Ligue de hockey Simulée Ste-Flanelle

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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	Nicklas Backstrom (A)	X				100.00	57	35	83	81	74	79	93	79	85	86	72	70	52	67	57	0	86	74	0
2	Derek Stepan (A)	X				100.00	56	43	87	78	67	76	88	75	81	77	72	74	66	60	50	0	58	71	0
3	Milan Lucic		X			100.00	79	56	73	75	83	75	95	72	43	73	71	63	56	68	60	0	82	70	0
4	Lee Stempniak			X		100.00	61	35	84	78	66	74	94	68	35	68	68	69	44	65	55	0	86	68	0
5	Josh Bailey		X	X		100.00	53	35	88	74	73	73	94	64	44	65	63	63	61	57	48	0	86	65	0
6	Anders Lee		X	X		100.00	68	50	81	72	81	68	94	63	35	59	67	62	48	42	39	0	83	64	0
7	Matt Moulson		X	X		100.00	54	35	90	71	73	69	94	61	35	60	62	65	52	58	50	0	86	64	0
8	Brandon Sutter	X				100.00	55	35	90	68	65	73	57	62	84	54	70	75	50	59	50	0	81	63	0
9	Scottie Upshall		X	X		100.00	63	35	81	70	69	65	84	57	40	52	61	70	53	68	61	0	84	63	0
10	Kevin Hayes	X	X	X		100.00	58	35	86	71	80	64	84	63	70	61	64	62	45	37	34	0	86	63	0
11	Brian Flynn	X	X	X		100.00	54	35	91	70	57	65	74	54	88	52	56	73	58	44	39	0	86	60	0
12	Sergey Kalinin	X	X	X		100.00	75	35	84	73	70	55	55	49	75	44	54	62	48	35	32	0	68	56	0
13	Brent Seabrook (C)				X	100.00	88	50	85	75	74	82	95	65	35	69	61	82	48	74	70	0	86	75	0
14	Mark Giordano				X	100.00	70	50	80	83	64	85	90	70	35	74	65	84	48	59	51	0	85	73	0
15	Marek Zidlicky				X	100.00	61	35	83	73	60	72	77	55	35	57	53	64	45	68	58	0	86	63	0
16	Kevin Connauton				X	100.00	68	50	76	68	68	69	69	51	35	51	51	72	45	45	37	0	80	62	0
17	Anthony Bitetto				X	100.00	77	43	79	66	69	53	39	45	35	46	43	62	48	35	32	0	21	55	0
18	Brady Skjei				X	100.00	52	35	81	62	68	55	35	31	35	30	32	76	48	35	32	0	89	54	0
Rayé
1	Jacob Josefson	X				84.52	58	35	86	69	62	66	64	54	82	51	56	70	68	49	43	0	73	59	0
2	Kevan Miller				X	91.29	78	61	79	67	69	73	75	50	35	52	47	83	48	45	38	0	73	65	0
3	Nikita Nikitin				X	100.00	57	35	83	60	73	62	40	47	35	46	47	71	48	50	43	0	20	57	0
4	Ryan Pulock (R)				X	97.85	50	43	86	65	71	53	36	49	35	43	55	67	48	35	32	0	45	55	0

	MOYENNE D'ÉQUIPE					98.73	63	41	83	71	70	68	74	58	51	58	59	70	51	53	46	0	74	64

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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	Frederik Andersen	100.00	90	70	91	85	87	89	91	83	87	84	80	46	39	0	66	79	0
2	Robin Lehner	100.00	64	45	91	94	67	62	68	69	56	81	70	46	43	0	83	66	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	77	58	91	90	77	76	80	76	72	83	75	46	41	0	75	73

