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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	Phillip Di Giuseppe		X	X		100.00	84	45	96	79	73	59	74	60	25	62	57	65	25	57	57	65	50	63	0
2	Dylan Gambrell (R)	X	X	X		100.00	71	55	89	62	72	61	79	63	72	61	59	73	25	50	51	65	50	62	0
3	Denis Malgin	X		X		100.00	71	42	89	84	62	61	64	66	52	64	59	55	25	63	63	63	50	62	0
4	Filip Chlapik	X				100.00	88	45	89	77	72	52	82	62	39	60	59	59	53	57	57	64	50	62	0
5	Greg McKegg	X	X			100.00	78	55	89	71	70	54	80	57	60	58	59	72	25	63	64	64	50	62	0
6	Frederick Gaudreau	X		X		100.00	74	65	95	64	65	65	66	64	80	60	62	68	59	58	58	65	50	62	0
7	Patrick Russell		X	X		100.00	79	44	91	62	75	59	68	61	44	60	55	77	25	49	49	64	50	61	0
8	Kole Lind			X		100.00	68	66	71	67	66	71	73	63	50	63	59	61	56	44	44	62	50	60	0
9	Justin Kirkland		X			100.00	72	70	77	61	70	75	80	57	50	61	48	62	46	45	45	58	50	59	0
10	Lukas Jasek			X		100.00	74	68	88	61	68	71	75	58	50	51	61	63	58	44	44	63	50	59	0
11	T.J. Tynan	X				100.00	63	40	95	62	58	51	82	63	34	56	55	56	25	45	45	59	50	57	0
12	Philippe Myers				X	100.00	81	45	82	80	76	71	82	60	25	58	49	65	25	53	53	63	50	66	0
13	Josh Brown				X	100.00	79	79	80	62	87	62	75	55	25	48	48	65	25	57	57	58	50	62	0
14	Sami Niku				X	100.00	73	43	80	75	66	69	61	61	25	63	47	66	25	48	48	61	50	62	0
15	Tommy Cross				X	100.00	75	77	69	67	77	72	77	54	25	45	48	62	46	44	44	57	50	60	0
16	Nikolai Knyzhov (R)				X	100.00	78	76	81	70	76	57	61	46	25	36	40	62	38	44	44	52	50	58	0
Rayé
1	Cory Conacher		X	X		100.00	67	63	75	71	63	74	77	67	50	65	62	66	59	64	65	65	50	64	1
2	Hunter Shinkaruk		X			100.00	73	65	93	63	65	57	58	56	50	47	58	64	55	52	52	60	50	57	0
3	Jack Rodewald			X		100.00	69	62	85	68	62	69	74	52	50	50	47	60	45	50	50	55	50	56	0
4	Brandon Baddock		X			100.00	69	80	43	66	80	71	76	51	50	48	50	58	48	44	44	55	50	56	0
5	Giovanni Fiore		X	X		100.00	77	71	91	55	71	55	56	53	50	38	61	64	58	50	50	59	50	55	0
6	Mason Geertsen				X	100.00	71	79	52	63	79	69	76	45	25	36	39	58	37	44	44	50	50	57	0
7	Evan McEneny				X	100.00	76	75	78	63	75	56	59	45	25	31	42	62	40	51	51	51	50	56	0

	MOYENNE D'ÉQUIPE					100.00	74	61	82	68	71	64	72	57	43	53	53	64	40	51	51	60	50	60

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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	Alex Lyon	100.00	61	68	85	75	61	64	54	64	59	58	30	45	45	61	50	61	0
2	Adam Wilcox	100.00	47	55	69	69	43	46	50	53	46	47	30	44	44	48	50	51	0
Rayé
1	Brad Thiessen	100.00	47	51	64	58	45	47	50	53	47	48	30	44	44	48	50	50	0

	MOYENNE D'ÉQUIPE	100.00	52	58	73	67	50	52	51	57	51	51	30	44	44	52	50	54

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Jim Montgomery	76	97	58	87	66	60	74	CAN	51	1	900,000$

