Stanley Cup Winners Hockey League

OTTAWA Senators
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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	Michael Raffl	0		X			100.00	80	39	83	64	74	79	82	63	58	66	62	72	63	80	74	28	98	71	0
2	Andrew Mangiapane	0		X			100.00	73	38	87	68	67	78	85	67	54	65	71	62	69	67	66	48	98	70	0
3	Nolan Patrick	0	X				100.00	67	41	76	70	80	83	85	69	84	70	67	60	68	56	52	91	98	70	0
4	Alex Formenton	0		X			100.00	56	56	78	68	73	87	88	64	57	60	63	59	62	61	63	72	98	68	0
5	Matt Puempel	0		X			100.00	62	45	89	64	78	82	79	62	57	61	61	63	65	73	67	69	98	68	0
6	Sam Bennett	0	X				100.00	83	55	67	65	76	73	82	64	71	62	66	57	65	67	64	86	98	68	0
7	Mike Vecchione	0	X				100.00	55	47	89	60	69	88	89	61	66	58	59	56	61	73	67	58	98	67	0
8	Sam Anas	0	X		X		100.00	57	37	83	65	61	87	88	63	68	64	58	56	63	73	67	58	98	67	0
9	Michael McLeod	0	X				100.00	64	49	85	62	78	82	76	63	77	61	58	64	59	63	62	85	98	67	0
10	Kurtis Gabriel	0			X		100.00	72	57	73	60	86	75	77	58	50	56	57	59	60	73	67	54	96	66	0
11	Andrew Poturalski	0	X				100.00	56	45	88	59	70	80	82	58	63	62	57	56	60	71	66	59	98	65	0
12	Dan Hamhuis	0				X	100.00	68	44	78	63	78	80	82	61	30	68	50	74	46	88	78	36	98	70	0
13	Calle Rosen	0				X	100.00	64	43	86	61	75	84	77	59	30	62	58	60	54	71	66	51	98	67	0
14	Timothy Liljegren (R)	0				X	100.00	71	45	89	62	74	73	72	63	30	61	57	64	55	61	64	83	96	66	0
15	Josh Mahura	0				X	100.00	59	43	88	66	72	82	75	63	30	61	57	58	54	63	61	66	98	66	0
16	Daniel Brickley	0				X	100.00	64	43	86	59	84	79	72	58	30	57	56	60	53	69	65	59	98	65	0
17	Jimmy Schuldt	0				X	100.00	62	51	82	60	75	85	84	58	30	57	54	56	47	69	65	61	98	65	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	65	46	83	63	75	81	81	62	52	62	59	61	59	69	66	63	98	67

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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	Dylan Ferguson	100.00	74	52	50	75	73	72	74	73	72	74	73	63	67	62	98	64	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	74	52	50	75	73	72	74	73	72	74	73	63	67	62	98	64

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Clément Jodoin	67	67	67	67	81	81	34	CAN	68	1	100,000$

