LHSIR - 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	Âge	Contrat	Salaire
1	Alexander Volkov (R)		X	X		100.00	75	71	83	64	71	81	86	63	50	62	58	67	55	55	55	65	50	0	0	22	3	925,000$
2	Filip Chlapik	X				100.00	88	45	89	77	72	52	82	62	39	60	59	59	53	57	57	64	50	0	0	23	1	925,000$
3	Joel Farabee (R)		X			100.00	67	81	79	84	63	67	64	67	34	70	68	63	53	49	49	69	50	0	0	20	3	1,425,000$
4	Jack Roslovic	X		X		100.00	71	43	94	79	69	68	91	70	49	69	70	56	25	62	63	70	50	0	0	23	1	900,000$
5	Marko Dano		X	X		100.00	68	75	51	76	75	67	71	56	50	57	47	63	45	58	58	57	50	0	0	25	2	925,000$
6	Michael McLeod	X				100.00	81	44	89	79	70	56	85	59	74	63	55	72	25	47	47	64	50	0	0	22	2	1,363,333$
7	Ryan Dzingel		X	X		100.00	67	62	86	82	71	67	88	75	35	76	64	63	25	64	65	69	45	0	0	28	2	1,800,000$
8	Nick Paul	X	X			100.00	87	69	87	71	87	67	78	61	77	65	70	72	25	56	56	71	50	0	0	25	2	750,000$
9	Tyson Jost	X	X			100.00	73	43	89	85	69	63	90	72	51	67	62	67	25	61	62	68	50	0	0	22	4	1,673,333$
10	Michael Dal Colle		X	X		100.00	80	44	92	66	74	61	75	60	56	60	58	62	25	55	56	63	49	0	0	24	2	700,000$
11	Andreas Englund				X	100.00	89	95	85	74	74	55	76	57	25	50	47	63	63	57	57	59	50	0	0	24	4	700,000$
12	Christian Jaros				X	100.00	76	44	86	63	76	55	72	53	25	57	47	65	25	54	54	59	50	0	0	24	2	755,000$
13	Christian Wolanin				X	100.00	72	70	78	79	70	55	59	45	25	28	43	62	41	57	57	51	50	0	0	25	2	925,000$
14	Jake Dotchin				X	100.00	70	79	49	65	79	56	59	48	25	39	40	61	38	56	56	51	50	0	0	26	4	700,000$
15	Samuel Girard				X	100.00	69	51	91	88	59	83	93	78	25	70	48	66	25	62	63	66	46	0	0	22	1	935,833$
16	Carl Dahlstrom				X	100.00	61	43	87	61	86	65	58	61	25	46	47	73	75	59	59	57	50	0	0	25	1	925,000$
Rayé

	MOYENNE D'ÉQUIPE					100.00	75	60	82	75	73	64	77	62	42	59	55	65	39	57	57	63	49	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 Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Antoine Bibeau	100.00	44	52	65	82	40	43	50	52	44	45	30	44	44	46	45	0	0
2	Chris Driedger	100.00	69	59	57	84	77	62	96	69	77	69	75	45	45	72	32	0	0
3	Artyom Zagidulin (R)	100.00	51	57	71	73	49	54	50	56	52	52	30	44	44	53	45	0	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	55	56	64	80	55	53	65	59	58	55	45	44	44	57	41	0

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Scott Gomez	59	65	56	59	59	55	86	USA	42	3	850,000$

