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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	Phillip Di Giuseppe (A)		X	X		100.00	90	46	87	79	70	58	71	63	38	68	56	64	25	60	61	63	50	64	0	28	3	800,000$
2	T.J. Tynan (C)	X				100.00	66	58	86	62	58	66	66	69	80	73	61	62	58	45	45	65	50	62	0	29	3	800,000$
3	Greg McKegg	X	X			100.00	71	70	72	71	70	55	54	62	78	73	44	67	42	63	64	59	50	61	0	29	1	925,000$
4	Lukas Jasek			X		100.00	75	68	90	61	68	65	66	62	78	67	53	64	50	44	44	62	50	60	0	24	2	853,333$
5	Jonah Gadjovich (R)		X			100.00	75	76	74	66	76	56	55	62	50	47	74	64	70	44	44	66	50	60	0	23	3	863,333$
6	Justin Kirkland		X			100.00	71	70	72	61	70	57	58	56	50	56	51	61	48	45	45	57	50	56	0	25	2	750,000$
7	Brent Pedersen (R)		X			100.00	81	76	94	60	76	61	65	49	50	47	46	64	44	44	44	55	50	55	0	26	3	750,000$
8	Brandon Baddock		X			100.00	69	82	39	66	82	59	62	49	50	46	46	58	44	44	44	52	50	54	0	26	2	750,000$
9	Nikolai Knyzhov (R)				X	100.00	80	45	80	70	79	70	76	63	25	51	48	72	25	50	50	63	50	65	0	23	1	800,000$
10	Tommy Cross				X	100.00	79	77	82	67	77	56	57	56	25	46	52	65	49	44	44	59	50	60	0	32	1	750,000$
11	Mason Geertsen				X	100.00	68	79	42	63	79	60	63	50	25	35	49	58	47	44	44	53	50	56	0	26	2	750,000$
12	Evan McEneny				X	100.00	76	75	78	63	75	56	59	45	25	31	42	62	40	51	51	51	50	56	0	27	3	750,000$
Rayé

	MOYENNE D'ÉQUIPE					100.00	75	69	75	66	73	60	63	57	48	53	52	63	45	48	48	59	50	59

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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	Scott Wedgewood	100.00	62	60	59	79	70	54	59	63	65	62	75	55	55	62	50	62	0
2	Jeff Glass	100.00	44	43	54	82	43	43	50	52	46	47	30	44	44	46	50	50	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	53	52	57	81	57	49	55	58	56	55	53	50	50	54	50	56

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Kris Knoblauch	65	68	69	70	65	61	83	CAN	42	2	700,000$

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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	HT	P/20	PSG	PSS	FT	S1	S2	S3
1	Jonah Gadjovich	Monsters (CLB)	LW	16	17	11	28	24	12	0	19	17	100	26	65	17.00%	2	269	16.82	2	1	3	17	47	0	0	0	1	40	2	0	60.98%	41	1	2.08	1	1	0	3	0	3
2	T.J. Tynan	Monsters (CLB)	C	16	9	12	21	12	0	0	10	40	46	14	39	19.57%	5	337	21.08	1	4	5	10	50	1	2	3	2	60	2	1	74.30%	323	0	1.25	1	1	0	2	2	1
3	Phillip Di Giuseppe	Monsters (CLB)	LW/RW	16	8	11	19	13	28	0	38	33	52	14	41	15.38%	2	325	20.35	0	4	4	3	49	2	1	3	2	44	0	1	61.47%	109	0	1.17	1	1	0	2	2	1
4	Brent Pedersen	Monsters (CLB)	LW	16	3	16	19	22	10	0	17	26	31	13	27	9.68%	4	287	17.98	1	0	1	1	46	0	0	0	0	33	0	0	67.79%	208	0	1.32	0	0	0	0	0	2
5	Justin Kirkland	Monsters (CLB)	LW	16	12	6	18	-5	14	0	37	37	78	24	50	15.38%	10	304	19.05	0	1	1	0	8	0	0	0	0	20	3	0	57.89%	19	0	1.18	0	0	0	2	1	1
6	Greg McKegg	Monsters (CLB)	C/LW	4	4	10	14	12	0	0	6	12	16	4	13	25.00%	0	76	19.04	1	1	2	3	11	0	0	0	0	15	0	0	80.49%	82	0	3.68	0	0	0	1	3	0
7	Brandon Baddock	Monsters (CLB)	LW	16	5	8	13	12	27	5	34	10	46	18	40	10.87%	3	256	16.03	0	1	1	6	38	0	0	0	0	23	1	0	64.71%	51	0	1.01	0	0	1	0	1	0
8	Lukas Jasek	Monsters (CLB)	RW	16	4	8	12	9	4	0	16	17	35	10	22	11.43%	2	308	19.30	1	0	1	6	48	0	0	0	0	38	0	1	85.19%	27	0	0.78	0	0	0	0	1	1
9	Nikolai Knyzhov	Monsters (CLB)	D	16	4	6	10	4	8	0	26	15	31	9	17	12.90%	6	328	20.51	2	2	4	16	48	0	1	1	0	59	2	1	0.00%	1	0	0.61	0	0	0	1	0	0
10	Mason Geertsen	Monsters (CLB)	D	16	3	6	9	16	15	5	38	6	21	9	13	14.29%	9	279	17.46	1	0	1	5	47	0	0	0	0	52	0	0	100.00%	1	0	0.64	0	0	0	0	1	1
11	Evan McEneny	Monsters (CLB)	D	16	1	4	5	15	6	0	10	8	16	3	16	6.25%	13	281	17.62	0	1	1	4	46	0	0	0	0	53	0	0	100.00%	1	0	0.35	0	0	0	0	0	0
12	Tommy Cross	Monsters (CLB)	D	16	0	5	5	2	18	0	21	10	20	8	12	0.00%	8	310	19.38	0	2	2	8	44	0	0	0	0	49	0	0	0.00%	0	0	0.32	0	0	0	0	0	1
Stats d'équipe Total ou en Moyenne				180	70	103	173	136	142	10	272	231	492	152	355	14.23%	64	3365	18.70	9	17	26	79	488	3	4	7	5	492	10	4	70.45%	863	1	1.03	3	3	1	11	11	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