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	EG	HT	P/20	PSG	PSS	S1	S2	S3
1	Andrew Mangiapane	BELLEVILLE Senators (OTW)	LW	39	19	15	34	22	20	51	72	185	43	125	10.27%	15	1010	25.91	2	4	6	24	110	0	0	0	2	120	8	0	47.69%	65	0	0	0.67	0	6	3	4	2
2	Michael Raffl	BELLEVILLE Senators (OTW)	LW	39	14	13	27	-1	46	120	92	178	55	104	7.87%	11	1035	26.55	2	2	4	32	117	0	0	0	5	98	2	2	50.71%	282	0	1	0.52	2	6	3	3	2
3	Sam Bennett	BELLEVILLE Senators (OTW)	C	39	12	11	23	7	50	78	58	116	30	80	10.34%	1	600	15.40	3	3	6	23	116	0	0	0	0	0	3	1	56.90%	659	0	0	0.77	0	1	0	4	1
4	Nolan Patrick	BELLEVILLE Senators (OTW)	C	39	10	13	23	-1	16	46	74	126	38	91	7.94%	7	779	19.98	2	4	6	24	127	1	0	1	2	107	1	1	64.39%	997	0	0	0.59	1	5	1	5	2
5	Dan Hamhuis	BELLEVILLE Senators (OTW)	D	39	2	18	20	9	34	80	41	78	22	48	2.56%	59	949	24.33	0	3	3	44	129	0	1	1	0	118	0	0	0.00%	0	0	0	0.42	0	0	0	0	1
6	Michael McLeod	BELLEVILLE Senators (OTW)	C	39	5	15	20	19	10	26	45	66	21	39	7.58%	5	398	10.21	0	0	0	0	0	0	0	0	0	0	1	0	57.96%	421	0	0	1.00	0	0	0	1	2
7	Matt Puempel	BELLEVILLE Senators (OTW)	LW	39	10	6	16	19	8	17	21	71	23	43	14.08%	3	435	11.17	1	0	1	3	3	0	0	0	0	15	2	0	46.43%	28	0	0	0.73	0	1	1	2	4
8	Kurtis Gabriel	BELLEVILLE Senators (OTW)	RW	39	2	14	16	7	16	30	27	56	16	50	3.57%	3	599	15.38	1	5	6	9	117	0	0	0	0	0	0	1	47.62%	42	0	0	0.53	0	0	1	0	2
9	Calle Rosen	BELLEVILLE Senators (OTW)	D	39	5	8	13	9	24	28	27	47	10	25	10.64%	32	949	24.35	4	1	5	26	130	0	0	0	0	122	1	0	0.00%	0	1	0	0.27	0	0	3	0	0
10	Alex Formenton	BELLEVILLE Senators (OTW)	LW	39	5	6	11	5	2	13	34	65	17	64	7.69%	5	537	13.78	1	0	1	0	0	0	1	1	2	112	1	1	52.94%	34	0	0	0.41	0	2	0	0	1
11	Timothy Liljegren	BELLEVILLE Senators (OTW)	D	39	1	10	11	5	32	68	32	54	20	28	1.85%	48	785	20.15	1	3	4	40	114	0	0	0	0	108	0	0	0.00%	0	0	0	0.28	0	0	0	0	2
12	Josh Mahura	BELLEVILLE Senators (OTW)	D	39	1	10	11	5	8	21	25	54	17	34	1.85%	30	812	20.84	1	2	3	33	114	0	0	0	0	126	0	0	0.00%	0	0	0	0.27	0	0	0	0	0
13	Sam Anas	BELLEVILLE Senators (OTW)	C/RW	39	2	7	9	-3	0	17	26	62	27	56	3.23%	1	655	16.80	1	1	2	14	127	0	0	0	0	0	0	0	53.85%	39	0	0	0.27	0	1	0	0	1
14	Daniel Brickley	BELLEVILLE Senators (OTW)	D	39	4	5	9	13	18	54	20	15	6	13	26.67%	32	593	15.21	0	0	0	1	2	0	0	0	0	9	0	1	0.00%	0	0	0	0.30	0	0	0	0	2
15	Mike Vecchione	BELLEVILLE Senators (OTW)	C	39	3	5	8	1	2	10	46	35	11	19	8.57%	11	470	12.08	0	0	0	0	0	0	0	0	0	66	0	0	55.98%	518	0	0	0.34	0	0	0	0	0
16	Jimmy Schuldt	BELLEVILLE Senators (OTW)	D	39	0	7	7	13	44	50	10	17	0	14	0.00%	32	617	15.82	0	0	0	0	0	0	0	0	0	42	0	0	0.00%	0	0	0	0.23	0	0	0	0	1
17	Andrew Poturalski	BELLEVILLE Senators (OTW)	C	39	1	3	4	2	4	3	12	23	8	21	4.35%	1	255	6.56	0	1	1	3	16	0	0	0	0	4	0	0	63.64%	22	0	0	0.31	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				663	96	166	262	131	334	712	662	1248	364	854	7.69%	296	11485	17.32	19	29	48	276	1228	1	2	3	11	1052	19	7	58.29%	3107	1	1	0.46	3	22	12	19	23

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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	Dylan Ferguson	BELLEVILLE Senators (OTW)	39	21	14	4	0.929	1.77	2367	0	6	70	985	0	0	1	0.818	22	39	0	5	5	4
Stats d'équipe Total ou en Moyenne			39	21	14	4	0.929	1.77	2367	0	6	70	985	0	0	1	0.818	22	39	0	5	5	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