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Jack Roslovic	Senators (OTT)	C/RW	48	23	26	49	11	22	0	48	131	120	35	101	19.17%	13	1024	21.34	6	11	17	34	207	2	0	2	5	163	3	4	48.80%	1297	1	0.96	1	7	0	0	0	6	4	2
2	Joel Farabee	Senators (OTT)	LW	48	18	26	44	12	39	15	67	100	124	38	82	14.52%	19	979	20.40	6	9	15	43	211	0	1	1	0	157	3	0	41.27%	63	0	0.90	1	7	0	1	2	2	5	5
3	Filip Chlapik	Senators (OTT)	C	48	14	15	29	-9	34	0	71	83	115	24	75	12.17%	5	889	18.53	5	6	11	39	192	0	0	0	0	76	1	2	42.84%	698	0	0.65	0	5	0	0	0	4	1	0
4	Marko Dano	Senators (OTT)	LW/RW	48	9	17	26	-12	48	10	65	76	83	35	53	10.84%	7	756	15.76	2	8	10	20	152	0	0	0	0	13	1	0	43.90%	41	0	0.69	0	0	0	1	1	0	3	2
5	Michael Dal Colle	Senators (OTT)	LW/RW	42	8	18	26	13	30	0	65	31	45	18	32	17.78%	8	700	16.67	2	3	5	14	142	0	2	2	1	60	4	1	46.75%	77	0	0.74	0	0	0	0	0	1	1	3
6	Michael McLeod	Senators (OTT)	C	48	15	9	24	-16	34	0	71	123	98	26	67	15.31%	19	755	15.74	2	3	5	12	53	1	0	1	3	140	2	0	58.40%	601	0	0.64	0	0	0	0	0	1	2	4
7	Carl Dahlstrom	Senators (OTT)	D	48	4	13	17	-6	52	0	76	43	40	12	28	10.00%	29	793	16.53	1	5	6	25	129	0	0	0	0	25	0	0	0.00%	0	0	0.43	0	0	0	0	0	1	1	0
8	Dante Fabbro	Senators	D	13	4	8	12	-1	18	0	28	18	28	5	18	14.29%	10	235	18.08	4	2	6	19	59	0	0	0	0	2	1	0	0.00%	0	0	1.02	0	0	0	0	0	2	1	0
9	Samuel Girard	Senators (OTT)	D	14	1	7	8	2	18	0	27	20	13	2	12	7.69%	13	332	23.72	1	1	2	13	72	0	0	0	0	29	0	0	0.00%	0	0	0.48	0	0	0	0	0	1	0	1
10	Christian Wolanin	Senators (OTT)	D	48	2	5	7	-2	26	0	36	26	14	5	7	14.29%	24	637	13.29	0	1	1	2	72	0	0	0	0	56	0	0	0.00%	0	0	0.22	0	0	0	0	0	0	0	0
11	Ryan Dzingel	Senators (OTT)	LW/RW	14	3	4	7	1	10	0	10	25	39	7	22	7.69%	0	251	17.93	0	1	1	12	66	0	0	0	0	0	1	0	23.08%	13	0	0.56	0	0	0	0	0	2	0	1
12	Alexander Volkov	Senators (OTT)	LW/RW	14	0	6	6	0	25	5	31	17	22	4	12	0.00%	2	295	21.14	0	3	3	5	73	0	0	0	2	22	0	0	40.00%	35	0	0.41	0	0	0	0	0	0	0	1
13	Nick Paul	Senators (OTT)	C/LW	14	1	5	6	5	12	0	17	21	31	6	16	3.23%	2	192	13.78	0	0	0	0	4	0	0	0	5	46	0	0	60.00%	10	0	0.62	0	0	0	0	0	0	0	1
14	Kevin Rooney	Senators	C	11	2	3	5	2	12	0	17	30	19	5	18	10.53%	2	231	21.06	0	0	0	5	45	0	0	0	1	45	0	0	60.97%	269	0	0.43	0	0	0	0	0	0	1	0
15	Andreas Englund	Senators (OTT)	D	14	0	4	4	-2	15	5	20	4	3	2	2	0.00%	7	232	16.59	0	1	1	1	7	0	0	0	0	55	0	0	0.00%	0	0	0.34	0	0	1	0	0	0	0	0
16	Christian Jaros	Senators (OTT)	D	14	1	3	4	7	8	0	19	11	6	1	4	16.67%	8	297	21.28	1	0	1	5	65	0	0	0	0	57	0	0	0.00%	0	0	0.27	0	0	0	0	0	0	1	0
17	Jake Dotchin	Senators (OTT)	D	48	0	3	3	-9	77	5	71	16	4	2	11	0.00%	28	578	12.05	0	0	0	0	16	0	0	0	0	59	0	0	0.00%	0	0	0.10	0	0	0	0	1	0	0	0
18	Tyson Jost	Senators (OTT)	C/LW	14	1	2	3	-1	0	0	4	10	10	2	7	10.00%	2	127	9.11	0	0	0	0	0	0	0	0	4	75	0	1	22.22%	9	0	0.47	0	0	0	0	0	0	1	0
Stats d'équipe Total ou en Moyenne				548	106	174	280	-5	480	40	743	785	814	229	567	13.02%	198	9311	16.99	30	54	84	249	1572	3	3	6	21	1089	16	8	49.86%	3113	1	0.60	2	19	1	2	4	20	21	20

