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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

#	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	Martin Necas (R)	X		X		100.00	75	43	90	86	63	67	84	73	35	75	76	60	25	53	53	75	50	68	0	22	3	1,462,000$
2	Alexander Nylander (R)		X	X		100.00	63	41	94	84	68	62	80	68	33	70	68	61	75	60	60	69	50	66	0	23	3	1,775,000$
3	Sam Steel	X				100.00	63	41	90	81	64	70	86	72	85	70	59	60	25	55	55	66	50	65	0	23	2	925,000$
4	Max Jones		X			100.00	79	44	82	73	76	67	87	63	38	57	66	66	25	55	55	67	50	64	0	23	2	863,333$
5	Trevor Lewis	X		X		100.00	77	44	90	74	75	61	67	59	44	60	60	69	25	74	78	64	50	64	0	34	2	800,000$
6	Emil Bemstrom (R)	X	X	X		100.00	73	42	95	82	65	62	69	68	34	64	71	54	25	50	50	68	50	63	0	22	3	1,633,333$
7	Luke Johnson	X				100.00	71	70	72	67	70	70	72	64	80	56	67	63	64	45	45	66	50	62	0	26	2	925,000$
8	Michael Dal Colle		X	X		100.00	80	44	92	66	74	61	75	60	56	60	58	62	25	55	56	63	50	61	0	25	2	3,063,333$
9	Michael McCarron	X		X		100.00	78	88	53	65	88	61	62	59	74	50	62	66	59	49	49	61	50	60	0	26	1	1,076,000$
10	Matthew Highmore		X	X		100.00	76	43	94	70	66	55	62	59	31	60	57	70	25	47	47	63	50	60	0	25	1	925,000$
11	Cooper Marody	X		X		100.00	69	64	79	66	64	63	64	60	75	60	56	61	53	44	44	61	50	59	0	24	2	1,000,000$
12	Erik Brannstrom (R)				X	100.00	65	41	84	83	62	65	72	69	25	50	47	66	25	47	47	60	50	63	0	21	3	1,375,000$
13	Alexandre Carrier				X	100.00	67	63	77	68	63	78	84	58	25	57	42	62	40	55	55	57	50	62	0	24	1	800,000$
14	Lucas Carlsson (R)				X	100.00	74	70	84	65	70	73	78	54	25	52	42	62	40	44	44	56	50	60	0	24	3	925,000$
15	Tyler Wotherspoon				X	100.00	79	76	85	61	76	72	78	51	25	47	41	63	39	44	44	55	50	60	0	28	1	800,000$
16	Dan Renouf				X	100.00	71	76	59	62	76	73	80	50	25	43	40	61	38	53	53	52	50	59	0	27	2	700,000$
17	Parker Wotherspoon				X	100.00	63	62	64	63	62	76	83	52	25	49	41	56	39	44	44	53	50	57	0	23	3	854,722$
Rayé

	MOYENNE D'ÉQUIPE					100.00	72	56	81	72	70	67	75	61	43	58	56	62	38	51	52	62	50	62

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

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Dan Vladar (R)	100.00	68	58	73	80	70	72	72	76	76	75	30	44	44	70	50	68	0
2	Jake Oettinger	100.00	59	52	65	86	63	63	56	63	63	62	30	44	44	60	50	61	0
Rayé
1	Ilya Sorokin (R)	100.00	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	0

	MOYENNE D'ÉQUIPE	100.00	59	53	63	72	61	62	59	63	63	62	37	46	46	60	50	60

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Chris Taylor	53	38	46	55	50	47	48	CAN	44	3	700,000$

