Ligue de hockey Simulée Ste-Flanelle

Baby Hawks
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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	Ryan Getzlaf (A)	X				100.00	61	61	79	76	78	81	92	80	86	88	72	75	58	76	68	0	50	75	0
2	Joe Pavelski	X		X		100.00	62	43	85	85	61	78	94	80	86	79	81	68	44	74	64	0	50	74	0
3	Ryan Kesler (A)	X				100.00	67	56	72	78	70	81	95	76	90	78	73	77	47	75	65	0	50	73	0
4	Brandon Saad		X	X		100.00	55	35	91	78	71	76	95	74	42	73	74	67	53	55	54	0	50	70	0
5	Mikael Backlund	X				100.00	57	35	84	77	69	74	95	69	82	68	70	75	45	56	50	0	50	69	0
6	Jordan Eberle			X		100.00	51	35	89	83	57	75	94	74	42	75	72	60	52	55	46	0	50	68	0
7	Nick Bonino	X				100.00	53	43	88	74	67	74	92	64	82	60	67	76	47	60	58	0	50	67	0
8	Patrick Eaves		X	X		100.00	58	43	86	81	69	69	82	64	39	55	73	62	61	70	62	0	50	65	0
9	Jonathan Drouin		X	X		100.00	56	35	89	82	59	73	74	70	56	71	68	57	61	40	37	0	50	65	0
10	Cal Clutterbuck			X		100.00	73	35	85	71	75	69	86	60	35	58	61	72	50	60	52	0	50	64	0
11	Matt Stajan	X				100.00	54	35	82	71	67	65	94	57	84	57	56	73	56	70	60	0	50	64	0
12	Ales Hemsky			X		100.00	49	35	90	79	58	69	53	66	35	64	68	58	53	71	61	0	50	63	0
13	Zdeno Chara (C)				X	98.00	74	61	76	63	93	82	93	61	35	63	58	86	48	94	85	0	50	74	0
14	Karl Alzner				X	100.00	69	43	86	62	73	76	95	47	35	47	47	88	48	63	55	0	50	67	0
15	John Klingberg				X	100.00	60	43	84	80	56	81	94	68	40	75	61	74	45	37	34	0	50	67	0
16	Dan Hamhuis				X	100.00	69	50	86	68	65	77	91	50	35	54	45	75	48	74	63	0	50	66	0
17	Danny DeKeyser				X	98.00	64	35	84	65	63	80	95	54	35	52	55	82	48	45	42	0	50	66	0
18	Stephen Johns (R)				X	100.00	80	50	81	62	79	68	49	46	35	45	47	75	48	37	34	0	50	60	0
Rayé
1	Jori Lehtera	X	X			100.00	57	35	82	71	74	70	82	61	83	60	61	62	53	41	38	0	50	62	0

	MOYENNE D'ÉQUIPE					99.79	62	43	84	74	69	75	87	64	56	64	64	72	51	61	54	0	50	67

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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	Jonathan Quick	100.00	90	59	95	82	87	85	85	83	84	80	94	66	63	0	50	79	0
2	Chad Johnson	99.00	74	46	90	73	74	72	70	75	74	75	93	48	45	0	50	68	0
Rayé

