Profi League - Senators

Senators
Formation
Points
Informations Joueurs
Lignes
Statistiques d'Équipe
Calendrier
Finance

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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	Sergei Plotnikov		X	X		100.00	77	46	66	77	58	53	67	65	60	37	38	60	50	48	48	0	86	57	0
2	Michael McCarron (R)	X		X		100.00	76	99	58	66	60	56	61	63	79	39	40	57	50	45	45	0	62	56	0
3	Viktor Tikhonov (A)	X	X	X		100.00	66	43	66	72	54	56	60	63	60	39	41	60	50	57	57	0	86	56	0
4	Zack Mitchell (R)			X		100.00	72	68	75	62	53	70	65	61	69	33	39	58	56	44	44	0	62	55	0
5	Stefan Noesen (R)		X	X		100.00	62	76	63	69	58	62	62	58	76	36	34	60	50	44	44	0	79	54	0
6	Jordan Weal	X		X		100.00	56	41	68	69	50	50	62	62	74	37	38	58	50	45	45	0	57	53	0
7	Tanner Richard (R)	X				100.00	56	65	59	62	50	69	66	60	74	43	34	56	50	44	44	0	77	53	0
8	Felix Girard (R)	X				100.00	57	66	59	60	51	68	65	55	65	32	30	54	50	44	44	0	79	50	0
9	Brian Hart (R)		X	X		100.00	60	75	62	63	56	48	57	52	59	25	31	55	50	44	44	0	59	49	0
10	Mark MacMillan (R)	X		X		100.00	56	64	59	63	50	53	60	55	64	31	32	52	50	44	44	0	24	49	0
11	Chris Wideman				X	100.00	69	42	81	74	66	66	85	69	25	41	43	73	50	51	51	0	87	62	0
12	Scott Harrington (R) (C)				X	100.00	72	45	88	73	72	62	66	68	25	39	37	71	50	60	60	0	81	61	0
13	Dylan Blujus (R)				X	100.00	83	73	95	70	69	79	78	51	25	30	32	62	50	55	55	0	64	60	0
14	Jakub Nakladal				X	100.00	74	45	67	67	61	65	65	69	25	38	40	69	50	46	46	0	81	58	0
15	Oliver Kylington (R)				X	100.00	59	67	67	73	57	58	63	56	25	32	35	59	50	55	55	0	40	54	0
16	Joey LaLeggia (R)				X	100.00	55	64	61	72	55	61	65	66	25	34	32	54	50	44	44	0	19	53	0
Rayé
1	Steve Bernier			X		100.00	71	76	67	75	92	73	68	65	57	42	41	58	53	75	79	0	74	64	0
2	Tom Kuhnhackl		X	X		100.00	68	44	66	70	55	58	73	64	60	44	42	65	50	47	47	0	83	57	0
3	Daniel Catenacci	X	X			100.00	72	44	77	73	52	50	60	60	58	37	38	57	50	45	45	0	69	54	0
4	Danny Biega				X	100.00	57	74	64	71	59	65	68	58	25	29	32	57	50	46	46	0	76	53	0
5	Matt Finn (R)				X	100.00	56	71	62	64	58	61	65	66	25	32	33	57	50	46	46	0	19	53	0
6	Patrick McNally (R)				X	100.00	59	69	67	61	57	57	63	53	25	24	27	58	50	46	46	0	20	50	0

	MOYENNE D'ÉQUIPE					100.00	65	62	68	68	59	61	66	61	49	35	36	60	50	49	49	0	63	55

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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	Jason Kasdorf (R)	100.00	66	68	65	75	66	79	65	72	73	66	48	55	55	0	86	66	0
2	Richard Bachman	100.00	67	72	64	70	68	74	69	71	69	66	53	59	59	0	85	66	0
Rayé
1	Rasmus Tirronen	100.00	58	70	72	65	66	68	64	62	60	61	49	55	55	0	19	61	0