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Barry Trotz	99	95	90	99	99	98	97	CAN	54	2	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	PIM5	HIT	HTT	SHT	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	TA	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Nicklas Backstrom	Tampa Bay	C	82	34	50	84	-1	16	0	49	183	243	0	13.99%	9	1504	18.34	14	28	42	77	397	1	0	1	1	5	7	2	54.56%	1875	0	0	1	1.12	1	1	0	0	0	3	5	3
2	Mark Giordano	Tampa Bay	D	82	20	62	82	3	163	45	120	133	183	0	10.93%	125	1977	24.11	12	21	33	132	396	0	0	0	0	303	5	1	0.00%	0	0	1	0	0.83	0	0	1	1	6	6	3	8
3	Derek Stepan	Tampa Bay	C	67	33	38	71	24	20	0	56	151	241	0	13.69%	12	1195	17.85	8	21	29	70	287	0	0	0	2	75	6	3	52.28%	1427	0	0	2	1.19	4	11	0	0	0	10	3	5
4	Brent Seabrook	Tampa Bay	D	82	16	53	69	3	150	10	166	115	134	0	11.94%	95	2070	25.25	11	19	30	86	409	0	2	2	3	311	2	1	0.00%	0	0	0	0	0.67	0	0	0	0	0	5	1	4
5	Milan Lucic	Tampa Bay	LW	82	30	34	64	10	147	25	268	71	232	0	12.93%	9	1492	18.20	15	10	25	62	389	0	0	0	0	0	3	1	38.18%	110	0	0	0	0.86	2	4	0	1	4	3	8	1
6	Lee Stempniak	Tampa Bay	RW	82	24	35	59	5	52	0	118	114	200	0	12.00%	21	1542	18.81	8	19	27	70	354	0	0	0	6	203	5	5	35.24%	105	0	0	0	0.77	1	1	0	0	0	5	5	0
7	Anders Lee	Tampa Bay	LW/RW	82	15	27	42	2	65	15	130	37	118	0	12.71%	6	1236	15.08	6	17	23	27	390	0	0	0	2	63	3	2	25.00%	84	0	0	0	0.68	0	0	0	2	1	3	2	3
8	Josh Bailey	Tampa Bay	LW/RW	82	15	27	42	9	12	0	70	73	173	0	8.67%	9	1371	16.72	4	10	14	47	331	0	0	0	0	11	2	2	32.99%	97	0	0	0	0.61	2	12	0	0	0	1	4	6
9	Brandon Sutter	Tampa Bay	C	73	14	12	26	-5	10	0	34	156	121	0	11.57%	21	1130	15.48	2	0	2	11	35	1	1	2	8	321	3	1	52.64%	1569	0	1	0	0.46	0	0	0	0	0	0	4	4
10	Brian Flynn	Tampa Bay	C/LW/RW	82	8	16	24	5	6	0	17	69	67	0	11.94%	11	882	10.76	0	2	2	1	47	1	0	1	5	272	1	0	51.18%	381	0	0	0	0.54	1	8	0	0	0	0	1	1
11	Kevin Hayes	Tampa Bay	C/LW/RW	82	13	11	24	4	16	0	40	51	86	0	15.12%	4	881	10.75	0	3	3	6	86	0	0	0	0	0	4	0	40.96%	166	0	0	0	0.54	0	0	0	0	0	1	2	1
12	Kevin Connauton	Tampa Bay	D	80	3	20	23	11	104	20	103	41	42	0	7.14%	48	1195	14.94	1	2	3	5	49	0	1	1	0	96	0	1	0.00%	0	0	0	0	0.38	0	0	1	1	2	2	0	0
13	Jacob Josefson	Tampa Bay	C	75	6	15	21	11	12	0	37	92	52	0	11.54%	7	677	9.04	0	0	0	0	0	0	0	0	0	134	1	0	48.09%	734	0	0	0	0.62	1	12	0	0	0	0	1	3
14	Matt Moulson	Tampa Bay	LW/RW	82	8	13	21	13	14	0	18	47	78	0	10.26%	8	1143	13.94	0	0	0	0	6	0	0	0	0	0	1	0	28.95%	76	0	0	0	0.37	0	0	0	0	0	0	0	2
15	Kevan Miller	Tampa Bay	D	69	3	15	18	6	101	35	94	75	33	0	9.09%	75	1308	18.97	0	1	1	3	13	0	0	0	2	280	0	0	0.00%	0	0	0	0	0.28	0	0	3	1	3	0	2	0
16	Marek Zidlicky	Tampa Bay	D	82	8	8	16	14	46	0	60	39	60	0	13.33%	70	1725	21.04	4	4	8	37	292	0	0	0	0	330	2	0	0.00%	0	0	0	0	0.19	0	0	0	0	0	0	1	2
17	Scottie Upshall	Tampa Bay	LW/RW	80	7	7	14	0	32	0	72	59	71	0	9.86%	6	882	11.03	0	0	0	0	2	0	0	0	2	328	0	0	35.62%	73	0	0	0	0.32	0	0	0	0	0	1	1	0
18	Brady Skjei	Tampa Bay	D	50	4	9	13	20	10	0	22	10	46	0	8.70%	54	884	17.69	0	2	2	23	200	0	0	0	0	80	1	0	0.00%	0	0	0	0	0.29	0	0	0	0	0	2	0	0
19	Ryan Pulock	Tampa Bay	D	45	4	5	9	-9	14	0	16	17	26	0	15.38%	29	714	15.88	4	2	6	18	172	0	0	0	0	12	0	0	0.00%	1	0	0	0	0.25	0	0	0	0	0	0	0	0
20	Sergey Kalinin	Tampa Bay	C/LW/RW	29	0	1	1	-5	31	5	31	12	10	0	0.00%	0	199	6.86	0	0	0	0	0	0	0	0	0	3	0	0	46.81%	94	0	0	0	0.10	0	0	0	0	0	0	0	0
21	Anthony Bitetto	Tampa Bay	D	5	0	0	0	3	21	5	8	2	1	0	0.00%	4	74	14.90	0	0	0	0	11	0	0	0	0	7	0	0	0.00%	0	0	0	0	0.00	0	0	0	0	1	0	0	0
Stats d'équipe Total ou en Moyenne			1475	265	458	723	123	1042	160	1529	1547	2217	0	11.95%	623	24089	16.33	89	161	250	675	3875	3	4	7	31	2842	46	19	50.57%	6792	0	2	3	0.60	12	49	5	6	17	42	43	43