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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	Chris Driedger	Senators (OTT)	14	7	4	3	0.915	1.53	864	0	3	22	259	0	1	0	0.714	14	14	0	1	0	3
Stats d'équipe Total ou en Moyenne			14	7	4	3	0.915	1.53	864	0	3	22	259	0	1	0	0.714	14	14	0	1	0	3

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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	Link
Alexander Volkov	Senators (OTT)	LW/RW	22	1997-08-02	Yes	191 Lbs	6 ft1	No	No	No	3	Pro & Farm	925,000$	380,444$	925,000$	380,444$	0$	0$	No	925,000$	925,000$
Andreas Englund	Senators (OTT)	D	24	1996-01-21	No	189 Lbs	6 ft3	No	No	No	4	Pro & Farm	700,000$	287,903$	700,000$	287,903$	0$	0$	No	700,000$	700,000$	700,000$	Lien
Antoine Bibeau (Contrat à 1 Volet)	Senators (OTT)	G	26	1994-05-01	No	207 Lbs	6 ft3	No	No	No	2	Pro & Farm	750,000$	308,468$	750,000$	308,468$	75,000$	30,847$	No	750,000$			Lien
Artyom Zagidulin	Senators (OTT)	G	25	1995-01-01	Yes	200 Lbs	6 ft0	No	No	No	2	Pro & Farm	750,000$	308,468$	750,000$	308,468$	0$	0$	No	750,000$			Lien
Carl Dahlstrom	Senators (OTT)	D	25	1995-01-28	No	231 Lbs	6 ft4	No	No	No	1	Pro & Farm	925,000$	380,444$	925,000$	380,444$	0$	0$	No				Lien
Chris Driedger	Senators (OTT)	G	26	1994-05-18	No	205 Lbs	6 ft4	No	No	No	2	Pro & Farm	850,000$	349,597$	850,000$	349,597$	0$	0$	No	850,000$			Lien
Christian Jaros	Senators (OTT)	D	24	1996-04-02	No	201 Lbs	6 ft3	No	No	No	2	Pro & Farm	755,000$	310,524$	450,000$	185,081$	0$	0$	No	755,000$			Lien
Christian Wolanin	Senators (OTT)	D	25	1995-03-17	No	185 Lbs	6 ft2	No	No	No	2	Pro & Farm	925,000$	380,444$	450,000$	185,081$	0$	0$	No	925,000$			Lien
Filip Chlapik	Senators (OTT)	C	23	1997-06-03	No	196 Lbs	6 ft1	No	No	No	1	Pro & Farm	925,000$	380,444$	925,000$	380,444$	0$	0$	No				Lien
Jack Roslovic	Senators (OTT)	C/RW	23	1997-01-28	No	187 Lbs	6 ft1	No	No	No	1	Pro & Farm	900,000$	370,161$	900,000$	370,161$	0$	0$	No				Lien
Jake Dotchin	Senators (OTT)	D	26	1994-03-24	No	210 Lbs	6 ft3	No	No	No	4	Pro & Farm	700,000$	287,903$	700,000$	287,903$	0$	0$	No	700,000$	700,000$	700,000$	Lien
Joel Farabee	Senators (OTT)	LW	20	2000-02-24	Yes	164 Lbs	6 ft0	No	No	No	3	Pro & Farm	1,425,000$	586,089$	1,425,000$	586,089$	0$	0$	No	1,425,000$	1,425,000$
Marko Dano	Senators (OTT)	LW/RW	25	1994-11-30	No	212 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	380,444$	925,000$	380,444$	0$	0$	No	925,000$			Lien
Michael Dal Colle	Senators (OTT)	LW/RW	24	1996-06-19	No	198 Lbs	6 ft3	No	No	No	2	Pro & Farm	700,000$	287,903$	700,000$	287,903$	0$	0$	No	700,000$			Lien
Michael McLeod	Senators (OTT)	C	22	1998-02-03	No	187 Lbs	6 ft2	No	No	No	2	Pro & Farm	1,363,333$	560,726$	450,000$	185,081$	0$	0$	No	1,363,333$			Lien
Nick Paul	Senators (OTT)	C/LW	25	1995-03-20	No	230 Lbs	6 ft4	No	No	No	2	Pro & Farm	750,000$	308,468$	450,000$	185,081$	0$	0$	No	750,000$			Lien
Ryan Dzingel	Senators (OTT)	LW/RW	28	1992-03-09	No	190 Lbs	6 ft0	No	No	No	2	Pro & Farm	1,800,000$	740,323$	1,800,000$	740,323$	0$	0$	No	1,800,000$			Lien
Samuel Girard	Senators (OTT)	D	22	1998-05-12	No	162 Lbs	5 ft10	No	No	No	1	Pro & Farm	935,833$	384,899$	935,833$	384,899$	0$	0$	No				Lien
Tyson Jost	Senators (OTT)	C/LW	22	1998-03-14	No	191 Lbs	5 ft11	No	No	No	4	Pro & Farm	1,673,333$	688,226$	1,673,333$	688,226$	0$	0$	No	1,673,333$	1,673,333$	1,673,333$	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
19	24.05	197 Lbs	6 ft2	2.21	983,026$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Joel Farabee	Jack Roslovic	Alexander Volkov	38	1	2	2
2	Ryan Dzingel			30	1	2	2
3	Nick Paul	Filip Chlapik	Marko Dano	27	1	2	2
4	Tyson Jost	Michael McLeod	Filip Chlapik	5	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Samuel Girard	Carl Dahlstrom	39	1	2	2
2	Christian Jaros	Christian Wolanin	33	1	2	2
3	Andreas Englund	Jake Dotchin	28	1	2	2
4	Samuel Girard	Carl Dahlstrom	0	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Joel Farabee	Jack Roslovic	Alexander Volkov	55	0	0	5
2	Ryan Dzingel		Filip Chlapik	45	0	0	5