	MOYENNE D'ÉQUIPE	100.00	64	70	67	70	67	74	66	68	67	64	50	56	56	0	63	64

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Kurt Kleinendorst	54	61	63	65	60	58	60	USA	55	5	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	PIM5	HIT	SHT	OSB	OSM	SHT%	SB	MP	PPG	PPA	PPP	PKG	PKA	PKP	PKM	GW	GT	FO%	FOT	GA	TA	EG	HT	P/20	PSG
1	Chris Wideman	Senators (OTT)		D	19	9	10	19	10	2	0	26	75	35	42	12.00%	42	26.:.54	2	2	4	0	1	1	48	2	1	0.00%	0	31	48	0	0	0.74	0
2	Viktor Tikhonov	Senators (OTT)		C/LW/RW	19	4	8	12	1	12	0	27	54	20	42	7.41%	12	21.:.21	1	5	6	0	1	1	58	1	0	47.95%	365	42	8	1	0	0.59	0
3	Tom Kuhnhackl	Senators (OTT)		LW/RW	14	5	5	10	1	10	0	29	45	19	27	11.11%	12	19.:.13	2	3	5	1	0	1	44	1	0	40.00%	10	34	11	0	0	0.74	0
4	Steve Bernier	Senators (OTT)		RW	16	4	5	9	3	57	35	33	48	10	39	8.33%	4	18.:.42	0	1	1	0	0	0	4	0	1	71.43%	21	40	8	0	0	0.60	0
5	Michael McCarron	Senators (OTT)		C/RW	19	4	2	6	-3	79	55	39	34	5	18	11.76%	4	16.:.58	0	0	0	0	0	0	7	1	0	60.87%	23	24	6	0	0	0.37	0
6	Tanner Richard	Senators (OTT)		C	15	0	6	6	9	28	10	18	9	4	15	0.00%	6	17.:.22	0	1	1	0	0	0	0	0	0	46.12%	232	4	5	0	0	0.46	0
7	Sergei Plotnikov	Senators (OTT)		LW/RW	19	3	2	5	0	4	0	29	33	11	23	9.09%	4	15.:.14	0	0	0	0	0	0	5	0	0	60.00%	10	48	3	0	0	0.35	0
8	Oliver Kylington	Senators (OTT)		D	19	0	4	4	-5	8	0	16	22	10	14	0.00%	26	16.:.42	0	0	0	0	0	0	11	0	0	0.00%	0	10	23	0	0	0.25	0
9	Daniel Catenacci	Senators (OTT)		C/LW	18	0	4	4	-3	0	0	21	39	11	35	0.00%	5	14.:.45	0	0	0	0	0	0	27	0	0	50.00%	106	33	6	0	0	0.30	0
10	Jakub Nakladal	Senators (OTT)		D	19	2	2	4	-7	12	0	38	51	22	23	3.92%	26	20.:.59	1	0	1	0	0	0	4	1	0	0.00%	0	18	25	0	0	0.20	0
11	Dylan Blujus	Senators (OTT)		D	19	1	3	4	-2	22	10	19	18	11	6	5.56%	37	17.:.43	1	0	1	0	0	0	36	0	0	0.00%	0	2	24	0	0	0.24	0
12	Scott Harrington	Senators (OTT)		D	19	0	3	3	-4	0	0	22	60	24	35	0.00%	41	23.:.17	0	2	2	0	0	0	40	0	0	0.00%	0	17	34	0	0	0.14	0
13	Jordan Weal	Senators (OTT)		C/RW	19	0	3	3	-6	4	0	14	40	20	23	0.00%	8	15.:.27	0	0	0	0	0	0	4	0	0	49.45%	182	28	4	0	0	0.20	0
14	Zack Mitchell	Senators (OTT)		RW	19	1	2	3	-1	4	0	33	35	14	26	2.86%	10	13.:.31	0	1	1	0	0	0	0	0	0	75.00%	4	9	7	0	0	0.23	0
15	Felix Girard	Senators (OTT)		C	6	0	1	1	-3	10	10	6	7	3	1	0.00%	3	16.:.39	0	0	0	0	0	0	1	0	0	58.33%	48	2	4	0	0	0.20	0
16	Stefan Noesen	Senators (OTT)		LW/RW	19	0	1	1	3	64	50	20	19	4	10	0.00%	5	14.:.28	0	0	0	0	0	0	4	0	0	46.15%	13	11	2	0	0	0.07	0
17	Joey LaLeggia	Senators (OTT)		D	2	0	0	0	-2	0	0	0	1	0	0	0.00%	0	15.:.26	0	0	0	0	0	0	0	0	0	0.00%	0	0	1	0	0	0.00	0
18	Brian Hart	Senators (OTT)		LW/RW	18	0	0	0	-4	44	40	16	18	2	6	0.00%	4	11.:.15	0	0	0	0	0	0	0	0	0	41.67%	12	2	5	0	0	0.00	0
19	Mark MacMillan	Senators (OTT)		C/RW	2	0	0	0	-2	2	0	4	2	0	1	0.00%	1	11.:.32	0	0	0	0	0	0	0	0	0	45.45%	11	2	1	0	0	0.00	0
20	Matt Finn	Senators (OTT)		D	1	0	0	0	0	0	0	0	0	0	0	0.00%	1	19.:.53	0	0	0	0	0	0	0	0	0	0.00%	0	0	2	0	0	0.00	0
Stats d'équipe Total ou en Moyenne			301	33	61	94	-15	362	210	410	478	610	225	386	5.41%	251	5322	17.68	7	15	22	66	321	1	2	3	12	300	6	2	49.28%	1037	357	227	1	0.35	0	0	9	17	15	3	5	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 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	Richard Bachman	Senators (OTT)	14	7	4	3	92.7%	2.56	845	2	0	36	495	307	0	0	0.0%	0	14	5	1	0	2
2	Jason Kasdorf	Senators (OTT)	6	2	2	1	95.0%	1.71	387	0	0	11	219	131	0	1	0.0%	0	5	14	2	1	0
Stats d'équipe Total ou en Moyenne		20	9	6	4	0.934	2.29	1232	2	0	47	714	438	0	1	0.000	0	19	19	3	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 Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Ballotage Forcé	Contrat	Status	Type	Salaire Actuel	Salaire Année 2	Salaire Année 3	Salaire Année 4	Salaire Année 5	Salaire Année 6	Salaire Année 7	Salaire Année 8	Salaire Année 9	Salaire Année 10	Link
