0	0
14	Joseph Cecconi	Stars (DAL)	D	43	0	5	5	1	59	5	56	20	10	1	11	0.00%	29	741	17.25	0	1	1	2	77	0	0	0	0	131	0	0	0.00%	2	0	0	0.13	0	0	1	0	0	0	1	0
15	Connor Hobbs	Stars (DAL)	D	43	0	4	4	0	46	0	52	12	8	4	14	0.00%	21	730	16.99	0	0	0	3	54	0	0	0	1	140	0	0	0.00%	1	0	0	0.11	0	0	0	0	0	0	1	0
Stats d'équipe Total ou en Moyenne				596	111	199	310	-14	419	35	711	817	926	253	633	11.99%	207	10891	18.27	36	65	101	266	2041	3	5	8	17	1337	19	13	55.76%	2932	1	1	0.57	5	16	3	1	3	25	21	25

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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	SO	GA	SA	A	PS %	PSA	ST	BG	S1	S2	S3
1	Joey Daccord	Stars (DAL)	34	19	10	3	0.874	2.55	1903	1	81	641	1	0.417	12	33	10	0	1	0
2	Michael Hutchinson	Stars (DAL)	13	4	6	1	0.912	1.88	701	1	22	250	0	0.833	6	10	23	1	1	1
Stats d'équipe Total ou en Moyenne			47	23	16	4	0.884	2.37	2604	2	103	891	1	0.556	18	43	33	1	2	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	Salaire Restant	Salaire Moyen	Salaire Moyen Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Adam Erne	Stars (DAL)	LW/RW	25	1995-04-20	No	214 Lbs	6 ft1	No	No	No	1	Pro & Farm	894,167$	418,239$	894,167$	418,239$	0$	0$	No				Lien
Alex Dostie	Stars (DAL)	C	23	1997-04-13	Yes	165 Lbs	5 ft10	No	No	No	3	Pro & Farm	925,000$	432,661$	925,000$	432,661$	0$	0$	No	925,000$	925,000$
Alex Petrovic	Stars (DAL)	D	28	1992-03-03	Yes	216 Lbs	6 ft4	No	No	No	1	Pro & Farm	1,050,000$	491,129$	1,050,000$	491,129$	0$	0$	No				Lien
Connor Hobbs	Stars (DAL)	D	23	1997-01-04	No	187 Lbs	6 ft1	No	No	No	1	Pro & Farm	720,000$	336,774$	720,000$	336,774$	0$	0$	No				Lien
Dominik Simon	Stars (DAL)	LW/RW	25	1994-08-08	No	190 Lbs	5 ft11	No	No	No	4	Pro & Farm	750,000$	350,806$	750,000$	350,806$	0$	0$	No	750,000$	750,000$	750,000$	Lien
Jan Rutta	Stars (DAL)	D	29	1990-07-29	No	200 Lbs	6 ft3	No	No	No	1	Pro & Farm	2,300,000$	1,075,806$	2,300,000$	1,075,806$	0$	0$	No				Lien
Joey Daccord	Stars (DAL)	G	23	1996-08-19	Yes	196 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,000,000$	467,742$	1,000,000$	467,742$	0$	0$	No				Lien
Joseph Cecconi	Stars (DAL)	D	23	1997-05-23	Yes	209 Lbs	6 ft2	No	No	No	3	Pro & Farm	1,293,750$	605,141$	1,293,750$	605,141$	0$	0$	No	1,293,750$	1,293,750$
Justin Dowling	Stars (DAL)	C/LW/RW	29	1990-10-01	No	185 Lbs	5 ft10	No	No	No	1	Pro & Farm	650,000$	304,032$	650,000$	304,032$	0$	0$	No				Lien
Matthew Strome	Stars (DAL)	LW	21	1999-01-05	Yes	206 Lbs	6 ft4	No	No	No	3	Pro & Farm	879,167$	411,223$	879,167$	411,223$	0$	0$	No	879,167$	879,167$
Michael Hutchinson	Stars (DAL)	G	30	1990-03-01	No	202 Lbs	6 ft3	No	No	No	1	Pro & Farm	1,000,000$	467,742$	1,000,000$	467,742$	0$	0$	No				Lien
Remi Elie	Stars (DAL)	LW	25	1995-04-15	No	210 Lbs	6 ft1	No	No	No	3	Pro & Farm	450,000$	210,484$	450,000$	210,484$	0$	0$	No	450,000$	450,000$		Lien
Tom Kuhnhackl	Stars (DAL)	LW/RW	28	1992-01-21	No	196 Lbs	6 ft2	No	No	No	2	Pro & Farm	700,000$	327,419$	700,000$	327,419$	0$	0$	No	700,000$			Lien
Tomas Nosek	Stars (DAL)	C/LW	27	1992-08-31	No	210 Lbs	6 ft3	No	No	No	2	Pro & Farm	962,500$	450,202$	962,500$	450,202$	0$	0$	No	962,500$			Lien
Victor Rask	Stars (DAL)	C/LW/RW	27	1993-03-01	No	200 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,000,000$	467,742$	1,000,000$	467,742$	0$	0$	No				Lien
Warren Foegele	Stars (DAL)	LW/RW	24	1996-04-01	No	190 Lbs	6 ft2	No	No	No	1	Pro & Farm	746,667$	349,247$	746,667$	349,247$	0$	0$	No				Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
16	25.63	199 Lbs	6 ft2	1.81	957,578$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Warren Foegele		Tom Kuhnhackl	35	0	1	4
2	Matthew Strome	Tomas Nosek	Adam Erne	35	0	1	4
3	Remi Elie	Alex Dostie	Dominik Simon	20	1	2	2
4	Justin Dowling		Tom Kuhnhackl	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Alex Petrovic	30	0	3	2
2	Joseph Cecconi	Connor Hobbs	30	0	3	2
3			25	0	3	2
4	Joseph Cecconi	Connor Hobbs	15	0	3	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Tom Kuhnhackl		Warren Foegele	60	0	0	5
2	Dominik Simon	Tomas Nosek	Adam Erne	40	0	1	4

