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	Max Pacioretty		X			100.00	65	35	85	80	74	79	95	78	46	76	79	68	63	57	50	0	80	72	0
2	Ryan Johansen	X				100.00	65	43	79	79	76	77	95	77	85	82	71	65	57	49	41	0	77	71	0
3	Taylor Hall		X			100.00	66	43	79	80	69	78	92	77	37	80	74	66	50	51	42	0	80	71	0
4	Frans Nielsen	X	X			100.00	51	43	90	77	61	76	95	73	83	74	71	73	62	60	52	0	81	70	0
5	Scott Hartnell		X			100.00	70	50	65	77	74	74	93	71	35	70	72	58	53	80	70	0	80	69	0
6	Reilly Smith		X	X		100.00	56	43	86	77	58	73	95	68	35	65	71	78	50	46	42	0	81	67	0
7	Boone Jenner	X	X			100.00	75	50	75	77	75	70	84	65	82	57	73	63	50	46	37	0	80	65	0
8	Riley Nash	X		X		100.00	59	35	88	71	69	67	78	59	83	56	62	73	61	45	40	0	74	63	0
9	Victor Rask	X				100.00	54	35	87	76	69	66	70	63	84	61	65	65	50	35	32	0	81	62	0
10	Alexander Burmistrov	X	X	X		100.00	61	35	85	72	56	58	56	52	73	52	52	70	48	45	36	0	80	58	0
11	Kyle Clifford		X	X		100.00	66	66	72	68	72	56	58	47	53	45	48	62	48	56	51	0	84	56	0
12	Roman Josi				X	100.00	61	56	83	82	66	87	95	69	35	76	62	85	53	50	42	0	76	72	0
13	Victor Hedman				X	100.00	71	43	81	72	77	83	91	66	35	73	59	81	56	60	51	0	80	71	0
14	Adam Larsson				X	100.00	79	50	75	64	68	78	93	49	35	52	45	88	48	49	41	0	82	67	0
15	Jan Hejda				X	100.00	76	50	81	61	79	69	87	45	35	46	43	79	47	59	49	0	59	65	0
16	Barret Jackman				X	100.00	73	66	74	66	66	67	92	45	35	46	43	72	48	72	62	0	76	64	0
17	Slava Voynov				X	100.00	60	43	82	61	62	73	46	53	35	61	45	74	47	52	49	0	69	61	0
Rayé
1	Cam Atkinson			X		91.74	52	35	87	83	53	75	94	68	35	62	73	70	57	47	41	0	71	66	0
2	Dalton Prout				X	100.00	77	65	62	63	78	68	71	43	50	43	43	70	48	45	41	0	33	60	0

	MOYENNE D'ÉQUIPE					99.53	65	47	80	73	69	72	83	61	52	62	61	72	52	53	46	0	75	66

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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	Roberto Luongo	100.00	87	95	96	83	88	79	81	89	76	84	85	79	69	0	70	82	0
2	Eddie Lack	100.00	61	45	92	69	61	60	63	61	57	85	77	43	37	0	80	61	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	74	70	94	76	75	70	72	75	67	85	81	61	53	0	75	72

