OTHL - Comets

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP	Âge	Contrat	Salaire
1	Kyle Criscuolo	X				100.00	69	63	84	62	63	62	63	62	78	55	65	61	62	44	44	63	50	59	0	29	1	750,000$
2	Brent Gates (R)	X	X			100.00	78	73	90	62	73	56	55	60	75	62	53	65	50	44	44	60	50	59	0	24	2	750,000$
3	Antti Suomela	X				100.00	73	67	86	74	67	55	55	56	70	60	46	63	44	49	49	57	50	58	0	27	2	950,000$
4	Antoine Morand (R)	X				100.00	70	66	79	67	66	70	74	57	71	56	54	60	51	44	44	59	50	58	0	22	2	927,000$
5	Jan Jenik (R)	X				100.00	69	62	85	66	62	58	58	59	74	55	58	60	55	44	44	60	50	57	0	21	3	902,500$
6	Blake Speers	X		X		100.00	72	67	83	68	67	58	61	51	64	47	47	63	45	55	55	54	50	56	0	24	2	935,833$
7	Jakob Stukel		X			100.00	75	67	92	62	67	60	64	49	50	44	48	61	46	44	44	55	50	54	0	24	2	750,000$
8	Nathan Noel	X	X			100.00	64	63	66	53	63	49	51	44	55	38	44	54	42	44	44	47	50	48	0	24	2	925,000$
9	Maxime Lajoie				X	100.00	73	68	85	77	68	69	74	51	25	44	41	62	39	51	51	55	50	61	0	24	1	800,000$
10	Thomas Schemitsch				X	100.00	78	77	80	60	77	63	66	55	25	54	41	64	39	44	44	56	50	59	0	25	2	750,000$
11	Mark Friedman				X	100.00	81	44	72	64	67	56	54	55	25	49	62	56	25	45	45	60	50	57	0	25	1	825,000$
12	Hunter Drew (R)				X	100.00	73	72	75	66	72	58	60	52	25	38	52	61	49	44	44	56	50	57	0	23	2	793,333$
13	Leon Gawanke (R)				X	100.00	78	72	91	63	72	59	62	48	25	41	40	62	38	44	44	53	50	57	0	22	2	810,000$
14	Jake Massie (R)				X	100.00	70	66	78	64	66	51	53	45	25	33	42	57	40	44	44	50	50	53	0	24	2	770,000$
Rayé

	MOYENNE D'ÉQUIPE					100.00	73	66	82	65	68	59	61	53	49	48	50	61	45	46	46	56	50	57

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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	Garret Sparks	100.00	58	55	69	80	61	60	54	62	58	57	30	47	47	58	50	59	0
2	Alex Lyon	100.00	47	54	68	75	43	46	50	53	46	47	30	46	46	48	50	51	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	53	55	69	78	52	53	52	58	52	52	30	47	47	53	50	55

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Mike Vellucci	68	70	69	70	81	76	67	USA	54	2	800,000$

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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	PPA	PPP	PPS	PPM	GW	GT	FO%	FOT	HT	P/20	FW	S1	S2	S3
1	Blake Speers	Comets (NJD)	C/RW	16	8	13	21	10	6	0	23	17	53	14	45	15.09%	10	150	9.40	0	0	0	0	0	0	76.92%	13	0	2.79	0	1	0	3
2	Kyle Criscuolo	Comets (NJD)	C	11	11	7	18	12	0	0	9	20	48	8	29	22.92%	1	107	9.73	0	0	0	0	1	1	75.57%	131	1	3.36	0	3	1	0
3	Joel Kiviranta	Devils	LW/RW	5	6	3	9	5	4	0	15	6	22	8	26	27.27%	4	77	15.52	0	0	0	0	1	0	75.00%	4	1	2.32	0	1	1	0
4	Leon Gawanke	Comets (NJD)	D	16	4	4	8	-12	21	5	49	24	18	9	14	22.22%	28	237	14.83	1	1	1	2	0	0	0.00%	0	0	0.67	1	1	1	1
5	Brent Gates	Comets (NJD)	C/LW	9	2	2	4	2	2	0	8	7	17	0	9	11.76%	4	43	4.85	0	0	0	0	0	0	83.33%	42	0	1.83	0	0	0	0
Stats d'équipe Total ou en Moyenne				57	31	29	60	17	33	5	104	74	158	39	123	19.62%	47	616	10.81	1	1	1	2	2	1	77.37%	190	2	1.95	1	6	3	4

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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
Stats d'équipe Total ou en Moyenne							0.000	0.00	0								0.000