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Samuel Girard	Carl Dahlstrom	55	0	0	5
2	Christian Jaros	Christian Wolanin	45	0	0	5

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Tyson Jost	55	2	3	0
2	Michael McLeod	Nick Paul	45	2	3	0

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Andreas Englund	Jake Dotchin	55	2	3	0
2	Christian Jaros	Christian Wolanin	45	2	3	0

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		55	1	4	0	Jake Dotchin	Andreas Englund	55	1	4	0
2	Michael McLeod	45	1	4	0	Christian Jaros	Christian Wolanin	45	1	4	0

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Jack Roslovic	Joel Farabee	50	1	2	2
2		Ryan Dzingel	50	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Samuel Girard	Carl Dahlstrom	50	1	2	2
2	Christian Jaros	Christian Wolanin	50	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Joel Farabee	Jack Roslovic	Filip Chlapik	Samuel Girard	Carl Dahlstrom

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Nick Paul		Michael McLeod	Carl Dahlstrom	Samuel Girard

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Joel Farabee, Jack Roslovic, Alexander Volkov	Joel Farabee, Jack Roslovic	Alexander Volkov

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Christian Wolanin, Jake Dotchin, Samuel Girard	Christian Wolanin	Jake Dotchin, Samuel Girard

Tirs de Pénalité
Jack Roslovic, Joel Farabee, Filip Chlapik, Michael McLeod,

Gardien
#1 : Chris Driedger, #2 : Antoine Bibeau

Lignes d'Attaque Perso. en Prol.
, , , , , , , , , ,

Lignes de Défense Perso. en Prol.
, , , ,

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	Admirals	2	2	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	4	0	4	1	1	0	0	0	0	0	0	5	2	3	4	1.000	9	16	25	0	1	41	38	35	6	42	305	287	328	33	30	8	29	49	13	3	23.08%	11	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
2	Bruins	6	0	3	0	1	0	0	2	8	18	-10	3	0	1	0	0	0	0	2	2	6	-4	3	0	2	0	1	0	0	0	6	12	-6	4	0.333	8	14	22	0	0	41	38	35	6	112	305	287	328	33	133	32	101	114	36	3	8.33%	42	7	83.33%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
3	Crunch	5	0	4	0	0	1	0	0	11	22	-11	2	0	2	0	0	0	0	0	4	8	-4	3	0	2	0	0	1	0	0	7	14	-7	1	0.100	11	20	31	0	0	41	38	35	6	99	305	287	328	33	127	42	66	93	29	3	10.34%	31	4	87.10%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
4	Flames	1	0	1	0	0	0	0	0	2	5	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	5	-3	0	0.000	2	4	6	0	0	41	38	35	6	7	305	287	328	33	23	6	14	17	9	0	0.00%	8	1	87.50%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
5	Griffins	6	2	3	0	0	0	0	1	18	21	-3	3	2	0	0	0	0	0	1	13	9	4	3	0	3	0	0	0	0	0	5	12	-7	5	0.417	18	28	46	0	0	41	38	35	6	138	305	287	328	33	153	45	62	118	30	6	20.00%	29	5	82.76%	2	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
6	IceHogs	2	0	2	0	0	0	0	0	4	6	-2	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	4	8	12	0	0	41	38	35	6	34	305	287	328	33	44	17	28	50	5	0	0.00%	15	4	73.33%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
7	Marlies	5	3	2	0	0	0	0	0	11	7	4	3	3	0	0	0	0	0	0	9	2	7	2	0	2	0	0	0	0	0	2	5	-3	6	0.600	11	19	30	0	1	41	38	35	6	124	305	287	328	33	90	25	54	84	35	5	14.29%	26	2	92.31%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
8	Monarchs	1	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	2	1.000	1	0	1	0	1	41	38	35	6	10	305	287	328	33	23	4	16	18	2	0	0.00%	8	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
9	Monsters	1	0	1	0	0	0	0	0	1	6	-5	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	6	-5	0	0.000	1	1	2	0	0	41	38	35	6	14	305	287	328	33	23	9	14	16	4	0	0.00%	7	3	57.14%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