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Alex Petrovic	Alex Dostie	50	0	2	3
2		Matthew Strome	50	0	2	3

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Tomas Nosek	Warren Foegele	50	0	4	1
2	Justin Dowling	Tom Kuhnhackl	50	0	4	1

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Connor Hobbs	50	0	5	0
2	Alex Petrovic	Joseph Cecconi	50	0	5	0

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Tomas Nosek	50	0	5	0		Connor Hobbs	50	0	5	0
2	Justin Dowling	50	0	5	0	Alex Petrovic	Joseph Cecconi	50	0	5	0

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Warren Foegele	50	0	2	3
2	Tomas Nosek	Tom Kuhnhackl	50	0	2	3

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Connor Hobbs	50	0	3	2
2	Alex Petrovic	Joseph Cecconi	50	0	3	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Warren Foegele		Dominik Simon		Alex Petrovic

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Warren Foegele	Tomas Nosek	Justin Dowling		Alex Petrovic

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, Warren Foegele, Tomas Nosek	Warren Foegele, Dominik Simon	Tomas Nosek

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, Alex Petrovic, Joseph Cecconi		, Alex Petrovic

Tirs de Pénalité
Adam Erne, Alex Dostie, Remi Elie, Matthew Strome,

Gardien
#1 : Joey Daccord, #2 : Michael Hutchinson

Lignes d'Attaque Perso. en Prol.
, , , , , , , , , ,

Lignes de Défense Perso. en Prol.
, , , ,

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Admirals	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	2	4	6	0	0	35	48	29	8	21	294	338	310	29	19	6	10	25	8	1	12.50%	5	1	80.00%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
2	Condors	1	1	0	0	0	0	0	0	2	1	1	1	1	0	0	0	0	0	0	2	1	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	2	4	6	0	0	35	48	29	8	21	294	338	310	29	16	4	16	22	2	0	0.00%	7	1	85.71%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
3	Flames	1	0	1	0	0	0	0	0	0	2	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	2	-2	0	0.000	0	0	0	0	0	35	48	29	8	15	294	338	310	29	12	5	10	19	7	0	0.00%	5	1	80.00%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
4	IceHogs	4	2	1	0	0	0	0	1	11	12	-1	1	1	0	0	0	0	0	0	5	3	2	3	1	1	0	0	0	0	1	6	9	-3	5	0.625	11	17	28	0	0	35	48	29	8	78	294	338	310	29	102	24	50	80	17	2	11.76%	22	3	86.36%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
5	Monarchs	2	1	1	0	0	0	0	0	7	6	1	1	0	1	0	0	0	0	0	3	4	-1	1	1	0	0	0	0	0	0	4	2	2	2	0.500	7	11	18	0	0	35	48	29	8	46	294	338	310	29	44	10	22	34	14	2	14.29%	10	1	90.00%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
6	Monsters	2	2	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	2	1	1	1	1	0	0	0	0	0	0	4	3	1	4	1.000	6	11	17	0	0	35	48	29	8	55	294	338	310	29	26	14	20	36	11	1	9.09%	8	1	87.50%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