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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	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
Alex Lyon	Comets (NJD)	G	28	1992-12-08	No	201 Lbs	6 ft1	Yes	No	No	1	Pro & Farm	800,000$	800,000$	0$	0$	No			Lien / Lien NHL
Antoine Morand	Comets (NJD)	C	22	1999-02-18	Yes	184 Lbs	5 ft11	No	No	No	2	Pro & Farm	927,000$	927,000$	0$	0$	No	927,000$		Lien / Lien NHL
Antti Suomela	Comets (NJD)	C	27	1994-03-16	No	180 Lbs	6 ft0	No	No	No	2	Pro & Farm	950,000$	950,000$	0$	0$	No	950,000$		Lien / Lien NHL
Blake Speers	Comets (NJD)	C/RW	24	1997-01-02	No	185 Lbs	5 ft11	No	No	No	2	Pro & Farm	935,833$	935,833$	0$	0$	No	935,833$		Lien / Lien NHL
Brent Gates	Comets (NJD)	C/LW	24	1997-08-12	Yes	198 Lbs	6 ft2	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien
Garret Sparks	Comets (NJD)	G	28	1993-06-28	No	201 Lbs	6 ft3	No	No	No	2	Pro & Farm	800,000$	800,000$	0$	0$	No	800,000$		Lien / Lien NHL
Hunter Drew	Comets (NJD)	D	23	1998-10-21	Yes	192 Lbs	6 ft2	No	No	No	2	Pro & Farm	793,333$	793,333$	0$	0$	No	793,333$		Lien / Lien NHL
Jake Massie	Comets (NJD)	D	24	1997-01-21	Yes	179 Lbs	6 ft1	No	No	No	2	Pro & Farm	770,000$	770,000$	0$	0$	No	770,000$		Lien / Lien NHL
Jakob Stukel	Comets (NJD)	LW	24	1997-03-06	No	182 Lbs	6 ft0	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien / Lien NHL
Jan Jenik	Comets (NJD)	C	21	2000-09-15	Yes	161 Lbs	6 ft1	No	No	No	3	Pro & Farm	902,500$	902,500$	0$	0$	No	902,500$	902,500$	Lien
Kyle Criscuolo	Comets (NJD)	C	29	1992-05-05	No	180 Lbs	5 ft8	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Lien / Lien NHL
Leon Gawanke	Comets (NJD)	D	22	1999-05-31	Yes	198 Lbs	6 ft1	No	No	No	2	Pro & Farm	810,000$	810,000$	0$	0$	No	810,000$		Lien / Lien NHL
Mark Friedman	Comets (NJD)	D	25	1995-12-25	No	185 Lbs	5 ft11	No	No	No	1	Pro & Farm	825,000$	825,000$	0$	0$	No			Lien / Lien NHL
Maxime Lajoie	Comets (NJD)	D	24	1997-11-05	No	183 Lbs	6 ft1	No	No	No	1	Pro & Farm	800,000$	800,000$	0$	0$	No			Lien / Lien NHL
Nathan Noel	Comets (NJD)	C/LW	24	1997-06-21	No	174 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$		Lien
Thomas Schemitsch	Comets (NJD)	D	25	1996-10-25	No	200 Lbs	6 ft4	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
16	24.63	186 Lbs	6 ft1	1.81	827,417$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1				40	1	2	2
2				30	1	2	2
3			Blake Speers	20	1	2	2
4				10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			40	1	2	2
2	Leon Gawanke		30	1	2	2
3			20	1	2	2
4			10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1				60	1	2	2
2				40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2	Leon Gawanke		40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2	Leon Gawanke		40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		60	1	2	2			60	1	2	2
2		40	1	2	2	Leon Gawanke		40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2	Leon Gawanke		40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,	,

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, , Leon Gawanke		, Leon Gawanke

Tirs de Pénalité
, , , ,

Gardien
#1 : , #2 :

