OTHL - Bears

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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	Carl Grundstrom (R)		X	X		100.00	90	46	87	76	70	68	71	75	25	57	75	56	25	45	45	71	50	64	0
2	Chris Conner		X	X		100.00	71	62	92	65	62	79	84	64	50	63	59	65	56	54	54	65	50	63	0
3	Laurent Dauphin	X	X	X		100.00	70	68	73	72	68	70	73	60	75	60	56	62	53	47	47	62	50	61	0
4	Matthew Peca	X	X	X		100.00	71	42	96	76	61	57	73	55	59	64	58	71	25	48	48	64	50	61	0
5	A.J. Greer (R)		X	X		100.00	78	44	74	72	76	45	77	56	37	57	58	58	25	46	46	60	50	58	0
6	Blake Lizotte (R)	X				100.00	51	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	50	54	1
7	Oscar Fantenberg				X	100.00	80	54	90	76	73	69	74	59	25	43	48	72	25	51	52	60	50	64	0
8	Steven Kampfer				X	100.00	78	44	82	68	70	65	53	64	25	49	50	64	25	61	61	59	50	62	0
9	Andrew Campbell				X	100.00	78	78	77	68	78	71	78	47	25	34	41	65	39	58	58	53	50	61	0
Rayé
1	Luke Witkowski			X	X	100.00	85	96	80	71	80	53	55	58	25	46	47	63	25	55	56	58	50	61	0

	MOYENNE D'ÉQUIPE					100.00	75	59	81	70	69	63	69	59	40	53	55	63	35	52	52	61	50	61

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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	Spencer Martin	100.00	53	58	73	83	52	60	50	57	57	56	30	44	44	55	50	57	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	53	58	73	83	52	60	50	57	57	56	30	44	44	55	50	57

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Dallas Eakins	64	61	41	44	62	48	53	USA	48	3	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	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	Blake Lizotte	Bears (WSH)	C	61	42	48	90	51	13	5	42	118	245	63	197	17.14%	24	995	16.32	3	13	16	36	144	1	1	2	8	130	6	1	68.13%	888	0	6	1.81	1	1	0	0	1	10	9	1
2	Matthew Peca	Bears (WSH)	C/LW/RW	61	37	53	90	57	16	0	24	143	238	68	143	15.55%	21	1135	18.61	4	14	18	29	140	1	0	1	10	160	6	1	71.04%	1630	1	1	1.59	0	6	0	0	0	7	4	8
3	A.J. Greer	Bears (WSH)	LW/RW	61	36	53	89	52	44	0	140	84	303	83	202	11.88%	16	964	15.81	5	8	13	36	137	0	2	2	3	102	8	2	58.70%	138	0	1	1.85	2	2	0	0	0	8	11	3
4	Laurent Dauphin	Bears (WSH)	C/LW/RW	61	32	56	88	59	27	5	71	83	218	60	189	14.68%	18	1144	18.76	5	13	18	27	140	1	0	1	7	171	4	3	75.76%	99	0	1	1.54	0	6	1	0	0	3	6	9
5	Steven Kampfer	Bears (WSH)	D	61	17	54	71	36	54	0	216	68	114	36	83	14.91%	71	1265	20.75	6	5	11	41	139	1	2	3	3	133	3	0	100.00%	3	0	0	1.12	0	0	0	0	0	5	5	4
6	Andrew Campbell	Bears (WSH)	D	61	21	41	62	26	104	10	206	61	116	44	111	18.10%	70	1114	18.27	12	8	20	51	132	0	0	0	3	96	1	1	71.43%	7	0	0	1.11	0	0	0	1	1	5	2	6
7	Luke Witkowski	Bears (WSH)	RW/D	58	20	31	51	50	92	10	129	53	145	34	92	13.79%	7	971	16.76	3	4	7	23	132	0	0	0	0	77	3	0	56.06%	66	0	0	1.05	5	5	1	0	1	2	3	5
Stats d'équipe Total ou en Moyenne				424	205	336	541	331	350	30	828	610	1379	388	1017	14.87%	227	7591	17.90	38	65	103	243	967	4	5	9	34	872	31	8	69.37%	2831	1	9	1.43	8	20	2	1	3	40	40	36