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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

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#	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	S1	S2	S3
1	Nicolas Aube-Kubel	Blue Jackets	RW	30	25	22	47	41	22	0	56	18	121	29	72	20.66%	2	603	20.12	3	4	7	12	85	1	1	2	4	104	2	0	86.11%	36	3	1.56	1	3	8	4	2
2	Phillip Di Giuseppe	Monsters (CLB)	LW/RW	30	16	29	45	40	12	0	25	45	81	19	58	19.75%	5	622	20.74	2	4	6	11	84	1	1	2	6	114	1	0	55.21%	259	1	1.45	1	3	3	3	1
3	Dylan Gambrell	Monsters (CLB)	C/LW/RW	30	18	23	41	28	16	0	26	41	81	24	65	22.22%	8	570	19.01	3	4	7	8	84	0	0	0	4	67	2	0	74.83%	437	0	1.44	0	0	6	0	2
4	Filip Chlapik	Monsters (CLB)	C	30	12	28	40	41	14	0	42	29	70	24	44	17.14%	1	554	18.47	2	4	6	11	84	0	0	0	1	62	3	2	63.06%	536	0	1.44	1	1	1	4	3
5	Frederick Gaudreau	Monsters (CLB)	C/RW	30	17	22	39	39	4	0	20	39	96	31	64	17.71%	1	469	15.66	2	1	3	4	19	0	0	0	3	25	4	0	81.76%	307	0	1.66	0	0	3	2	3
6	Greg McKegg	Monsters (CLB)	C/LW	30	9	22	31	25	18	0	42	30	90	28	66	10.00%	4	489	16.33	2	3	5	15	86	0	0	0	0	3	2	2	53.85%	26	0	1.27	0	0	1	3	1
7	Denis Malgin	Monsters (CLB)	C/RW	30	14	16	30	25	20	0	24	28	79	25	54	17.72%	0	489	16.31	2	5	7	10	88	0	0	0	0	1	0	1	71.43%	28	0	1.23	0	0	0	2	2
8	Kole Lind	Monsters (CLB)	RW	30	8	22	30	37	8	0	26	15	67	18	57	11.94%	2	438	14.62	0	1	1	3	11	0	1	1	2	58	1	0	71.11%	45	0	1.37	0	0	1	0	2
9	Patrick Russell	Monsters (CLB)	LW/RW	30	14	14	28	37	22	0	28	34	84	22	58	16.67%	2	399	13.31	0	2	2	3	11	1	0	1	2	42	2	0	73.33%	15	0	1.40	0	0	1	4	0
10	Sami Niku	Monsters (CLB)	D	30	1	24	25	30	20	0	34	18	27	10	10	3.70%	19	690	23.01	1	5	6	18	84	0	0	0	0	106	0	0	0.00%	0	0	0.72	0	0	0	0	3
11	Tommy Cross	Monsters (CLB)	D	30	7	17	24	44	30	0	61	6	24	8	21	29.17%	14	648	21.63	3	2	5	13	87	0	1	1	1	102	0	0	0.00%	0	0	0.74	0	0	1	1	2
12	Josh Brown	Monsters (CLB)	D	30	5	17	22	43	16	0	42	11	27	8	17	18.52%	12	626	20.87	2	3	5	13	81	0	0	0	2	97	2	0	0.00%	0	0	0.70	0	0	1	0	2
13	Philippe Myers	Monsters (CLB)	D	30	4	16	20	30	20	0	56	15	38	11	17	10.53%	22	696	23.22	0	4	4	14	85	0	1	1	2	102	0	0	0.00%	0	0	0.57	0	0	0	0	0
14	Justin Kirkland	Monsters (CLB)	LW	30	6	11	17	17	6	0	25	9	35	12	19	17.14%	0	252	8.41	0	0	0	0	1	0	0	0	0	0	1	0	66.67%	12	0	1.35	0	0	0	1	1
15	Lukas Jasek	Monsters (CLB)	RW	30	5	11	16	50	29	5	40	8	30	9	24	16.67%	4	503	16.78	1	0	1	1	2	0	0	0	0	0	3	0	62.50%	8	0	0.64	0	0	0	1	0
16	T.J. Tynan	Monsters (CLB)	C	30	6	9	15	17	2	0	2	16	35	7	24	17.14%	0	251	8.40	0	0	0	0	0	0	0	0	0	0	1	1	63.76%	218	0	1.19	0	0	1	1	1
17	Nikolai Knyzhov	Monsters (CLB)	D	30	4	8	12	44	26	0	46	7	19	3	7	21.05%	9	504	16.82	0	2	2	3	19	0	0	0	1	41	1	0	0.00%	0	0	0.48	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				510	171	311	482	588	285	5	595	369	1004	288	677	17.03%	105	8810	17.28	23	44	67	139	918	3	5	8	28	932	25	6	68.45%	1927	4	1.09	3	7	27	26	25