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	Salaire Année 5	Salaire Année 6	Link
Alex Formenton	BELLEVILLE Senators (OTW)	LW	20	1999-09-13	No	75 Kg	188 CM	No	No	No	6	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$	750,000$	750,000$	Lien
Andrew Mangiapane	BELLEVILLE Senators (OTW)	LW	24	1996-04-04	No	84 Kg	178 CM	No	No	No	5	Pro & Farm	1,100,000$	575,229$	1,100,000$	575,229$	0$	0$	No	1,100,000$	1,100,000$	1,100,000$	1,100,000$		Lien
Andrew Poturalski	BELLEVILLE Senators (OTW)	C	26	1994-01-14	No	86 Kg	180 CM	No	No	No	2	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$					Lien
Calle Rosen	BELLEVILLE Senators (OTW)	D	26	1994-02-02	No	85 Kg	185 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	500,000$	261,467$	0$	0$	No	750,000$	750,000$				Lien
Dan Hamhuis	BELLEVILLE Senators (OTW)	D	37	1982-12-13	No	93 Kg	185 CM	No	No	No	1	Pro & Farm	1,250,000$	653,669$	1,250,000$	653,669$	0$	0$	No						Lien
Daniel Brickley	BELLEVILLE Senators (OTW)	D	25	1995-03-30	No	92 Kg	191 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	500,000$	261,467$	0$	0$	No	750,000$	750,000$				Lien
Dylan Ferguson	BELLEVILLE Senators (OTW)	G	21	1998-09-20	No	86 Kg	185 CM	No	No	No	6	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$	750,000$	750,000$	Lien
Jimmy Schuldt	BELLEVILLE Senators (OTW)	D	25	1995-05-11	No	92 Kg	183 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	500,000$	261,467$	0$	0$	No	750,000$	750,000$				Lien
Josh Mahura	BELLEVILLE Senators (OTW)	D	22	1998-05-05	No	85 Kg	183 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$				Lien
Kurtis Gabriel	BELLEVILLE Senators (OTW)	RW	27	1993-04-20	No	91 Kg	193 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No						Lien
Matt Puempel	BELLEVILLE Senators (OTW)	LW	27	1993-01-24	No	91 Kg	185 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$				Lien
Michael McLeod	BELLEVILLE Senators (OTW)	C	22	1998-02-03	No	85 Kg	188 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	500,000$	261,467$	0$	0$	No	750,000$	750,000$				Lien
Michael Raffl	BELLEVILLE Senators (OTW)	LW	31	1988-12-01	No	91 Kg	183 CM	No	No	No	4	Pro & Farm	1,600,000$	836,697$	1,600,000$	836,697$	0$	0$	No	1,600,000$	1,600,000$	1,600,000$			Lien
Mike Vecchione	BELLEVILLE Senators (OTW)	C	27	1993-02-25	No	88 Kg	178 CM	No	No	No	3	Pro & Farm	750,000$	392,201$	500,000$	261,467$	0$	0$	No	750,000$	750,000$				Lien
Nolan Patrick	BELLEVILLE Senators (OTW)	C	21	1998-09-19	No	90 Kg	188 CM	No	No	No	2	Pro & Farm	925,000$	483,715$	925,000$	483,715$	0$	0$	No	925,000$					Lien
Sam Anas	BELLEVILLE Senators (OTW)	C/RW	27	1993-06-01	No	74 Kg	173 CM	No	No	No	2	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$					Lien
Sam Bennett	BELLEVILLE Senators (OTW)	C	24	1996-06-20	No	89 Kg	185 CM	No	No	No	2	Pro & Farm	2,400,000$	1,255,045$	2,400,000$	1,255,045$	0$	0$	No	2,400,000$					Lien
Timothy Liljegren	BELLEVILLE Senators (OTW)	D	21	1999-04-30	Yes	90 Kg	183 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
18	25.17	87 Kg	185 CM	3.11	945,833$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Michael Raffl	Nolan Patrick	Sam Anas	31	1	2	2
2	Andrew Mangiapane	Sam Bennett	Kurtis Gabriel	26	1	2	2
3	Alex Formenton	Mike Vecchione	Michael Raffl	23	1	2	2
4	Matt Puempel	Michael McLeod	Andrew Mangiapane	20	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dan Hamhuis	Calle Rosen	31	1	2	2
2	Josh Mahura	Timothy Liljegren	26	1	2	2
3	Daniel Brickley	Jimmy Schuldt	23	1	2	2
4	Dan Hamhuis	Calle Rosen	20	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Michael Raffl	Nolan Patrick	Sam Anas	55	1	2	2
2	Andrew Mangiapane	Sam Bennett	Kurtis Gabriel	45	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dan Hamhuis	Calle Rosen	55	1	2	2
2	Josh Mahura	Timothy Liljegren	45	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Michael Raffl	Andrew Mangiapane	55	1	2	2
2	Nolan Patrick	Alex Formenton	45	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dan Hamhuis	Calle Rosen	55	1	2	2
2	Josh Mahura	Timothy Liljegren	45	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Michael Raffl	55	1	2	2	Dan Hamhuis	Calle Rosen	55	1	2	2
2	Andrew Mangiapane	45	1	2	2	Josh Mahura	Timothy Liljegren	45	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Michael Raffl	Andrew Mangiapane	55	1	2	2
2	Nolan Patrick	Alex Formenton	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dan Hamhuis	Calle Rosen	55	1	2	2
2	Josh Mahura	Timothy Liljegren	45	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Michael Raffl	Nolan Patrick	Sam Anas	Dan Hamhuis	Calle Rosen