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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	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Spencer Martin	Bears (WSH)	61	44	15	1	0.902	2.38	3533	10	5	140	1433	0	3	1	0.762	21	61	0	1	2	1
Stats d'équipe Total ou en Moyenne			61	44	15	1	0.902	2.38	3533	10	5	140	1433	0	3	1	0.762	21	61	0	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	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
A.J. Greer	Bears (WSH)	LW/RW	23	1996-12-14	Yes	204 Lbs	6 ft3	No	No	No	3	Pro & Farm	825,000$	0$	0$	No	825,000$	825,000$	Lien
Andrew Campbell	Bears (WSH)	D	32	1988-02-04	No	205 Lbs	6 ft4	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$		Lien
Blake Lizotte	Bears (WSH)	C	22	1997-12-13 11:41:36 AM	Yes	172 Lbs	5 ft7	No	No	No	3	Pro & Farm	925,000$	0$	0$	No	925,000$	925,000$
Carl Grundstrom	Bears (WSH)	LW/RW	22	1997-12-01	Yes	194 Lbs	6 ft0	No	No	No	3	Pro & Farm	925,000$	0$	0$	No	925,000$	925,000$	Lien
Chris Conner	Bears (WSH)	LW/RW	36	1983-12-23	No	181 Lbs	5 ft7	No	No	No	1	Pro & Farm	650,000$	0$	0$	No			Lien
Laurent Dauphin	Bears (WSH)	C/LW/RW	25	1995-03-27	No	180 Lbs	6 ft1	No	No	No	1	Pro & Farm	894,167$	0$	0$	No			Lien
Luke Witkowski	Bears (WSH)	RW/D	30	1990-04-14	No	210 Lbs	6 ft2	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Lien
Matthew Peca	Bears (WSH)	C/LW/RW	26	1993-04-27	No	178 Lbs	5 ft8	No	No	No	1	Pro & Farm	925,000$	0$	0$	No			Lien
Oscar Fantenberg	Bears (WSH)	D	28	1991-10-07	No	203 Lbs	6 ft0	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$		Lien
Spencer Martin	Bears (WSH)	G	24	1995-06-07	No	210 Lbs	6 ft3	No	No	No	1	Pro & Farm	894,000$	0$	0$	No			Lien
Steven Kampfer	Bears (WSH)	D	31	1988-09-24	No	195 Lbs	5 ft11	No	No	No	1	Pro & Farm	650,000$	0$	0$	No			Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
11	27.18	194 Lbs	6 ft0	1.73	794,379$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Laurent Dauphin	Matthew Peca		40	1	2	2
2	A.J. Greer	Blake Lizotte		30	1	2	2
3				20	1	2	2
4	Laurent Dauphin	Matthew Peca		10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Steven Kampfer	40	1	2	2
2	Andrew Campbell		30	1	2	2
3			20	1	2	2
4		Steven Kampfer	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Laurent Dauphin	Matthew Peca		60	1	2	2
2	A.J. Greer	Blake Lizotte		40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Steven Kampfer	60	1	2	2
2	Andrew Campbell		40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Matthew Peca	Laurent Dauphin	60	1	2	2
2	A.J. Greer	Blake Lizotte	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Steven Kampfer	60	1	2	2
2	Andrew Campbell		40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Matthew Peca	60	1	2	2		Steven Kampfer	60	1	2	2
2	Laurent Dauphin	40	1	2	2	Andrew Campbell		40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Matthew Peca	Laurent Dauphin	60	1	2	2
2	A.J. Greer	Blake Lizotte	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Steven Kampfer	60	1	2	2
2	Andrew Campbell		40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Laurent Dauphin	Matthew Peca			Steven Kampfer

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Laurent Dauphin	Matthew Peca			Steven Kampfer

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
A.J. Greer, Blake Lizotte, Matthew Peca	A.J. Greer, Blake Lizotte	Matthew Peca

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Andrew Campbell, , Steven Kampfer	Andrew Campbell	, Steven Kampfer