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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	Alex Lyon	Monsters (CLB)	30	27	2	1	0.910	1.25	1819	0	8	38	422	0	0	0	0.714	7	30	0	1	0	0
Stats d'équipe Total ou en Moyenne			30	27	2	1	0.910	1.25	1819	0	8	38	422	0	0	0	0.714	7	30	0	1	0	0

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Adam Wilcox	Monsters (CLB)	G	26	1994-04-25	No	187 Lbs	6 ft0	No	No	No	1	Pro & Farm	650,000$	650,000$	0$	0$	No				Lien / Lien NHL
Alex Lyon	Monsters (CLB)	G	27	1992-12-08	No	201 Lbs	6 ft1	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No				Lien / Lien NHL
Brad Thiessen	Monsters (CLB)	G	34	1986-03-18	No	171 Lbs	5 ft11	No	No	No	1	Pro & Farm	700,000$	700,000$	0$	0$	No				Lien / Lien NHL
Brandon Baddock	Monsters (CLB)	LW	25	1995-03-29	No	215 Lbs	6 ft3	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$		Lien / Lien NHL
Cory Conacher	Monsters (CLB)	LW/RW	30	1989-12-14	No	180 Lbs	5 ft8	No	No	No	1	Pro & Farm	850,000$	850,000$	0$	0$	No				Lien / Lien NHL
Denis Malgin	Monsters (CLB)	C/RW	23	1997-01-18	No	177 Lbs	5 ft9	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No				Lien / Lien NHL
Dylan Gambrell	Monsters (CLB)	C/LW/RW	24	1996-08-26	Yes	195 Lbs	6 ft0	No	No	No	3	Pro & Farm	700,000$	700,000$	0$	0$	No	700,000$	700,000$		Lien / Lien NHL
Evan McEneny	Monsters (CLB)	D	26	1994-05-22	No	203 Lbs	6 ft2	No	No	No	1	Pro & Farm	700,000$	700,000$	0$	0$	No				Lien / Lien NHL
Filip Chlapik	Monsters (CLB)	C	23	1997-06-03	No	196 Lbs	6 ft1	No	No	No	1	Pro & Farm	894,167$	894,167$	0$	0$	No				Lien / Lien NHL
Frederick Gaudreau	Monsters (CLB)	C/RW	27	1993-05-01	No	179 Lbs	6 ft0	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$		Lien / Lien NHL
Giovanni Fiore	Monsters (CLB)	LW/RW	24	1996-08-13	No	194 Lbs	6 ft1	No	No	No	2	Pro & Farm	900,000$	900,000$	0$	0$	No	900,000$			Lien / Lien NHL
Greg McKegg	Monsters (CLB)	C/LW	28	1992-06-17	No	191 Lbs	6 ft0	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$			Lien / Lien NHL
Hunter Shinkaruk	Monsters (CLB)	LW	26	1994-10-13	No	181 Lbs	5 ft10	No	No	No	0	Pro & Farm			0$	0$	No				Lien / Lien NHL
Jack Rodewald	Monsters (CLB)	RW	26	1994-02-14	No	169 Lbs	6 ft0	No	No	No	1	Pro & Farm	700,000$	700,000$	0$	0$	No				Lien / Lien NHL
Josh Brown	Monsters (CLB)	D	26	1994-01-21	No	225 Lbs	6 ft5	No	No	No	1	Pro & Farm	700,000$	700,000$	0$	0$	No				Lien / Lien NHL
Justin Kirkland	Monsters (CLB)	LW	24	1996-08-02	No	183 Lbs	6 ft3	No	No	No	3	Pro & Farm	700,000$	700,000$	0$	0$	No	700,000$	700,000$		Lien / Lien NHL
Kole Lind	Monsters (CLB)	RW	22	1998-10-16	No	178 Lbs	6 ft1	No	No	No	3	Pro & Farm	891,333$	891,333$	0$	0$	No	891,333$	891,333$		Lien / Lien NHL
