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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	Âge	Contrat	Salaire
1	Matthew Knies (R)			X		99.00	86	56	83	80	81	65	77	68	25	70	71	69	25	54	55	0	45	70	0	21	3	925,000$
2	Pierre Engvall		X	X		100.00	63	43	91	79	81	70	92	72	42	69	66	59	25	66	68	0	51	68	0	28	1	1,833,000$
3	Brett Leason			X		99.00	77	45	92	71	82	64	78	64	39	63	70	80	25	60	61	0	51	68	0	25	1	842,500$
4	Max Jones			X		100.00	89	58	82	74	82	62	78	71	34	65	59	68	25	65	65	0	50	67	0	26	3	1,295,000$
5	Taylor Raddysh			X		100.00	77	44	90	74	73	72	93	78	37	61	58	78	25	64	64	0	50	67	0	26	1	758,333$
6	Jesse Puljujarvi			X		98.00	83	45	91	80	77	53	72	66	31	55	66	65	25	63	65	0	50	66	0	26	1	1,175,000$
7	Mark Kastelic	X		X		100.00	85	99	72	72	86	48	85	64	76	58	58	66	25	59	59	0	50	64	0	25	3	825,000$
8	Vinni Lettieri	X		X		100.00	77	43	84	69	69	54	68	68	50	59	60	69	25	58	58	0	50	63	0	29	1	2,100,000$
9	Isak Rosen (R)			X		100.00	69	58	93	66	58	80	85	64	50	61	63	62	60	44	44	0	50	63	0	21	3	894,167$
10	Austin Czarnik	X		X		100.00	67	41	89	80	59	51	88	64	69	53	55	65	25	65	65	0	50	62	0	31	3	762,500$
11	Emil Heineman			X		100.00	74	69	84	65	69	54	53	62	50	56	63	64	60	44	44	0	50	61	0	22	3	897,500$
12	Niko Mikkola				X	98.00	89	56	76	73	73	33	99	62	25	52	48	93	25	64	66	0	50	69	0	28	1	2,125,000$
13	Braden Schneider				X	100.00	85	45	93	78	75	68	99	63	25	52	48	81	25	63	65	0	50	69	0	23	1	925,000$
14	Dylan Samberg				X	98.00	71	53	84	77	72	67	88	63	25	56	47	85	25	59	60	0	50	69	0	25	1	925,000$
15	Kaedan Korczak				X	100.00	82	45	90	73	73	69	72	65	25	64	48	71	25	47	47	0	50	68	0	23	1	789,167$
16	Ben Hutton				X	100.00	63	55	87	81	77	69	57	69	25	54	48	67	75	72	73	0	50	66	0	31	1	850,000$
17	Jacob MacDonald				X	100.00	77	64	80	67	75	59	66	73	31	45	61	68	75	62	62	0	50	64	0	31	1	2,125,000$
18	Cale Fleury				X	100.00	79	75	89	74	75	57	58	55	25	50	43	66	41	51	51	0	48	62	0	25	1	800,000$
19	Alex Petrovic				X	100.00	82	81	84	70	81	78	86	50	25	43	41	65	39	44	44	0	50	61	0	32	1	775,000$
Rayé
1	Roland McKeown				X	100.00	73	71	77	65	71	76	83	50	25	43	42	60	40	44	44	0	42	57	0	28	3	762,500$
2	Drew Helleson (R)				X	100.00	78	77	79	65	77	72	79	48	25	39	41	62	39	44	44	0	42	57	0	23	2	925,000$
3	Lukas Cormier (R)				X	100.00	70	65	83	67	65	72	78	50	25	45	41	59	39	44	44	0	42	57	0	22	3	793,333$
4	Libor Hajek				X	100.00	72	75	66	65	75	48	50	43	25	28	39	61	37	57	57	0	42	52	0	26	1	874,125$