Tirs de Pénalité
Matthew Peca, Laurent Dauphin, , A.J. Greer, Blake Lizotte

Gardien
#1 : Spencer Martin, #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	Americans	3	0	2	0	1	0	0	0	10	20	-10	1	0	1	0	0	0	0	0	2	9	-7	2	0	1	0	1	0	0	0	8	11	-3	2	0.333	10	18	28	0	0	112	90	100	12	53	859	876	900	41	113	22	43	27	8	2	25.00%	9	1	88.89%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
2	Admirals	2	1	1	0	0	0	0	0	8	6	2	1	1	0	0	0	0	0	0	5	1	4	1	0	1	0	0	0	0	0	3	5	-2	2	0.500	8	15	23	1	0	112	90	100	12	104	859	876	900	41	64	19	4	52	1	0	0.00%	2	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
3	Barracudas	2	2	0	0	0	0	0	0	9	4	5	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	4	2	2	4	1.000	9	17	26	0	0	112	90	100	12	73	859	876	900	41	36	5	6	53	6	2	33.33%	3	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
4	Bruins	3	0	2	0	0	0	1	0	8	11	-3	1	0	0	0	0	0	1	0	3	2	1	2	0	2	0	0	0	0	0	5	9	-4	2	0.333	8	12	20	0	0	112	90	100	12	66	859	876	900	41	63	16	31	31	9	1	11.11%	11	1	90.91%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
5	Checkers	4	2	2	0	0	0	0	0	9	17	-8	2	1	1	0	0	0	0	0	3	8	-5	2	1	1	0	0	0	0	0	6	9	-3	4	0.500	9	14	23	0	0	112	90	100	12	75	859	876	900	41	129	35	24	59	12	2	16.67%	10	1	90.00%	1	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
6	Comets	2	0	1	0	0	0	0	1	5	12	-7	1	0	0	0	0	0	0	1	3	4	-1	1	0	1	0	0	0	0	0	2	8	-6	1	0.250	5	9	14	0	0	112	90	100	12	34	859	876	900	41	73	14	10	20	12	1	8.33%	5	1	80.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
7	Condors	2	0	2	0	0	0	0	0	4	11	-7	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	8	-6	0	0.000	4	8	12	0	0	112	90	100	12	34	859	876	900	41	58	15	10	18	10	2	20.00%	5	3	40.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
8	Crunch	3	1	2	0	0	0	0	0	7	9	-2	2	1	1	0	0	0	0	0	6	7	-1	1	0	1	0	0	0	0	0	1	2	-1	2	0.333	7	13	20	0	0	112	90	100	12	92	859	876	900	41	80	17	30	79	9	0	0.00%	15	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
9	Devils	4	4	0	0	0	0	0	0	25	3	22	2	2	0	0	0	0	0	0	11	1	10	2	2	0	0	0	0	0	0	14	2	12	8	1.000	25	44	69	0	2	112	90	100	12	211	859	876	900	41	67	18	26	77	6	2	33.33%	11	1	90.91%	1	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
10	Eagles	2	2	0	0	0	0	0	0	15	8	7	1	1	0	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	7	4	3	4	1.000	15	30	45	0	0	112	90	100	12	94	859	876	900	41	76	21	6	49	6	2	33.33%	3	1	66.67%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
11	Griffins	3	1	0	0	1	0	1	0	9	6	3	1	1	0	0	0	0	0	0	2	1	1	2	0	0	0	1	0	1	0	7	5	2	6	1.000	9	12	21	0	0	112	90	100	12	108	859	876	900	41	74	22	22	73	11	3	27.27%	11	1	90.91%	1	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
