National Hockey League

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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	Aleksander Barkov	X				100.00	61	42	96	91	80	90	98	90	95	97	89	82	85	67	68	0	90	89	0
2	Patrice Bergeron (A)	X				97.00	69	43	86	87	72	78	82	87	98	96	92	71	59	85	91	0	90	86	0
3	Blake Wheeler	X		X		98.00	75	58	79	86	86	85	99	89	39	99	75	67	65	82	85	0	91	83	0
4	Reilly Smith		X	X		100.00	65	42	93	80	68	79	88	77	55	83	77	74	43	70	72	0	80	78	0
5	Jaden Schwartz		X			100.00	73	43	92	86	68	78	83	77	43	78	70	63	25	70	72	0	91	74	0
6	Micheal Ferland		X	X		100.00	91	96	77	80	79	67	91	73	38	76	75	59	25	64	65	0	90	74	0
7	Joel Armia			X		100.00	85	45	91	81	77	72	85	71	31	64	74	72	75	62	63	0	91	73	0
8	Valtteri Filppula	X				100.00	59	41	92	77	71	68	94	63	83	65	75	83	52	82	90	0	91	72	0
9	Troy Brouwer		X	X		100.00	89	69	82	73	81	63	93	61	38	61	70	76	57	79	84	0	90	70	0
10	Tanner Pearson		X			100.00	81	55	90	80	74	67	98	67	33	60	74	67	45	66	68	0	78	70	0
11	Nate Thompson	X	X			100.00	76	54	92	69	77	65	91	61	88	60	57	85	25	75	78	0	48	67	0
12	John Carlson				X	100.00	69	43	87	85	80	93	98	97	25	85	60	82	75	75	80	0	91	83	0
13	Roman Josi (A)				X	100.00	67	42	85	90	74	93	96	81	25	75	60	86	63	71	74	0	91	81	0
14	Ben Lovejoy				X	100.00	76	54	84	70	76	72	78	60	25	50	48	99	25	73	77	0	85	74	0
15	Zdeno Chara				X	100.00	78	78	74	72	98	83	85	61	25	52	49	90	57	98	99	0	89	73	0
16	Brayden McNabb				X	100.00	90	47	81	76	80	79	96	58	25	52	48	90	25	69	71	0	90	73	0
17	Roman Polak				X	100.00	90	57	75	66	86	78	83	59	25	50	47	96	25	77	80	0	80	73	0
Rayé
1	Paul Byron	X	X	X		100.00	85	55	90	85	58	68	86	64	36	74	78	69	80	67	67	0	84	74	0
2	Mark Jankowski	X				100.00	63	42	94	75	77	63	96	58	86	68	71	77	25	55	55	0	88	71	0
3	Nick Holden				X	100.00	82	45	92	77	80	75	84	61	25	55	48	75	25	68	69	0	68	69	0

	MOYENNE D'ÉQUIPE					99.75	76	53	87	79	77	76	90	71	47	70	67	78	48	73	75	0	85	75

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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	Sergei Bobrovsky	98.00	80	92	89	71	75	84	68	82	83	80	95	71	72	0	90	80	0
Rayé
1	David Rittich	100.00	79	69	66	81	83	75	66	79	81	79	85	52	52	0	90	76	0

	MOYENNE D'ÉQUIPE	99.00	80	81	78	76	79	80	67	81	82	80	90	62	62	0	90	78

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Sheldon Keefe	70	70	70	70	70	70	70	CAN	38	5	500,000$