10	Moose	1	0	1	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	1	5	-4	0	0	0	0	0	0	0	0	0	0	0	0	0.000	1	1	2	0	0	41	38	35	6	21	305	287	328	33	27	11	12	18	7	0	0.00%	5	1	80.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
11	Penguins	3	0	2	0	0	0	1	0	6	9	-3	2	0	2	0	0	0	0	0	3	7	-4	1	0	0	0	0	0	1	0	3	2	1	2	0.333	6	9	15	0	0	41	38	35	6	47	305	287	328	33	66	29	21	56	18	3	16.67%	7	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
12	Phantoms	2	2	0	0	0	0	0	0	8	5	3	1	1	0	0	0	0	0	0	5	3	2	1	1	0	0	0	0	0	0	3	2	1	4	1.000	8	13	21	0	0	41	38	35	6	37	305	287	328	33	42	10	10	46	17	4	23.53%	4	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
13	Rampage	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	6	9	0	0	41	38	35	6	18	305	287	328	33	28	5	12	16	5	1	20.00%	6	2	66.67%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
14	Rocket	6	2	2	0	2	0	0	0	19	19	0	3	2	0	0	1	0	0	0	12	8	4	3	0	2	0	1	0	0	0	7	11	-4	8	0.667	19	28	47	0	0	41	38	35	6	109	305	287	328	33	119	31	70	101	33	6	18.18%	28	4	85.71%	1	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
15	Sharks	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	6	9	0	1	41	38	35	6	21	305	287	328	33	13	1	6	18	5	0	0.00%	3	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
16	Soldiers	1	0	0	0	1	0	0	0	3	2	1	1	0	0	0	1	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	3	6	0	0	41	38	35	6	20	305	287	328	33	15	4	10	15	8	0	0.00%	4	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
17	Sound Tigers	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	2	2	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	8	12	0	0	41	38	35	6	28	305	287	328	33	29	6	8	21	5	0	0.00%	4	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
18	Stars	1	0	0	0	0	0	0	1	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	4	5	-1	1	0.500	4	8	12	0	0	41	38	35	6	18	305	287	328	33	23	10	33	21	3	0	0.00%	8	3	62.50%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
	Total	48	14	22	0	4	1	2	5	120	142	-22	23	11	7	0	2	0	0	3	65	57	8	25	3	15	0	2	1	2	2	55	85	-30	46	0.479	120	199	319	0	4	41	38	35	6	934	305	287	328	33	1050	305	594	906	277	34	12.27%	260	37	85.77%	3	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
19	Wolf Pack	1	0	1	0	0	0	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	2	4	6	0	0	41	38	35	6	13	305	287	328	33	24	8	14	15	3	0	0.00%	7	1	85.71%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
20	Wolves	1	0	0	0	0	0	0	1	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	2	3	-1	1	0.500	2	3	5	0	0	41	38	35	6	22	305	287	328	33	18	2	14	20	10	0	0.00%	7	0	100.00%	0	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
	_Since Last GM Reset	48	14	22	0	4	1	2	5	120	142	-22	23	11	7	0	2	0	0	3	65	57	8	25	3	15	0	2	1	2	2	55	85	-30	46	0.479	120	199	319	0	4	41	38	35	6	934	305	287	328	33	1050	305	594	906	277	34	12.27%	260	37	85.77%	3	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
	_Vs Conference	30	7	16	0	3	1	1	2	68	94	-26	14	6	5	0	1	0	0	2	35	34	1	16	1	11	0	2	1	1	0	33	60	-27	25	0.417	68	112	180	0	1	41	38	35	6	562	305	287	328	33	647	192	364	542	184	24	13.04%	160	22	86.25%	1	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278
	_Vs Division	28	5	11	0	3	1	0	2	67	87	-20	14	5	3	0	1	0	0	2	40	33	7	14	0	8	0	2	1	0	0	27	54	-27	19	0.339	67	109	176	0	1	41	38	35	6	582	305	287	328	33	622	175	353	510	163	23	14.11%	156	22	85.90%	3	657	1304	50.38%	630	1305	48.28%	339	700	48.43%	1133	794	1216	353	572	278