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Michael Raffl	Nolan Patrick	Sam Anas	Dan Hamhuis	Calle Rosen

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Andrew Poturalski, Matt Puempel, Mike Vecchione	Andrew Poturalski, Matt Puempel	Mike Vecchione

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Daniel Brickley, Jimmy Schuldt, Josh Mahura	Daniel Brickley	Jimmy Schuldt, Josh Mahura

Tirs de Pénalité
Michael Raffl, Andrew Mangiapane, Nolan Patrick, Alex Formenton, Matt Puempel

Gardien
#1 : Dylan Ferguson, #2 :

Lignes d'Attaque Perso. en Prol.
Michael Raffl, Andrew Mangiapane, Nolan Patrick, Alex Formenton, Matt Puempel, Sam Bennett, Sam Bennett, Mike Vecchione, Michael McLeod, Sam Anas, Kurtis Gabriel

Lignes de Défense Perso. en Prol.
Dan Hamhuis, Calle Rosen, Josh Mahura, Timothy Liljegren, Daniel Brickley

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	BRIDGEPORT Sound Tigers	3	1	2	0	0	0	0	0	7	9	-2	1	1	0	0	0	0	0	0	4	2	2	2	0	2	0	0	0	0	0	3	7	-4	2	0.333	7	9	16	1	0	39	34	22	4	86	378	422	428	42	77	24	24	65	9	1	11.11%	11	3	72.73%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
2	BROOKLYN Wolfpack	2	1	1	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	3	0	3	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	4	7	11	0	1	39	34	22	4	51	378	422	428	42	51	13	14	28	5	0	0.00%	7	1	85.71%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
3	CHICAGO Wolves	1	1	0	0	0	0	0	0	3	0	3	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	3	0	3	2	1.000	3	5	8	0	1	39	34	22	4	24	378	422	428	42	25	5	10	16	2	0	0.00%	4	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
4	COLORADO Eagles	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	39	34	22	4	40	378	422	428	42	24	7	12	19	5	0	0.00%	6	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
5	HERSEY Bears	1	0	0	0	0	0	1	0	2	1	1	1	0	0	0	0	0	1	0	2	1	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	2	2	4	0	0	39	34	22	4	42	378	422	428	42	14	4	4	19	4	0	0.00%	2	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
6	HOLLYWOOD Oscar	2	1	1	0	0	0	0	0	6	7	-1	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	6	7	-1	2	0.500	6	10	16	0	0	39	34	22	4	62	378	422	428	42	69	27	18	39	12	3	25.00%	7	2	71.43%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
7	IOWA Wild	2	1	1	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	2	0	2	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	4	6	10	0	1	39	34	22	4	73	378	422	428	42	44	11	6	31	6	0	0.00%	3	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
8	LAVAL Rockets	2	1	1	0	0	0	0	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	4	3	1	2	0.500	4	7	11	0	0	39	34	22	4	46	378	422	428	42	61	16	18	39	10	1	10.00%	9	2	77.78%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
9	MILWAUKEE Admirals	4	2	1	0	0	0	0	1	8	6	2	2	1	0	0	0	0	0	1	5	4	1	2	1	1	0	0	0	0	0	3	2	1	5	0.625	8	13	21	0	0	39	34	22	4	94	378	422	428	42	87	28	38	58	11	2	18.18%	16	3	81.25%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
10	MONT-LAURIER Sommet	2	1	0	0	0	0	1	0	7	5	2	1	1	0	0	0	0	0	0	2	1	1	1	0	0	0	0	0	1	0	5	4	1	4	1.000	7	12	19	0	0	39	34	22	4	85	378	422	428	42	54	13	26	44	4	0	0.00%	12	1	91.67%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
11	Manitoba Moose	1	0	1	0	0	0	0	0	1	3	-2	1	0	1	0	0	0	0	0	1	3	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	1	2	3	0	0	39	34	22	4	25	378	422	428	42	25	7	6	17	1	0	0.00%	3	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