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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	PKM	GW	FO%	FOT	P/20	S1	S2	S3
1	Blake Wheeler	Senateurs	C/RW	2	2	3	5	1	4	4	6	5	4	6	40.00%	0	41	20.73	0	1	1	2	7	6	0	0.00%	1	2.41	1	0	0
2	Aleksander Barkov	Senateurs	C	2	2	2	4	1	2	1	7	9	1	4	22.22%	0	36	18.40	0	1	1	4	7	1	1	52.63%	57	2.17	0	0	1
3	John Carlson	Senateurs	D	2	1	2	3	1	2	2	2	5	0	5	20.00%	0	44	22.16	1	0	1	4	11	0	0	0.00%	0	1.35	1	0	0
4	Patrice Bergeron	Senateurs	C	2	0	2	2	1	0	9	7	5	3	10	0.00%	1	44	22.10	0	0	0	1	8	12	0	62.90%	62	0.90	0	0	0
5	Roman Josi	Senateurs	D	2	1	1	2	3	0	1	9	8	0	3	12.50%	4	44	22.19	0	0	0	3	10	1	0	0.00%	0	0.90	0	1	0
6	Reilly Smith	Senateurs	LW/RW	2	0	1	1	1	0	1	2	2	2	5	0.00%	1	36	18.12	0	0	0	1	8	4	0	100.00%	1	0.55	0	0	0
7	Valtteri Filppula	Senateurs	C	2	0	1	1	1	0	0	3	3	3	4	0.00%	0	23	11.99	0	0	0	0	0	7	0	50.00%	24	0.83	0	0	0
8	Joel Armia	Senateurs	RW	2	1	0	1	1	0	2	1	5	1	1	20.00%	0	16	8.45	0	0	0	0	0	0	1	0.00%	1	1.18	0	0	1
9	Jaden Schwartz	Senateurs	LW	2	0	1	1	1	2	4	1	3	1	2	0.00%	0	34	17.30	0	0	0	1	7	0	0	0.00%	1	0.58	0	0	0
10	Zdeno Chara	Senateurs	D	2	0	0	0	1	0	6	5	2	0	3	0.00%	11	46	23.36	0	0	0	0	5	13	0	0.00%	0	0.00	0	0	0
11	Ben Lovejoy	Senateurs	D	2	0	0	0	0	2	1	0	0	0	1	0.00%	3	35	17.63	0	0	0	0	4	14	0	0.00%	0	0.00	0	0	0
12	Brayden McNabb	Senateurs	D	2	0	0	0	3	4	7	3	1	1	0	0.00%	2	39	19.63	0	0	0	0	0	11	0	0.00%	0	0.00	0	0	0
13	Paul Byron	Senateurs	C/LW/RW	2	0	0	0	1	2	2	3	1	0	1	0.00%	0	15	7.67	0	0	0	0	0	6	0	37.50%	8	0.00	0	0	0
14	Troy Brouwer	Senateurs	LW/RW	2	0	0	0	1	4	2	1	2	0	1	0.00%	0	15	7.57	0	0	0	0	0	6	0	0.00%	1	0.00	0	0	0
15	Roman Polak	Senateurs	D	2	0	0	0	0	2	9	1	1	0	3	0.00%	2	28	14.47	0	0	0	0	0	11	0	0.00%	0	0.00	0	0	0
16	Tanner Pearson	Senateurs	LW	2	0	0	0	1	4	2	1	3	0	1	0.00%	1	22	11.29	0	0	0	0	0	0	0	66.67%	3	0.00	0	0	0
17	Mark Jankowski	Senateurs	C	2	0	0	0	1	0	0	1	0	1	0	0.00%	0	17	8.50	0	0	0	0	0	8	0	35.00%	20	0.00	0	0	0
18	Micheal Ferland	Senateurs	LW/RW	2	0	0	0	1	4	5	2	3	2	4	0.00%	0	25	12.67	0	0	0	2	8	0	0	0.00%	1	0.00	0	0	0
Stats d'équipe Total ou en Moyenne				36	7	13	20	20	32	58	55	58	19	54	12.07%	25	568	15.79	1	2	3	18	80	104	2	52.22%	180	0.70	2	1	2

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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	Sergei Bobrovsky	Senateurs	2	2	0	0	0.945	1.50	120	0	0	3	55	0	0	0	0.000	0	2	0	0	1	0
Stats d'équipe Total ou en Moyenne			2	2	0	0	0.945	1.50	120	0	0	3	55	0	0	0	0.000	0	2	0	0	1	0