	MOYENNE D'ÉQUIPE					99.65	77	59	84	72	74	63	78	62	35	54	54	69	36	56	57	0	48	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	John Gibson	100.00	65	79	78	82	65	64	69	64	65	65	61	71	71	0	45	68	0
2	Yaroslav Askarov (R)	100.00	60	55	69	71	61	66	61	66	67	66	30	44	44	0	50	63	0
Rayé
1	Erik Kallgren	100.00	46	50	63	75	43	47	50	53	48	48	30	44	44	0	42	50	0

	MOYENNE D'ÉQUIPE	100.00	57	61	70	76	56	59	60	61	60	60	40	53	53	0	46	60

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Dan Hinote	40	40	40	40	40	40	40	TUR	80	2	1,000,000$

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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	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKS	PKM	GW	GT	FO%	FOT	P/20	PSG	PSS	S1	S2	S3
1	Taylor Raddysh	Phantoms (Phi)	RW	8	6	2	8	6	2	11	18	29	11	26	20.69%	2	152	19.05	0	0	0	1	17	0	13	1	0	38.89%	18	1.05	1	1	2	0	0
2	Jesse Puljujarvi	Phantoms (Phi)	RW	8	2	4	6	4	6	20	9	20	7	19	10.00%	1	145	18.19	1	0	1	7	17	0	1	0	0	37.50%	16	0.82	0	0	0	0	0
3	Braden Schneider	Phantoms (Phi)	D	8	1	5	6	0	6	18	4	18	4	13	5.56%	7	180	22.57	0	1	1	8	27	0	28	0	0	0.00%	0	0.66	0	0	0	0	1
4	Vinni Lettieri	Phantoms (Phi)	C/RW	8	2	3	5	6	0	8	12	18	7	14	11.11%	1	133	16.63	0	0	0	3	21	0	1	0	0	44.03%	159	0.75	0	0	0	1	0
5	Max Jones	Phantoms (Phi)	RW	8	3	1	4	-4	6	23	14	22	6	15	13.64%	0	131	16.48	0	0	0	0	3	0	8	0	1	50.00%	6	0.61	0	1	0	1	0
6	Dylan Samberg	Phantoms (Phi)	D	8	2	2	4	1	10	14	13	19	8	4	10.53%	10	172	21.59	1	0	1	9	27	1	26	0	0	0.00%	0	0.46	0	0	0	1	1
7	Emil Heineman	Phantoms (Phi)	RW	8	1	2	3	0	0	1	1	4	1	0	25.00%	0	15	2.00	1	1	2	3	5	0	0	0	0	22.22%	9	3.75	0	0	0	0	0