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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	Frederik Andersen	Tampa Bay	69	43	16	7	0.907	2.40	3993	6	6	160	1728	0	3	2	0.684	38	68	14	8	7	3
2	Robin Lehner	Tampa Bay	19	8	6	2	0.895	2.60	992	0	0	43	409	0	1	0	0.700	10	14	68	0	0	0
Stats d'équipe Total ou en Moyenne		88	51	22	9	0.905	2.44	4986	6	6	203	2137	0	4	2	0.688	48	82	82	8	7	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	Ballotage Forcé	Contrat	Status	Type	Salaire Actuel	Salaire Année 2	Salaire Année 3	Salaire Année 4	Salaire Année 5	Salaire Année 6	Link
Anders Lee	Tampa Bay	LW/RW	25	1990-07-03	No	228 Lbs	6 ft3	No	No	1	Avec Restriction	Pro & Farm	900,000$						Lien
Anthony Bitetto	Tampa Bay	D	25	1990-07-15	No	210 Lbs	6 ft1	No	No	3	Avec Restriction	Pro & Farm	605,000$	605,000$	605,000$				Lien
Brady Skjei	Tampa Bay	D	21	1994-03-26	No	206 Lbs	6 ft3	No	No	4	Contrat d'Entrée	Pro & Farm	925,000$	925,000$	925,000$	925,000$			Lien
Brandon Sutter	Tampa Bay	C	26	1989-02-14	No	190 Lbs	6 ft3	No	No	1	Avec Restriction	Pro & Farm	1,350,000$						Lien
Brent Seabrook	Tampa Bay	D	30	1985-04-20	No	220 Lbs	6 ft3	No	No	5	Sans Restriction	Pro & Farm	6,500,000$	6,000,000$	6,000,000$	5,750,000$	5,500,000$		Lien
Brian Flynn	Tampa Bay	C/LW/RW	27	1988-07-26	No	183 Lbs	6 ft1	No	No	1	Avec Restriction	Pro & Farm	925,000$						Lien
Derek Stepan	Tampa Bay	C	25	1990-06-18	No	196 Lbs	6 ft0	No	No	1	Avec Restriction	Pro & Farm	4,000,000$						Lien
Frederik Andersen	Tampa Bay	G	26	1989-10-02	No	220 Lbs	6 ft4	No	No	2	Avec Restriction	Pro & Farm	925,000$	925,000$					Lien
Jacob Josefson	Tampa Bay	C	24	1991-03-02	No	190 Lbs	6 ft0	No	No	3	Avec Restriction	Pro & Farm	1,100,000$	1,100,000$	1,100,000$				Lien
Josh Bailey	Tampa Bay	LW/RW	26	1989-10-02	No	210 Lbs	6 ft1	No	No	1	Avec Restriction	Pro & Farm	1,600,000$						Lien
Kevan Miller	Tampa Bay	D	27	1987-11-15	No	210 Lbs	6 ft2	No	No	2	Avec Restriction	Pro & Farm	550,000$	550,000$					Lien
Kevin Connauton	Tampa Bay	D	25	1990-02-23	No	205 Lbs	6 ft2	No	No	2	Avec Restriction	Pro & Farm	600,000$	600,000$					Lien
Kevin Hayes	Tampa Bay	C/LW/RW	23	1992-05-08	No	225 Lbs	6 ft5	No	No	3	Avec Restriction	Pro & Farm	900,000$	900,000$	900,000$				Lien
Lee Stempniak	Tampa Bay	RW	32	1983-02-04	No	195 Lbs	5 ft11	Yes	No	5	Sans Restriction	Pro & Farm	5,000,000$	5,000,000$	5,000,000$	5,000,000$	5,000,000$		Lien
Marek Zidlicky (Contrat à 1 Volet)	Tampa Bay	D	38	1977-02-03	No	190 Lbs	5 ft11	No	No	1	Sans Restriction	Pro & Farm	2,500,000$						Lien
Mark Giordano	Tampa Bay	D	32	1983-10-03	No	198 Lbs	6 ft0	No	No	1	Sans Restriction	Pro & Farm	3,000,000$						Lien
Matt Moulson	Tampa Bay	LW/RW	31	1983-11-01	No	212 Lbs	6 ft1	Yes	No	5	Sans Restriction	Pro & Farm	2,074,000$	2,074,000$	2,074,000$	2,074,000$	2,074,000$		Lien
Milan Lucic	Tampa Bay	LW	27	1988-06-07	No	233 Lbs	6 ft3	Yes	No	5	Avec Restriction	Pro & Farm	6,000,000$	5,000,000$	5,000,000$	5,000,000$	4,500,000$		Lien
Nicklas Backstrom	Tampa Bay	C	27	1987-11-23	No	213 Lbs	6 ft1	Yes	No	6	Avec Restriction	Pro & Farm	8,000,000$	6,000,000$	6,000,000$	6,000,000$	5,800,000$	5,800,000$	Lien
Nikita Nikitin	Tampa Bay	D	29	1986-06-16	No	217 Lbs	6 ft4	No	No	0	Avec Restriction	Pro & Farm							Lien
Robin Lehner	Tampa Bay	G	24	1991-07-24	No	240 Lbs	6 ft5	No	No	3	Avec Restriction	Pro & Farm	1,600,000$	1,600,000$	1,600,000$				Lien
Ryan Pulock	Tampa Bay	D	20	1994-10-06	Yes	215 Lbs	6 ft2	No	No	4	Contrat d'Entrée	Pro & Farm	925,000$	925,000$	925,000$	925,000$			Lien
Scottie Upshall	Tampa Bay	LW/RW	31	1983-10-07	No	200 Lbs	6 ft0	Yes	No	5	Sans Restriction	Pro & Farm	2,000,000$	1,750,000$	1,650,000$	1,650,000$	1,300,000$		Lien
Sergey Kalinin	Tampa Bay	C/LW/RW	24	1991-03-17	No	200 Lbs	6 ft3	No	No	4	Avec Restriction	Pro & Farm	925,000$	925,000$	925,000$	925,000$			Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
24	26.88	209 Lbs	6 ft2	2.83	2,204,333$

Somme Salaire 1e Année	Somme Salaire 2e Année	Somme Salaire 3e Année	Somme Salaire 4e Année	Somme Salaire 5e Année
52,904,000$	34,879,000$	32,704,000$	28,249,000$	24,174,000$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Milan Lucic	Derek Stepan	Lee Stempniak	32	1	2	2
2	Josh Bailey	Nicklas Backstrom	Matt Moulson	30	1	2	2
3	Kevin Hayes	Brandon Sutter	Anders Lee	23	1	2	2
4	Sergey Kalinin	Brian Flynn	Scottie Upshall	15	1	3	1

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Brent Seabrook	Kevin Connauton	31	1	2	2
2	Marek Zidlicky	Mark Giordano	29	1	2	2
3	Anthony Bitetto	Brady Skjei	23	1	3	1
4	Brent Seabrook	Mark Giordano	17	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Milan Lucic	Nicklas Backstrom	Anders Lee	60	1	1	3
2	Kevin Hayes	Derek Stepan	Lee Stempniak	40	1	1	3

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Brent Seabrook	Mark Giordano	60	1	1	3
2	Brady Skjei	Anthony Bitetto	40	1	1	3

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Brandon Sutter	Lee Stempniak	57	1	3	1
2	Brian Flynn	Scottie Upshall	43	1	3	1

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Brent Seabrook	Kevin Connauton	58	1	3	1
2	Marek Zidlicky	Mark Giordano	42	1	3	1

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Brian Flynn	61	1	4	0	Brent Seabrook	Mark Giordano	60	1	4	0
2	Nicklas Backstrom	39	1	4	0	Marek Zidlicky	Brady Skjei	40	1	4	0