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Status	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Link
Aleksander Barkov	Senateurs	C	24	1995-09-02	No	213 Lbs	6 ft4	No	No	Yes	4	Avec Restriction	Pro & Farm	5,900,000$	5,900,000$	4,635,714$	No	Lien
Ben Lovejoy	Senateurs	D	36	1984-02-20	No	205 Lbs	6 ft1	No	No	Yes	1	Sans Restriction	Pro & Farm	2,666,667$	2,666,667$	2,095,238$	No	Lien
Blake Wheeler	Senateurs	C/RW	33	1986-08-30	No	225 Lbs	6 ft5	No	No	Yes	6	Sans Restriction	Pro & Farm	5,600,000$	5,600,000$	4,400,000$	No	Lien
Brayden McNabb	Senateurs	D	29	1991-01-20	No	212 Lbs	6 ft4	No	No	Yes	4	Sans Restriction	Pro & Farm	2,500,000$	2,500,000$	1,964,286$	No	Lien
David Rittich	Senateurs	G	27	1992-08-18	No	202 Lbs	6 ft3	No	No	No	1	Sans Restriction	Pro & Farm	800,000$	800,000$	628,571$	No	Lien
Jaden Schwartz	Senateurs	LW	27	1992-06-25	No	190 Lbs	5 ft10	No	No	Yes	3	Sans Restriction	Pro & Farm	5,350,000$	5,350,000$	4,203,571$	No	Lien
Joel Armia	Senateurs	RW	26	1993-05-31	No	205 Lbs	6 ft3	No	No	Yes	1	Avec Restriction	Pro & Farm	1,850,000$	1,850,000$	1,453,571$	No	Lien
John Carlson	Senateurs	D	30	1990-01-10	No	218 Lbs	6 ft3	No	No	Yes	6	Sans Restriction	Pro & Farm	8,000,000$	8,000,000$	6,285,714$	No	Lien
Mark Jankowski	Senateurs	C	25	1994-09-13	No	202 Lbs	6 ft4	No	No	Yes	2	Avec Restriction	Pro & Farm	1,675,000$	1,675,000$	1,316,071$	No	Lien
Micheal Ferland	Senateurs	LW/RW	27	1992-04-19	No	208 Lbs	6 ft2	No	No	Yes	1	Sans Restriction	Pro & Farm	1,750,000$	1,750,000$	1,375,000$	No	Lien
Nate Thompson	Senateurs	C/LW	35	1984-10-05	No	214 Lbs	6 ft1	No	No	Yes	1	Sans Restriction	Pro & Farm	1,650,000$	1,650,000$	1,296,429$	No	Lien
Nick Holden	Senateurs	D	32	1987-05-14	No	214 Lbs	6 ft4	No	No	Yes	2	Sans Restriction	Pro & Farm	2,200,000$	2,200,000$	1,728,571$	No	Lien
Patrice Bergeron	Senateurs	C	34	1985-07-24	No	195 Lbs	6 ft1	No	No	Yes	4	Sans Restriction	Pro & Farm	6,875,000$	6,875,000$	5,401,786$	No	Lien
Paul Byron	Senateurs	C/LW/RW	30	1989-04-26	No	162 Lbs	5 ft9	No	No	Yes	5	Sans Restriction	Pro & Farm	1,167,000$	1,167,000$	916,929$	No	Lien
Reilly Smith	Senateurs	LW/RW	29	1991-04-01	No	185 Lbs	6 ft0	No	No	Yes	4	Sans Restriction	Pro & Farm	5,000,000$	5,000,000$	3,928,571$	No	Lien
Roman Josi	Senateurs	D	29	1990-05-31	No	201 Lbs	6 ft1	No	No	Yes	2	Sans Restriction	Pro & Farm	4,000,000$	4,000,000$	3,142,857$	No	Lien
Roman Polak	Senateurs	D	33	1986-04-28	No	235 Lbs	6 ft2	No	No	Yes	1	Sans Restriction	Pro & Farm	1,300,000$	1,300,000$	1,021,429$	No	Lien
Sergei Bobrovsky	Senateurs	G	31	1988-09-19	No	182 Lbs	6 ft2	No	No	Yes	1	Sans Restriction	Pro & Farm	7,425,000$	7,425,000$	5,833,929$	No	Lien
Tanner Pearson	Senateurs	LW	27	1992-08-09	No	201 Lbs	6 ft1	No	No	Yes	3	Sans Restriction	Pro & Farm	3,750,000$	3,750,000$	2,946,429$	No	Lien
Troy Brouwer	Senateurs	LW/RW	34	1985-08-17	No	215 Lbs	6 ft3	No	No	Yes	1	Sans Restriction	Pro & Farm	850,000$	850,000$	667,857$	No	Lien
Valtteri Filppula	Senateurs	C	36	1984-03-20	No	196 Lbs	6 ft0	No	No	Yes	1	Sans Restriction	Pro & Farm	2,750,000$	2,750,000$	2,160,714$	No	Lien
Zdeno Chara	Senateurs	D	43	1977-03-18	No	250 Lbs	6 ft11	No	No	Yes	1	Sans Restriction	Pro & Farm	5,000,000$	5,000,000$	3,928,571$	No	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
22	30.77	206 Lbs	6 ft2	2.50	3,548,121$