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#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Scott Wedgewood	Monsters (CLB)	13	9	4	0	0.903	1.93	746	0	1	24	248	0	1	0	0.000	0	13	0	0	0	0
Stats d'équipe Total ou en Moyenne			13	9	4	0	0.903	1.93	746	0	1	24	248	0	1	0	0.000	0	13	0	0	0	0

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
Brandon Baddock	Monsters (CLB)	LW	26	1995-03-28	No	221 Lbs	6 ft3	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien / Lien NHL
Brent Pedersen	Monsters (CLB)	LW	26	1995-07-05	Yes	205 Lbs	6 ft2	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$	Lien / Lien NHL
Evan McEneny	Monsters (CLB)	D	27	1994-05-22	No	203 Lbs	6 ft2	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$	Lien / Lien NHL
Greg McKegg	Monsters (CLB)	C/LW	29	1992-06-17	No	192 Lbs	6 ft0	No	No	No	1	Pro & Farm	925,000$	925,000$	0$	0$	No			Lien / Lien NHL
Jeff Glass	Monsters (CLB)	G	35	1985-11-18	No	206 Lbs	6 ft3	Yes	No	No	2	Pro & Farm	800,000$	800,000$	0$	0$	No	800,000$		Lien
Jonah Gadjovich	Monsters (CLB)	LW	23	1998-10-12	Yes	209 Lbs	6 ft2	No	No	No	3	Pro & Farm	863,333$	863,333$	0$	0$	No	863,333$	863,333$	Lien / Lien NHL
Justin Kirkland	Monsters (CLB)	LW	25	1996-08-02	No	183 Lbs	6 ft3	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien / Lien NHL
Lukas Jasek	Monsters (CLB)	RW	24	1997-08-28	No	183 Lbs	6 ft1	No	No	No	2	Pro & Farm	853,333$	853,333$	0$	0$	No	853,333$		Lien / Lien NHL
Mason Geertsen	Monsters (CLB)	D	26	1995-04-18	No	215 Lbs	6 ft2	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien / Lien NHL
Nikolai Knyzhov	Monsters (CLB)	D	23	1998-03-20	Yes	218 Lbs	6 ft2	No	No	No	1	Pro & Farm	800,000$	800,000$	0$	0$	No			Lien / Lien NHL
Phillip Di Giuseppe	Monsters (CLB)	LW/RW	28	1993-10-09	No	193 Lbs	6 ft0	No	No	No	3	Pro & Farm	800,000$	800,000$	0$	0$	No	800,000$	800,000$	Lien / Lien NHL
Scott Wedgewood	Monsters (CLB)	G	29	1992-08-13	No	207 Lbs	6 ft2	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Lien / Lien NHL
T.J. Tynan	Monsters (CLB)	C	29	1992-02-25	No	165 Lbs	5 ft8	No	No	No	3	Pro & Farm	800,000$	800,000$	0$	0$	No	800,000$	800,000$	Lien / Lien NHL
Tommy Cross	Monsters (CLB)	D	32	1989-09-12	No	205 Lbs	6 ft3	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
14	27.29	200 Lbs	6 ft2	2.07	792,262$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	T.J. Tynan	Lukas Jasek	40	1	2	2
2	Jonah Gadjovich	Greg McKegg	Brent Pedersen	30	1	2	2
3	Justin Kirkland	Brandon Baddock		20	1	2	2
4	Brent Pedersen	Phillip Di Giuseppe	T.J. Tynan	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nikolai Knyzhov	Tommy Cross	40	1	2	2
2	Evan McEneny	Mason Geertsen	30	1	2	2
3			20	1	2	2
4	Nikolai Knyzhov	Tommy Cross	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	T.J. Tynan	Lukas Jasek	60	1	2	2
2	Jonah Gadjovich	Greg McKegg	Brent Pedersen	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nikolai Knyzhov	Tommy Cross	60	1	2	2
2	Evan McEneny	Mason Geertsen	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	T.J. Tynan	60	1	2	2
2	Greg McKegg	Jonah Gadjovich	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nikolai Knyzhov	Tommy Cross	60	1	2	2
2	Evan McEneny	Mason Geertsen	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	60	1	2	2	Nikolai Knyzhov	Tommy Cross	60	1	2	2
2	T.J. Tynan	40	1	2	2	Evan McEneny	Mason Geertsen	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	T.J. Tynan	60	1	2	2
2	Greg McKegg	Jonah Gadjovich	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Nikolai Knyzhov	Tommy Cross	60	1	2	2
2	Evan McEneny	Mason Geertsen	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Phillip Di Giuseppe	T.J. Tynan	Lukas Jasek	Nikolai Knyzhov	Tommy Cross