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

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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	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	FW	FT	S1	S2	S3
1	Max Jones	Thunderbirds (FLA)	LW	19	7	8	15	0	4	0	17	23	52	15	27	13.46%	0	326	17.16	4	5	9	25	92	1	41	0	0	44.44%	18	0	0.92	0	0	2	1	1
2	Emil Bemstrom	Thunderbirds (FLA)	C/LW/RW	19	6	9	15	5	2	0	18	22	29	11	32	20.69%	0	314	16.56	1	4	5	9	95	0	2	1	1	28.57%	14	1	0.95	0	0	1	1	1
3	Erik Brannstrom	Thunderbirds (FLA)	D	19	3	7	10	1	18	0	25	19	28	3	20	10.71%	19	446	23.50	3	2	5	22	91	0	61	1	0	0.00%	0	0	0.45	0	0	1	0	2
4	Luke Johnson	Thunderbirds (FLA)	C	19	2	8	10	1	8	0	21	52	43	13	26	4.65%	0	331	17.44	1	2	3	13	55	1	78	0	0	61.71%	410	0	0.60	0	0	0	0	1
5	Sam Steel	Thunderbirds (FLA)	C	19	0	9	9	5	4	0	10	39	31	6	26	0.00%	1	349	18.38	0	4	4	7	106	0	2	0	0	66.75%	397	0	0.52	0	0	0	1	1
6	Martin Necas	Thunderbirds (FLA)	C/RW	19	7	2	9	5	8	0	34	21	57	16	46	12.28%	1	360	18.97	1	2	3	16	106	0	17	1	0	46.43%	28	1	0.50	0	0	0	2	1
7	Alexandre Carrier	Thunderbirds (FLA)	D	19	2	6	8	1	12	0	24	25	16	2	8	12.50%	15	427	22.52	1	4	5	10	97	1	60	1	0	0.00%	0	0	0.37	0	0	1	0	1
8	Michael McCarron	Thunderbirds (FLA)	C/RW	19	2	4	6	-5	29	5	29	27	25	7	18	8.00%	1	264	13.95	1	2	3	8	51	1	52	0	0	62.04%	245	0	0.45	1	0	1	0	0
9	Alexander Nylander	Thunderbirds (FLA)	LW/RW	19	0	6	6	0	0	0	10	25	45	16	36	0.00%	2	388	20.45	0	5	5	10	100	0	71	0	0	44.44%	27	0	0.31	0	0	0	0	0
10	Matthew Highmore	Thunderbirds (FLA)	LW/RW	19	2	3	5	-4	6	0	8	27	14	8	16	14.29%	1	197	10.39	0	1	1	1	12	0	22	1	0	38.46%	13	0	0.51	0	0	0	1	0
11	Cooper Marody	Thunderbirds (FLA)	C/RW	19	0	4	4	1	4	0	10	23	9	1	11	0.00%	4	158	8.34	0	0	0	0	0	0	0	0	0	68.35%	79	0	0.50	0	0	0	0	0
12	Michael Dal Colle	Thunderbirds (FLA)	LW/RW	19	2	1	3	-6	2	0	1	8	9	2	6	22.22%	1	92	4.85	2	0	2	3	5	0	0	2	0	25.00%	4	0	0.65	0	0	0	1	0
13	Lucas Carlsson	Thunderbirds (FLA)	D	19	1	2	3	-6	20	0	25	11	9	1	3	11.11%	10	310	16.32	0	0	0	1	24	0	15	0	0	0.00%	0	0	0.19	0	0	0	0	0
14	Tyler Wotherspoon	Thunderbirds (FLA)	D	19	2	1	3	1	14	0	29	18	15	2	7	13.33%	20	413	21.75	1	0	1	6	96	0	53	1	0	0.00%	0	0	0.15	0	0	0	0	0
15	Parker Wotherspoon	Thunderbirds (FLA)	D	17	2	0	2	2	27	5	28	8	8	2	7	25.00%	18	348	20.47	2	0	2	6	87	0	60	2	0	0.00%	0	0	0.11	0	1	0	1	0
16	Trevor Lewis	Thunderbirds (FLA)	C/RW	9	1	1	2	0	4	0	7	13	17	1	7	5.88%	2	81	9.11	0	0	0	0	0	0	0	0	0	66.67%	3	0	0.49	0	0	0	1	0
17	Brendan Guhle	Panthers	D	2	0	1	1	0	2	0	2	4	4	1	0	0.00%	0	43	21.55	0	1	1	4	10	0	3	0	0	0.00%	0	0	0.46	0	0	0	0	0
18	Dan Renouf	Thunderbirds (FLA)	D	19	0	0	0	-6	18	0	42	16	4	5	6	0.00%	17	307	16.19	0	0	0	1	8	0	32	0	0	0.00%	0	0	0.00	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				313	39	72	111	-5	182	10	340	381	415	112	302	9.40%	112	5161	16.49	17	32	49	142	1045	4	576	10	1	62.12%	1238	2	0.43	1	1	6	9	8