8	Niko Mikkola	Phantoms (Phi)	D	8	0	3	3	0	4	13	6	18	2	9	0.00%	9	150	18.84	0	1	1	5	21	0	21	0	0	0.00%	0	0.40	0	0	0	0	1
9	Brett Leason	Phantoms (Phi)	RW	5	2	1	3	-3	0	11	11	16	4	8	12.50%	2	117	23.42	1	0	1	4	15	0	18	0	0	53.85%	13	0.51	0	3	1	0	0
10	Austin Czarnik	Phantoms (Phi)	C/RW	8	0	2	2	-5	0	2	14	10	4	11	0.00%	0	119	14.99	0	0	0	0	0	1	5	0	0	55.80%	138	0.33	0	0	0	0	0
11	Ben Hutton	Phantoms (Phi)	D	8	0	2	2	-6	0	6	5	2	3	1	0.00%	5	116	14.61	0	0	0	1	1	0	11	0	0	0.00%	0	0.34	1	1	0	0	0
12	Isak Rosen	Phantoms (Phi)	RW	8	0	2	2	-4	2	2	3	11	2	8	0.00%	0	104	13.07	0	1	1	0	1	0	0	0	0	0.00%	7	0.38	0	0	0	0	0
13	Pierre Engvall	Phantoms (Phi)	LW/RW	5	0	2	2	-3	0	0	14	15	6	13	0.00%	1	118	23.65	0	1	1	1	17	2	20	0	0	52.31%	65	0.34	1	3	0	0	0
14	Jacob MacDonald	Phantoms (Phi)	D	8	1	1	2	-6	14	13	9	6	1	0	16.67%	9	114	14.27	0	0	0	0	0	0	10	0	0	0.00%	0	0.35	1	1	0	0	0
15	Mark Kastelic	Phantoms (Phi)	C/RW	8	0	2	2	-3	16	20	18	16	2	19	0.00%	4	137	17.14	0	1	1	3	27	0	0	0	0	59.52%	168	0.29	0	0	0	0	0
16	Kaedan Korczak	Phantoms (Phi)	D	8	0	2	2	0	12	5	7	8	5	4	0.00%	8	153	19.19	0	1	1	6	23	0	20	0	0	0.00%	0	0.26	0	0	0	0	0
17	Alex Petrovic	Phantoms (Phi)	D	8	1	0	1	1	2	6	0	3	1	0	33.33%	6	50	6.25	0	0	0	0	0	0	4	0	0	0.00%	0	0.40	0	0	0	0	0
18	Cale Fleury	Phantoms (Phi)	D	2	0	1	1	0	0	1	1	2	0	0	0.00%	0	16	8.11	0	0	0	0	0	0	2	0	0	0.00%	0	1.23	0	0	0	0	0
19	Matthew Knies	Phantoms (Phi)	RW	1	0	0	0	0	0	2	2	1	1	5	0.00%	0	25	25.20	0	0	0	0	5	0	4	0	0	40.00%	10	0.00	0	1	0	0	0
Stats d'équipe Total ou en Moyenne				133	21	37	58	-16	80	176	161	238	75	169	8.82%	65	2155	16.21	4	7	11	51	233	4	199	1	1	50.90%	609	0.54	4	11	3	3	3