12	PROVIDENCE Bruins	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	39	34	22	4	47	378	422	428	42	15	3	6	15	6	1	16.67%	2	1	50.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
13	PV Sharapovas	2	1	0	0	0	0	0	1	3	3	0	2	1	0	0	0	0	0	1	3	3	0	0	0	0	0	0	0	0	0	0	0	0	3	0.750	3	5	8	0	0	39	34	22	4	63	378	422	428	42	40	16	16	34	14	1	7.14%	7	1	85.71%	1	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
14	Quebec Nordiques	1	1	0	0	0	0	0	0	3	0	3	1	1	0	0	0	0	0	0	3	0	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	4	7	0	1	39	34	22	4	44	378	422	428	42	18	8	10	19	2	0	0.00%	4	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
15	SAN DIEGO Gulls	1	1	0	0	0	0	0	0	12	2	10	1	1	0	0	0	0	0	0	12	2	10	0	0	0	0	0	0	0	0	0	0	0	2	1.000	12	24	36	0	0	39	34	22	4	63	378	422	428	42	13	7	12	14	0	0	0.00%	5	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
16	STOCKTON Flames	2	1	0	0	1	0	0	0	8	3	5	2	1	0	0	1	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	4	1.000	8	15	23	0	0	39	34	22	4	72	378	422	428	42	44	16	16	40	5	2	40.00%	7	1	85.71%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
17	Syracruse Crunch	3	2	1	0	0	0	0	0	6	4	2	1	0	1	0	0	0	0	0	1	2	-1	2	2	0	0	0	0	0	0	5	2	3	4	0.667	6	11	17	0	0	39	34	22	4	105	378	422	428	42	80	15	20	54	14	2	14.29%	10	1	90.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
18	TUSCON Roadrunners	3	1	2	0	0	0	0	0	5	7	-2	1	0	1	0	0	0	0	0	2	4	-2	2	1	1	0	0	0	0	0	3	3	0	2	0.333	5	8	13	0	1	39	34	22	4	76	378	422	428	42	94	31	30	59	7	1	14.29%	14	2	85.71%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
	Total	39	18	14	0	1	0	2	4	98	75	23	20	9	5	0	1	0	1	4	55	36	19	19	9	9	0	0	0	1	0	43	39	4	46	0.590	98	166	264	1	6	39	34	22	4	1248	378	422	428	42	986	296	334	712	140	19	13.57%	150	21	86.00%	1	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
20	UTICA Comets	1	0	1	0	0	0	0	0	3	4	-1	1	0	1	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	6	9	0	0	39	34	22	4	26	378	422	428	42	37	10	12	19	2	1	50.00%	6	1	83.33%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
21	VICTORIAVILLE Tigres	2	1	0	0	0	0	0	1	5	3	2	1	0	0	0	0	0	0	1	2	3	-1	1	1	0	0	0	0	0	0	3	0	3	3	0.750	5	8	13	0	1	39	34	22	4	53	378	422	428	42	62	22	14	33	5	2	40.00%	6	0	100.00%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
22	WILKIES-BARRIE Penguins	2	1	0	0	0	0	0	1	5	4	1	1	0	0	0	0	0	0	1	2	3	-1	1	1	0	0	0	0	0	0	3	1	2	3	0.750	5	8	13	0	0	39	34	22	4	71	378	422	428	42	52	13	22	50	16	2	12.50%	9	2	77.78%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
	_Since Last GM Reset	39	18	14	0	1	0	2	4	98	75	23	20	9	5	0	1	0	1	4	55	36	19	19	9	9	0	0	0	1	0	43	39	4	46	0.590	98	166	264	1	6	39	34	22	4	1248	378	422	428	42	986	296	334	712	140	19	13.57%	150	21	86.00%	1	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
	_Vs Conference	19	10	4	0	1	0	1	3	48	35	13	11	5	2	0	1	0	0	3	24	18	6	8	5	2	0	0	0	1	0	24	17	7	27	0.711	48	81	129	0	3	39	34	22	4	653	378	422	428	42	488	148	154	361	79	12	15.19%	68	8	88.24%	1	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
	_Vs Division	8	3	1	0	0	0	0	2	18	17	1	4	2	1	0	0	0	0	2	11	6	5	4	1	0	0	0	0	0	0	7	11	-4	8	0.500	18	26	44	1	1	39	34	22	4	250	378	422	428	42	194	54	64	162	34	3	8.82%	29	6	79.31%	0	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251