Somme Salaire 1e Année	Somme Salaire 2e Année	Somme Salaire 3e Année	Somme Salaire 4e Année	Somme Salaire 5e Année
78,058,667$	52,017,000$	44,142,000$	35,042,000$	14,767,000$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Jaden Schwartz	Aleksander Barkov	Blake Wheeler	34	1	1	3
2	Tanner Pearson	Patrice Bergeron	Reilly Smith	32	1	1	3
3	Micheal Ferland	Valtteri Filppula	Joel Armia	24	1	2	2
4			Troy Brouwer	10	1	3	1

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Roman Josi	Brayden McNabb	39	1	2	2
2	John Carlson	Zdeno Chara	39	1	2	2
3	Roman Polak	Ben Lovejoy	16	2	3	0
4	John Carlson	Roman Josi	6	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Jaden Schwartz	Aleksander Barkov	Blake Wheeler	60	1	0	4
2	Micheal Ferland	Patrice Bergeron	Reilly Smith	40	1	0	4

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	John Carlson	Roman Josi	60	1	1	3
2	John Carlson	Roman Josi	40	1	1	3

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

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Roman Polak	Zdeno Chara	60	1	2	2
2	Brayden McNabb	Ben Lovejoy	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Patrice Bergeron	60	1	2	2	Zdeno Chara	Ben Lovejoy	60	1	2	2
2	Valtteri Filppula	40	1	2	2	Roman Polak	Brayden McNabb	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Patrice Bergeron	Reilly Smith	60	1	2	2
2	Aleksander Barkov	Blake Wheeler	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Roman Josi	Brayden McNabb	60	1	2	2
2	Zdeno Chara	John Carlson	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Jaden Schwartz	Aleksander Barkov	Blake Wheeler	Roman Josi	John Carlson

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
	Patrice Bergeron	Joel Armia	Ben Lovejoy	Zdeno Chara

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Blake Wheeler, Joel Armia, Jaden Schwartz	Tanner Pearson, Joel Armia	Patrice Bergeron

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Roman Polak, Ben Lovejoy, John Carlson	John Carlson	Zdeno Chara, Ben Lovejoy

Tirs de Pénalité
Jaden Schwartz, Aleksander Barkov, Patrice Bergeron, Micheal Ferland, Blake Wheeler

Gardien
#1 : Sergei Bobrovsky, #2 :

Lignes d'Attaque Perso. en Prol.
Aleksander Barkov, Jaden Schwartz, Patrice Bergeron, Reilly Smith, Tanner Pearson, Blake Wheeler, Blake Wheeler, Micheal Ferland, Valtteri Filppula, Joel Armia,

Lignes de Défense Perso. en Prol.
Roman Josi, Brayden McNabb, John Carlson, Zdeno Chara, Roman Polak