			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
48	46	W2	120	199	319	934	1050	305	594	906	0	4

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
48	14	22	4	1	2	5	120	142

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
23	11	7	2	0	0	3	65	57

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
25	3	15	2	1	2	2	55	85

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	4	0	0	0	2

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
277	34	12.27%	260	37	85.77%	3

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
305	287	328	33	41	38	35	6

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
657	1304	50.38%	630	1305	48.28%	339	700	48.43%

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
1133	794	1216	353	572	278

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 - 2020-09-27	8	Senators	2	Griffins	6	L			Sommaire du Match
2 - 2020-09-28	16	Rocket	2	Senators	4	W			Sommaire du Match
4 - 2020-09-30	24	Senators	3	Rocket	5	L			Sommaire du Match
5 - 2020-10-01	38	Griffins	4	Senators	3	L	X	X	Sommaire du Match
6 - 2020-10-02	51	Marlies	1	Senators	3	W			Sommaire du Match
8 - 2020-10-04	58	Senators	3	Rocket	2	W	X		Sommaire du Match
10 - 2020-10-06	71	Senators	1	Marlies	2	L			Sommaire du Match
12 - 2020-10-08	84	Griffins	4	Senators	5	W			Sommaire du Match
14 - 2020-10-10	95	Marlies	0	Senators	2	W			Sommaire du Match
16 - 2020-10-12	105	Senators	0	Griffins	1	L			Sommaire du Match
18 - 2020-10-14	112	Rocket	4	Senators	5	W			Sommaire du Match
21 - 2020-10-17	133	Crunch	6	Senators	4	L			Sommaire du Match
22 - 2020-10-18	136	Senators	0	Bruins	4	L			Sommaire du Match
23 - 2020-10-19	152	Senators	2	Crunch	5	L			Sommaire du Match
24 - 2020-10-20	157	Bruins	2	Senators	0	L			Sommaire du Match
26 - 2020-10-22	177	Sound Tigers	2	Senators	4	W			Sommaire du Match
27 - 2020-10-23	181	Senators	2	Wolf Pack	3	L			Sommaire du Match
28 - 2020-10-24	194	Senators	1	Rocket	4	L			Sommaire du Match
31 - 2020-10-27	206	Senators	3	Phantoms	2	W			Sommaire du Match
32 - 2020-10-28	215	IceHogs	3	Senators	2	L			Sommaire du Match
34 - 2020-10-30	227	Penguins	4	Senators	1	L			Sommaire du Match
36 - 2020-11-01	231	Senators	4	Stars	5	L	X	X	Sommaire du Match
38 - 2020-11-03	245	Soldiers	2	Senators	3	W	X		Sommaire du Match
40 - 2020-11-05	258	Senators	1	Monsters	6	L			Sommaire du Match
43 - 2020-11-08	272	Rocket	2	Senators	3	W	X		Sommaire du Match
44 - 2020-11-09	281	Senators	2	Bruins	5	L			Sommaire du Match
45 - 2020-11-10	289	Senators	4	Crunch	5	L	X		Sommaire du Match
46 - 2020-11-11	301	Phantoms	3	Senators	5	W			Sommaire du Match
48 - 2020-11-13	312	Senators	1	Crunch	4	L			Sommaire du Match
49 - 2020-11-14	321	Moose	5	Senators	1	L			Sommaire du Match
50 - 2020-11-15	333	Senators	3	Griffins	5	L			Sommaire du Match
51 - 2020-11-16	341	Senators	2	Flames	5	L			Sommaire du Match