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

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	SO	GA	SA	A	EG	ST	BG	S1	S2	S3
1	Dan Vladar	Thunderbirds (FLA)	19	10	5	4	0.913	1.58	1179	3	31	357	1	1	19	0	2	1	1
2	Jake Oettinger	Thunderbirds (FLA)	1	0	0	0	1.000	0.00	15	0	0	9	0	0	0	19	0	0	0
Stats d'équipe Total ou en Moyenne			20	10	5	4	0.915	1.56	1195	3	31	366	1	1	19	19	2	1	1

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

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	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
Alexander Nylander	Thunderbirds (FLA)	LW/RW	23	1998-03-01	Yes	180 Lbs	6 ft1	No	No	No	3	Pro & Farm	1,775,000$	0$	0$	No	1,775,000$	1,775,000$	Lien
Alexandre Carrier	Thunderbirds (FLA)	D	24	1996-10-08	No	174 Lbs	5 ft11	No	No	No	1	Pro & Farm	800,000$	0$	0$	No			Lien / Lien NHL
Cooper Marody	Thunderbirds (FLA)	C/RW	24	1996-12-20	No	173 Lbs	6 ft0	No	No	No	2	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$		Lien / Lien NHL
Dan Renouf	Thunderbirds (FLA)	D	27	1994-06-01	No	205 Lbs	6 ft2	Yes	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$		Lien / Lien NHL
Dan Vladar	Thunderbirds (FLA)	G	23	1997-08-20	Yes	185 Lbs	6 ft5	No	No	No	3	Pro & Farm	750,000$	0$	0$	No	750,000$	750,000$	Lien / Lien NHL
Emil Bemstrom	Thunderbirds (FLA)	C/LW/RW	22	1999-06-01	Yes	181 Lbs	5 ft10	No	No	No	3	Pro & Farm	1,633,333$	0$	0$	No	1,633,333$	1,633,333$	Lien / Lien NHL
Erik Brannstrom	Thunderbirds (FLA)	D	21	1999-09-02	Yes	173 Lbs	5 ft10	No	No	No	3	Pro & Farm	1,375,000$	0$	0$	No	1,375,000$	1,375,000$	Lien / Lien NHL
Ilya Sorokin	Thunderbirds (FLA)	G	25	1995-08-04 2:47:45 PM	Yes	190 Lbs	6 ft3	No	No	No	3	Pro & Farm	2,000,000$	0$	0$	No	2,000,000$	2,000,000$
Jake Oettinger	Thunderbirds (FLA)	G	22	1998-12-18	No	212 Lbs	6 ft4	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$		Lien / Lien NHL
Lucas Carlsson	Thunderbirds (FLA)	D	24	1997-07-05	Yes	190 Lbs	6 ft0	No	No	No	3	Pro & Farm	925,000$	0$	0$	No	925,000$	925,000$	Lien / Lien NHL
Luke Johnson	Thunderbirds (FLA)	C	26	1994-09-18	No	198 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$		Lien / Lien NHL
Martin Necas	Thunderbirds (FLA)	C/RW	22	1999-01-14	Yes	167 Lbs	6 ft1	No	No	No	3	Pro & Farm	1,462,000$	0$	0$	No	1,462,000$	1,462,000$	Lien / Lien NHL
Matthew Highmore	Thunderbirds (FLA)	LW/RW	25	1996-02-27	No	181 Lbs	5 ft11	No	No	No	1	Pro & Farm	925,000$	0$	0$	No			Lien / Lien NHL
Max Jones	Thunderbirds (FLA)	LW	23	1998-02-16	No	203 Lbs	6 ft3	No	No	No	2	Pro & Farm	863,333$	0$	0$	No	863,333$		Lien / Lien NHL
Michael Dal Colle	Thunderbirds (FLA)	LW/RW	25	1996-06-19	No	198 Lbs	6 ft3	No	No	No	2	Pro & Farm	3,063,333$	0$	0$	No	3,063,333$		Lien / Lien NHL
Michael McCarron	Thunderbirds (FLA)	C/RW	26	1995-03-06	No	231 Lbs	6 ft6	No	No	No	1	Pro & Farm	1,076,000$	0$	0$	No			Lien / Lien NHL
Parker Wotherspoon	Thunderbirds (FLA)	D	23	1997-08-24	No	168 Lbs	6 ft0	No	No	No	3	Pro & Farm	854,722$	0$	0$	No	854,722$	854,722$	Lien / Lien NHL
Sam Steel	Thunderbirds (FLA)	C	23	1998-02-03	No	178 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$		Lien / Lien NHL
Trevor Lewis	Thunderbirds (FLA)	C/RW	34	1987-01-07	No	205 Lbs	6 ft1	Yes	No	No	2	Pro & Farm	800,000$	0$	0$	No	800,000$		Lien / Lien NHL
Tyler Wotherspoon	Thunderbirds (FLA)	D	28	1993-03-11	No	207 Lbs	6 ft2	No	No	No	1	Pro & Farm	800,000$	0$	0$	No			Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
20	24.50	190 Lbs	6 ft1	2.20	1,178,886$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Emil Bemstrom	Sam Steel	Martin Necas	40	0	1	4
2	Max Jones	Luke Johnson	Alexander Nylander	30	0	1	4
3	Michael Dal Colle	Michael McCarron	Matthew Highmore	20	1	2	2
4		Cooper Marody	Trevor Lewis	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Parker Wotherspoon	Erik Brannstrom	40	1	2	2
2	Alexandre Carrier	Tyler Wotherspoon	30	1	2	2
3	Lucas Carlsson	Dan Renouf	20	1	2	2
4		Erik Brannstrom	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Emil Bemstrom	Sam Steel	Martin Necas	60	0	0	5
2	Max Jones	Luke Johnson	Alexander Nylander	40	0	0	5