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	GA	SA	A	PS %	PSA	ST	BG	S3
1	John Gibson	Phantoms (Phi)	8	3	3	1	0.902	3.09	447	23	234	1	0.692	13	8	0	1
2	Yaroslav Askarov	Phantoms (Phi)	1	0	1	0	0.885	3.83	47	3	26	0	0.000	0	0	8	0
Stats d'équipe Total ou en Moyenne			9	3	4	1	0.900	3.15	495	26	260	1	0.692	13	8	8	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	Type	Salaire Actuel	Salaire Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
Alex Petrovic	Phantoms (Phi)	D	32	1992-03-03	No	216 Lbs	6 ft4	No	No	No	1	Pro & Farm	775,000$	775,000$	0$	0$	No			Lien
Austin Czarnik	Phantoms (Phi)	C/RW	31	1992-12-11	No	169 Lbs	5 ft9	No	No	No	3	Pro & Farm	762,500$	762,500$	0$	0$	No	762,500$	762,500$	Lien / Lien NHL
Ben Hutton	Phantoms (Phi)	D	31	1993-04-19	No	205 Lbs	6 ft2	No	No	No	1	Pro & Farm	850,000$	850,000$	0$	0$	No			Lien / Lien NHL
Braden Schneider	Phantoms (Phi)	D	23	2001-09-20	No	202 Lbs	6 ft2	No	No	No	1	Pro & Farm	925,000$	925,000$	0$	0$	No			Lien
Brett Leason	Phantoms (Phi)	RW	25	1999-04-30	No	215 Lbs	6 ft5	No	No	No	1	Pro & Farm	842,500$	842,500$	0$	0$	No			Lien / Lien NHL
Cale Fleury	Phantoms (Phi)	D	25	1998-11-18	No	205 Lbs	6 ft1	No	No	No	1	Pro & Farm	800,000$	800,000$	0$	0$	No			Lien / Lien NHL
Drew Helleson	Phantoms (Phi)	D	23	2001-03-26	Yes	205 Lbs	6 ft3	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$		Lien
Dylan Samberg	Phantoms (Phi)	D	25	1999-01-24	No	189 Lbs	6 ft3	No	No	No	1	Pro & Farm	925,000$	925,000$	0$	0$	No			Lien
Emil Heineman	Phantoms (Phi)	RW	22	2001-11-16	No	185 Lbs	6 ft1	No	No	No	3	Pro & Farm	897,500$	897,500$	0$	0$	No	897,500$	897,500$	Lien
Erik Kallgren	Phantoms (Phi)	G	27	1996-10-14	No	190 Lbs	6 ft2	No	No	No	2	Pro & Farm	2,750,000$	2,750,000$	0$	0$	No	2,750,000$		Lien
Isak Rosen	Phantoms (Phi)	RW	21	2003-03-15	Yes	156 Lbs	5 ft11	No	No	No	3	Pro & Farm	894,167$	894,167$	0$	0$	No	894,167$	894,167$	Lien
Jacob MacDonald	Phantoms (Phi)	D	31	1993-02-26	No	205 Lbs	6 ft0	No	No	No	1	Pro & Farm	2,125,000$	2,125,000$	0$	0$	No			Lien / Lien NHL
Jesse Puljujarvi	Phantoms (Phi)	RW	26	1998-05-07	No	200 Lbs	6 ft4	No	No	No	1	Pro & Farm	1,175,000$	1,175,000$	0$	0$	No			Lien
John Gibson	Phantoms (Phi)	G	31	1993-07-14	No	216 Lbs	6 ft2	No	No	No	2	Pro & Farm	6,400,000$	6,400,000$	0$	0$	No	6,400,000$		Lien / Lien NHL
Kaedan Korczak	Phantoms (Phi)	D	23	2001-01-29	No	191 Lbs	6 ft3	No	No	No	1	Pro & Farm	789,167$	789,167$	0$	0$	No			Lien
Libor Hajek	Phantoms (Phi)	D	26	1998-02-04	No	203 Lbs	6 ft2	No	No	No	1	Pro & Farm	874,125$	874,125$	0$	0$	No			Lien / Lien NHL
Lukas Cormier	Phantoms (Phi)	D	22	2002-03-27	Yes	180 Lbs	5 ft10	No	No	No	3	Pro & Farm	793,333$	793,333$	0$	0$	No	793,333$	793,333$	Lien
Mark Kastelic	Phantoms (Phi)	C/RW	25	1999-03-10	No	222 Lbs	6 ft4	No	No	No	3	Pro & Farm	825,000$	825,000$	0$	0$	No	825,000$	825,000$	Lien