12	Gulls	2	1	0	0	1	0	0	0	9	6	3	1	1	0	0	0	0	0	0	5	3	2	1	0	0	0	1	0	0	0	4	3	1	4	1.000	9	15	24	0	0	112	90	100	12	81	859	876	900	41	50	20	24	47	8	2	25.00%	11	1	90.91%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
13	Heat	2	0	2	0	0	0	0	0	1	24	-23	1	0	1	0	0	0	0	0	0	12	-12	1	0	1	0	0	0	0	0	1	12	-11	0	0.000	1	2	3	0	0	112	90	100	12	17	859	876	900	41	129	29	10	33	3	0	0.00%	4	2	50.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
14	IceHogs	2	0	2	0	0	0	0	0	3	14	-11	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	0	9	-9	0	0.000	3	5	8	0	0	112	90	100	12	31	859	876	900	41	95	25	8	37	5	2	40.00%	4	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
15	Marlies	3	1	2	0	0	0	0	0	12	14	-2	2	1	1	0	0	0	0	0	9	6	3	1	0	1	0	0	0	0	0	3	8	-5	2	0.333	12	23	35	0	0	112	90	100	12	68	859	876	900	41	142	42	13	44	3	1	33.33%	5	2	60.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
16	Monsters	4	0	3	0	0	0	1	0	5	11	-6	2	0	2	0	0	0	0	0	1	7	-6	2	0	1	0	0	0	1	0	4	4	0	2	0.250	5	7	12	0	0	112	90	100	12	73	859	876	900	41	104	35	34	44	16	2	12.50%	17	2	88.24%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
17	Moose	2	2	0	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	4	2	2	4	1.000	7	12	19	0	0	112	90	100	12	62	859	876	900	41	48	19	18	48	9	2	22.22%	8	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
18	Penguins	4	2	1	0	0	0	1	0	18	11	7	2	1	1	0	0	0	0	0	11	7	4	2	1	0	0	0	0	1	0	7	4	3	6	0.750	18	31	49	1	0	112	90	100	12	113	859	876	900	41	91	37	26	83	19	6	31.58%	13	1	92.31%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
19	Phantoms	4	2	2	0	0	0	0	0	19	16	3	2	1	1	0	0	0	0	0	9	6	3	2	1	1	0	0	0	0	0	10	10	0	4	0.500	19	34	53	0	0	112	90	100	12	134	859	876	900	41	153	55	14	69	12	1	8.33%	7	1	85.71%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
20	Rampage	2	1	0	0	0	1	0	0	9	5	4	1	1	0	0	0	0	0	0	6	1	5	1	0	0	0	0	1	0	0	3	4	-1	3	0.750	9	17	26	0	0	112	90	100	12	75	859	876	900	41	87	24	10	47	6	2	33.33%	5	1	80.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
21	Reign	2	2	0	0	0	0	0	0	12	3	9	1	1	0	0	0	0	0	0	4	1	3	1	1	0	0	0	0	0	0	8	2	6	4	1.000	12	20	32	0	0	112	90	100	12	101	859	876	900	41	51	18	6	46	0	0	0.00%	2	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
22	Roadrunners	2	1	0	0	0	0	1	0	9	5	4	1	1	0	0	0	0	0	0	5	2	3	1	0	0	0	0	0	1	0	4	3	1	4	1.000	9	16	25	0	0	112	90	100	12	77	859	876	900	41	68	24	16	33	9	3	33.33%	8	1	87.50%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
23	Rocket	3	0	3	0	0	0	0	0	3	22	-19	2	0	2	0	0	0	0	0	2	14	-12	1	0	1	0	0	0	0	0	1	8	-7	0	0.000	3	4	7	0	0	112	90	100	12	43	859	876	900	41	125	43	28	41	8	0	0.00%	13	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