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
John Tortorella	71	66	70	75	99	99	80	USA	58	3	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	Frans Nielsen	Calgary	C/LW	82	37	54	91	12	29	5	36	204	263	0	14.07%	11	1656	20.20	8	25	33	70	301	0	1	1	7	255	5	3	48.69%	2025	0	1	1	1.10	4	6	0	0	1	8	2	5
2	Max Pacioretty	Calgary	LW	82	29	48	77	6	46	0	114	105	285	0	10.18%	7	1577	19.24	13	19	32	81	349	0	0	0	2	123	5	1	35.93%	359	0	0	1	0.98	3	8	0	0	0	7	2	2
3	Roman Josi	Calgary	D	74	14	57	71	12	63	25	44	120	139	0	10.07%	110	1773	23.96	10	26	36	96	327	0	0	0	2	365	1	1	0.00%	0	0	0	0	0.80	0	0	1	1	3	6	1	4
4	Reilly Smith	Calgary	LW/RW	82	34	35	69	-3	30	0	46	120	241	0	14.11%	15	1416	17.27	16	12	28	82	345	0	0	0	0	0	4	2	27.93%	111	0	0	0	0.97	0	2	0	0	0	0	5	2
5	Taylor Hall	Calgary	LW	81	25	44	69	-2	76	0	163	80	247	0	10.12%	5	1384	17.09	8	15	23	65	280	0	0	0	0	3	4	3	35.87%	92	0	0	0	1.00	2	2	0	0	0	2	6	3
6	Victor Hedman	Calgary	D	82	9	52	61	15	48	0	90	88	104	0	8.65%	104	1912	23.32	4	17	21	60	294	1	2	3	1	324	1	1	0.00%	0	0	0	0	0.64	0	0	0	0	0	3	4	3
7	Cam Atkinson	Calgary	RW	82	26	34	60	7	16	0	38	75	198	0	13.13%	6	1380	16.83	8	11	19	61	297	0	1	1	0	5	2	1	23.91%	92	0	0	0	0.87	2	2	0	0	0	4	3	4
8	Ryan Johansen	Calgary	C	81	17	43	60	-2	61	5	149	140	191	0	8.90%	7	1563	19.30	5	22	27	49	340	1	0	1	4	119	4	0	52.88%	1825	0	0	0	0.77	0	3	0	0	0	3	4	1
9	Scott Hartnell	Calgary	LW	82	24	22	46	8	94	0	152	68	182	0	13.19%	5	1346	16.42	8	12	20	55	345	1	0	1	3	210	4	2	38.10%	63	0	0	0	0.68	1	3	0	0	0	3	3	4
10	Riley Nash	Calgary	C/RW	78	12	18	30	8	10	0	22	79	102	0	11.76%	13	1022	13.11	0	0	0	0	0	0	3	3	4	318	2	1	52.59%	483	0	0	0	0.59	0	1	0	0	0	2	4	2
11	Boone Jenner	Calgary	C/LW	82	9	21	30	9	95	5	121	93	120	0	7.50%	13	1166	14.22	1	1	2	4	40	2	0	2	6	303	1	1	51.98%	910	0	0	0	0.51	2	5	0	0	0	0	3	1
12	Adam Larsson	Calgary	D	82	6	22	28	1	185	35	197	71	65	0	9.23%	97	1813	22.11	3	6	9	42	289	0	0	0	0	315	1	0	0.00%	0	0	0	0	0.31	0	0	5	1	1	2	0	0
13	Victor Rask	Calgary	C	82	11	13	24	12	0	0	13	78	96	0	11.46%	1	771	9.40	0	0	0	0	0	0	0	0	7	140	0	1	50.68%	734	0	0	0	0.62	0	0	0	0	0	0	2	2
14	Alexander Burmistrov	Calgary	C/LW/RW	82	7	8	15	10	12	0	26	34	54	0	12.96%	4	619	7.56	0	1	1	0	15	0	1	1	3	79	3	0	46.07%	178	0	0	0	0.48	0	0	0	0	0	0	0	2
15	Jan Hejda	Calgary	D	78	1	14	15	2	119	15	135	39	29	0	3.45%	93	1275	16.36	0	0	0	2	24	0	0	0	0	222	0	0	0.00%	0	0	0	0	0.24	0	0	1	1	1	0	0	0
16	Barret Jackman	Calgary	D	80	5	8	13	12	154	50	168	43	40	0	12.50%	73	1502	18.78	2	0	2	12	121	0	0	0	1	271	1	0	0.00%	0	0	0	0	0.17	0	0	3	3	4	1	2	0
17	Slava Voynov	Calgary	D	62	5	7	12	10	12	0	34	34	23	0	21.74%	42	862	13.91	2	0	2	4	32	0	0	0	0	22	1	1	0.00%	0	0	0	0	0.28	0	0	0	0	0	0	1	0
18	Dalton Prout	Calgary	D	47	2	5	7	4	156	30	110	15	12	0	16.67%	21	686	14.60	0	0	0	0	7	0	0	0	0	86	1	0	0.00%	0	0	0	0	0.20	0	0	2	0	4	0	1	1
19	Kyle Clifford	Calgary	LW/RW	82	2	5	7	13	70	30	68	24	22	0	9.09%	2	519	6.34	0	0	0	1	10	0	0	0	0	6	0	1	43.48%	23	0	0	0	0.27	0	0	3	0	3	0	0	0
Stats d'équipe Total ou en Moyenne			1483	275	510	785	134	1276	200	1726	1510	2413	0	11.40%	629	24248	16.35	88	167	255	684	3422	5	8	13	40	3174	40	19	49.04%	6895	0	1	2	0.65	14	32	15	6	17	41	43	36

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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	Roberto Luongo	Calgary	74	43	25	3	0.890	2.78	4247	0	4	197	1791	0	3	1	0.720	25	71	11	6	5	2
2	Eddie Lack	Calgary	14	5	4	2	0.854	4.24	707	0	0	50	342	0	0	1	0.500	10	11	60	0	0	0
Stats d'équipe Total ou en Moyenne		88	48	29	5	0.884	2.99	4954	0	4	247	2133	0	3	2	0.657	35	82	71	6	5	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	Link
Adam Larsson	Calgary	D	22	1992-11-12	No	205 Lbs	6 ft3	No	No	6	Avec Restriction	Pro & Farm	2,500,000$	2,750,000$	3,000,000$	3,000,000$	3,250,000$	3,500,000$	Lien
Alexander Burmistrov	Calgary	C/LW/RW	23	1991-10-21	No	180 Lbs	6 ft1	No	No	4	Avec Restriction	Pro & Farm	1,000,000$	1,000,000$	1,000,000$	1,000,000$			Lien
Barret Jackman	Calgary	D	34	1981-03-05	No	203 Lbs	6 ft0	No	No	1	Sans Restriction	Pro & Farm	1,100,000$						Lien
Boone Jenner	Calgary	C/LW	22	1993-06-15	No	215 Lbs	6 ft2	No	No	2	Avec Restriction	Pro & Farm	767,000$	767,000$					Lien
Cam Atkinson	Calgary	RW	26	1989-06-05	No	180 Lbs	5 ft8	No	No	6	Avec Restriction	Pro & Farm	3,300,000$	3,300,000$	3,300,000$	3,300,000$	3,300,000$	3,300,000$	Lien
Dalton Prout	Calgary	D	25	1990-03-13	No	230 Lbs	6 ft3	No	No	3	Avec Restriction	Pro & Farm	1,100,000$	1,100,000$	1,100,000$				Lien
Eddie Lack	Calgary	G	27	1988-01-05	No	187 Lbs	6 ft4	No	No	2	Avec Restriction	Pro & Farm	850,000$	850,000$					Lien
Frans Nielsen	Calgary	C/LW	31	1984-04-24	No	188 Lbs	6 ft1	No	No	4	Sans Restriction	Pro & Farm	3,500,000$	3,500,000$	3,500,000$	3,500,000$			Lien
Jan Hejda (Contrat à 1 Volet)	Calgary	D	37	1978-06-18	No	230 Lbs	6 ft4	No	No	3	Sans Restriction	Pro & Farm	1,800,000$	501,000$	500,000$				Lien
Kyle Clifford	Calgary	LW/RW	24	1991-01-13	No	206 Lbs	6 ft2	No	No	4	Avec Restriction	Pro & Farm	850,000$	850,000$	850,000$	850,000$			Lien
Max Pacioretty	Calgary	LW	26	1988-11-20	No	213 Lbs	6 ft2	Yes	No	1	Avec Restriction	Pro & Farm	3,700,000$						Lien
Reilly Smith	Calgary	LW/RW	24	1991-04-01	No	185 Lbs	6 ft0	No	No	4	Avec Restriction	Pro & Farm	2,500,000$	2,500,000$	2,500,000$	2,500,000$			Lien
Riley Nash	Calgary	C/RW	26	1989-05-09	No	200 Lbs	6 ft1	No	No	4	Avec Restriction	Pro & Farm	1,300,000$	1,300,000$	1,300,000$	1,300,000$			Lien
Roberto Luongo	Calgary	G	36	1979-04-04	No	217 Lbs	6 ft3	No	No	2	Sans Restriction	Pro & Farm	8,000,000$	8,000,000$					Lien
Roman Josi	Calgary	D	25	1990-06-01	No	201 Lbs	6 ft1	No	No	3	Avec Restriction	Pro & Farm	4,000,000$	4,000,000$	4,000,000$				Lien
Ryan Johansen	Calgary	C	23	1992-07-31	No	218 Lbs	6 ft3	No	No	5	Avec Restriction	Pro & Farm	4,300,000$	4,300,000$	4,300,000$	4,300,000$	4,300,000$		Lien
Scott Hartnell	Calgary	LW	33	1982-04-18	No	214 Lbs	6 ft2	No	No	1	Sans Restriction	Pro & Farm	3,500,000$						Lien
Slava Voynov	Calgary	D	25	1990-01-15	No	194 Lbs	6 ft0	Yes	No	4	Avec Restriction	Pro & Farm	849,000$	849,000$	849,000$	849,000$			Lien
Taylor Hall	Calgary	LW	23	1991-11-14	No	201 Lbs	6 ft1	No	No	4	Avec Restriction	Pro & Farm	4,800,000$	5,000,000$	5,200,000$	5,600,000$			Lien
Victor Hedman	Calgary	D	24	1990-12-18	No	223 Lbs	6 ft6	No	No	2	Avec Restriction	Pro & Farm	3,200,000$	3,200,000$					Lien
Victor Rask	Calgary	C	22	1993-03-01	No	200 Lbs	6 ft2	No	No	3	Avec Restriction	Pro & Farm	650,000$	650,000$	650,000$				Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
21	26.57	204 Lbs	6 ft2	3.24	2,550,762$