Matthew Knies	Phantoms (Phi)	RW	21	2002-10-17	Yes	216 Lbs	6 ft3	No	No	No	3	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$	925,000$	Lien
Max Jones	Phantoms (Phi)	RW	26	1998-02-17	No	220 Lbs	6 ft3	No	No	No	3	Pro & Farm	1,295,000$	1,295,000$	0$	0$	No	1,295,000$	1,295,000$	Lien
Niko Mikkola	Phantoms (Phi)	D	28	1996-04-27	No	185 Lbs	6 ft4	No	No	No	1	Pro & Farm	2,125,000$	2,125,000$	0$	0$	No			Lien / Lien NHL
Pierre Engvall	Phantoms (Phi)	LW/RW	28	1996-05-31	No	213 Lbs	6 ft5	No	No	No	1	Pro & Farm	1,833,000$	1,833,000$	0$	0$	No			Lien / Lien NHL
Roland McKeown	Phantoms (Phi)	D	28	1996-01-20	No	194 Lbs	6 ft1	No	No	No	3	Pro & Farm	762,500$	762,500$	0$	0$	No	762,500$	762,500$	Lien
Taylor Raddysh	Phantoms (Phi)	RW	26	1998-02-18	No	198 Lbs	6 ft2	No	No	No	1	Pro & Farm	758,333$	758,333$	0$	0$	No			Lien / Lien NHL
Vinni Lettieri	Phantoms (Phi)	C/RW	29	1995-02-06	No	191 Lbs	5 ft11	No	No	No	1	Pro & Farm	2,100,000$	2,100,000$	0$	0$	No			Lien / Lien NHL
Yaroslav Askarov	Phantoms (Phi)	G	22	2002-06-16	Yes	176 Lbs	6 ft3	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$		Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
26	26.04	198 Lbs	6 ft2	1.77	1,348,159$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Pierre Engvall	Mark Kastelic	Matthew Knies	30	1	2	2
2	Max Jones	Vinni Lettieri	Brett Leason	30	1	2	2
3	Jesse Puljujarvi	Austin Czarnik	Taylor Raddysh	25	1	2	2
4	Brett Leason	Matthew Knies	Max Jones	15	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Braden Schneider	Dylan Samberg	30	1	2	2
2	Niko Mikkola	Kaedan Korczak	30	1	2	2
3	Ben Hutton	Jacob MacDonald	25	1	2	2
4	Cale Fleury	Alex Petrovic	15	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Pierre Engvall	Mark Kastelic	Matthew Knies	60	1	2	2
2	Max Jones	Vinni Lettieri	Brett Leason	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Braden Schneider	Dylan Samberg	60	1	2	2
2	Niko Mikkola	Kaedan Korczak	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Matthew Knies	Brett Leason	60	1	2	2
2	Pierre Engvall	Taylor Raddysh	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Braden Schneider	Dylan Samberg	60	1	2	2
2	Niko Mikkola	Kaedan Korczak	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Matthew Knies	60	1	2	2	Braden Schneider	Dylan Samberg	60	1	2	2
2	Brett Leason	40	1	2	2	Niko Mikkola	Kaedan Korczak	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Matthew Knies	Brett Leason	60	1	2	2
2	Pierre Engvall	Taylor Raddysh	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Braden Schneider	Dylan Samberg	60	1	2	2
2	Niko Mikkola	Kaedan Korczak	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Pierre Engvall	Mark Kastelic	Matthew Knies	Braden Schneider	Dylan Samberg