24	Senators	3	2	0	0	1	0	0	0	17	4	13	2	2	0	0	0	0	0	0	14	2	12	1	0	0	0	1	0	0	0	3	2	1	6	1.000	17	25	42	0	1	112	90	100	12	121	859	876	900	41	73	22	8	67	8	3	37.50%	3	1	66.67%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
25	Sound Tigers	4	3	1	0	0	0	0	0	22	11	11	2	2	0	0	0	0	0	0	14	3	11	2	1	1	0	0	0	0	0	8	8	0	6	0.750	22	37	59	0	0	112	90	100	12	200	859	876	900	41	113	22	27	104	8	1	12.50%	10	0	100.00%	1	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
26	Stars	2	0	2	0	0	0	0	0	4	10	-6	1	0	1	0	0	0	0	0	2	5	-3	1	0	1	0	0	0	0	0	2	5	-3	0	0.000	4	8	12	0	0	112	90	100	12	71	859	876	900	41	108	39	4	41	4	0	0.00%	1	1	0.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
27	Thunderbirds	3	2	1	0	0	0	0	0	13	15	-2	1	1	0	0	0	0	0	0	3	0	3	2	1	1	0	0	0	0	0	10	15	-5	4	0.667	13	23	36	0	1	112	90	100	12	85	859	876	900	41	85	25	25	42	15	4	26.67%	4	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
	Total	82	38	33	0	4	1	5	1	311	299	12	41	24	15	0	0	0	1	1	162	129	33	41	14	18	0	4	1	4	0	149	170	-21	96	0.585	311	544	855	2	5	112	90	100	12	2656	859	876	900	41	2586	738	526	1537	234	46	19.66%	218	24	88.99%	4	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
29	Wild	2	2	0	0	0	0	0	0	12	1	11	1	1	0	0	0	0	0	0	7	0	7	1	1	0	0	0	0	0	0	5	1	4	4	1.000	12	22	34	0	1	112	90	100	12	112	859	876	900	41	34	14	12	62	0	0	0.00%	6	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
30	Wolf Pack	4	4	0	0	0	0	0	0	27	8	19	2	2	0	0	0	0	0	0	14	5	9	2	2	0	0	0	0	0	0	13	3	10	8	1.000	27	51	78	0	0	112	90	100	12	207	859	876	900	41	133	28	8	82	4	0	0.00%	4	0	100.00%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
31	Wolves	2	0	2	0	0	0	0	0	0	8	-8	1	0	1	0	0	0	0	0	0	6	-6	1	0	1	0	0	0	0	0	0	2	-2	0	0.000	0	0	0	0	0	112	90	100	12	41	859	876	900	41	64	13	23	29	7	0	0.00%	8	1	87.50%	0	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
	_Since Last GM Reset	82	38	33	0	4	1	5	1	311	299	12	41	24	15	0	0	0	1	1	162	129	33	41	14	18	0	4	1	4	0	149	170	-21	96	0.585	311	544	855	2	5	112	90	100	12	2656	859	876	900	41	2586	738	526	1537	234	46	19.66%	218	24	88.99%	4	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
	_Vs Conference	51	24	20	0	2	0	4	1	199	170	29	26	15	9	0	0	0	1	1	105	73	32	25	9	11	0	2	0	3	0	94	97	-3	61	0.598	199	339	538	1	4	112	90	100	12	1630	859	876	900	41	1505	431	326	915	152	27	17.76%	139	12	91.37%	4	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
	_Vs Division	28	15	9	0	0	0	2	0	125	77	48	14	8	3	0	0	0	1	0	63	37	26	14	7	6	0	0	0	1	0	62	40	22	34	0.607	125	218	343	1	2	112	90	100	12	1013	859	876	900	41	790	230	159	518	77	14	18.18%	72	6	91.67%	3	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429