7	Moose	3	1	2	0	0	0	0	0	6	7	-1	1	1	0	0	0	0	0	0	3	2	1	2	0	2	0	0	0	0	0	3	5	-2	2	0.333	6	12	18	0	0	35	48	29	8	49	294	338	310	29	49	23	32	54	18	2	11.11%	14	3	78.57%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
8	Penguins	1	0	1	0	0	0	0	0	0	1	-1	1	0	1	0	0	0	0	0	0	1	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	35	48	29	8	18	294	338	310	29	25	7	6	20	5	0	0.00%	3	0	100.00%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
9	Rampage	7	4	0	0	2	0	0	1	32	18	14	4	3	0	0	0	0	0	1	18	7	11	3	1	0	0	2	0	0	0	14	11	3	13	0.929	32	60	92	0	1	35	48	29	8	208	294	338	310	29	178	54	74	140	34	8	23.53%	29	7	75.86%	1	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
10	Senators	1	0	0	0	0	0	1	0	5	4	1	1	0	0	0	0	0	1	0	5	4	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	5	5	10	0	0	35	48	29	8	23	294	338	310	29	18	5	13	21	8	3	37.50%	3	0	100.00%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
11	Sharks	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	5	8	0	0	35	48	29	8	29	294	338	310	29	24	8	18	21	3	1	33.33%	8	1	87.50%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
12	Soldiers	11	3	6	0	0	0	0	2	22	30	-8	5	1	2	0	0	0	0	2	7	10	-3	6	2	4	0	0	0	0	0	15	20	-5	8	0.364	22	40	62	0	1	35	48	29	8	219	294	338	310	29	236	52	150	212	73	8	10.96%	53	5	90.57%	1	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
	Total	43	19	16	0	3	0	1	4	116	107	9	21	12	5	0	0	0	1	3	60	40	20	22	7	11	0	3	0	0	1	56	67	-11	50	0.581	116	205	321	0	2	35	48	29	8	954	294	338	310	29	891	252	489	815	251	36	14.34%	199	26	86.93%	3	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
13	Wolf Pack	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	4	6	0	0	35	48	29	8	22	294	338	310	29	21	6	10	16	5	0	0.00%	5	0	100.00%	1	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
14	Wolves	7	4	2	0	1	0	0	0	18	14	4	3	3	0	0	0	0	0	0	10	3	7	4	1	2	0	1	0	0	0	8	11	-3	10	0.714	18	32	50	0	0	35	48	29	8	150	294	338	310	29	121	34	58	115	46	8	17.39%	27	2	92.59%	0	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
	_Since Last GM Reset	43	19	16	0	3	0	1	4	116	107	9	21	12	5	0	0	0	1	3	60	40	20	22	7	11	0	3	0	0	1	56	67	-11	50	0.581	116	205	321	0	2	35	48	29	8	954	294	338	310	29	891	252	489	815	251	36	14.34%	199	26	86.93%	3	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
	_Vs Conference	37	17	13	0	3	0	0	4	103	92	11	18	11	4	0	0	0	0	3	53	34	19	19	6	9	0	3	0	0	1	50	58	-8	44	0.595	103	185	288	0	2	35	48	29	8	821	294	338	310	29	789	215	430	703	215	32	14.88%	175	24	86.29%	2	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273
	_Vs Division	29	13	9	0	3	0	0	4	83	74	9	13	8	2	0	0	0	0	3	40	23	17	16	5	7	0	3	0	0	1	43	51	-8	36	0.621	83	149	232	0	2	35	48	29	8	655	294	338	310	29	637	164	332	547	170	26	15.29%	131	17	87.02%	2	668	1211	55.16%	635	1150	55.22%	349	622	56.11%	1013	693	1052	324	542	273