Somme Salaire 1e Année	Somme Salaire 2e Année	Somme Salaire 3e Année	Somme Salaire 4e Année	Somme Salaire 5e Année
53,566,000$	44,417,000$	32,049,000$	26,199,000$	10,850,000$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Max Pacioretty	Frans Nielsen		35	0	2	3
2	Taylor Hall	Ryan Johansen	Reilly Smith	35	0	2	3
3	Scott Hartnell	Boone Jenner	Riley Nash	23	1	2	2
4	Alexander Burmistrov	Victor Rask	Kyle Clifford	7	2	3	0

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Barret Jackman	Victor Hedman	35	1	2	2
2	Adam Larsson	Roman Josi	35	1	2	2
3	Jan Hejda	Slava Voynov	20	2	3	0
4	Victor Hedman	Roman Josi	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Max Pacioretty	Ryan Johansen	Reilly Smith	60	0	0	5
2	Taylor Hall	Frans Nielsen		40	0	1	4

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Scott Hartnell	Roman Josi	60	0	1	4
2	Victor Hedman	Adam Larsson	40	0	1	4

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Boone Jenner	Riley Nash	60	1	2	2
2	Frans Nielsen	Scott Hartnell	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Victor Hedman	Barret Jackman	60	1	4	0
2	Roman Josi	Adam Larsson	40	1	4	0

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Riley Nash	60	1	2	2	Victor Hedman	Roman Josi	60	1	2	2
2	Boone Jenner	40	1	2	2	Victor Hedman	Adam Larsson	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Ryan Johansen	Boone Jenner	60	1	2	2
2	Frans Nielsen	Scott Hartnell	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Roman Josi	Barret Jackman	60	1	2	2
2	Victor Hedman	Adam Larsson	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Max Pacioretty	Frans Nielsen	Reilly Smith	Victor Hedman	Roman Josi

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Kyle Clifford	Frans Nielsen		Adam Larsson	Victor Hedman

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Boone Jenner, Victor Rask, Riley Nash	Boone Jenner, Reilly Smith	Victor Rask

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Barret Jackman, Roman Josi, Victor Hedman	Barret Jackman	Roman Josi, Victor Hedman

Tirs de Pénalité
Max Pacioretty, Boone Jenner, Frans Nielsen, Scott Hartnell, Reilly Smith