Lukas Jasek	Monsters (CLB)	RW	23	1997-08-28	No	183 Lbs	6 ft1	No	No	No	3	Pro & Farm	853,333$	853,333$	0$	0$	No	853,333$	853,333$		Lien / Lien NHL
Mason Geertsen	Monsters (CLB)	D	25	1995-04-18	No	215 Lbs	6 ft2	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$		Lien / Lien NHL
Nikolai Knyzhov	Monsters (CLB)	D	22	1998-05-20	Yes	203 Lbs	6 ft3	No	No	No	1	Pro & Farm	810,000$	810,000$	0$	0$	No				Lien / Lien NHL
Patrick Russell	Monsters (CLB)	LW/RW	27	1993-01-03	No	205 Lbs	6 ft1	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No				Lien / Lien NHL
Philippe Myers	Monsters (CLB)	D	23	1997-01-25	No	196 Lbs	6 ft5	No	No	No	1	Pro & Farm	678,333$	678,333$	0$	0$	No				Lien / Lien NHL
Phillip Di Giuseppe	Monsters (CLB)	LW/RW	27	1993-10-09	No	201 Lbs	6 ft0	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No				Lien / Lien NHL
Sami Niku	Monsters (CLB)	D	24	1996-10-10	No	176 Lbs	6 ft1	No	No	No	2	Pro & Farm	916,666$	916,666$	0$	0$	No	916,666$			Lien / Lien NHL
T.J. Tynan	Monsters (CLB)	C	28	1992-02-24	No	165 Lbs	5 ft8	No	No	No	4	Pro & Farm	800,000$	800,000$	0$	0$	No	800,000$	800,000$	800,000$	Lien / Lien NHL
Tommy Cross	Monsters (CLB)	D	31	1989-09-12	No	205 Lbs	6 ft3	No	No	No	2	Pro & Farm	700,000$	700,000$	0$	0$	No	700,000$			Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
26	25.81	191 Lbs	6 ft1	1.77	743,032$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	Filip Chlapik		40	1	2	2
2	Greg McKegg	Dylan Gambrell	Denis Malgin	30	1	2	2
3	Patrick Russell	Frederick Gaudreau	Kole Lind	20	1	2	2
4	Justin Kirkland	T.J. Tynan	Lukas Jasek	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Philippe Myers	Sami Niku	40	1	2	2
2	Josh Brown	Tommy Cross	30	1	2	2
3	Nikolai Knyzhov	Lukas Jasek	20	1	2	2
4	Philippe Myers	Sami Niku	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	Filip Chlapik		60	1	2	2
2	Greg McKegg	Dylan Gambrell	Denis Malgin	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Philippe Myers	Sami Niku	60	1	2	2
2	Josh Brown	Tommy Cross	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe		60	1	2	2
2	Filip Chlapik	Dylan Gambrell	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Philippe Myers	Sami Niku	60	1	2	2
2	Josh Brown	Tommy Cross	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe	60	1	2	2	Philippe Myers	Sami Niku	60	1	2	2
2		40	1	2	2	Josh Brown	Tommy Cross	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Phillip Di Giuseppe		60	1	2	2
2	Filip Chlapik	Dylan Gambrell	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Philippe Myers	Sami Niku	60	1	2	2
2	Josh Brown	Tommy Cross	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Phillip Di Giuseppe	Filip Chlapik		Philippe Myers	Sami Niku