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Pierre Engvall	Mark Kastelic	Matthew Knies	Braden Schneider	Dylan Samberg

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Isak Rosen, Emil Heineman, Jesse Puljujarvi	Isak Rosen, Emil Heineman	Jesse Puljujarvi

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Ben Hutton, Jacob MacDonald, Cale Fleury	Ben Hutton	Jacob MacDonald, Cale Fleury

Tirs de Pénalité
Matthew Knies, Brett Leason, Pierre Engvall, Taylor Raddysh, Max Jones

Gardien
#1 : John Gibson, #2 : Yaroslav Askarov

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	Bulldogs	2	0	1	0	0	0	1	0	8	9	-1	1	0	0	0	0	0	1	0	3	2	1	1	0	1	0	0	0	0	0	5	7	-2	2	0.500	8	13	21	0	0	6	9	6	4	62	72	82	84	15	58	11	20	54	8	2	25.00%	10	3	70.00%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
2	Hit Man	2	0	2	0	0	0	0	0	4	6	-2	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	4	6	10	0	0	6	9	6	4	58	72	82	84	15	74	25	22	38	9	0	0.00%	11	1	90.91%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
3	Saints	2	1	0	0	0	0	0	1	7	7	0	1	0	0	0	0	0	0	1	2	3	-1	1	1	0	0	0	0	0	0	5	4	1	3	0.750	7	13	20	0	0	6	9	6	4	61	72	82	84	15	64	14	22	47	8	2	25.00%	8	1	87.50%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
4	Titans	2	0	1	0	0	0	1	0	4	5	-1	1	0	0	0	0	0	1	0	3	2	1	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	4	6	10	0	0	6	9	6	4	61	72	82	84	15	64	19	18	40	6	0	0.00%	7	0	100.00%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
	Total	8	1	4	0	0	0	2	1	23	27	-4	4	0	1	0	0	0	2	1	10	10	0	4	1	3	0	0	0	0	0	13	17	-4	7	0.438	23	38	61	0	0	6	9	6	4	242	72	82	84	15	260	69	82	179	31	4	12.90%	36	5	86.11%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
	_Since Last GM Reset	8	1	4	0	0	0	2	1	23	27	-4	4	0	1	0	0	0	2	1	10	10	0	4	1	3	0	0	0	0	0	13	17	-4	7	0.438	23	38	61	0	0	6	9	6	4	242	72	82	84	15	260	69	82	179	31	4	12.90%	36	5	86.11%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
	_Vs Conference	8	1	4	0	0	0	2	1	23	27	-4	4	0	1	0	0	0	2	1	10	10	0	4	1	3	0	0	0	0	0	13	17	-4	7	0.438	23	38	61	0	0	6	9	6	4	242	72	82	84	15	260	69	82	179	31	4	12.90%	36	5	86.11%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51
	_Vs Division	4	1	4	0	0	0	2	1	12	14	-2	2	0	1	0	0	0	2	1	6	4	2	2	1	3	0	0	0	0	0	6	10	-4	7	0.875	12	19	31	0	0	6	9	6	4	123	72	82	84	15	122	30	38	94	14	2	14.29%	17	3	82.35%	0	140	267	52.43%	134	285	47.02%	64	131	48.85%	196	133	193	61	105	51

			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
8	7	W2	23	38	61	242	260	69	82	179	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	1	4	0	0	2	1	23	27

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	0	1	0	0	2	1	10	10

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	1	3	0	0	0	0	13	17

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
3	4	0	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
31	4	12.90%	36	5	86.11%	0

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
72	82	84	15	6	9	6	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
140	267	52.43%	134	285	47.02%	64	131	48.85%

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
196	133	193	61	105	51

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
3 - 2024-09-19	11	Hit Man	3	Phantoms	2	L			Sommaire du Match
5 - 2024-09-21	23	Phantoms	5	Bulldogs	7	L			Sommaire du Match
6 - 2024-09-22	30	Titans	2	Phantoms	3	W	X	X	Sommaire du Match
7 - 2024-09-23	41	Saints	3	Phantoms	2	L	X	X	Sommaire du Match
8 - 2024-09-24	47	Phantoms	2	Hit Man	3	L			Sommaire du Match
11 - 2024-09-27	58	Phantoms	1	Titans	3	L			Sommaire du Match
12 - 2024-09-28	64	Phantoms	5	Saints	4	W			Sommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
15 - 2024-10-01	77	Bulldogs	2	Phantoms	3	W	X	X	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	30	15
Assistance	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
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$	3,505,212$	3,316,262$	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$	1	0$	0$

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
Séries
2021	9	4	5	0	0	0	0	0	29	34	-5	5	2	3	0	0	0	0	0	16	18	-2	4	2	2	0	0	0	0	0	13	16	-3	8	29	51	80	0	0	10	10	7	2	282	88	91	74	29	357	105	93	245	42	3	7.14%	41	7	82.93%	0	125	301	41.53%	132	370	35.68%	70	161	43.48%	216	142	246	78	128	63
Total Séries	9	4	5	0	0	0	0	0	29	34	-5	5	2	3	0	0	0	0	0	16	18	-2	4	2	2	0	0	0	0	0	13	16	-3	8	29	51	80	0	0	10	10	7	2	282	88	91	74	29	357	105	93	245	42	3	7.14%	41	7	82.93%	0	125	301	41.53%	132	370	35.68%	70	161	43.48%	216	142	246	78	128	63