Brian Hart	Senators (OTT)	LW/RW	23	25.11.1993	Yes	203 Lbs	6 ft2	No	No	10	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Chris Wideman	Senators (OTT)	D	27	7.1.1990 11:24:59	No	180 Lbs	5 ft10	No	No	1	Sans Restriction	Pro & Farm	1,300,000$										Lien
Daniel Catenacci	Senators (OTT)	C/LW	24	9.3.1993	No	192 Lbs	5 ft9	No	No	2	Avec Restriction	Pro & Farm	900,000$	900,000$									Lien
Danny Biega	Senators (OTT)	D	25	29.9.1991 15:13:00	No	205 Lbs	6 ft0	No	No	1	Avec Restriction	Pro & Farm	1,000,000$										Lien
Dylan Blujus	Senators (OTT)	D	23	22.1.1994	Yes	191 Lbs	6 ft3	No	No	9	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Felix Girard	Senators (OTT)	C	23	9.5.1994	Yes	185 Lbs	5 ft10	No	No	9	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Jakub Nakladal	Senators (OTT)	D	29	29.12.1987	No	205 Lbs	6 ft2	No	No	1	Sans Restriction	Pro & Farm	1,500,000$										Lien
Jason Kasdorf	Senators (OTT)	G	25	8.5.1992 17:28:26	Yes	178 Lbs	6 ft3	No	No	9	Avec Restriction	Pro & Farm	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Joey LaLeggia	Senators (OTT)	D	24	24.6.1992 15:05:47	Yes	183 Lbs	5 ft9	No	No	9	Avec Restriction	Pro & Farm	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Jordan Weal	Senators (OTT)	C/RW	25	15.4.1992 14:38:36	No	179 Lbs	5 ft10	No	No	1	Avec Restriction	Pro & Farm	1,000,000$										Lien
Mark MacMillan	Senators (OTT)	C/RW	25	23.1.1992	Yes	172 Lbs	6 ft0	No	No	10	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Matt Finn	Senators (OTT)	D	23	24.2.1994	Yes	199 Lbs	6 ft0	No	No	9	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Michael McCarron	Senators (OTT)	C/RW	22	7.3.1995 15:07:56	Yes	237 Lbs	6 ft5	No	No	3	Contrat d'Entrée	Pro & Farm	1,200,000$	1,200,000$	1,200,000$								Lien
Oliver Kylington	Senators (OTT)	D	20	19.5.1997 15:04:29	Yes	181 Lbs	6 ft0	No	No	9	Contrat d'Entrée	Pro & Farm	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Patrick McNally	Senators (OTT)	D	25	4.12.1991	Yes	181 Lbs	6 ft2	No	No	10	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	Lien
Rasmus Tirronen	Senators (OTT)	G	26	9.11.1990 0:01:39	No	200 Lbs	6 ft3	No	No	9	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Richard Bachman	Senators (OTT)	G	29	25.7.1987	No	183 Lbs	5 ft10	No	No	2	Sans Restriction	Pro & Farm	900,000$	900,000$									Lien
Scott Harrington	Senators (OTT)	D	24	1.1.1993 15:15:41	Yes	201 Lbs	6 ft2	No	No	1	Avec Restriction	Pro & Farm	1,000,000$										Lien
Sergei Plotnikov	Senators (OTT)	LW/RW	26	3.6.1990 3:23:02	No	205 Lbs	6 ft2	No	No	1	Avec Restriction	Pro & Farm	1,000,000$										Lien
Stefan Noesen	Senators (OTT)	LW/RW	24	12.2.1993	Yes	205 Lbs	6 ft2	No	No	9	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien
Steve Bernier	Senators (OTT)	RW	32	31.3.1985	No	215 Lbs	6 ft3	No	No	2	Sans Restriction	Pro & Farm	1,200,000$	1,200,000$									Lien
Tanner Richard	Senators (OTT)	C	24	6.4.1993 11:30:09	Yes	176 Lbs	6 ft0	No	No	7	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$				Lien
Tom Kuhnhackl	Senators (OTT)	LW/RW	25	21.2.1992 11:31:02	No	196 Lbs	6 ft2	No	No	1	Avec Restriction	Pro & Farm	1,200,000$										Lien
Viktor Tikhonov	Senators (OTT)	C/LW/RW	29	12.5.1988 4:06:53	No	189 Lbs	6 ft2	No	No	1	Sans Restriction	Pro & Farm	1,100,000$										Lien
Zack Mitchell	Senators (OTT)	RW	24	7.1.1993	Yes	185 Lbs	6 ft0	No	No	9	Avec Restriction	Farm Only	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$	100,000$		Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
25	25.04	193 Lbs	6 ft1	5.40	584,000$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Sergei Plotnikov	Viktor Tikhonov		40	1	1	3
2		Tanner Richard	Michael McCarron	30	0	2	3
3	Stefan Noesen	Jordan Weal	Zack Mitchell	20	0	3	2
4		Felix Girard	Brian Hart	10	1	4	0