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Phillip Di Giuseppe	T.J. Tynan	Lukas Jasek	Nikolai Knyzhov	Tommy Cross

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Justin Kirkland, Brandon Baddock, Lukas Jasek	Justin Kirkland, Brandon Baddock	Lukas Jasek

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Evan McEneny, Mason Geertsen, Nikolai Knyzhov	Evan McEneny	Mason Geertsen, Nikolai Knyzhov

Tirs de Pénalité
Phillip Di Giuseppe, T.J. Tynan, Greg McKegg, Jonah Gadjovich, Lukas Jasek

Gardien
#1 : Scott Wedgewood, #2 : Jeff Glass

Lignes d'Attaque Perso. en Prol.
Phillip Di Giuseppe, T.J. Tynan, Greg McKegg, Jonah Gadjovich, Lukas Jasek, Justin Kirkland, Justin Kirkland, Brent Pedersen, Brandon Baddock, ,

Lignes de Défense Perso. en Prol.
Nikolai Knyzhov, Tommy Cross, Evan McEneny, Mason Geertsen,

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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	Canucks	2	1	1	0	0	0	0	0	8	6	2	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	6	2	4	2	0.500	8	15	23	0	0	39	16	18	3	50	195	175	164	8	62	13	26	39	10	5	50.00%	12	2	83.33%	0	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
2	Comets	2	2	0	0	0	0	0	0	11	2	9	1	1	0	0	0	0	0	0	6	1	5	1	1	0	0	0	0	0	0	5	1	4	4	1.000	11	17	28	0	0	39	16	18	3	67	195	175	164	8	40	6	12	48	3	1	33.33%	6	0	100.00%	1	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
3	Crunch	2	2	0	0	0	0	0	0	15	1	14	1	1	0	0	0	0	0	0	8	0	8	1	1	0	0	0	0	0	0	7	1	6	4	1.000	15	24	39	0	1	39	16	18	3	82	195	175	164	8	24	8	12	47	6	0	0.00%	6	0	100.00%	0	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
4	Griffins	2	1	0	0	0	0	1	0	9	5	4	1	0	0	0	0	0	1	0	5	4	1	1	1	0	0	0	0	0	0	4	1	3	4	1.000	9	16	25	0	0	39	16	18	3	59	195	175	164	8	67	12	24	42	6	0	0.00%	12	1	91.67%	1	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
5	Marlies	2	0	2	0	0	0	0	0	1	10	-9	1	0	1	0	0	0	0	0	1	3	-2	1	0	1	0	0	0	0	0	0	7	-7	0	0.000	1	2	3	0	0	39	16	18	3	40	195	175	164	8	47	13	24	39	16	0	0.00%	11	3	72.73%	0	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
6	Penguins	1	1	0	0	0	0	0	0	8	5	3	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	8	5	3	2	1.000	8	15	23	0	0	39	16	18	3	47	195	175	164	8	23	8	12	20	2	1	50.00%	6	1	83.33%	0	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
7	Roadrunners	1	1	0	0	0	0	0	0	4	1	3	1	1	0	0	0	0	0	0	4	1	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	7	11	0	0	39	16	18	3	52	195	175	164	8	5	1	6	9	2	0	0.00%	3	0	100.00%	0	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
8	Rocket	2	0	1	0	0	1	0	0	3	5	-2	1	0	1	0	0	0	0	0	0	1	-1	1	0	0	0	0	1	0	0	3	4	-1	1	0.250	3	5	8	0	0	39	16	18	3	45	195	175	164	8	24	7	10	25	8	0	0.00%	5	1	80.00%	0	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
9	Senators	2	2	0	0	0	0	0	0	15	5	10	1	1	0	0	0	0	0	0	9	3	6	1	1	0	0	0	0	0	0	6	2	4	4	1.000	15	28	43	0	0	39	16	18	3	97	195	175	164	8	43	7	32	40	7	2	28.57%	12	1	91.67%	1	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
	Total	16	10	4	0	0	1	1	0	74	40	34	8	4	3	0	0	0	1	0	35	17	18	8	6	1	0	0	1	0	0	39	23	16	23	0.719	74	129	203	0	1	39	16	18	3	539	195	175	164	8	335	75	158	309	60	9	15.00%	73	9	87.67%	3	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