			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
82	96	W3	311	544	855	2656	2586	738	526	1537	2	5

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	38	33	4	1	5	1	311	299

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	24	15	0	0	1	1	162	129

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	14	18	4	1	4	0	149	170

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
7	2	0	0	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
234	46	19.66%	218	24	88.99%	4

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
859	876	900	41	112	90	100	12

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
1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%

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
1988	1508	2086	502	886	429

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 - 2019-10-04	2	Bears	3	Rampage	4	L	X		Sommaire du Match
3 - 2019-10-06	15	Bears	3	Sound Tigers	7	L			Sommaire du Match
4 - 2019-10-07	30	Checkers	6	Bears	0	L			Sommaire du Match
7 - 2019-10-10	43	Stars	5	Bears	2	L			Sommaire du Match
9 - 2019-10-12	53	Bears	3	Admirals	5	L			Sommaire du Match
11 - 2019-10-14	65	Bears	2	Stars	5	L			Sommaire du Match
13 - 2019-10-16	84	Eagles	4	Bears	8	W			Sommaire du Match
15 - 2019-10-18	97	Marlies	4	Bears	2	L			Sommaire du Match
17 - 2019-10-20	112	Wolf Pack	3	Bears	6	W			Sommaire du Match
19 - 2019-10-22	125	Bears	0	IceHogs	9	L			Sommaire du Match
21 - 2019-10-24	135	Bears	1	Heat	12	L			Sommaire du Match
23 - 2019-10-26	149	Bears	2	Condors	8	L			Sommaire du Match
24 - 2019-10-27	159	Bears	2	Comets	8	L			Sommaire du Match
28 - 2019-10-31	187	Bears	3	Marlies	8	L			Sommaire du Match
31 - 2019-11-03	203	Americans	9	Bears	2	L			Sommaire du Match
33 - 2019-11-05	219	Heat	12	Bears	0	L			Sommaire du Match
37 - 2019-11-09	242	Bears	3	Thunderbirds	10	L			Sommaire du Match
39 - 2019-11-11	262	Wolves	6	Bears	0	L			Sommaire du Match
41 - 2019-11-13	271	Roadrunners	2	Bears	5	W			Sommaire du Match
43 - 2019-11-15	284	Bears	4	Phantoms	9	L			Sommaire du Match
45 - 2019-11-17	298	Rocket	8	Bears	0	L			Sommaire du Match
46 - 2019-11-18	300	Bears	2	Bruins	4	L			Sommaire du Match
48 - 2019-11-20	317	Gulls	3	Bears	5	W			Sommaire du Match
50 - 2019-11-22	332	Bears	5	Wolf Pack	1	W			Sommaire du Match
53 - 2019-11-25	361	Comets	4	Bears	3	L	X	X	Sommaire du Match
57 - 2019-11-29	389	Thunderbirds	0	Bears	3	W			Sommaire du Match
59 - 2019-12-01	402	Crunch	5	Bears	1	L			Sommaire du Match
60 - 2019-12-02	406	Bears	3	Griffins	2	W	X		Sommaire du Match
63 - 2019-12-05	431	Bears	4	Barracudas	2	W			Sommaire du Match
64 - 2019-12-06	435	Bears	8	Reign	2	W			Sommaire du Match
66 - 2019-12-08	447	Bears	4	Gulls	3	W	X		Sommaire du Match
69 - 2019-12-11	471	Monsters	3	Bears	0	L			Sommaire du Match
71 - 2019-12-13	486	Bruins	2	Bears	3	W	X	X	Sommaire du Match
74 - 2019-12-16	514	Bears	1	Crunch	2	L			Sommaire du Match
76 - 2019-12-18	519	Bears	2	Monsters	1	W	X	X	Sommaire du Match
80 - 2019-12-22	550	Bears	6	Devils	2	W			Sommaire du Match
81 - 2019-12-23	564	Crunch	2	Bears	5	W			Sommaire du Match
83 - 2019-12-25	569	Bears	3	Bruins	5	L			Sommaire du Match
87 - 2019-12-29	592	Monsters	4	Bears	1	L			Sommaire du Match
88 - 2019-12-30	593	Bears	2	Checkers	6	L			Sommaire du Match
91 - 2020-01-02	625	Sound Tigers	1	Bears	8	W			Sommaire du Match
94 - 2020-01-05	639	Bears	4	Checkers	3	W			Sommaire du Match
96 - 2020-01-07	658	Barracudas	2	Bears	5	W			Sommaire du Match