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jakub Nakladal	Chris Wideman	45	1	2	2
2	Scott Harrington	Dylan Blujus	40	1	2	2
3	Joey LaLeggia	Oliver Kylington	15	1	2	2
4	Jakub Nakladal	Chris Wideman	0	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Sergei Plotnikov	Michael McCarron		60	0	0	5
2		Viktor Tikhonov	Zack Mitchell	40	0	1	4

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jakub Nakladal	Chris Wideman	60	1	2	2
2	Scott Harrington	Dylan Blujus	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Viktor Tikhonov		60	1	2	2
2	Michael McCarron	Sergei Plotnikov	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jakub Nakladal	Chris Wideman	60	1	2	2
2	Scott Harrington	Dylan Blujus	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Viktor Tikhonov	60	1	2	2	Jakub Nakladal	Chris Wideman	60	1	2	2
2		40	1	2	2	Scott Harrington	Dylan Blujus	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Sergei Plotnikov	60	1	2	2
2	Michael McCarron	Viktor Tikhonov	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jakub Nakladal	Chris Wideman	60	1	2	2
2	Scott Harrington	Dylan Blujus	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Sergei Plotnikov	Michael McCarron		Jakub Nakladal	Chris Wideman

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Sergei Plotnikov	Michael McCarron		Scott Harrington	Chris Wideman