Gardien
#1 : Roberto Luongo, #2 : Eddie Lack

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
Anaheim	5	3	2	0	0	0	0	0	16	16	0	3	2	1	0	0	0	0	0	10	10	0	2	1	1	0	0	0	0	0	6	6	0	6	0.600	16	30	46	0	0	5	4	7	0	133	34	38	61	0	133	37	104	104	20	2	10.00%	28	5	82.14%	1	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	114	74	120	38	65	32
Atlanta	2	1	1	0	0	0	0	0	6	6	0	1	1	0	0	0	0	0	0	3	2	1	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	6	11	17	0	0	1	3	2	0	46	11	20	15	0	52	14	22	40	12	2	16.67%	11	2	81.82%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	45	31	49	15	24	11
Boston	2	0	1	0	0	0	1	0	8	8	0	1	0	0	0	0	0	1	0	5	4	1	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	8	12	20	0	0	2	4	1	1	57	17	15	20	7	59	12	38	41	11	3	27.27%	11	2	81.82%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	55	38	43	14	26	14
Buffalo	2	2	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	2	1	4	1.000	6	12	18	0	0	2	2	2	0	61	12	16	33	0	47	16	42	45	11	1	9.09%	16	2	87.50%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	45	29	49	16	25	12
Philadelphie	2	1	1	0	0	0	0	0	5	6	-1	1	1	0	0	0	0	0	0	3	1	2	1	0	1	0	0	0	0	0	2	5	-3	2	0.500	5	10	15	0	0	2	0	3	0	61	22	21	18	0	39	16	33	42	14	2	14.29%	14	4	71.43%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	48	32	43	17	27	13
Chicago	3	1	1	0	0	0	1	0	12	9	3	1	0	0	0	0	0	1	0	5	4	1	2	1	1	0	0	0	0	0	7	5	2	4	0.667	12	20	32	0	0	6	3	2	2	81	30	21	28	9	81	19	53	61	17	3	17.65%	18	5	72.22%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	73	49	72	23	39	19
Colorado	3	2	1	0	0	0	0	0	8	6	2	2	1	1	0	0	0	0	0	6	5	1	1	1	0	0	0	0	0	0	2	1	1	4	0.667	8	14	22	0	1	2	5	1	0	81	24	37	20	0	76	19	33	60	16	4	25.00%	13	2	84.62%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	68	45	72	23	38	19
Kansas City	3	2	1	0	0	0	0	0	13	10	3	2	2	0	0	0	0	0	0	11	6	5	1	0	1	0	0	0	0	0	2	4	-2	4	0.667	13	26	39	0	0	3	5	5	0	83	18	30	35	0	73	24	50	67	17	3	17.65%	20	2	90.00%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	75	52	67	22	37	19
Detroit	2	0	2	0	0	0	0	0	4	8	-4	1	0	1	0	0	0	0	0	1	4	-3	1	0	1	0	0	0	0	0	3	4	-1	0	0.000	4	7	11	0	0	3	0	1	0	70	31	16	23	0	68	15	29	37	11	2	18.18%	12	4	66.67%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	45	31	49	15	24	11
Edmonton	4	4	0	0	0	0	0	0	20	8	12	2	2	0	0	0	0	0	0	10	3	7	2	2	0	0	0	0	0	0	10	5	5	8	1.000	20	36	56	0	0	6	5	9	0	130	42	43	45	0	85	27	48	86	27	7	25.93%	18	2	88.89%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	105	73	83	30	51	26
Islanders	2	1	1	0	0	0	0	0	9	8	1	1	1	0	0	0	0	0	0	4	1	3	1	0	1	0	0	0	0	0	5	7	-2	2	0.500	9	15	24	0	0	5	3	1	0	85	29	33	23	0	60	16	28	56	14	6	42.86%	14	3	78.57%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	50	34	45	14	24	11
LA Kings	5	2	2	0	0	0	1	0	15	15	0	3	1	1	0	0	0	1	0	9	9	0	2	1	1	0	0	0	0	0	6	6	0	6	0.600	15	21	36	0	0	2	10	2	2	132	53	46	30	9	128	36	87	105	22	3	13.64%	26	5	80.77%	1	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	116	77	125	40	63	31
Minnesota	3	2	0	0	0	0	0	1	9	4	5	2	2	0	0	0	0	0	0	8	2	6	1	0	0	0	0	0	0	1	1	2	-1	5	0.833	9	17	26	0	0	3	3	3	1	82	25	29	25	6	49	21	37	76	17	3	17.65%	15	1	93.33%	1	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	70	45	74	24	39	19
Hartford	2	1	0	0	1	0	0	0	10	5	5	1	0	0	0	1	0	0	0	5	4	1	1	1	0	0	0	0	0	0	5	1	4	4	1.000	10	18	28	0	0	5	2	2	1	62	20	13	27	2	56	15	26	43	10	2	20.00%	12	1	91.67%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	50	34	45	14	26	14
Ottawa	2	2	0	0	0	0	0	0	13	7	6	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	9	4	5	4	1.000	13	23	36	0	0	8	2	3	0	92	30	34	28	0	50	15	34	38	18	4	22.22%	12	2	83.33%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	57	41	38	13	24	13
Pittsburgh	2	0	1	0	0	1	0	0	5	9	-4	1	0	1	0	0	0	0	0	3	6	-3	1	0	0	0	0	1	0	0	2	3	-1	1	0.250	5	8	13	0	0	2	1	2	0	37	11	8	16	2	44	14	19	36	12	2	16.67%	7	1	85.71%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	50	34	47	16	26	13
Rangers	2	1	1	0	0	0	0	0	6	7	-1	1	1	0	0	0	0	0	0	2	1	1	1	0	1	0	0	0	0	0	4	6	-2	2	0.500	6	10	16	1	0	0	1	5	0	50	15	12	23	0	54	24	28	40	9	3	33.33%	14	1	92.86%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	42	27	49	17	27	13