	MOYENNE D'ÉQUIPE	99.50	82	53	93	78	81	79	78	79	79	78	94	57	54	0	50	74

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Bruce Boudreau	83	95	89	84	99	99	92	CAN	62	1	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	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Joe Pavelski	Chicago	C/RW	76	48	59	107	46	46	10	97	144	318	30	54	15.09%	8	1354	17.82	13	19	32	82	349	0	0	0	0	11	7	3	56.10%	467	3	1.58	0	3	1	0	1	13	5	7
2	Ryan Kesler	Chicago	C	82	40	65	105	22	115	15	192	192	284	14	54	14.08%	8	1483	18.10	14	28	42	86	351	0	0	0	2	82	10	4	59.69%	1858	1	1.42	0	0	1	0	2	8	9	6
3	Ryan Getzlaf	Chicago	C	81	31	58	89	26	134	30	148	155	239	12	36	12.97%	14	1480	18.28	8	25	33	67	355	0	0	0	2	92	6	3	56.35%	1803	0	1.20	0	4	2	0	4	5	11	5
4	John Klingberg	Chicago	D	82	10	66	76	11	84	0	78	92	133	11	26	7.52%	62	1725	21.05	9	21	30	96	362	0	0	0	0	60	3	2	0.00%	0	0	0.88	0	0	0	0	0	2	6	4
5	Jonathan Drouin	Chicago	LW/RW	82	27	38	65	19	38	0	51	78	169	16	25	15.98%	1	1340	16.35	10	13	23	50	350	0	0	0	0	0	4	3	35.80%	81	1	0.97	1	4	0	0	0	4	4	5
6	Brandon Saad	Chicago	LW/RW	82	32	30	62	31	24	0	53	100	211	12	29	15.17%	5	1527	18.63	14	8	22	70	366	0	0	0	3	75	4	4	39.29%	84	0	0.81	1	2	0	0	0	7	2	7
7	Zdeno Chara	Chicago	D	82	15	45	60	17	170	50	142	76	141	9	18	10.64%	73	1809	22.06	9	13	22	85	370	0	0	0	4	335	3	1	0.00%	0	0	0.66	0	0	5	1	3	3	3	3
8	Jordan Eberle	Chicago	RW	82	25	28	53	28	14	0	38	93	190	17	29	13.16%	7	1434	17.49	6	12	18	62	361	0	0	0	0	0	3	4	29.81%	104	2	0.74	0	0	0	0	0	3	8	3
9	Jori Lehtera	Chicago	C/LW	64	14	36	50	0	34	0	66	110	131	0	0	10.69%	6	1101	17.22	5	16	21	34	256	0	0	0	2	47	4	0	51.80%	1305	0	0.91	0	1	0	0	0	3	4	2
10	Mikael Backlund	Chicago	C	82	24	25	49	24	58	0	88	183	204	18	35	11.76%	18	1292	15.76	0	1	1	7	16	2	2	4	10	406	8	0	51.80%	1718	0	0.76	0	0	0	0	0	1	2	8
11	Danny DeKeyser	Chicago	D	82	12	28	40	37	50	0	56	58	116	9	11	10.34%	92	1768	21.57	6	8	14	65	333	0	1	1	2	317	2	2	0.00%	0	0	0.45	0	0	0	0	0	3	2	4
12	Nick Bonino	Chicago	C	82	14	15	29	20	23	5	56	76	108	14	20	12.96%	22	1232	15.04	0	0	0	1	2	2	1	3	13	432	0	0	53.24%	216	1	0.47	0	0	0	0	1	3	0	2
13	Karl Alzner	Chicago	D	82	3	26	29	26	75	5	91	54	48	4	5	6.25%	110	1660	20.25	0	0	0	5	21	0	2	2	3	354	0	0	0.00%	0	0	0.35	0	0	0	1	0	0	1	0
14	Patrick Eaves	Chicago	LW/RW	82	15	13	28	16	39	5	49	56	127	3	5	11.81%	2	957	11.68	1	2	3	16	74	0	0	0	2	44	3	0	34.78%	69	0	0.58	0	4	1	0	0	1	1	4
15	Dan Hamhuis	Chicago	D	82	4	22	26	22	119	15	91	43	54	4	12	7.41%	57	1554	18.96	3	7	10	33	302	0	1	1	1	167	0	0	0.00%	0	0	0.33	0	0	0	0	3	0	2	2
16	Ales Hemsky	Chicago	RW	82	10	14	24	9	12	0	16	39	88	5	17	11.36%	6	657	8.02	0	0	0	0	5	0	0	0	0	3	1	0	30.36%	56	0	0.73	0	0	0	0	0	0	0	3
17	Cal Clutterbuck	Chicago	RW	78	11	9	20	-3	58	0	111	77	106	7	21	10.38%	15	1201	15.40	2	2	4	23	193	0	0	0	4	155	2	0	35.12%	168	0	0.33	0	2	0	0	0	0	1	3
18	Stephen Johns	Chicago	D	82	3	12	15	32	143	35	152	34	28	3	4	10.71%	80	1353	16.51	0	1	1	5	52	0	0	0	1	277	0	0	0.00%	0	0	0.22	0	0	2	1	4	0	1	0
19	Matt Stajan	Chicago	C	76	4	4	8	-2	12	0	37	74	40	3	12	10.00%	14	663	8.73	0	0	0	0	0	0	0	0	1	196	0	0	54.59%	632	0	0.24	1	1	0	0	0	0	0	0
20	Tyler Gaudet	Baby Hawks (Chi)	C/LW	23	2	2	4	-3	4	0	4	6	10	0	0	20.00%	3	191	8.31	0	0	0	0	0	0	0	0	1	49	1	0	0.00%	9	0	0.42	0	0	0	0	0	0	1	0
Stats d'équipe Total ou en Moyenne				1546	344	595	939	378	1252	170	1616	1740	2745	191	413	12.53%	603	25793	16.68	100	176	276	787	4128	4	7	11	51	3111	61	26	53.75%	8570	8	0.73	3	21	12	3	18	56	63	68

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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	SO	GA	SA	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Jonathan Quick	Chicago	74	48	20	5	0.896	2.49	4306	6	179	1728	7	1	0.786	14	72	10	6	1	1
2	Chad Johnson	Chicago	15	8	1	0	0.903	2.50	649	1	27	278	1	0	0.000	0	10	72	0	0	0
Stats d'équipe Total ou en Moyenne			89	56	21	5	0.897	2.49	4956	7	206	2006	8	1	0.786	14	82	82	6	1	1

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Status	Type	Salaire Actuel	Salaire Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	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
Ales Hemsky	Chicago	RW	33	1983-08-13	No	185 Lbs	6 ft0	No	No	No	1	Sans Restriction	Pro & Farm	2,500,000$