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Brandon Sutter	Josh Bailey	55	1	2	2
2	Nicklas Backstrom	Anders Lee	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Marek Zidlicky	Mark Giordano	56	1	2	2
2	Anthony Bitetto	Brady Skjei	44	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Milan Lucic	Nicklas Backstrom	Lee Stempniak	Brent Seabrook	Mark Giordano

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Brian Flynn	Nicklas Backstrom	Lee Stempniak	Brent Seabrook	Mark Giordano

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Nicklas Backstrom, Milan Lucic, Josh Bailey	Nicklas Backstrom, Derek Stepan	Brian Flynn

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Mark Giordano, Brent Seabrook, Marek Zidlicky	Mark Giordano	Brent Seabrook, Mark Giordano

Tirs de Pénalité
Derek Stepan, Josh Bailey, Brian Flynn, Brandon Sutter, Milan Lucic

Gardien
#1 : Frederik Andersen, #2 : Robin Lehner

Lignes d'Attaque Perso. en Prol.
Nicklas Backstrom, Lee Stempniak, Milan Lucic, Brian Flynn, Josh Bailey, , , Anders Lee, , Matt Moulson, Kevin Hayes

Lignes de Défense Perso. en Prol.
Brent Seabrook, Mark Giordano, , Marek Zidlicky, Kevin Connauton