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	Canucks	2	0	1	0	0	0	0	1	7	10	-3	1	0	0	0	0	0	0	1	4	5	-1	1	0	1	0	0	0	0	0	3	5	-2	1	0.250	7	12	19	0	0	22	12	18	0	64	127	137	182	8	64	19	8	35	6	1	16.67%	4	2	50.00%	0	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
2	Crunch	3	2	1	0	0	0	0	0	18	13	5	2	1	1	0	0	0	0	0	9	8	1	1	1	0	0	0	0	0	0	9	5	4	4	0.667	18	29	47	0	0	22	12	18	0	113	127	137	182	8	90	29	12	72	6	3	50.00%	6	3	50.00%	0	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
3	Griffins	2	1	1	0	0	0	0	0	8	8	0	1	0	1	0	0	0	0	0	4	5	-1	1	1	0	0	0	0	0	0	4	3	1	2	0.500	8	13	21	0	0	22	12	18	0	51	127	137	182	8	53	19	8	40	6	1	16.67%	4	2	50.00%	1	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
4	Marlies	3	0	3	0	0	0	0	0	3	31	-28	1	0	1	0	0	0	0	0	2	10	-8	2	0	2	0	0	0	0	0	1	21	-20	0	0.000	3	6	9	0	0	22	12	18	0	53	127	137	182	8	182	51	23	33	12	1	8.33%	8	3	62.50%	0	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
5	Monsters	2	0	2	0	0	0	0	0	2	11	-9	1	0	1	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	1	6	-5	0	0.000	2	3	5	0	0	22	12	18	0	40	127	137	182	8	67	24	6	26	6	0	0.00%	3	1	66.67%	0	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
6	Rocket	2	0	2	0	0	0	0	0	2	11	-9	1	0	1	0	0	0	0	0	2	6	-4	1	0	1	0	0	0	0	0	0	5	-5	0	0.000	2	4	6	0	0	22	12	18	0	29	127	137	182	8	58	12	10	27	3	0	0.00%	5	1	80.00%	0	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
7	Senators	2	1	0	0	0	1	0	0	12	11	1	1	0	0	0	0	1	0	0	5	6	-1	1	1	0	0	0	0	0	0	7	5	2	3	0.750	12	18	30	0	0	22	12	18	0	99	127	137	182	8	66	29	6	39	3	0	0.00%	3	0	100.00%	0	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
	Total	16	4	10	0	0	1	0	1	52	95	-43	8	1	5	0	0	1	0	1	27	45	-18	8	3	5	0	0	0	0	0	25	50	-25	10	0.313	52	85	137	0	0	22	12	18	0	449	127	137	182	8	580	183	73	272	42	6	14.29%	33	12	63.64%	1	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
	_Since Last GM Reset	16	4	10	0	0	1	0	1	52	95	-43	8	1	5	0	0	1	0	1	27	45	-18	8	3	5	0	0	0	0	0	25	50	-25	10	0.313	52	85	137	0	0	22	12	18	0	449	127	137	182	8	580	183	73	272	42	6	14.29%	33	12	63.64%	1	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
	_Vs Conference	16	4	10	0	0	1	0	1	52	95	-43	8	1	5	0	0	1	0	1	27	45	-18	8	3	5	0	0	0	0	0	25	50	-25	10	0.313	52	85	137	0	0	22	12	18	0	449	127	137	182	8	580	183	73	272	42	6	14.29%	33	12	63.64%	1	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77
	_Vs Division	12	4	10	0	0	1	0	1	43	74	-31	6	1	5	0	0	1	0	1	22	35	-13	6	3	5	0	0	0	0	0	21	39	-18	10	0.417	43	70	113	0	0	22	12	18	0	345	127	137	182	8	449	140	59	211	30	5	16.67%	26	9	65.38%	1	157	334	47.01%	168	408	41.18%	131	274	47.81%	307	225	482	105	176	77

			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	10	L3	52	85	137	449	580	183	73	272	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
16	4	10	0	1	0	1	52	95

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	1	5	0	1	0	1	27	45

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	3	5	0	0	0	0	25	50

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
1	8	0	1	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
42	6	14.29%	33	12	63.64%	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
127	137	182	8	22	12	18	0

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
157	334	47.01%	168	408	41.18%	131	274	47.81%

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
307	225	482	105	176	77

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	1	Comets	1	Marlies	11	L			Sommaire du Match
2 - 2021-10-19	19	Crunch	2	Comets	4	W			Sommaire du Match
4 - 2021-10-21	47	Griffins	5	Comets	4	L			Sommaire du Match
5 - 2021-10-22	53	Comets	7	Senators	5	W			Sommaire du Match
6 - 2021-10-23	63	Comets	9	Crunch	5	W			Sommaire du Match
7 - 2021-10-24	88	Canucks	5	Comets	4	L	X	X	Sommaire du Match
8 - 2021-10-25	100	Comets	1	Monsters	6	L			Sommaire du Match
9 - 2021-10-26	114	Comets	3	Canucks	5	L			Sommaire du Match
10 - 2021-10-27	126	Senators	6	Comets	5	L	X		Sommaire du Match
12 - 2021-10-29	148	Monsters	5	Comets	1	L			Sommaire du Match
13 - 2021-10-30	162	Comets	0	Marlies	10	L			Sommaire du Match
15 - 2021-11-01	182	Rocket	6	Comets	2	L			Sommaire du Match
16 - 2021-11-02	189	Comets	4	Griffins	3	W			Sommaire du Match
18 - 2021-11-04	213	Crunch	6	Comets	5	L			Sommaire du Match
20 - 2021-11-06	235	Comets	0	Rocket	5	L			Sommaire du Match
21 - 2021-11-07	244	Marlies	10	Comets	2	L			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité	2000	1000
Prix des Billets	40	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$	1,323,866$	1,320,366$	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

	Comets
	GP: 16 \| W: 4 \| L: 10 \| OTL: 2 \| P: 10 GF: 52 \| GA: 95 \| PP%: 14.29% \| PK%: 63.64% DG: Mathias \| Morale : 50 \| Moyenne d'Équipe : 56