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	Maple Leafs	2	2	0	0	0	0	0	0	7	3	4	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	13	20	0	0	3	3	1	0	58	15	20	23	0	55	25	32	58	10	1	10.00%	15	1	93.33%	0	32	69	46.38%	47	81	58.02%	15	30	50.00%	49	34	45	15	24	12
	Total	2	2	0	0	0	0	0	0	7	3	4	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	13	20	0	0	3	3	1	0	58	15	20	23	0	55	25	32	58	10	1	10.00%	15	1	93.33%	0	32	69	46.38%	47	81	58.02%	15	30	50.00%	49	34	45	15	24	12
	_Since Last GM Reset	2	2	0	0	0	0	0	0	7	3	4	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	13	20	0	0	3	3	1	0	58	15	20	23	0	55	25	32	58	10	1	10.00%	15	1	93.33%	0	32	69	46.38%	47	81	58.02%	15	30	50.00%	49	34	45	15	24	12
	_Vs Conference	2	2	0	0	0	0	0	0	7	3	4	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	13	20	0	0	3	3	1	0	58	15	20	23	0	55	25	32	58	10	1	10.00%	15	1	93.33%	0	32	69	46.38%	47	81	58.02%	15	30	50.00%	49	34	45	15	24	12
	_Vs Division	2	2	0	0	0	0	0	0	7	3	4	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	13	20	0	0	3	3	1	0	58	15	20	23	0	55	25	32	58	10	1	10.00%	15	1	93.33%	0	32	69	46.38%	47	81	58.02%	15	30	50.00%	49	34	45	15	24	12

			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
2	4	W2	7	13	20	58	55	25	32	58	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
2	2	0	0	0	0	0	7	3

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
2	2	0	0	0	0	0	7	3

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
2	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
10	1	10.00%	15	1	93.33%	0

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
15	20	23	0	3	3	1	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
32	69	46.38%	47	81	58.02%	15	30	50.00%

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
49	34	45	15	24	12

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	RI	Lien
1 - 2019-09-19	2	Maple Leafs	2	Senateurs	4	W	R3	Sommaire du Match
3 - 2019-09-21	10	Maple Leafs	1	Senateurs	3	W		Sommaire du Match
5 - 2019-09-23	18	Senateurs	-	Maple Leafs	-		R3
7 - 2019-09-25	26	Senateurs	-	Maple Leafs	-
9 - 2019-09-27	34	Maple Leafs	-	Senateurs	-		R3
11 - 2019-09-29	42	Senateurs	-	Maple Leafs	-
13 - 2019-10-01	50	Maple Leafs	-	Senateurs	-		R3

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2	Niveau 3	Niveau 4	Luxe
Capacité de l'Aréna	6000	5000	2000	4000	1000
Prix des Billets	100	60	35	25	200
Assistance	12,000	10,000	4,000	8,000	2,000
Attendance PCT	100.00%	100.00%	100.00%	100.00%	100.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
39	18000 - 100.00%	1,892,300$	3,784,600$	18000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs	Valeur du Cap Salarial Spécial
0$	78,058,667$	78,058,667$	0$	-2,500,000$

Cap Salarial Par Jour	Cap salarial à ce jour	Taxe de Luxe Totale	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	0$	0$	22	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	11	0$	0$

Total de l'Équipe Éstimé
Dépenses de la Saison Éstimées	Cap Salarial de la Saison Éstimé	Compte Bancaire Actuel	Compte Bancaire Projeté
0$	0$	5,219,397$	5,219,397$

Charte de Profondeur

Ailier Gauche

Centre

Ailier Droit

Reilly Smith	AGE:29	PO:0	OV:78
Paul Byron	AGE:30	PO:0	OV:74
Jaden Schwartz	AGE:27	PO:0	OV:74
Micheal Ferland	AGE:27	PO:0	OV:74
Troy Brouwer	AGE:34	PO:0	OV:70
Tanner Pearson	AGE:27	PO:0	OV:70
Nate Thompson	AGE:35	PO:0	OV:67
*Joseph Gambardella	AGE:26	PO:0	OV:64
Brad Malone	AGE:30	PO:0	OV:63
Anthony Greco	AGE:26	PO:0	OV:59
Brendan Gaunce	AGE:26	PO:0	OV:59