San Jose	5	2	2	0	1	0	0	0	18	14	4	2	1	1	0	0	0	0	0	7	5	2	3	1	1	0	1	0	0	0	11	9	2	6	0.600	18	32	50	0	1	3	8	6	1	150	43	48	56	3	119	38	81	91	26	7	26.92%	32	3	90.63%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	129	88	108	38	66	33
St-Louis	3	0	3	0	0	0	0	0	7	15	-8	1	0	1	0	0	0	0	0	3	5	-2	2	0	2	0	0	0	0	0	4	10	-6	0	0.000	7	14	21	0	0	3	4	0	0	55	18	21	16	0	98	25	74	71	13	2	15.38%	24	6	75.00%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	64	42	75	24	39	17
Tampa Bay	2	0	2	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	1	2	-1	1	0	1	0	0	0	0	0	0	3	-3	0	0.000	1	2	3	0	0	0	0	1	0	44	19	9	16	0	43	10	32	41	7	0	0.00%	16	2	87.50%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	46	29	47	15	26	12
Toronto	2	1	1	0	0	0	0	0	7	8	-1	1	0	1	0	0	0	0	0	3	5	-2	1	1	0	0	0	0	0	0	4	3	1	2	0.500	7	13	20	0	0	3	2	2	0	70	32	21	17	0	61	20	39	31	14	4	28.57%	16	4	75.00%	1	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	50	35	45	14	23	12
Vancouver	5	3	1	0	0	0	1	0	20	15	5	2	1	1	0	0	0	0	0	5	7	-2	3	2	0	0	0	0	1	0	15	8	7	8	0.800	20	33	53	0	1	7	7	5	1	130	46	40	42	5	121	29	75	92	23	9	39.13%	23	6	73.91%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	118	81	123	38	63	31
Quebec	2	2	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	2	0	2	1	1	0	0	0	0	0	0	3	1	2	4	1.000	5	10	15	0	1	3	1	1	0	66	21	28	17	0	35	7	18	36	10	1	10.00%	7	0	100.00%	1	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	51	35	42	15	25	13
Winnipeg	3	1	2	0	0	0	0	0	10	10	0	1	1	0	0	0	0	0	0	7	2	5	2	0	2	0	0	0	0	0	3	8	-5	2	0.333	10	18	28	0	0	5	1	4	0	101	32	27	42	0	94	25	26	56	11	2	18.18%	12	6	50.00%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	73	51	66	20	40	20
Nashville	5	2	1	0	1	0	0	1	17	15	2	3	1	0	0	1	0	0	1	10	8	2	2	1	1	0	0	0	0	0	7	7	0	7	0.700	17	31	48	0	0	6	4	6	2	146	56	39	48	5	143	45	94	98	14	6	42.86%	31	3	90.32%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	116	76	125	38	65	32
Montreal	2	0	2	0	0	0	0	0	5	10	-5	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	3	6	-3	0	0.000	5	9	14	1	0	3	0	2	0	63	31	13	19	0	55	15	22	43	11	1	9.09%	10	3	70.00%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	47	33	47	14	25	11
Washington	2	1	1	0	0	0	0	0	7	7	0	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	5	3	2	2	0.500	7	13	20	0	0	2	5	0	0	48	12	23	13	0	77	22	40	51	7	3	42.86%	20	6	70.00%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	42	26	51	15	26	12
Caroline	2	0	1	0	1	0	0	0	7	8	-1	1	0	1	0	0	0	0	0	3	5	-2	1	0	0	0	1	0	0	0	4	3	1	2	0.500	7	13	20	0	0	0	4	2	1	67	20	23	20	4	58	15	20	48	10	1	10.00%	10	4	60.00%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	53	36	43	15	28	15
Las Vegas	3	1	0	0	0	0	1	1	15	12	3	1	0	0	0	0	0	0	1	3	4	-1	2	1	0	0	0	0	1	0	12	8	4	5	0.833	15	26	41	0	0	5	5	4	4	90	34	32	20	11	93	29	42	67	11	1	9.09%	21	6	71.43%	0	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	75	50	73	23	41	20
Vs Division	29	16	8	0	2	0	2	1	106	83	23	15	8	4	0	1	0	1	1	51	42	9	14	8	4	0	1	0	1	0	55	41	14	41	0.707	106	183	289	0	2	29	38	35	6	821	274	254	282	22	729	212	489	576	132	34	25.76%	158	24	84.81%	2	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	700	472	686	224	374	187
Vs Conference	50	25	16	0	2	0	4	3	180	149	31	25	14	6	0	1	0	2	2	94	70	24	25	11	10	0	1	0	2	1	86	79	7	65	0.650	180	318	498	0	3	56	64	54	13	1394	455	451	468	48	1293	374	804	1034	234	52	22.22%	281	52	81.49%	3	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	1202	809	1188	387	650	324
Since Last GM Reset	82	38	31	0	4	1	5	3	284	256	28	41	21	13	0	2	0	3	2	140	118	22	41	17	18	0	2	1	2	1	144	138	6	98	0.598	284	504	788	2	4	97	94	84	16	2373	788	756	796	63	2151	620	1274	1702	415	89	21.45%	483	93	80.75%	5	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	1984	1342	1929	633	1064	531
Total	82	38	31	0	4	1	5	3	284	256	28	41	21	13	0	2	0	3	2	140	118	22	41	17	18	0	2	1	2	1	144	138	6	98	0.598	284	504	788	2	4	97	94	84	16	2373	788	756	796	63	2151	620	1274	1702	415	89	21.45%	483	93	80.75%	5	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	1984	1342	1929	633	1064	531