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
Anaheim	2	2	0	0	0	0	0	0	12	5	7	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	9	3	6	4	1.000	12	21	33	0	0	5	2	5	0	57	18	18	21	0	57	15	26	45	11	2	18.18%	13	4	69.23%	1	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	50	34	43	14	26	13
Atlanta	3	1	2	0	0	0	0	0	6	11	-5	2	0	2	0	0	0	0	0	3	9	-6	1	1	0	0	0	0	0	0	3	2	1	2	0.333	6	12	18	0	0	3	1	2	0	78	28	18	32	0	81	16	55	55	19	4	21.05%	15	4	73.33%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	69	46	73	24	37	18
Boston	5	2	2	0	0	0	0	1	20	13	7	2	0	2	0	0	0	0	0	7	10	-3	3	2	0	0	0	0	0	1	13	3	10	5	0.500	20	34	54	0	2	4	6	10	0	155	45	53	53	9	127	37	57	80	39	10	25.64%	23	2	91.30%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	129	89	113	38	62	31
Buffalo	4	2	0	0	1	0	0	1	14	8	6	2	1	0	0	1	0	0	0	8	2	6	2	1	0	0	0	0	0	1	6	6	0	7	0.875	14	25	39	0	0	2	10	1	2	102	25	51	23	8	75	25	52	75	20	3	15.00%	25	3	88.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	99	68	99	30	49	24
Calgary	2	2	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	3	0	3	1	1	0	0	0	0	0	0	2	1	1	4	1.000	5	8	13	0	1	1	2	2	0	43	15	12	16	0	44	20	14	33	16	2	12.50%	7	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	47	31	46	16	26	13
Philadelphie	3	1	1	0	1	0	0	0	11	9	2	1	1	0	0	0	0	0	0	7	4	3	2	0	1	0	1	0	0	0	4	5	-1	4	0.667	11	21	32	0	0	6	2	2	1	90	33	28	27	2	74	25	50	69	21	4	19.05%	15	2	86.67%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	76	51	66	22	38	20
Chicago	2	1	1	0	0	0	0	0	5	7	-2	1	1	0	0	0	0	0	0	2	1	1	1	0	1	0	0	0	0	0	3	6	-3	2	0.500	5	9	14	0	0	1	1	3	0	47	17	11	19	0	46	11	23	27	14	2	14.29%	11	2	81.82%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	48	30	42	15	28	15
Colorado	2	0	1	0	0	0	0	1	3	5	-2	1	0	1	0	0	0	0	0	1	2	-1	1	0	0	0	0	0	0	1	2	3	-1	1	0.250	3	5	8	0	0	2	0	1	1	52	10	24	13	9	48	15	27	36	8	0	0.00%	11	1	90.91%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	47	33	48	16	28	14
Kansas City	2	1	1	0	0	0	0	0	11	9	2	1	1	0	0	0	0	0	0	6	2	4	1	0	1	0	0	0	0	0	5	7	-2	2	0.500	11	20	31	0	0	6	4	1	0	55	26	14	15	0	63	24	39	40	14	4	28.57%	12	4	66.67%	1	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	48	33	45	14	25	13
Detroit	5	1	3	0	0	0	0	1	13	17	-4	3	0	2	0	0	0	0	1	7	11	-4	2	1	1	0	0	0	0	0	6	6	0	3	0.300	13	19	32	0	0	2	3	8	0	147	48	52	47	3	155	52	67	97	24	5	20.83%	31	5	83.87%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	121	82	121	36	61	30
Edmonton	2	1	1	0	0	0	0	0	7	6	1	1	1	0	0	0	0	0	0	5	1	4	1	0	1	0	0	0	0	0	2	5	-3	2	0.500	7	12	19	0	0	2	2	3	0	57	10	26	21	0	46	16	20	29	17	3	17.65%	9	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	47	31	46	17	26	12
Islanders	3	3	0	0	0	0	0	0	9	5	4	1	1	0	0	0	0	0	0	3	2	1	2	2	0	0	0	0	0	0	6	3	3	6	1.000	9	14	23	0	0	2	4	3	0	80	31	28	21	0	74	11	35	61	17	1	5.88%	14	2	85.71%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	78	55	63	21	38	19
LA Kings	2	2	0	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	5	3	2	4	1.000	8	14	22	0	0	3	3	2	0	46	12	15	19	0	58	9	30	42	9	2	22.22%	9	2	77.78%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	46	30	46	15	26	12
Minnesota	2	0	2	0	0	0	0	0	3	6	-3	1	0	1	0	0	0	0	0	1	3	-2	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	3	4	7	1	0	1	2	0	0	53	13	19	21	0	42	13	16	41	8	1	12.50%	7	1	85.71%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	51	36	43	13	24	12
Hartford	3	1	0	0	0	1	0	1	7	6	1	2	0	0	0	0	1	0	1	3	5	-2	1	1	0	0	0	0	0	0	4	1	3	4	0.667	7	14	21	0	0	1	4	2	0	87	26	30	28	7	87	23	43	66	18	3	16.67%	14	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	73	50	76	21	37	18
Ottawa	4	2	1	0	0	0	0	1	13	12	1	2	1	0	0	0	0	0	1	6	6	0	2	1	1	0	0	0	0	0	7	6	1	5	0.625	13	25	38	0	0	2	5	6	0	103	35	35	32	5	112	23	51	84	17	5	29.41%	22	4	81.82%	1	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	98	68	93	28	52	26
Pittsburgh	3	2	1	0	0	0	0	0	10	7	3	1	1	0	0	0	0	0	0	4	1	3	2	1	1	0	0	0	0	0	6	6	0	4	0.667	10	15	25	0	0	4	2	4	0	77	26	24	27	0	86	23	28	49	15	5	33.33%	14	1	92.86%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	70	49	72	23	36	17
Rangers	3	1	0	0	0	0	1	1	10	7	3	1	0	0	0	0	0	1	0	3	2	1	2	1	0	0	0	0	0	1	7	5	2	5	0.833	10	15	25	0	0	4	3	2	1	77	27	29	17	9	73	30	28	53	19	4	21.05%	14	2	85.71%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	79	55	72	24	38	19
San Jose	2	1	1	0	0	0	0	0	7	8	-1	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	5	4	1	2	0.500	7	11	18	1	0	2	4	1	0	41	14	13	14	0	63	15	33	35	7	3	42.86%	9	2	77.78%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	48	33	47	14	24	12
St-Louis	2	0	2	0	0	0	0	0	4	8	-4	1	0	1	0	0	0	0	0	3	6	-3	1	0	1	0	0	0	0	0	1	2	-1	0	0.000	4	7	11	0	0	1	2	1	0	58	14	21	23	0	54	15	12	44	7	1	14.29%	6	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	47	31	46	15	26	12
Toronto	4	3	1	0	0	0	0	0	13	6	7	2	2	0	0	0	0	0	0	8	3	5	2	1	1	0	0	0	0	0	5	3	2	6	0.750	13	21	34	0	1	5	5	3	0	105	37	38	30	0	92	34	46	72	21	3	14.29%	19	2	89.47%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	105	71	85	29	49	25
Vancouver	2	2	0	0	0	0	0	0	7	5	2	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	3	2	1	4	1.000	7	13	20	0	0	2	2	3	0	50	19	17	14	0	46	21	23	40	10	2	20.00%	4	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	46	31	48	16	25	12
Quebec	4	2	0	0	0	0	2	0	13	9	4	2	1	0	0	0	0	1	0	6	4	2	2	1	0	0	0	0	1	0	7	5	2	8	1.000	13	20	33	0	0	3	5	3	4	121	40	40	38	10	104	31	47	77	33	4	12.12%	20	2	90.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	103	70	94	32	51	26
Winnipeg	2	1	0	0	0	0	1	0	6	3	3	1	1	0	0	0	0	0	0	2	0	2	1	0	0	0	0	0	1	0	4	3	1	4	1.000	6	9	15	0	1	2	2	1	1	61	16	20	25	4	59	12	42	36	9	1	11.11%	11	3	72.73%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	53	35	42	14	28	15
Nashville	2	2	0	0	0	0	0	0	6	3	3	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	1	2	4	1.000	6	9	15	0	0	2	2	2	0	49	15	17	17	0	53	14	24	40	9	2	22.22%	12	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	45	29	49	15	25	12
Montreal	4	3	1	0	0	0	0	0	14	11	3	2	2	0	0	0	0	0	0	7	4	3	2	1	1	0	0	0	0	0	7	7	0	6	0.750	14	24	38	0	0	4	8	2	0	106	26	46	34	0	102	45	54	74	21	5	23.81%	22	2	90.91%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	89	58	100	32	49	24
Washington	3	3	0	0	0	0	0	0	15	7	8	2	2	0	0	0	0	0	0	10	5	5	1	1	0	0	0	0	0	0	5	2	3	6	1.000	15	24	39	0	0	5	8	2	0	88	17	39	32	0	82	14	47	49	21	4	19.05%	21	2	90.48%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	78	54	63	22	38	19
Caroline	3	2	0	0	0	0	0	1	9	6	3	2	2	0	0	0	0	0	0	7	3	4	1	0	0	0	0	0	0	1	2	3	-1	5	0.833	9	17	26	0	1	2	4	3	1	79	24	21	32	5	78	20	38	49	17	3	17.65%	14	2	85.71%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	78	54	66	22	39	20
Las Vegas	2	0	1	0	0	0	1	0	9	9	0	1	0	0	0	0	0	1	0	6	5	1	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	9	16	25	0	0	4	1	3	3	53	20	12	21	5	58	15	21	31	13	1	7.69%	8	0	100.00%	0	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	56	38	41	14	27	15
Vs Division	30	15	8	0	1	0	2	4	100	76	24	15	7	4	0	1	0	1	2	49	40	9	15	8	4	0	0	0	1	2	51	36	15	40	0.667	100	168	268	0	3	22	42	33	6	839	256	315	257	35	767	247	374	559	175	35	20.00%	162	20	87.65%	1	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	747	509	708	227	377	190
Vs Conference	54	29	12	0	2	1	3	7	177	134	43	27	14	6	0	1	1	2	3	89	71	18	27	15	6	0	1	0	1	4	88	63	25	76	0.704	177	300	477	0	4	49	70	53	9	1495	468	532	473	58	1402	409	698	1010	322	63	19.57%	283	35	87.63%	1	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	1352	926	1264	410	680	344
Since Last GM Reset	82	44	22	0	2	1	5	8	270	213	57	41	23	10	0	1	1	3	3	133	103	30	41	21	12	0	1	0	2	5	137	110	27	111	0.677	270	458	728	2	6	83	99	81	14	2217	687	771	732	76	2139	624	1048	1529	474	89	18.78%	412	54	86.89%	3	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	2038	1388	1903	625	1051	535
Total	82	44	22	0	2	1	5	8	270	213	57	41	23	10	0	1	1	3	3	133	103	30	41	21	12	0	1	0	2	5	137	110	27	111	0.677	270	458	728	2	6	83	99	81	14	2217	687	771	732	76	2139	624	1048	1529	474	89	18.78%	412	54	86.89%	3	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	2038	1388	1903	625	1051	535