Aleksander Barkov	AGE:24	PO:0	OV:89
Patrice Bergeron	AGE:34	PO:0	OV:86
Blake Wheeler	AGE:33	PO:0	OV:83
Paul Byron	AGE:30	PO:0	OV:74
Valtteri Filppula	AGE:36	PO:0	OV:72
Mark Jankowski	AGE:25	PO:0	OV:71
Teddy Blueger	AGE:25	PO:0	OV:70
Nate Thompson	AGE:35	PO:0	OV:67
*Joseph Gambardella	AGE:26	PO:0	OV:64
Brad Malone	AGE:30	PO:0	OV:63
J.T. Brown	AGE:29	PO:0	OV:63
Brendan Gaunce	AGE:26	PO:0	OV:59

Blake Wheeler	AGE:33	PO:0	OV:83
Reilly Smith	AGE:29	PO:0	OV:78
Paul Byron	AGE:30	PO:0	OV:74
Micheal Ferland	AGE:27	PO:0	OV:74
Joel Armia	AGE:26	PO:0	OV:73
Troy Brouwer	AGE:34	PO:0	OV:70
*Matt Luff	AGE:22	PO:0	OV:65
J.T. Brown	AGE:29	PO:0	OV:63
Anthony Greco	AGE:26	PO:0	OV:59

Défense #1

Défense #2

Gardien

John Carlson	AGE:30	PO:0	OV:83
Roman Josi	AGE:29	PO:0	OV:81
Ben Lovejoy	AGE:36	PO:0	OV:74
Zdeno Chara	AGE:43	PO:0	OV:73
Brayden McNabb	AGE:29	PO:0	OV:73

Roman Polak	AGE:33	PO:0	OV:73
Nick Holden	AGE:32	PO:0	OV:69
Ashton Sautner	AGE:25	PO:0	OV:64
Kyle Capobianco	AGE:22	PO:0	OV:58
Jake Chelios	AGE:29	PO:0	OV:52

Sergei Bobrovsky	AGE:31	PO:0	OV:80
David Rittich	AGE:27	PO:0	OV:76
*Pavel Francouz	AGE:29	PO:0	OV:66

Éspoirs

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

Éspoir	Nom de l'Équipe	Année de Repêchage	Choix Total	Information
Adam Edstrom	Senateurs
Alfons Malmstrom	Senateurs	2016	107	D
Ben Thomas	Senateurs
Brendan Woods	Senateurs
Calvin Thurkauf	Senateurs
Daniel Altshuller	Senateurs
Dominik Bokk	Senateurs	12	16
Dylan Blujus	Senateurs
Felix Sandstrom	Senateurs	2015	70
Frederik Nissen	Senateurs	13	88
Jasper Weatherby	Senateurs	12	101
Jesper Eliasson	Senateurs	12	89
Julius Vahatalo	Senateurs	2014	166	LW/C
Kasimir Kaskisuo	Senateurs
Marcus Westfalt	Senateurs	12	202
Markus Phillips	Senateurs
Oskari Laaksonen	Senateurs
Ryan Fitzgerald	Senateurs
Ryan Gropp	Senateurs
Scott Perunovich	Senateurs	12	67
Shane Gersich	Senateurs
Thomas Schemitsch	Senateurs
Tyler Moy	Senateurs
Victor Brattstrom	Senateurs	12	180
Victor Ejdsell	Senateurs
Zachary Bouthillier	Senateurs	12	167

Choix au Repêchage

Année	R1	R2	R3	R4	R5	R6	R7
14				NYI	PHI
15	OTT NYR			ARZ OTT	OTT	OTT	OTT
16	OTT	OTT	OTT	OTT	OTT	OTT	ARZ OTT
17	OTT	OTT	OTT	OTT	OTT	OTT	OTT
18	OTT	OTT	OTT	OTT	OTT	OTT	OTT