52 - 2020-11-17	350	Griffins	1	Senators	5	W			Sommaire du Match
53 - 2020-11-18	357	Senators	4	Bruins	3	W	X		Sommaire du Match
55 - 2020-11-20	374	Marlies	1	Senators	4	W			Sommaire du Match
56 - 2020-11-21	388	Bruins	2	Senators	1	L	X	X	Sommaire du Match
58 - 2020-11-23	401	Admirals	0	Senators	4	W			Sommaire du Match
59 - 2020-11-24	410	Senators	5	Admirals	2	W			Sommaire du Match
60 - 2020-11-25	420	Senators	2	Wolves	3	L	X	X	Sommaire du Match
61 - 2020-11-26	428	Senators	2	IceHogs	3	L			Sommaire du Match
63 - 2020-11-28	437	Senators	3	Sharks	0	W			Sommaire du Match
64 - 2020-11-29	444	Bruins	2	Senators	1	L	X	X	Sommaire du Match
66 - 2020-12-01	460	Penguins	3	Senators	2	L			Sommaire du Match
67 - 2020-12-02	471	Senators	1	Monarchs	0	W	X	X	Sommaire du Match
68 - 2020-12-03	480	Crunch	2	Senators	0	L			Sommaire du Match
70 - 2020-12-05	491	Senators	1	Marlies	3	L			Sommaire du Match
71 - 2020-12-06	502	Rampage	2	Senators	3	W			Sommaire du Match
72 - 2020-12-07	515	Senators	3	Penguins	2	W	X	X	Sommaire du Match
74 - 2020-12-09	521	Senators	-	Condors	-
75 - 2020-12-10	532	Bruins	-	Senators	-
77 - 2020-12-12	547	Marlies	-	Senators	-
78 - 2020-12-13	557	Senators	-	Rocket	-
79 - 2020-12-14	569	Crunch	-	Senators	-
80 - 2020-12-15	576	Senators	-	Monsters	-
82 - 2020-12-17	589	Monsters	-	Senators	-
85 - 2020-12-20	604	Senators	-	Sound Tigers	-
86 - 2020-12-21	612	Moose	-	Senators	-
87 - 2020-12-22	623	Senators	-	Soldiers	-
88 - 2020-12-23	635	Senators	-	Moose	-
90 - 2020-12-25	643	Rocket	-	Senators	-
92 - 2020-12-27	658	Griffins	-	Senators	-
93 - 2020-12-28	667	Senators	-	Penguins	-
94 - 2020-12-29	674	Senators	-	Crunch	-
95 - 2020-12-30	682	Sound Tigers	-	Senators	-
96 - 2020-12-31	700	Flames	-	Senators	-
97 - 2021-01-01	711	Senators	-	Griffins	-
99 - 2021-01-03	722	Stars	-	Senators	-
100 - 2021-01-04	729	Senators	-	Griffins	-
102 - 2021-01-06	744	Monarchs	-	Senators	-
104 - 2021-01-08	760	Senators	-	Bruins	-
105 - 2021-01-09	769	Phantoms	-	Senators	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10	784	Condors	-	Senators	-
108 - 2021-01-12	798	Wolf Pack	-	Senators	-
110 - 2021-01-14	809	Senators	-	Rampage	-
111 - 2021-01-15	816	Senators	-	Wolf Pack	-
112 - 2021-01-16	829	Rampage	-	Senators	-
114 - 2021-01-18	844	Wolves	-	Senators	-
117 - 2021-01-21	863	Sharks	-	Senators	-
118 - 2021-01-22	870	Senators	-	Marlies	-
119 - 2021-01-23	878	Senators	-	Stars	-
120 - 2021-01-24	887	Senators	-	Marlies	-
122 - 2021-01-26	896	Crunch	-	Senators	-

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
18	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
1,576,388$	1,792,749$	1,593,416$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	1,076,001$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	51	21,312$	1,086,912$

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