			Total Pour les Joueurs
Matchs Joués	Points	Séquence	Buts	Passes	Points	Tirs Pour	Tirs Contre	Tirs Bloqués	Minutes de Pénalité	Mises en Échec	Buts en Filet Désert	Blanchissage
82	111	OTW1	270	458	728	2217	2139	624	1048	1529	2	6

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	44	22	2	1	5	8	270	213

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	23	10	1	1	3	3	133	103

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	21	12	1	0	2	5	137	110

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
6	2	1	0	0	1

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
474	89	18.78%	412	54	86.89%	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
687	771	732	76	83	99	81	14

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
1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%

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
2038	1388	1903	625	1051	535

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

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	Lien
2 - 2016-10-13	5	Detroit	5	Tampa Bay	3	L			Sommaire du Match
4 - 2016-10-15	20	Hartford	2	Tampa Bay	1	L	X		Sommaire du Match
7 - 2016-10-18	43	Quebec	3	Tampa Bay	4	W			Sommaire du Match
9 - 2016-10-20	55	Colorado	2	Tampa Bay	1	L			Sommaire du Match
11 - 2016-10-22	75	Tampa Bay	3	Ottawa	5	L			Sommaire du Match
14 - 2016-10-25	87	Tampa Bay	3	Toronto	0	W			Sommaire du Match
16 - 2016-10-27	102	Tampa Bay	4	Montreal	5	L			Sommaire du Match
18 - 2016-10-29	114	Tampa Bay	4	Hartford	1	W			Sommaire du Match
19 - 2016-10-30	127	Tampa Bay	2	Rangers	3	L	X	X	Sommaire du Match
21 - 2016-11-01	139	Tampa Bay	3	Islanders	2	W			Sommaire du Match
23 - 2016-11-03	154	Boston	5	Tampa Bay	4	L			Sommaire du Match
25 - 2016-11-05	166	Hartford	3	Tampa Bay	2	L	X	X	Sommaire du Match
27 - 2016-11-07	182	Tampa Bay	4	Quebec	3	W	X	X	Sommaire du Match
30 - 2016-11-10	198	Islanders	2	Tampa Bay	3	W			Sommaire du Match
32 - 2016-11-12	217	San Jose	4	Tampa Bay	2	L			Sommaire du Match
34 - 2016-11-14	229	Tampa Bay	3	Islanders	1	W			Sommaire du Match
35 - 2016-11-15	230	Tampa Bay	6	Detroit	2	W			Sommaire du Match
37 - 2016-11-17	253	Tampa Bay	2	Buffalo	1	W			Sommaire du Match
39 - 2016-11-19	265	Tampa Bay	3	Philadelphie	2	W	X		Sommaire du Match
41 - 2016-11-21	279	Tampa Bay	3	Las Vegas	4	L			Sommaire du Match
43 - 2016-11-23	294	Philadelphie	4	Tampa Bay	7	W			Sommaire du Match
45 - 2016-11-25	305	Atlanta	5	Tampa Bay	1	L			Sommaire du Match
47 - 2016-11-27	325	Tampa Bay	2	Boston	3	L	X	X	Sommaire du Match
49 - 2016-11-29	332	Tampa Bay	3	Atlanta	2	W			Sommaire du Match
51 - 2016-12-01	351	Tampa Bay	1	St-Louis	2	L			Sommaire du Match
53 - 2016-12-03	359	Washington	3	Tampa Bay	4	W			Sommaire du Match
54 - 2016-12-04	371	Tampa Bay	2	Caroline	3	L	X	X	Sommaire du Match
58 - 2016-12-08	399	Vancouver	3	Tampa Bay	4	W			Sommaire du Match
60 - 2016-12-10	411	Pittsburgh	1	Tampa Bay	4	W			Sommaire du Match
64 - 2016-12-14	443	Tampa Bay	2	Calgary	1	W			Sommaire du Match
66 - 2016-12-16	458	Tampa Bay	3	Vancouver	2	W			Sommaire du Match
67 - 2016-12-17	469	Tampa Bay	2	Edmonton	5	L			Sommaire du Match
70 - 2016-12-20	486	Detroit	3	Tampa Bay	2	L			Sommaire du Match
72 - 2016-12-22	500	St-Louis	6	Tampa Bay	3	L			Sommaire du Match
73 - 2016-12-23	512	Tampa Bay	5	Washington	2	W			Sommaire du Match
78 - 2016-12-28	529	Montreal	1	Tampa Bay	3	W			Sommaire du Match
79 - 2016-12-29	535	Toronto	2	Tampa Bay	3	W			Sommaire du Match
81 - 2016-12-31	555	Caroline	0	Tampa Bay	3	W			Sommaire du Match
84 - 2017-01-03	569	Winnipeg	0	Tampa Bay	2	W			Sommaire du Match
86 - 2017-01-05	580	Las Vegas	5	Tampa Bay	6	W	X	X	Sommaire du Match
88 - 2017-01-07	601	Tampa Bay	1	Philadelphie	3	L			Sommaire du Match
89 - 2017-01-08	603	Tampa Bay	4	Pittsburgh	3	W			Sommaire du Match
93 - 2017-01-12	626	Buffalo	1	Tampa Bay	6	W			Sommaire du Match
94 - 2017-01-13	635	Atlanta	4	Tampa Bay	2	L			Sommaire du Match
97 - 2017-01-16	659	Tampa Bay	5	LA Kings	3	W			Sommaire du Match
98 - 2017-01-17	671	Tampa Bay	9	Anaheim	3	W			Sommaire du Match
100 - 2017-01-19	684	Tampa Bay	5	San Jose	4	W			Sommaire du Match
102 - 2017-01-21	697	Tampa Bay	3	Nashville	1	W			Sommaire du Match
105 - 2017-01-24	722	Tampa Bay	3	Chicago	6	L			Sommaire du Match
107 - 2017-01-26	731	Tampa Bay	3	Quebec	2	W			Sommaire du Match
112 - 2017-01-31	752	Boston	5	Tampa Bay	3	L			Sommaire du Match
114 - 2017-02-02	760	Ottawa	2	Tampa Bay	3	W			Sommaire du Match
116 - 2017-02-04	776	Anaheim	2	Tampa Bay	3	W			Sommaire du Match
119 - 2017-02-07	797	LA Kings	1	Tampa Bay	3	W			Sommaire du Match
122 - 2017-02-10	813	Tampa Bay	2	Minnesota	3	L			Sommaire du Match
123 - 2017-02-11	825	Tampa Bay	4	Winnipeg	3	W	X	X	Sommaire du Match
130 - 2017-02-18	864	Tampa Bay	5	Kansas City	7	L			Sommaire du Match
131 - 2017-02-19	873	Tampa Bay	2	Colorado	3	L	X	X	Sommaire du Match
133 - 2017-02-21	887	Edmonton	1	Tampa Bay	5	W			Sommaire du Match
135 - 2017-02-23	896	Calgary	0	Tampa Bay	3	W			Sommaire du Match
139 - 2017-02-27	920	Ottawa	4	Tampa Bay	3	L	X	X	Sommaire du Match
141 - 2017-03-01	934	Caroline	3	Tampa Bay	4	W			Sommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2017-03-03	948	Tampa Bay	2	Pittsburgh	3	L			Sommaire du Match
144 - 2017-03-04	953	Tampa Bay	4	Buffalo	5	L	X	X	Sommaire du Match
146 - 2017-03-06	971	Rangers	2	Tampa Bay	3	W	X	X	Sommaire du Match
149 - 2017-03-09	993	Minnesota	3	Tampa Bay	1	L			Sommaire du Match
151 - 2017-03-11	1005	Quebec	1	Tampa Bay	2	W	X	X	Sommaire du Match
153 - 2017-03-13	1017	Tampa Bay	5	Rangers	2	W			Sommaire du Match
154 - 2017-03-14	1024	Tampa Bay	4	Ottawa	1	W			Sommaire du Match
156 - 2017-03-16	1045	Toronto	1	Tampa Bay	5	W			Sommaire du Match
158 - 2017-03-18	1057	Washington	2	Tampa Bay	6	W			Sommaire du Match
161 - 2017-03-21	1080	Nashville	2	Tampa Bay	3	W			Sommaire du Match
163 - 2017-03-23	1089	Tampa Bay	2	Boston	0	W			Sommaire du Match
164 - 2017-03-24	1102	Tampa Bay	0	Detroit	4	L			Sommaire du Match
167 - 2017-03-27	1126	Chicago	1	Tampa Bay	2	W			Sommaire du Match
170 - 2017-03-30	1141	Detroit	3	Tampa Bay	2	L	X	X	Sommaire du Match
172 - 2017-04-01	1160	Montreal	3	Tampa Bay	4	W			Sommaire du Match
173 - 2017-04-02	1168	Kansas City	2	Tampa Bay	6	W			Sommaire du Match
175 - 2017-04-04	1187	Tampa Bay	9	Boston	0	W			Sommaire du Match
177 - 2017-04-06	1201	Tampa Bay	2	Toronto	3	L			Sommaire du Match
178 - 2017-04-07	1206	Tampa Bay	3	Montreal	2	W			Sommaire du Match
180 - 2017-04-09	1230	Buffalo	1	Tampa Bay	2	W	X		Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2	Niveau 3	Niveau 4	Luxe
Capacité de l'Aréna	6000	5000	2500	4000	1000
Prix des Billets	100	60	35	20	205
Assistance	245259	204037	101949	163960	41000
Attendance PCT	99.70%	99.53%	99.46%	99.98%	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
0	18444 - 99.70%	1,273,855$	52,228,035$	18500	100