			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	98	L1	284	504	788	2373	2151	620	1274	1702	2	4

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	38	31	4	1	5	3	284	256

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	21	13	2	0	3	2	140	118

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	17	18	2	1	2	1	144	138

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	5	0	0	1	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
415	89	21.45%	483	93	80.75%	5

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
788	756	796	63	97	94	84	16

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
1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%

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
1984	1342	1929	633	1064	531

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
1 - 2016-10-12	3	Calgary	5	Edmonton	2	W			Sommaire du Match
3 - 2016-10-14	15	Edmonton	1	Calgary	6	W			Sommaire du Match
4 - 2016-10-15	17	Calgary	5	Vancouver	3	W			Sommaire du Match
7 - 2016-10-18	47	Buffalo	2	Calgary	3	W			Sommaire du Match
9 - 2016-10-20	59	Caroline	5	Calgary	3	L			Sommaire du Match
11 - 2016-10-22	72	St-Louis	5	Calgary	3	L			Sommaire du Match
13 - 2016-10-24	82	Calgary	1	Chicago	2	L			Sommaire du Match
14 - 2016-10-25	89	Calgary	2	St-Louis	6	L			Sommaire du Match
17 - 2016-10-28	110	Ottawa	3	Calgary	4	W			Sommaire du Match
19 - 2016-10-30	130	Washington	4	Calgary	2	L			Sommaire du Match
21 - 2016-11-01	135	Calgary	6	Chicago	3	W			Sommaire du Match
23 - 2016-11-03	155	Calgary	5	San Jose	3	W			Sommaire du Match
25 - 2016-11-05	172	Calgary	3	LA Kings	4	L			Sommaire du Match
26 - 2016-11-06	179	Calgary	2	Anaheim	5	L			Sommaire du Match
30 - 2016-11-10	201	Kansas City	3	Calgary	6	W			Sommaire du Match
32 - 2016-11-12	215	Rangers	1	Calgary	2	W			Sommaire du Match
35 - 2016-11-15	232	Calgary	1	Minnesota	2	L	X	X	Sommaire du Match
36 - 2016-11-16	244	Nashville	4	Calgary	5	W	X		Sommaire du Match
38 - 2016-11-18	259	Chicago	4	Calgary	5	W	X	X	Sommaire du Match
40 - 2016-11-20	274	Calgary	3	Detroit	4	L			Sommaire du Match
41 - 2016-11-21	276	Calgary	3	Buffalo	2	W			Sommaire du Match
43 - 2016-11-23	292	Calgary	3	Atlanta	4	L			Sommaire du Match
45 - 2016-11-25	304	Calgary	3	Boston	4	L			Sommaire du Match
47 - 2016-11-27	324	Calgary	2	Philadelphie	5	L			Sommaire du Match
48 - 2016-11-28	328	Calgary	5	Islanders	7	L			Sommaire du Match
50 - 2016-11-30	344	Toronto	5	Calgary	3	L			Sommaire du Match
52 - 2016-12-02	357	Minnesota	1	Calgary	4	W			Sommaire du Match
54 - 2016-12-04	375	Anaheim	3	Calgary	4	W			Sommaire du Match
56 - 2016-12-06	388	Calgary	2	Kansas City	4	L			Sommaire du Match
58 - 2016-12-08	403	Calgary	2	Nashville	4	L			Sommaire du Match
60 - 2016-12-10	415	Winnipeg	2	Calgary	7	W			Sommaire du Match
64 - 2016-12-14	443	Tampa Bay	2	Calgary	1	L			Sommaire du Match
66 - 2016-12-16	457	Atlanta	2	Calgary	3	W			Sommaire du Match
69 - 2016-12-19	480	Calgary	5	Nashville	3	W			Sommaire du Match
70 - 2016-12-20	492	Calgary	4	San Jose	3	W	X		Sommaire du Match
73 - 2016-12-23	506	Vancouver	4	Calgary	5	W			Sommaire du Match
77 - 2016-12-27	524	Calgary	2	Colorado	1	W			Sommaire du Match
79 - 2016-12-29	541	Anaheim	1	Calgary	3	W			Sommaire du Match
81 - 2016-12-31	549	Nashville	2	Calgary	1	L	X	X	Sommaire du Match
85 - 2017-01-04	572	Colorado	0	Calgary	4	W			Sommaire du Match
87 - 2017-01-06	589	Calgary	4	Vancouver	0	W			Sommaire du Match
88 - 2017-01-07	598	Vancouver	3	Calgary	0	L			Sommaire du Match
90 - 2017-01-09	611	Calgary	2	Winnipeg	3	L			Sommaire du Match
92 - 2017-01-11	623	San Jose	0	Calgary	3	W			Sommaire du Match
94 - 2017-01-13	638	Hartford	4	Calgary	5	W	X		Sommaire du Match
95 - 2017-01-14	649	Calgary	5	Edmonton	3	W			Sommaire du Match
98 - 2017-01-17	669	Quebec	0	Calgary	2	W			Sommaire du Match
100 - 2017-01-19	682	Las Vegas	4	Calgary	3	L	X	X	Sommaire du Match
102 - 2017-01-21	699	Edmonton	2	Calgary	4	W			Sommaire du Match
104 - 2017-01-23	713	Calgary	4	Toronto	3	W			Sommaire du Match
105 - 2017-01-24	718	Calgary	3	Montreal	6	L			Sommaire du Match
107 - 2017-01-26	730	Calgary	9	Ottawa	4	W			Sommaire du Match
113 - 2017-02-01	756	Minnesota	1	Calgary	4	W			Sommaire du Match
115 - 2017-02-03	770	Calgary	5	Hartford	1	W			Sommaire du Match
117 - 2017-02-05	786	Calgary	4	Rangers	6	L			Sommaire du Match
119 - 2017-02-07	792	Calgary	2	Pittsburgh	3	L	X		Sommaire du Match
125 - 2017-02-13	835	Nashville	2	Calgary	4	W			Sommaire du Match
127 - 2017-02-15	845	Philadelphie	1	Calgary	3	W			Sommaire du Match
130 - 2017-02-18	867	Calgary	6	Vancouver	5	W	X	X	Sommaire du Match
133 - 2017-02-21	888	Calgary	8	Las Vegas	5	W			Sommaire du Match
135 - 2017-02-23	896	Calgary	0	Tampa Bay	3	L			Sommaire du Match
136 - 2017-02-24	901	Calgary	3	Quebec	1	W			Sommaire du Match
138 - 2017-02-26	913	Calgary	4	Caroline	3	W	X		Sommaire du Match
140 - 2017-02-28	922	LA Kings	3	Calgary	1	L			Sommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2017-03-03	946	Detroit	4	Calgary	1	L			Sommaire du Match
145 - 2017-03-05	962	Islanders	1	Calgary	4	W			Sommaire du Match
149 - 2017-03-09	986	Montreal	4	Calgary	2	L			Sommaire du Match
151 - 2017-03-11	1007	Calgary	1	Winnipeg	5	L			Sommaire du Match
153 - 2017-03-13	1020	Pittsburgh	6	Calgary	3	L			Sommaire du Match
155 - 2017-03-15	1034	Boston	4	Calgary	5	W	X	X	Sommaire du Match
157 - 2017-03-17	1051	Kansas City	3	Calgary	5	W			Sommaire du Match
159 - 2017-03-19	1065	LA Kings	3	Calgary	4	W	X	X	Sommaire du Match
161 - 2017-03-21	1078	Calgary	5	Washington	3	W			Sommaire du Match
163 - 2017-03-23	1096	Calgary	4	Las Vegas	3	W	X	X	Sommaire du Match
165 - 2017-03-25	1112	Calgary	2	St-Louis	4	L			Sommaire du Match
167 - 2017-03-27	1123	Colorado	5	Calgary	2	L			Sommaire du Match
169 - 2017-03-29	1138	LA Kings	3	Calgary	4	W			Sommaire du Match
171 - 2017-03-31	1154	San Jose	5	Calgary	4	L			Sommaire du Match
173 - 2017-04-02	1174	Anaheim	6	Calgary	3	L			Sommaire du Match
175 - 2017-04-04	1184	Calgary	4	Anaheim	1	W			Sommaire du Match
177 - 2017-04-06	1195	Calgary	3	LA Kings	2	W			Sommaire du Match
179 - 2017-04-08	1219	Calgary	2	San Jose	3	L			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	6500	5250	2250	4000	1500
Prix des Billets	200	100	100	100	400
Assistance	138463	137075	46574	65631	29560
Attendance PCT	51.96%	63.68%	50.49%	40.02%	48.07%