[2020-03-04 10:00:16] - TRADE : From Senateurs to Sharks : Steven Santini (68).
[2020-03-01 15:17:04] - TRADE : From Senateurs to Canucks : Pierre-Edouard Bellemare (66), Y:14-RND:6-OTT, Y:14-RND:6-CHI.
[2020-03-01 15:17:04] - TRADE : From Canucks to Senateurs : Valtteri Filppula (72), 1 000 000 $ (Salary Cap).
[2020-03-01 15:15:02] - TRADE : From Senateurs to Capitals : Christian Wolanin (69), Y:14-RND:2-NYR.
[2020-03-01 15:15:02] - TRADE : From Capitals to Senateurs : Roman Polak (73), Y:14-RND:5-PHI.
[2020-03-01 15:14:10] - TRADE : From Senateurs to Predators : Joakim Nordstrom (65), Bulat Shafigullin (P), Y:14-RND:2-OTT, Y:14-RND:3-OTT, Y:15-RND:2-OTT.
[2020-03-01 15:14:10] - TRADE : From Predators to Senateurs : Tanner Pearson (70), Nate Thompson (67), 1 500 000 $ (Salary Cap).
[2020-03-01 15:12:51] - TRADE : From Senateurs to Coyotes : Patrik Nemeth (74).
[2020-03-01 15:12:51] - TRADE : From Coyotes to Senateurs : Nick Holden (69), Y:15-RND:4-ARZ, Y:16-RND:7-ARZ.
[2020-02-21 13:59:36] - TRADE : From Senateurs to Devils : Viktor Arvidsson (78), Kevin Stenlund (57), Y:14-RND:5-MTL.
[2020-02-21 13:59:36] - TRADE : From Devils to Senateurs : David Rittich (76).
[2020-02-13 08:24:23] - TRADE : From Devils to Senateurs : Ben Lovejoy (74).
[2020-02-13 08:24:23] - TRADE : From Senateurs to Devils : Drew Stafford (66), Y:14-RND:3-VAN.
[2020-02-11 09:07:35] - TRADE : From Canucks to Senateurs : Viktor Arvidsson (78), Mark Jankowski (71), Brayden McNabb (73), Y:14-RND:2-OTT, Y:14-RND:3-VAN.
[2020-02-11 09:07:35] - TRADE : From Senateurs to Canucks : Drew Doughty (79), Eric Staal (76), Y:14-RND:4-BOS, Y:14-RND:5-OTT.
[2019-11-24 21:17:10] - Senateurs hired Sheldon Keefe for 500 000 $ for 5 year(s).
[2019-11-24 21:16:50] - Senateurs fired Ken Hithcock.
[2019-09-30 21:10:03] - Team Name Change : Senators changed name to Senateurs
[2019-09-30 21:09:02] - Team Name Change : Senateurs changed name to Senators
[2019-09-12 22:14:23] - Jake Chelios was added to Senateurs.
[2019-09-12 22:14:17] - Joseph Gambardella was added to Senateurs.
[2019-09-12 22:14:07] - J.T. Brown was added to Senateurs.
[2019-09-12 22:14:00] - Brad Malone was added to Senateurs.
[2019-09-12 22:13:54] - Drew Stafford was added to Senateurs.
[2019-09-12 22:13:48] - Pavel Francouz was added to Senateurs.
[2019-07-10 19:20:16] - Adam Edstrom has been added to Senateurs.
[2019-07-05 16:53:33] - Ahti Oksanen has been deleted from Senateurs.
[2019-07-05 16:53:26] - Aidan Muir has been deleted from Senateurs.
[2019-07-05 16:53:21] - Derek MacKenzie has been deleted from Senateurs.
[2019-07-03 16:44:17] - TRADE : From Senateurs to Oilers : Y:13-RND:5-CLB.
[2019-07-03 16:44:17] - TRADE : From Oilers to Senateurs : Y:14-RND:5-MTL.
[2019-07-01 21:01:45] - TRADE : From Senateurs to Jets : Y:13-RND:6-ANA, Y:13-RND:6-CLB, Y:13-RND:6-OTT.
[2019-07-01 21:01:45] - TRADE : From Jets to Senateurs : Y:14-RND:4-BOS.
[2019-06-28 22:47:39] - Senateurs drafts Frederik Nissen as the #88 overall pick in the Entry Draft of year 13.