			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
43	50	W3	116	205	321	954	891	252	489	815	0	2

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
43	19	16	3	0	1	4	116	107

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
21	12	5	0	0	1	3	60	40

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
22	7	11	3	0	0	1	56	67

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	3	0	0	0	3

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
251	36	14.34%	199	26	86.93%	3

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
294	338	310	29	35	48	29	8

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
668	1211	55.16%	635	1150	55.22%	349	622	56.11%

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
1013	693	1052	324	542	273

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
1 - 2020-09-27	3	Soldiers	1	Stars	0	L			Sommaire du Match
2 - 2020-09-28	15	Stars	2	Soldiers	5	L			Sommaire du Match
4 - 2020-09-30	26	Stars	2	Soldiers	3	L			Sommaire du Match
5 - 2020-10-01	39	Soldiers	3	Stars	1	L			Sommaire du Match
6 - 2020-10-02	49	Rampage	0	Stars	2	W			Sommaire du Match
9 - 2020-10-05	66	Soldiers	2	Stars	1	L	X	X	Sommaire du Match
10 - 2020-10-06	70	Stars	4	Soldiers	3	W			Sommaire du Match
12 - 2020-10-08	86	Penguins	1	Stars	0	L			Sommaire du Match
14 - 2020-10-10	93	Stars	2	Wolves	1	W	X		Sommaire du Match
16 - 2020-10-12	102	Stars	1	Soldiers	4	L			Sommaire du Match
18 - 2020-10-14	111	Stars	6	Rampage	5	W	X		Sommaire du Match
20 - 2020-10-16	128	Admirals	3	Stars	2	L			Sommaire du Match
22 - 2020-10-18	143	Wolves	1	Stars	3	W			Sommaire du Match
23 - 2020-10-19	154	Stars	2	Wolves	5	L			Sommaire du Match
24 - 2020-10-20	159	Stars	4	Monsters	3	W			Sommaire du Match
25 - 2020-10-21	171	Moose	2	Stars	3	W			Sommaire du Match
27 - 2020-10-23	180	Stars	3	IceHogs	2	W			Sommaire du Match
28 - 2020-10-24	189	Sharks	1	Stars	3	W			Sommaire du Match
30 - 2020-10-26	204	Condors	1	Stars	2	W			Sommaire du Match
32 - 2020-10-28	211	Stars	4	Rampage	3	W	X		Sommaire du Match
34 - 2020-10-30	226	Stars	4	Soldiers	2	W			Sommaire du Match
36 - 2020-11-01	231	Senators	4	Stars	5	W	X	X	Sommaire du Match
38 - 2020-11-03	243	Stars	1	Moose	2	L			Sommaire du Match
40 - 2020-11-05	256	Wolves	1	Stars	5	W			Sommaire du Match
42 - 2020-11-07	266	Stars	1	IceHogs	4	L			Sommaire du Match
44 - 2020-11-09	276	Stars	2	Moose	3	L			Sommaire du Match
45 - 2020-11-10	284	Wolves	1	Stars	2	W			Sommaire du Match
46 - 2020-11-11	298	Rampage	1	Stars	7	W			Sommaire du Match
48 - 2020-11-13	314	Rampage	1	Stars	5	W			Sommaire du Match
49 - 2020-11-14	319	Stars	1	Wolves	4	L			Sommaire du Match
50 - 2020-11-15	326	Stars	0	Flames	2	L			Sommaire du Match