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Phillip Di Giuseppe	Filip Chlapik		Philippe Myers	Sami Niku

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Frederick Gaudreau, Patrick Russell, Kole Lind	Frederick Gaudreau, Patrick Russell	Kole Lind

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Nikolai Knyzhov, Josh Brown, Tommy Cross	Nikolai Knyzhov	Josh Brown, Tommy Cross

Tirs de Pénalité
Phillip Di Giuseppe, , Filip Chlapik, Dylan Gambrell, Denis Malgin

Gardien
#1 : Alex Lyon, #2 : Adam Wilcox

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

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

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Bears	2	2	0	0	0	0	0	0	15	2	13	1	1	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	8	1	7	4	1.000	15	24	39	0	0	78	50	42	4	97	356	311	330	14	20	5	10	44	4	0	0.00%	4	0	100.00%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
2	Bruins	2	1	0	0	0	0	1	0	5	2	3	1	1	0	0	0	0	0	0	2	0	2	1	0	0	0	0	0	1	0	3	2	1	4	1.000	5	7	12	0	1	78	50	42	4	41	356	311	330	14	27	10	20	42	14	3	21.43%	9	1	88.89%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
3	Checkers	2	2	0	0	0	0	0	0	15	3	12	1	1	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	6	1	5	4	1.000	15	27	42	0	0	78	50	42	4	81	356	311	330	14	29	6	18	30	4	1	25.00%	9	1	88.89%	1	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
4	Comets	2	0	1	0	1	0	0	0	4	4	0	1	0	0	0	1	0	0	0	3	2	1	1	0	1	0	0	0	0	0	1	2	-1	2	0.500	4	8	12	0	0	78	50	42	4	33	356	311	330	14	40	6	26	42	14	2	14.29%	12	0	100.00%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
5	Crunch	2	2	0	0	0	0	0	0	9	5	4	1	1	0	0	0	0	0	0	5	4	1	1	1	0	0	0	0	0	0	4	1	3	4	1.000	9	16	25	0	0	78	50	42	4	60	356	311	330	14	21	7	24	35	11	2	18.18%	12	1	91.67%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
6	Devils	2	2	0	0	0	0	0	0	16	1	15	1	1	0	0	0	0	0	0	8	1	7	1	1	0	0	0	0	0	0	8	0	8	4	1.000	16	32	48	0	1	78	50	42	4	88	356	311	330	14	26	3	16	37	5	1	20.00%	7	0	100.00%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
7	Griffins	2	2	0	0	0	0	0	0	16	1	15	1	1	0	0	0	0	0	0	9	0	9	1	1	0	0	0	0	0	0	7	1	6	4	1.000	16	30	46	0	1	78	50	42	4	69	356	311	330	14	27	5	22	35	10	3	30.00%	11	1	90.91%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
8	Marlies	2	2	0	0	0	0	0	0	17	3	14	1	1	0	0	0	0	0	0	9	1	8	1	1	0	0	0	0	0	0	8	2	6	4	1.000	17	31	48	0	0	78	50	42	4	82	356	311	330	14	25	3	16	46	8	3	37.50%	8	0	100.00%	1	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
9	Penguins	2	2	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	3	0	3	4	1.000	7	14	21	0	1	78	50	42	4	51	356	311	330	14	20	10	16	39	8	2	25.00%	8	1	87.50%	1	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
10	Phantoms	2	2	0	0	0	0	0	0	11	2	9	1	1	0	0	0	0	0	0	6	1	5	1	1	0	0	0	0	0	0	5	1	4	4	1.000	11	21	32	0	0	78	50	42	4	64	356	311	330	14	38	10	18	46	4	1	25.00%	9	1	88.89%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