98 - 2020-01-09	674	Senators	2	Bears	6	W			Sommaire du Match
99 - 2020-01-10	676	Bears	6	Phantoms	1	W			Sommaire du Match
102 - 2020-01-13	702	Devils	0	Bears	5	W			Sommaire du Match
104 - 2020-01-15	713	Checkers	2	Bears	3	W			Sommaire du Match
107 - 2020-01-18	739	Devils	1	Bears	6	W			Sommaire du Match
109 - 2020-01-20	750	Bears	5	Sound Tigers	1	W			Sommaire du Match
118 - 2020-01-29	769	Bears	1	Rocket	8	L			Sommaire du Match
120 - 2020-01-31	781	Admirals	1	Bears	5	W			Sommaire du Match
122 - 2020-02-02	789	Bears	3	Senators	2	W	X		Sommaire du Match
124 - 2020-02-04	808	Penguins	2	Bears	8	W			Sommaire du Match
126 - 2020-02-06	824	Reign	1	Bears	4	W			Sommaire du Match
130 - 2020-02-10	854	Phantoms	1	Bears	8	W			Sommaire du Match
132 - 2020-02-12	865	Sound Tigers	2	Bears	6	W			Sommaire du Match
135 - 2020-02-15	882	Bears	7	Eagles	4	W			Sommaire du Match
137 - 2020-02-17	895	Bears	4	Roadrunners	3	W	X	X	Sommaire du Match
139 - 2020-02-19	919	Bears	0	Wolves	2	L			Sommaire du Match
142 - 2020-02-22	940	Rocket	6	Bears	2	L			Sommaire du Match
144 - 2020-02-24	950	Bears	8	Devils	0	W			Sommaire du Match
145 - 2020-02-25	966	Penguins	5	Bears	3	L			Sommaire du Match
147 - 2020-02-27	980	Moose	2	Bears	3	W			Sommaire du Match
149 - 2020-02-29	993	Bears	4	Moose	2	W			Sommaire du Match
152 - 2020-03-03	1014	Bears	5	Wild	1	W			Sommaire du Match
155 - 2020-03-06	1032	Phantoms	5	Bears	1	L			Sommaire du Match
156 - 2020-03-07	1038	Bears	8	Wolf Pack	2	W			Sommaire du Match
158 - 2020-03-09	1057	Bears	3	Penguins	2	W	X	X	Sommaire du Match
160 - 2020-03-11	1066	Bears	4	Americans	3	W	X		Sommaire du Match
163 - 2020-03-14	1092	Griffins	1	Bears	2	W			Sommaire du Match
165 - 2020-03-16	1108	IceHogs	5	Bears	3	L			Sommaire du Match
167 - 2020-03-18	1122	Condors	3	Bears	2	L			Sommaire du Match
170 - 2020-03-21	1138	Bears	2	Monsters	3	L			Sommaire du Match
171 - 2020-03-22	1152	Senators	0	Bears	8	W			Sommaire du Match
173 - 2020-03-24	1169	Bears	4	Penguins	2	W			Sommaire du Match
175 - 2020-03-26	1184	Rampage	1	Bears	6	W			Sommaire du Match
177 - 2020-03-28	1198	Wolf Pack	2	Bears	8	W			Sommaire du Match
179 - 2020-03-30	1209	Bears	4	Griffins	3	W	X	X	Sommaire du Match
181 - 2020-04-01	1224	Bears	4	Americans	8	L			Sommaire du Match
182 - 2020-04-02	1238	Marlies	2	Bears	7	W			Sommaire du Match
184 - 2020-04-04	1254	Wild	0	Bears	7	W			Sommaire du Match
186 - 2020-04-06	1263	Bears	7	Thunderbirds	5	W			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	35	0
Assistance	74,186	36,026
Assistance PCT	90.47%	87.87%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
0	2688 - 89.60%	63,330$	2,596,510$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,649,180$	873,817$	873,817$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
4,698$	849,118$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	8,999$	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
2019	82	38	33	0	4	1	5	1	311	299	12	41	24	15	0	0	0	1	1	162	129	33	41	14	18	0	4	1	4	0	149	170	-21	96	311	544	855	2	5	112	90	100	12	2656	859	876	900	41	2586	738	526	1537	234	46	19.66%	218	24	88.99%	4	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429
Total Saison Régulière	82	38	33	0	4	1	5	1	311	299	12	41	24	15	0	0	0	1	1	162	129	33	41	14	18	0	4	1	4	0	149	170	-21	96	311	544	855	2	5	112	90	100	12	2656	859	876	900	41	2586	738	526	1537	234	46	19.66%	218	24	88.99%	4	1249	2155	57.96%	1026	2139	47.97%	648	1299	49.88%	1988	1508	2086	502	886	429