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Parker Wotherspoon	Erik Brannstrom	60	1	2	2
2	Alexandre Carrier	Tyler Wotherspoon	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Luke Johnson	Alexander Nylander	60	1	2	2
2	Michael McCarron	Max Jones	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Parker Wotherspoon	Erik Brannstrom	60	1	2	2
2	Alexandre Carrier	Tyler Wotherspoon	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Luke Johnson	60	1	2	2	Parker Wotherspoon	Erik Brannstrom	60	1	2	2
2	Michael McCarron	40	1	2	2	Alexandre Carrier	Tyler Wotherspoon	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Luke Johnson	Alexander Nylander	60	1	2	2
2	Sam Steel	Max Jones	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Parker Wotherspoon	Erik Brannstrom	60	1	2	2
2	Alexandre Carrier	Tyler Wotherspoon	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Emil Bemstrom	Sam Steel	Martin Necas	Parker Wotherspoon	Erik Brannstrom

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Max Jones	Luke Johnson	Michael McCarron	Parker Wotherspoon	Erik Brannstrom

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Luke Johnson, Matthew Highmore, Michael McCarron	Luke Johnson, Matthew Highmore	Michael McCarron

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Lucas Carlsson, Dan Renouf, Alexandre Carrier	Lucas Carlsson	Dan Renouf, Alexandre Carrier