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Felix Girard, Stefan Noesen, Jordan Weal	Felix Girard, Stefan Noesen	Jordan Weal

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Jakub Nakladal, Oliver Kylington, Scott Harrington	Jakub Nakladal	Oliver Kylington, Scott Harrington

Tirs de Pénalité
, Sergei Plotnikov, Michael McCarron, Viktor Tikhonov, Zack Mitchell

Gardien
#1 : Richard Bachman, #2 : Jason Kasdorf

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

#	VS Équipe	GP	W	L	GF	GA	Diff	P	PCT	G	A	TP	SO	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	Penguins	7	4	3	18	19	-1	8	0.571	18	29	47	0	17	16	16	1	234	235	219	224	46	249	101	67	153	15	3	20.00%	17	1	94.12%	1	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161
2	Checkers	7	4	3	26	19	7	8	0.571	26	42	68	0	17	16	16	1	270	235	219	224	46	259	111	209	166	19	5	26.32%	22	6	72.73%	0	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161
3	Crunch	5	1	4	6	10	-4	2	0.200	6	9	15	1	17	16	16	1	220	235	219	224	46	207	81	90	142	13	2	15.38%	10	1	90.00%	0	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161
	Vs Division	14	8	6	44	38	6	16	0.571	44	71	115	0	17	16	16	1	504	235	219	224	46	508	212	276	319	34	8	23.53%	39	7	82.05%	1	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161
	Vs Conference	19	9	10	50	48	2	18	0.474	50	80	130	1	17	16	16	1	724	235	219	224	46	715	293	366	461	47	10	21.28%	49	8	83.67%	1	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161
	Since Last GM Reset	19	9	10	50	48	2	18	0.474	50	80	130	1	17	16	16	1	724	235	219	224	46	715	293	366	461	47	10	21.28%	49	8	83.67%	1	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161
	Total	19	9	10	50	48	2	18	0.474	50	80	130	1	17	16	16	1	724	235	219	224	46	715	293	366	461	47	10	21.28%	49	8	83.67%	1	209	429	48.72%	199	413	48.18%	123	235	52.34%	447	280	459	175	327	161

			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
19	18	L1	50	80	130	724	715	293	366	461	1	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
19	9	10	0	0	0	0	50	48

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	5	3	0	0	0	0	26	19

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
11	4	7	0	0	0	0	24	29

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
3	3	1	3	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
47	10	21.28%	49	8	83.67%	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
235	219	224	46	17	16	16	1

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
209	429	48.72%	199	413	48.18%	123	235	52.34%

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
447	280	459	175	327	161

Derniers Match Joués

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

Date		Result	Detail
Mon, Apr 21	@	Senators5,Checkers3	RECAP
Wed, Apr 23	@	Senators1,Checkers2 (OT)	RECAP
Fri, Apr 25	@	Checkers2,Senators4	RECAP
Sun, Apr 27	@	Checkers4,Senators3	RECAP
Tue, Apr 29	@	Senators3,Checkers4	RECAP
Thu, May 1	@	Checkers3,Senators7	RECAP
Sat, May 3	@	Senators3,Checkers1	RECAP
Mon, May 5	@	Senators1,Penguins3	RECAP
Wed, May 7	@	Senators4,Penguins1	RECAP
Fri, May 9	@	Penguins1,Senators3	RECAP
Sun, May 11	@	Penguins3,Senators2 (OT)	RECAP
Tue, May 13	@	Senators1,Penguins7	RECAP
Thu, May 15	@	Penguins3,Senators4	RECAP
Sat, May 17	@	Senators3,Penguins1	RECAP
Mon, May 19	@	Senators1,Crunch2 (OT)	RECAP
Wed, May 21	@	Senators1,Crunch3	RECAP
Fri, May 23	@	Crunch1,Senators2 (OT)	RECAP
Sun, May 25	@	Crunch2,Senators1 (OT)	RECAP
Tue, May 27	@	Senators1,Crunch2	RECAP

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
33	0 - 0.00%	0$	0$	3000	100

Dépenses
Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,340,000$	430,000$	0$

Dépenses Annuelles à Ce Jour	Cap Salarial Par Jour	Cap salarial à ce jour
0$	0$	0$

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

Afficher ou Masquer des Colonnes

	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