			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
39	46	W10	98	166	264	1248	986	296	334	712	1	6

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
39	18	14	1	0	2	4	98	75

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
20	9	5	1	0	1	4	55	36

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
19	9	9	0	0	1	0	43	39

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
10	0	0	0	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
140	19	13.57%	150	21	86.00%	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
378	422	428	42	39	34	22	4

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
774	1323	58.50%	707	1197	59.06%	330	546	60.44%

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
1033	740	853	275	483	251

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	RI	Lien
1 - 2020-08-02	13	UTICA Comets	4	BELLEVILLE Senators	3	L				Sommaire du Match
3 - 2020-08-04	26	BELLEVILLE Senators	1	BRIDGEPORT Sound Tigers	3	L				Sommaire du Match
4 - 2020-08-05	36	MONT-LAURIER Sommet	1	BELLEVILLE Senators	2	W				Sommaire du Match
6 - 2020-08-07	55	COLORADO Eagles	1	BELLEVILLE Senators	0	L				Sommaire du Match
8 - 2020-08-09	74	BELLEVILLE Senators	3	MILWAUKEE Admirals	1	W				Sommaire du Match
9 - 2020-08-10	80	Quebec Nordiques	0	BELLEVILLE Senators	3	W				Sommaire du Match
10 - 2020-08-11	94	BELLEVILLE Senators	2	Syracruse Crunch	1	W				Sommaire du Match
11 - 2020-08-12	106	Manitoba Moose	3	BELLEVILLE Senators	1	L				Sommaire du Match
12 - 2020-08-13	123	BELLEVILLE Senators	0	MILWAUKEE Admirals	1	L				Sommaire du Match
13 - 2020-08-14	132	WILKIES-BARRIE Penguins	3	BELLEVILLE Senators	2	L	X	X	R3	Sommaire du Match
15 - 2020-08-16	146	BELLEVILLE Senators	4	HOLLYWOOD Oscar	3	W				Sommaire du Match
16 - 2020-08-17	155	BELLEVILLE Senators	1	LAVAL Rockets	2	L				Sommaire du Match
17 - 2020-08-18	166	VICTORIAVILLE Tigres	3	BELLEVILLE Senators	2	L	X	X		Sommaire du Match
19 - 2020-08-20	185	STOCKTON Flames	1	BELLEVILLE Senators	5	W				Sommaire du Match
21 - 2020-08-22	202	BELLEVILLE Senators	2	IOWA Wild	3	L				Sommaire du Match
22 - 2020-08-23	211	Syracruse Crunch	2	BELLEVILLE Senators	1	L				Sommaire du Match
23 - 2020-08-24	225	BELLEVILLE Senators	3	VICTORIAVILLE Tigres	0	W				Sommaire du Match
24 - 2020-08-25	227	BELLEVILLE Senators	3	Syracruse Crunch	1	W				Sommaire du Match
25 - 2020-08-26	241	BELLEVILLE Senators	2	BRIDGEPORT Sound Tigers	4	L				Sommaire du Match
26 - 2020-08-27	253	MILWAUKEE Admirals	2	BELLEVILLE Senators	1	L	X	X		Sommaire du Match
27 - 2020-08-28	269	PV Sharapovas	2	BELLEVILLE Senators	1	L	X	X		Sommaire du Match
29 - 2020-08-30	288	BELLEVILLE Senators	2	PROVIDENCE Bruins	4	L				Sommaire du Match
30 - 2020-08-31	294	TUSCON Roadrunners	4	BELLEVILLE Senators	2	L				Sommaire du Match
32 - 2020-09-02	315	HERSEY Bears	1	BELLEVILLE Senators	2	W	X	X		Sommaire du Match
33 - 2020-09-03	326	BELLEVILLE Senators	2	HOLLYWOOD Oscar	4	L				Sommaire du Match
34 - 2020-09-04	329	BELLEVILLE Senators	1	TUSCON Roadrunners	3	L				Sommaire du Match
36 - 2020-09-06	347	BELLEVILLE Senators	3	WILKIES-BARRIE Penguins	1	W			R3	Sommaire du Match
37 - 2020-09-07	354	BROOKLYN Wolfpack	0	BELLEVILLE Senators	3	W				Sommaire du Match
38 - 2020-09-08	374	BELLEVILLE Senators	1	BROOKLYN Wolfpack	3	L				Sommaire du Match
39 - 2020-09-09	382	MILWAUKEE Admirals	2	BELLEVILLE Senators	4	W				Sommaire du Match