[2020-04-14 07:14:00] Auto Lines Partial Function has been run for Senateurs.
[2020-04-14 07:14:00] Auto Roster Partial Function has been run for Senateurs.
[2020-04-14 07:14:00] Senateurs roster errors : Not enough Goalie available in Pro Team! 1 Dressed. 2 Required. Not enough Players available in Pro Team! 17 Dressed. 18 Required. Some errors will be automatically fixed.
[2020-04-12 12:24:04] Successfully loaded Senateurs lines done with Web Base Client and SQLite Database.
[2020-04-08 13:09:29] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-04-08 10:55:18] Current fund for Senateurs is under 0 $.
[2020-04-07 08:29:30] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-04-06 07:48:34] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-04-02 09:37:32] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-04-02 09:36:33] Auto Lines Partial Function has been run for Senateurs.
[2020-04-02 09:36:33] Auto Roster Partial Function has been run for Senateurs.
[2020-04-02 09:36:33] Senateurs roster errors : Not enough Goalie available in Pro Team! 1 Dressed. 2 Required. Some errors will be automatically fixed.
[2020-04-01 09:28:49] David Rittich from Senateurs is back from Exhaustion.
[2020-03-31 09:55:33] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-03-31 09:55:32] David Rittich from Senateurs injured (Exhaustion)
[2020-03-29 13:41:18] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-03-29 13:41:18] Last 7 Days Pro Star : 1 - Mark Scheifele of Flyers (6-0-6) / 2 - Matt Murray of Coyotes (0,965) / 3 - Reilly Smith of Senateurs (3-2-5)
[2020-03-26 10:45:11] Senateurs lines for next game are empty. Current rosters/lines are not erased.
[2020-03-25 10:07:10] Senateurs lines for next game are empty. Current rosters/lines are not erased.

Pas de Blessure ou de Suspension.

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
12	82	43	27	0	2	2	3	5	264	216	48	41	27	9	0	1	1	1	2	144	96	48	41	16	18	0	1	1	2	3	120	120	0	86	264	477	741	2	5	93	89	77	10	2798	937	900	930	70	2328	682	787	1956	296	57	19.26%	305	59	80.66%	1	1736	3132	55.43%	1423	2643	53.84%	690	1242	55.56%	2163	1568	1812	556	995	513
13	82	33	29	0	8	3	5	4	235	201	34	41	18	11	0	5	2	2	3	124	100	24	41	15	18	0	3	1	3	1	111	101	10	99	235	415	650	1	7	96	63	63	18	2371	847	748	739	69	2040	584	857	1894	373	61	16.35%	348	61	82.47%	0	1539	2746	56.05%	1405	2552	55.05%	687	1220	56.31%	2089	1459	1854	603	1045	532
Total Saison Régulière	164	76	56	0	10	5	8	9	499	417	82	82	45	20	0	6	3	3	5	268	196	72	82	31	36	0	4	2	5	4	231	221	10	185	499	892	1391	3	12	189	152	140	28	5169	1784	1648	1669	139	4368	1266	1644	3850	669	118	17.64%	653	120	81.62%	1	3275	5878	55.72%	2828	5195	54.44%	1377	2462	55.93%	4252	3027	3666	1160	2041	1045
Séries
12	26	16	10	0	0	0	0	0	73	63	10	14	10	4	0	0	0	0	0	41	37	4	12	6	6	0	0	0	0	0	32	26	6	32	73	134	207	0	0	24	26	16	7	868	262	255	265	86	825	243	241	638	102	12	11.76%	92	14	84.78%	0	495	986	50.20%	521	942	55.31%	222	401	55.36%	712	512	643	192	340	173
13	2	2	0	0	0	0	0	0	7	3	4	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	4	7	13	20	0	0	3	3	1	0	58	15	20	23	0	55	25	32	58	10	1	10.00%	15	1	93.33%	0	32	69	46.38%	47	81	58.02%	15	30	50.00%	49	34	45	15	24	12
Total Séries	28	18	10	0	0	0	0	0	80	66	14	16	12	4	0	0	0	0	0	48	40	8	12	6	6	0	0	0	0	0	32	26	6	36	80	147	227	0	0	27	29	17	7	926	277	275	288	86	880	268	273	696	112	13	11.61%	107	15	85.98%	0	527	1055	49.95%	568	1023	55.52%	237	431	54.99%	762	546	689	207	364	185