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	10178 - 52.20%	1,571,820$	64,444,600$	19500	100

Dépenses
Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs	Valeur du Cap Salarial Spécial
53,566,000$	53,856,250$	0$	0$

Dépenses Annuelles à Ce Jour	Cap Salarial Par Jour	Cap salarial à ce jour	Taxe de Luxe Totale
54,911,490$	295,945$	54,408,782$	0$

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	298,707$	0$

Total de l'Équipe Éstimé
Dépenses de la Saison Éstimées	Cap Salarial de la Saison Éstimé	Compte Bancaire Actuel	Compte Bancaire Projeté
0$	56,998,926$	153,774,733$	153,774,733$

Charte de Profondeur

Ailier Gauche

Centre

Ailier Droit

Max Pacioretty	AGE:26	PO:0	OV:72
Taylor Hall	AGE:23	PO:0	OV:71
Frans Nielsen	AGE:31	PO:0	OV:70
Scott Hartnell	AGE:33	PO:0	OV:69
Reilly Smith	AGE:24	PO:0	OV:67
Boone Jenner	AGE:22	PO:0	OV:65
Alexander Burmistrov	AGE:23	PO:0	OV:58
Matt Nieto	AGE:22	PO:0	OV:58
Kyle Clifford	AGE:24	PO:0	OV:56
Viktor Arvidsson	AGE:22	PO:0	OV:55
Josh Leivo	AGE:22	PO:0	OV:52
Anthony Mantha	AGE:21	PO:0	OV:50
Kevin Fiala	AGE:19	PO:0	OV:50
*Anton Slepyshev	AGE:21	PO:0	OV:49
Garrett Wilson	AGE:24	PO:0	OV:49
*Max Friberg	AGE:22	PO:0	OV:48
*Sergey Tolchinsky	AGE:20	PO:0	OV:48
*Cristoval Nieves	AGE:21	PO:0	OV:46
*Nicolas Kerdiles	AGE:21	PO:0	OV:46
*Ryan MacInnis	AGE:19	PO:0	OV:45
*Theodor Blueger	AGE:21	PO:0	OV:45
*Nikolay Prokhorkin	AGE:22	PO:0	OV:39
*Tyler Motte	AGE:20	PO:0	OV:39

Ryan Johansen	AGE:23	PO:0	OV:71
Frans Nielsen	AGE:31	PO:0	OV:70
Boone Jenner	AGE:22	PO:0	OV:65
Riley Nash	AGE:26	PO:0	OV:63
Victor Rask	AGE:22	PO:0	OV:62
Alexander Burmistrov	AGE:23	PO:0	OV:58
Colton Sissons	AGE:21	PO:0	OV:55
*Cristoval Nieves	AGE:21	PO:0	OV:46
*Eric Cornel	AGE:19	PO:0	OV:46
*Nicolas Kerdiles	AGE:21	PO:0	OV:46
*Ryan MacInnis	AGE:19	PO:0	OV:45
*Theodor Blueger	AGE:21	PO:0	OV:45
*Tyler Motte	AGE:20	PO:0	OV:39

Reilly Smith	AGE:24	PO:0	OV:67
Cam Atkinson	AGE:26	PO:0	OV:66
Riley Nash	AGE:26	PO:0	OV:63
Alexander Burmistrov	AGE:23	PO:0	OV:58
Matt Nieto	AGE:22	PO:0	OV:58
Kyle Clifford	AGE:24	PO:0	OV:56
*Hudson Fasching	AGE:20	PO:0	OV:55
Viktor Arvidsson	AGE:22	PO:0	OV:55
Colton Sissons	AGE:21	PO:0	OV:55
Josh Leivo	AGE:22	PO:0	OV:52
Anthony Mantha	AGE:21	PO:0	OV:50
Kevin Fiala	AGE:19	PO:0	OV:50
*Anton Slepyshev	AGE:21	PO:0	OV:49
*Max Friberg	AGE:22	PO:0	OV:48
*Sergey Tolchinsky	AGE:20	PO:0	OV:48
*Matej Stransky	AGE:22	PO:0	OV:39
*Riley Barber	AGE:21	PO:0	OV:39