41 - 2020-09-11	401	BRIDGEPORT Sound Tigers	2	BELLEVILLE Senators	4	W				Sommaire du Match
42 - 2020-09-12	415	BELLEVILLE Senators	3	CHICAGO Wolves	0	W				Sommaire du Match
44 - 2020-09-14	427	IOWA Wild	0	BELLEVILLE Senators	2	W				Sommaire du Match
45 - 2020-09-15	434	BELLEVILLE Senators	2	TUSCON Roadrunners	0	W				Sommaire du Match
46 - 2020-09-16	449	PV Sharapovas	1	BELLEVILLE Senators	2	W				Sommaire du Match
48 - 2020-09-18	465	BELLEVILLE Senators	5	MONT-LAURIER Sommet	4	W	X	X		Sommaire du Match
49 - 2020-09-19	478	STOCKTON Flames	2	BELLEVILLE Senators	3	W	X			Sommaire du Match
50 - 2020-09-20	482	BELLEVILLE Senators	3	LAVAL Rockets	1	W				Sommaire du Match
52 - 2020-09-22	503	SAN DIEGO Gulls	2	BELLEVILLE Senators	12	W				Sommaire du Match
53 - 2020-09-23	514	BELLEVILLE Senators	-	Manitoba Moose	-
54 - 2020-09-24	525	BELLEVILLE Senators	-	Marlies de Toronto	-
55 - 2020-09-25	535	BROOKLYN Wolfpack	-	BELLEVILLE Senators	-
57 - 2020-09-27	554	BELLEVILLE Senators	-	LEHIGH VALLEY Phantoms	-
58 - 2020-09-28	561	COLORADO Eagles	-	BELLEVILLE Senators	-
59 - 2020-09-29	577	BELLEVILLE Senators	-	PROVIDENCE Bruins	-
60 - 2020-09-30	589	Binghampton Devils	-	BELLEVILLE Senators	-
61 - 2020-10-01	604	Manitoba Moose	-	BELLEVILLE Senators	-
63 - 2020-10-03	617	BELLEVILLE Senators	-	Quebec Nordiques	-
64 - 2020-10-04	632	Quebec Nordiques	-	BELLEVILLE Senators	-
65 - 2020-10-05	643	BELLEVILLE Senators	-	IOWA Wild	-
67 - 2020-10-07	659	PROVIDENCE Bruins	-	BELLEVILLE Senators	-
69 - 2020-10-09	676	BELLEVILLE Senators	-	ROCKFORD IceHogs	-
70 - 2020-10-10	689	HOLLYWOOD Oscar	-	BELLEVILLE Senators	-
71 - 2020-10-11	702	BELLEVILLE Senators	-	Binghampton Devils	-
72 - 2020-10-12	714	WILKIES-BARRIE Penguins	-	BELLEVILLE Senators	-
74 - 2020-10-14	732	LAVAL Rockets	-	BELLEVILLE Senators	-
75 - 2020-10-15	737	BELLEVILLE Senators	-	UTICA Comets	-
76 - 2020-10-16	752	BELLEVILLE Senators	-	ROCKFORD IceHogs	-
77 - 2020-10-17	763	BELLEVILLE Senators	-	UTICA Comets	-
78 - 2020-10-18	770	BRIDGEPORT Sound Tigers	-	BELLEVILLE Senators	-
80 - 2020-10-20	790	MONT-LAURIER Sommet	-	BELLEVILLE Senators	-
82 - 2020-10-22	812	VICTORIAVILLE Tigres	-	BELLEVILLE Senators	-
83 - 2020-10-23	820	BELLEVILLE Senators	-	SAN DIEGO Gulls	-
84 - 2020-10-24	835	LEHIGH VALLEY Phantoms	-	BELLEVILLE Senators	-
86 - 2020-10-26	847	BELLEVILLE Senators	-	SAN DIEGO Gulls	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
87 - 2020-10-27	855	BELLEVILLE Senators	-	HERSEY Bears	-
88 - 2020-10-28	868	ROCKFORD IceHogs	-	BELLEVILLE Senators	-
90 - 2020-10-30	891	Marlies de Toronto	-	BELLEVILLE Senators	-
91 - 2020-10-31	900	BELLEVILLE Senators	-	BRIDGEPORT Sound Tigers	-
92 - 2020-11-01	909	BELLEVILLE Senators	-	HERSEY Bears	-
93 - 2020-11-02	922	Marlies de Toronto	-	BELLEVILLE Senators	-
95 - 2020-11-04	940	Binghampton Devils	-	BELLEVILLE Senators	-
96 - 2020-11-05	954	BELLEVILLE Senators	-	COLORADO Eagles	-
97 - 2020-11-06	965	CHICAGO Wolves	-	BELLEVILLE Senators	-
98 - 2020-11-07	968	BELLEVILLE Senators	-	LEHIGH VALLEY Phantoms	-
99 - 2020-11-08	983	BELLEVILLE Senators	-	PV Sharapovas	-
103 - 2020-11-12	1001	CHICAGO Wolves	-	BELLEVILLE Senators	-
104 - 2020-11-13	1014	BELLEVILLE Senators	-	STOCKTON Flames	-
106 - 2020-11-15	1024	PROVIDENCE Bruins	-	BELLEVILLE Senators	-
107 - 2020-11-16	1037	BELLEVILLE Senators	-	STOCKTON Flames	-