11	Rocket	2	1	0	0	0	0	0	1	9	6	3	1	1	0	0	0	0	0	0	7	3	4	1	0	0	0	0	0	0	1	2	3	-1	3	0.750	9	16	25	0	0	78	50	42	4	55	356	311	330	14	26	15	18	41	10	2	20.00%	9	2	77.78%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
12	Senators	2	1	0	0	0	0	1	0	9	5	4	1	0	0	0	0	0	1	0	4	3	1	1	1	0	0	0	0	0	0	5	2	3	4	1.000	9	14	23	0	0	78	50	42	4	54	356	311	330	14	34	7	26	47	5	1	20.00%	13	1	92.31%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
13	Sound Tigers	2	2	0	0	0	0	0	0	15	0	15	1	1	0	0	0	0	0	0	8	0	8	1	1	0	0	0	0	0	0	7	0	7	4	1.000	15	25	40	0	2	78	50	42	4	92	356	311	330	14	20	3	6	35	3	0	0.00%	3	0	100.00%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
14	Thunderbirds	2	1	1	0	0	0	0	0	5	3	2	1	1	0	0	0	0	0	0	5	2	3	1	0	1	0	0	0	0	0	0	1	-1	2	0.500	5	10	15	0	0	78	50	42	4	48	356	311	330	14	47	13	43	36	4	1	25.00%	13	0	100.00%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
	Total	30	24	2	0	1	0	2	1	173	39	134	15	13	0	0	1	0	1	0	95	22	73	15	11	2	0	0	0	1	1	78	17	61	55	0.917	173	312	485	0	8	78	50	42	4	1004	356	311	330	14	422	105	285	595	109	23	21.10%	130	9	93.08%	3	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
16	Wolf Pack	2	2	0	0	0	0	0	0	20	0	20	1	1	0	0	0	0	0	0	9	0	9	1	1	0	0	0	0	0	0	11	0	11	4	1.000	20	37	57	0	2	78	50	42	4	89	356	311	330	14	22	2	6	40	5	1	20.00%	3	0	100.00%	0	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
	_Since Last GM Reset	30	24	2	0	1	0	2	1	173	39	134	15	13	0	0	1	0	1	0	95	22	73	15	11	2	0	0	0	1	1	78	17	61	55	0.917	173	312	485	0	8	78	50	42	4	1004	356	311	330	14	422	105	285	595	109	23	21.10%	130	9	93.08%	3	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
	_Vs Conference	30	24	2	0	1	0	2	1	173	39	134	15	13	0	0	1	0	1	0	95	22	73	15	11	2	0	0	0	1	1	78	17	61	55	0.917	173	312	485	0	8	78	50	42	4	1004	356	311	330	14	422	105	285	595	109	23	21.10%	130	9	93.08%	3	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
	_Vs Division	14	10	1	0	1	0	1	1	70	25	45	7	5	0	0	1	0	1	0	41	13	28	7	5	1	0	0	0	0	1	29	12	17	25	0.893	70	124	194	0	2	78	50	42	4	409	356	311	330	14	207	60	169	282	62	15	24.19%	75	6	92.00%	1	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205

			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
30	55	W1	173	312	485	1004	422	105	285	595	0	8

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
30	24	2	1	0	2	1	173	39

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	13	0	1	0	1	0	95	22

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	11	2	0	0	1	1	78	17

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
8	1	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
109	23	21.10%	130	9	93.08%	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
356	311	330	14	78	50	42	4

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
602	840	71.67%	380	597	63.65%	337	490	68.78%