Dépenses
Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs	Valeur du Cap Salarial Spécial
52,904,000$	49,390,666$	0$	0$

Dépenses Annuelles à Ce Jour	Cap Salarial Par Jour	Cap salarial à ce jour	Taxe de Luxe Totale
52,232,920$	292,287$	51,730,189$	0$

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	295,050$	0$

Total de l'Équipe Éstimé
Dépenses de la Saison Éstimées	Cap Salarial de la Saison Éstimé	Compte Bancaire Actuel	Compte Bancaire Projeté
0$	54,242,570$	160,828,453$	160,828,453$

Charte de Profondeur

Ailier Gauche

Centre

Ailier Droit

Milan Lucic	AGE:27	PO:0	OV:70
Josh Bailey	AGE:26	PO:0	OV:65
Anders Lee	AGE:25	PO:0	OV:64
Matt Moulson	AGE:31	PO:0	OV:64
Scottie Upshall	AGE:31	PO:0	OV:63
Kevin Hayes	AGE:23	PO:0	OV:63
Brian Flynn	AGE:27	PO:0	OV:60
Sergey Kalinin	AGE:24	PO:0	OV:56
Ronalds Kenins	AGE:24	PO:0	OV:55
Nicklas Jensen	AGE:22	PO:0	OV:52
Tommy Sestito	AGE:28	PO:0	OV:52
Derek Grant	AGE:25	PO:0	OV:50
Carter Ashton	AGE:24	PO:0	OV:48
Kasperi Kapanen	AGE:19	PO:0	OV:48
Petr Straka	AGE:23	PO:0	OV:46
*Samuel Henley	AGE:22	PO:0	OV:46
John Persson	AGE:23	PO:0	OV:42
Luca Caputi	AGE:27	PO:0	OV:40
Nicolas Deschamps	AGE:25	PO:0	OV:37