	_Since Last GM Reset	16	10	4	0	0	1	1	0	74	40	34	8	4	3	0	0	0	1	0	35	17	18	8	6	1	0	0	1	0	0	39	23	16	23	0.719	74	129	203	0	1	39	16	18	3	539	195	175	164	8	335	75	158	309	60	9	15.00%	73	9	87.67%	3	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
	_Vs Conference	15	9	4	0	0	1	1	0	70	39	31	7	3	3	0	0	0	1	0	31	16	15	8	6	1	0	0	1	0	0	39	23	16	21	0.700	70	122	192	0	1	39	16	18	3	487	195	175	164	8	330	74	152	300	58	9	15.52%	70	9	87.14%	3	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100
	_Vs Division	10	8	4	0	0	1	1	0	43	26	17	5	3	3	0	0	0	1	0	23	11	12	5	5	1	0	0	1	0	0	20	15	5	19	0.950	43	75	118	0	1	39	16	18	3	323	195	175	164	8	205	47	102	193	43	2	4.65%	46	6	86.96%	2	311	445	69.89%	206	352	58.52%	164	247	66.40%	481	364	300	96	183	100

			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
16	23	W5	74	129	203	539	335	75	158	309	0	1

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
16	10	4	0	1	1	0	74	40

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	4	3	0	0	1	0	35	17

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	6	1	0	1	0	0	39	23

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
8	0	0	1	1	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
60	9	15.00%	73	9	87.67%	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
195	175	164	8	39	16	18	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
311	445	69.89%	206	352	58.52%	164	247	66.40%

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
481	364	300	96	183	100

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
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
1 - 2021-10-18	7	Canucks	4	Monsters	2	L			Sommaire du Match
2 - 2021-10-19	29	Monsters	4	Griffins	1	W			Sommaire du Match
4 - 2021-10-21	44	Rocket	1	Monsters	0	L			Sommaire du Match
6 - 2021-10-23	65	Marlies	3	Monsters	1	L			Sommaire du Match
7 - 2021-10-24	79	Monsters	0	Marlies	7	L			Sommaire du Match
8 - 2021-10-25	100	Comets	1	Monsters	6	W			Sommaire du Match
9 - 2021-10-26	108	Monsters	7	Crunch	1	W			Sommaire du Match
11 - 2021-10-28	132	Senators	3	Monsters	9	W			Sommaire du Match
12 - 2021-10-29	148	Monsters	5	Comets	1	W			Sommaire du Match
13 - 2021-10-30	152	Monsters	3	Rocket	4	L	X		Sommaire du Match
14 - 2021-10-31	171	Griffins	4	Monsters	5	W	X	X	Sommaire du Match
15 - 2021-11-01	179	Monsters	8	Penguins	5	W			Sommaire du Match
16 - 2021-11-02	190	Monsters	6	Canucks	2	W			Sommaire du Match
18 - 2021-11-04	217	Roadrunners	1	Monsters	4	W			Sommaire du Match
21 - 2021-11-07	237	Crunch	0	Monsters	8	W			Sommaire du Match
22 - 2021-11-08	249	Monsters	6	Senators	2	W			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité	2000	1000
Prix des Billets	100	0
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	Popularité de l'Équipe
0	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
0$	1,109,166$	1,104,166$	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

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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

	Monsters
	GP: 16 \| W: 11 \| L: 4 \| OTL: 1 \| P: 23 GF: 74 \| GA: 40 \| PP%: 15.00% \| PK%: 87.67% DG: Tyler Bell \| Morale : 50 \| Moyenne d'Équipe : 59