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
1029	803	449	157	340	205

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	Lien
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
1 - 2020-10-24	15	Rocket	3	Monsters	7	W			Sommaire du Match
2 - 2020-10-25	30	Monsters	3	Penguins	0	W			Sommaire du Match
3 - 2020-10-26	39	Marlies	1	Monsters	9	W			Sommaire du Match
4 - 2020-10-27	48	Monsters	7	Sound Tigers	0	W			Sommaire du Match
5 - 2020-10-28	73	Griffins	0	Monsters	9	W			Sommaire du Match
6 - 2020-10-29	81	Monsters	11	Wolf Pack	0	W			Sommaire du Match
8 - 2020-10-31	105	Monsters	8	Marlies	2	W			Sommaire du Match
9 - 2020-11-01	115	Thunderbirds	2	Monsters	5	W			Sommaire du Match
10 - 2020-11-02	129	Monsters	6	Checkers	1	W			Sommaire du Match
11 - 2020-11-03	149	Sound Tigers	0	Monsters	8	W			Sommaire du Match
12 - 2020-11-04	164	Bears	1	Monsters	7	W			Sommaire du Match
13 - 2020-11-05	178	Monsters	8	Bears	1	W			Sommaire du Match
15 - 2020-11-07	191	Senators	3	Monsters	4	W	X	X	Sommaire du Match
17 - 2020-11-09	223	Phantoms	1	Monsters	6	W			Sommaire du Match
19 - 2020-11-11	249	Comets	2	Monsters	3	W	X		Sommaire du Match
21 - 2020-11-13	270	Monsters	2	Rocket	3	L	X	X	Sommaire du Match
22 - 2020-11-14	280	Checkers	2	Monsters	9	W			Sommaire du Match
23 - 2020-11-15	299	Monsters	0	Thunderbirds	1	L			Sommaire du Match
24 - 2020-11-16	312	Devils	1	Monsters	8	W			Sommaire du Match
25 - 2020-11-17	323	Monsters	8	Devils	0	W			Sommaire du Match
26 - 2020-11-18	341	Crunch	4	Monsters	5	W			Sommaire du Match
27 - 2020-11-19	353	Monsters	7	Griffins	1	W			Sommaire du Match
29 - 2020-11-21	373	Bruins	0	Monsters	2	W			Sommaire du Match
32 - 2020-11-24	404	Penguins	2	Monsters	4	W			Sommaire du Match
33 - 2020-11-25	427	Monsters	5	Senators	2	W			Sommaire du Match
34 - 2020-11-26	434	Monsters	1	Comets	2	L			Sommaire du Match
35 - 2020-11-27	436	Wolf Pack	0	Monsters	9	W			Sommaire du Match
36 - 2020-11-28	438	Monsters	4	Crunch	1	W			Sommaire du Match
37 - 2020-11-29	440	Monsters	3	Bruins	2	W	X	X	Sommaire du Match
38 - 2020-11-30	449	Monsters	5	Phantoms	1	W			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	100	0
Assistance	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
0	0 - 0.00%	0$	0$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
0$	1,931,883$	1,930,550$	0$

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

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

Afficher ou Masquer des Colonnes

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	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
2020	30	24	2	0	1	0	2	1	173	39	134	15	13	0	0	1	0	1	0	95	22	73	15	11	2	0	0	0	1	1	78	17	61	55	173	312	485	0	8	78	50	42	4	1004	356	311	330	14	422	105	285	595	109	23	21.10%	130	9	93.08%	3	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205
Total Saison Régulière	30	24	2	0	1	0	2	1	173	39	134	15	13	0	0	1	0	1	0	95	22	73	15	11	2	0	0	0	1	1	78	17	61	55	173	312	485	0	8	78	50	42	4	1004	356	311	330	14	422	105	285	595	109	23	21.10%	130	9	93.08%	3	602	840	71.67%	380	597	63.65%	337	490	68.78%	1029	803	449	157	340	205