Nicklas Backstrom	AGE:27	PO:0	OV:74
Derek Stepan	AGE:25	PO:0	OV:71
Brandon Sutter	AGE:26	PO:0	OV:63
Kevin Hayes	AGE:23	PO:0	OV:63
Brian Flynn	AGE:27	PO:0	OV:60
Jacob Josefson	AGE:24	PO:0	OV:59
Sergey Kalinin	AGE:24	PO:0	OV:56
Derek Grant	AGE:25	PO:0	OV:50
*Brody Sutter	AGE:24	PO:0	OV:49
*Samuel Henley	AGE:22	PO:0	OV:46
*Ryan Martindale	AGE:23	PO:0	OV:44
*Adam Gilmour	AGE:21	PO:0	OV:42
*Thomas Di Pauli	AGE:21	PO:0	OV:41
Jamie Arniel	AGE:25	PO:0	OV:36

Lee Stempniak	AGE:32	PO:0	OV:68
Josh Bailey	AGE:26	PO:0	OV:65
Anders Lee	AGE:25	PO:0	OV:64
Matt Moulson	AGE:31	PO:0	OV:64
Scottie Upshall	AGE:31	PO:0	OV:63
Kevin Hayes	AGE:23	PO:0	OV:63
Brian Flynn	AGE:27	PO:0	OV:60
Sergey Kalinin	AGE:24	PO:0	OV:56
Brett Ritchie	AGE:22	PO:0	OV:54
Nicklas Jensen	AGE:22	PO:0	OV:52
*Brody Sutter	AGE:24	PO:0	OV:49
Kasperi Kapanen	AGE:19	PO:0	OV:48
Petr Straka	AGE:23	PO:0	OV:46
*Adam Gilmour	AGE:21	PO:0	OV:42
Spencer Machacek	AGE:26	PO:0	OV:40
Jamie Arniel	AGE:25	PO:0	OV:36

Défense #1

Défense #2

Gardien

Brent Seabrook	AGE:30	PO:0	OV:75
Mark Giordano	AGE:32	PO:0	OV:73
Kevan Miller	AGE:27	PO:0	OV:65
Marek Zidlicky	AGE:38	PO:0	OV:63
Kevin Connauton	AGE:25	PO:0	OV:62
Nikita Nikitin	AGE:29	PO:0	OV:57
Anthony Bitetto	AGE:25	PO:0	OV:55
*Ryan Pulock	AGE:20	PO:0	OV:55
Brady Skjei	AGE:21	PO:0	OV:54

Rinat Valiev	AGE:20	PO:0	OV:52
*Brett Lernout	AGE:20	PO:0	OV:50
*Thomas Vannelli	AGE:20	PO:0	OV:45
*James Melindy	AGE:21	PO:0	OV:44
*Simon Bertilsson	AGE:24	PO:0	OV:44
*Calle Andersson	AGE:21	PO:0	OV:42
*Michael Brodzinski	AGE:20	PO:0	OV:40
*Adam Polasek	AGE:24	PO:0	OV:39
*Mikael Wikstrand	AGE:21	PO:0	OV:39

Frederik Andersen	AGE:26	PO:0	OV:79
Robin Lehner	AGE:24	PO:0	OV:66
Calvin Pickard	AGE:23	PO:0	OV:65
Linus Ullmark	AGE:22	PO:0	OV:53
*Jake Paterson	AGE:21	PO:0	OV:44
*Zachary Nagelvoort	AGE:21	PO:0	OV:42
*Janne Juvonen	AGE:21	PO:0	OV:40
Timo Pielmeier	AGE:26	PO:0	OV:39

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
Brendan Guhle	Tampa Bay	2015	57
Jacob Larsson	Tampa Bay	2015	34
Mitchell Marner	Tampa Bay	2015	4
Samuel Blais	Tampa Bay	2015	154
Tage Thompson	Tampa Bay	2016	24

Choix au Repêchage

Année	R1	R2	R3	R4	R5	R6	R7
2017	Tam	Tam	Tam	Tam	KC Tam Edm	KC	KC Tam
2018	Tam	Tam	Tam	Tam	Tam	Tam	Que Tam
2019	Tam	Tam	Tam	Tam	Tam	Tam	Tam
2020	Tam	Tam	Tam	Tam	Tam	Tam	Tam
2021	Tam	Tam	Tam	Tam	Tam	Tam	Tam

[2016-09-16 21:15:38] - Kevan Miller was added to Tampa Bay.
[2016-09-16 21:15:39] - TRADE : From Tampa Bay to Caroline : Tomas Plekanec (71).
[2016-09-16 21:15:39] - TRADE : From Caroline to Tampa Bay : Kevan Miller (65), Y:2017-RND:5-Edm.
[2017-01-17 20:00:21] - New Record for Team's Most Goals (9) in 1 Game for Tampa Bay!
[2017-01-17 20:00:21] - New Record for Team's Most Assists (16) in 1 Game for Tampa Bay!
[2017-01-17 20:00:21] - New Record for Team's Most Points (25) in 1 Game for Tampa Bay!

Nikita Nikitin est suspendu indéfiniement.

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
2016	82	44	22	0	2	1	5	8	270	213	57	41	23	10	0	1	1	3	3	133	103	30	41	21	12	0	1	0	2	5	137	110	27	88	270	458	728	2	6	83	99	81	14	2217	687	771	732	76	2139	624	1048	1529	474	89	18.78%	412	54	86.89%	3	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	2038	1388	1903	625	1051	535
Total Saison Régulière	82	44	22	0	2	1	5	8	270	213	57	41	23	10	0	1	1	3	3	133	103	30	41	21	12	0	1	0	2	5	137	110	27	88	270	458	728	2	6	83	99	81	14	2217	687	771	732	76	2139	624	1048	1529	474	89	18.78%	412	54	86.89%	3	1432	2811	50.94%	1322	2670	49.51%	681	1311	51.95%	2038	1388	1903	625	1051	535