51 - 2020-11-16	340	Soldiers	0	Stars	2	W			Sommaire du Match
52 - 2020-11-17	346	Stars	4	Monarchs	2	W			Sommaire du Match
54 - 2020-11-19	364	Stars	2	IceHogs	3	L	X	X	Sommaire du Match
55 - 2020-11-20	369	Soldiers	4	Stars	3	L	X	X	Sommaire du Match
56 - 2020-11-21	380	Stars	2	Soldiers	3	L			Sommaire du Match
57 - 2020-11-22	392	Rampage	5	Stars	4	L	X	X	Sommaire du Match
59 - 2020-11-24	409	Monsters	1	Stars	2	W			Sommaire du Match
60 - 2020-11-25	421	Stars	2	Wolf Pack	4	L			Sommaire du Match
61 - 2020-11-26	430	Monarchs	4	Stars	3	L			Sommaire du Match
64 - 2020-11-29	443	Stars	4	Rampage	3	W			Sommaire du Match
65 - 2020-11-30	454	IceHogs	3	Stars	5	W			Sommaire du Match
66 - 2020-12-01	463	Stars	3	Wolves	1	W			Sommaire du Match
67 - 2020-12-02	473	Monsters	-	Stars	-
68 - 2020-12-03	485	Stars	-	Monsters	-
70 - 2020-12-05	493	Stars	-	Griffins	-
71 - 2020-12-06	503	IceHogs	-	Stars	-
72 - 2020-12-07	514	Stars	-	Marlies	-
74 - 2020-12-09	525	Admirals	-	Stars	-
75 - 2020-12-10	539	Stars	-	Penguins	-
77 - 2020-12-12	545	Griffins	-	Stars	-
79 - 2020-12-14	561	Wolves	-	Stars	-
80 - 2020-12-15	573	Stars	-	Sound Tigers	-
81 - 2020-12-16	581	Stars	-	Rocket	-
83 - 2020-12-18	595	Sound Tigers	-	Stars	-
85 - 2020-12-20	605	Stars	-	Monarchs	-
86 - 2020-12-21	614	Phantoms	-	Stars	-
87 - 2020-12-22	628	Sharks	-	Stars	-
89 - 2020-12-24	640	Stars	-	Bruins	-
91 - 2020-12-26	650	Stars	-	Rampage	-
92 - 2020-12-27	660	Bruins	-	Stars	-
94 - 2020-12-29	672	Wolf Pack	-	Stars	-
95 - 2020-12-30	686	Crunch	-	Stars	-
96 - 2020-12-31	693	Stars	-	IceHogs	-
97 - 2021-01-01	706	Stars	-	Soldiers	-
98 - 2021-01-02	716	Moose	-	Stars	-
99 - 2021-01-03	722	Stars	-	Senators	-
101 - 2021-01-05	738	Condors	-	Stars	-
103 - 2021-01-07	752	Marlies	-	Stars	-
104 - 2021-01-08	766	Stars	-	Flames	-
105 - 2021-01-09	774	Flames	-	Stars	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10	783	Stars	-	Sharks	-
108 - 2021-01-12	796	Stars	-	Phantoms	-
109 - 2021-01-13	806	Stars	-	Condors	-
110 - 2021-01-14	811	Soldiers	-	Stars	-
112 - 2021-01-16	828	Stars	-	Crunch	-
113 - 2021-01-17	834	Rocket	-	Stars	-
115 - 2021-01-19	846	Stars	-	Moose	-
116 - 2021-01-20	853	Stars	-	Admirals	-
117 - 2021-01-21	859	IceHogs	-	Stars	-
119 - 2021-01-23	878	Senators	-	Stars	-
122 - 2021-01-26	895	IceHogs	-	Stars	-

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	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
20	0 - 0.00%	0$	0$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,450,884$	1,532,126$	1,532,126$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	785,567$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	58	22,436$	1,301,288$

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