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
813,106$	1,702,500$	1,577,500$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	812,188$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	57	15,619$	890,283$

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
Saison Régulière
33	82	18	64	0	0	0	0	0	154	443	-289	41	9	32	0	0	0	0	0	78	223	-145	41	9	32	0	0	0	0	0	76	220	-144	36	154	276	430	1	2	76	40	38	0	1557	512	505	540	0	6319	1714	87	1047	75	0	0.00%	26	3	88.46%	0	349	1096	31.84%	835	3764	22.18%	324	1207	26.84%	726	468	3450	504	742	250
34	39	18	14	0	1	0	2	4	98	75	23	20	9	5	0	1	0	1	4	55	36	19	19	9	9	0	0	0	1	0	43	39	4	46	98	166	264	1	6	39	34	22	4	1248	378	422	428	42	986	296	334	712	140	19	13.57%	150	21	86.00%	1	774	1323	58.50%	707	1197	59.06%	330	546	60.44%	1033	740	853	275	483	251
Total Saison Régulière	121	36	78	0	1	0	2	4	252	518	-266	61	18	37	0	1	0	1	4	133	259	-126	60	18	41	0	0	0	1	0	119	259	-140	82	252	442	694	2	8	115	74	60	4	2805	890	927	968	42	7305	2010	421	1759	215	19	8.84%	176	24	86.36%	1	1123	2419	46.42%	1542	4961	31.08%	654	1753	37.31%	1760	1209	4303	780	1226	501