Défense #1

Défense #2

Gardien

Roman Josi	AGE:25	PO:0	OV:72
Victor Hedman	AGE:24	PO:0	OV:71
Adam Larsson	AGE:22	PO:0	OV:67
Jan Hejda	AGE:37	PO:0	OV:65
Barret Jackman	AGE:34	PO:0	OV:64
Slava Voynov	AGE:25	PO:0	OV:61
Dalton Prout	AGE:25	PO:0	OV:60
*John Ramage	AGE:24	PO:0	OV:53
*Andrey Pedan	AGE:22	PO:0	OV:52

Brett Kulak	AGE:21	PO:0	OV:52
Karl Stollery	AGE:27	PO:0	OV:52
Kyle Cumiskey	AGE:28	PO:0	OV:51
*Julius Honka	AGE:19	PO:0	OV:49
*Ville Pokka	AGE:21	PO:0	OV:46
*Anthony Florentino	AGE:20	PO:0	OV:41
*Luc Snuggerud	AGE:20	PO:0	OV:39
Evan Oberg	AGE:27	PO:0	OV:37

Roberto Luongo	AGE:36	PO:0	OV:82
Eddie Lack	AGE:27	PO:0	OV:61
Andrey Makarov	AGE:22	PO:0	OV:48
Calvin Heeter	AGE:26	PO:0	OV:47
*Thatcher Demko	AGE:19	PO:0	OV:46
*Ilya Sorokin	AGE:20	PO:0	OV:43

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
Aapeli Rasanen	Calgary	2016	148
Alexander DeBrincat	Calgary	2016	25
Colton Point	Calgary	2016	91
Erik Cernak	Calgary	2015	104
Ivan Provorov	Calgary	2015	8
Jack Roslovic	Calgary	2015	38
Jacob Moverare	Calgary	2016	85
Jonathan Dahlen	Calgary	2016	44
MacKenzie Blackwood	Calgary	2015	40
Ryan Pilon	Calgary	2015	98

Choix au Repêchage

Année	R1	R2	R3	R4	R5	R6	R7
2017	Cal	Cal		Was	Cal	Cal	Atl Cal
2018	Cal	Cal	Cal	Cal	Har Cal	Cal	LA Cal
2019	Cal	Cal	Cal	Cal	Cal	Cal	Cal Edm
2020	Cal	Cal	Cal	Cal	Cal	Cal	Cal
2021	Cal	Cal	Cal	Cal	Cal	Cal	Cal

[2016-10-28 20:00:15] - New Record for Team's Most Shots (49) in 1 Game for Calgary!
[2016-10-30 20:00:19] - New Record for Team's Most Hits (31) in 1 Game for Calgary!
[2016-11-13 09:14:52] - Taylor Hall was added to Calgary.
[2016-11-13 09:14:53] - TRADE : From Calgary to Edmonton : Dustin Byfuglien (74), Y:2017-RND:3-Cal.
[2016-11-13 09:14:53] - TRADE : From Edmonton to Calgary : Taylor Hall (70), Y:2018-RND:4-Edm, Y:2019-RND:7-Edm.
[2016-11-28 20:00:18] - New Record for Team's Most Hits (33) in 1 Game for Calgary!
[2016-11-29 19:54:02] - Calgary fired Willie Desjardins.
[2016-11-29 19:54:03] - Calgary hired John Tortorella for 500 000 $ for 3 year(s).
[2016-12-04 20:00:18] - New Record for Team's Most Penalties (46) in 1 Game for Calgary!
[2016-12-21 20:48:18] - Barret Jackman was added to Calgary.
[2016-12-21 20:48:20] - TRADE : From Calgary to Ottawa : Y:2017-RND:6-Stl.
[2016-12-21 20:48:20] - TRADE : From Ottawa to Calgary : Barret Jackman (64).
[2017-01-15 14:51:13] - Eddie Lack was added to Calgary.
[2017-01-15 14:51:14] - TRADE : From St-Louis to Calgary : Eddie Lack (61).
[2017-01-15 14:51:14] - TRADE : From Calgary to St-Louis : Y:2017-RND:7-Que, Y:2018-RND:4-Edm.
[2017-01-15 14:51:42] - TRADE : From Hartford to Calgary : Y:2018-RND:5-Har.
[2017-01-15 14:51:42] - TRADE : From Calgary to Hartford : Anton Khudobin (60).
[2017-01-26 20:00:18] - New Record for Team's Most Goals (9) in 1 Game for Calgary!
[2017-01-26 20:00:18] - New Record for Team's Most Assists (16) in 1 Game for Calgary!
[2017-01-26 20:00:18] - New Record for Team's Most Points (25) in 1 Game for Calgary!

Pas de Blessure ou de Suspension.

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
Saison Régulière
2016	82	38	31	0	4	1	5	3	284	256	28	41	21	13	0	2	0	3	2	140	118	22	41	17	18	0	2	1	2	1	144	138	6	76	284	504	788	2	4	97	94	84	16	2373	788	756	796	63	2151	620	1274	1702	415	89	21.45%	483	93	80.75%	5	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	1984	1342	1929	633	1064	531
Total Saison Régulière	82	38	31	0	4	1	5	3	284	256	28	41	21	13	0	2	0	3	2	140	118	22	41	17	18	0	2	1	2	1	144	138	6	76	284	504	788	2	4	97	94	84	16	2373	788	756	796	63	2151	620	1274	1702	415	89	21.45%	483	93	80.75%	5	1370	2836	48.31%	1307	2703	48.35%	702	1343	52.27%	1984	1342	1929	633	1064	531