Tirs de Pénalité
Martin Necas, Alexander Nylander, Sam Steel, Max Jones, Emil Bemstrom

Gardien
#1 : Dan Vladar, #2 : Jake Oettinger

Lignes d'Attaque Perso. en Prol.
Martin Necas, Alexander Nylander, Sam Steel, Max Jones, Emil Bemstrom, Luke Johnson, Luke Johnson, Michael Dal Colle, Matthew Highmore, Michael McCarron, Cooper Marody

Lignes de Défense Perso. en Prol.
Parker Wotherspoon, Erik Brannstrom, Alexandre Carrier, Tyler Wotherspoon, Lucas Carlsson

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	Bruins	7	4	3	0	0	0	0	0	14	12	2	4	4	0	0	0	0	0	0	10	4	6	3	0	3	0	0	0	0	0	4	8	-4	8	0.571	14	27	41	1	1	18	11	9	1	145	152	111	131	25	147	35	71	126	55	8	14.55%	32	1	96.88%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
2	Comets	6	2	4	0	0	0	0	0	10	12	-2	3	1	2	0	0	0	0	0	3	5	-2	3	1	2	0	0	0	0	0	7	7	0	4	0.333	10	19	29	0	0	18	11	9	1	138	152	111	131	25	95	45	52	102	44	6	13.64%	25	2	92.00%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
3	Monsters	6	4	2	0	0	0	0	0	15	8	7	3	2	1	0	0	0	0	0	10	5	5	3	2	1	0	0	0	0	0	5	3	2	8	0.667	15	26	41	0	2	18	11	9	1	136	152	111	131	25	125	33	59	121	26	3	11.54%	26	2	92.31%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
	Total	19	10	9	0	0	0	0	0	39	32	7	10	7	3	0	0	0	0	0	23	14	9	9	3	6	0	0	0	0	0	16	18	-2	20	0.526	39	72	111	1	3	18	11	9	1	419	152	111	131	25	367	113	182	349	125	17	13.60%	83	5	93.98%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
	_Since Last GM Reset	19	10	9	0	0	0	0	0	39	32	7	10	7	3	0	0	0	0	0	23	14	9	9	3	6	0	0	0	0	0	16	18	-2	20	0.526	39	72	111	1	3	18	11	9	1	419	152	111	131	25	367	113	182	349	125	17	13.60%	83	5	93.98%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
	_Vs Conference	19	10	9	0	0	0	0	0	39	32	7	10	7	3	0	0	0	0	0	23	14	9	9	3	6	0	0	0	0	0	16	18	-2	20	0.526	39	72	111	1	3	18	11	9	1	419	152	111	131	25	367	113	182	349	125	17	13.60%	83	5	93.98%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
	_Vs Division	6	4	3	0	0	0	0	0	15	8	7	3	4	0	0	0	0	0	0	10	5	5	3	0	3	0	0	0	0	0	5	3	2	8	0.667	15	26	41	0	2	18	11	9	1	136	152	111	131	25	125	33	59	121	26	3	11.54%	26	2	92.31%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124

			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
19	20	OTL1	39	72	111	419	367	113	182	349	1	3

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
19	10	9	0	0	0	0	39	32

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
10	7	3	0	0	0	0	23	14

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
9	3	6	0	0	0	0	16	18

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
3	4	1	2	0	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
125	17	13.60%	83	5	93.98%	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
152	111	131	25	18	11	9	1

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
345	570	60.53%	271	497	54.53%	169	273	61.90%

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
514	367	437	141	243	124

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	Lien
1 - 2020-10-24	2	Bruins	1	Thunderbirds	2	W		Sommaire du Match
3 - 2020-10-26	10	Bruins	1	Thunderbirds	2	W		Sommaire du Match
5 - 2020-10-28	18	Thunderbirds	0	Bruins	2	L		Sommaire du Match
7 - 2020-10-30	26	Thunderbirds	1	Bruins	2	L	X	Sommaire du Match
9 - 2020-11-01	34	Bruins	2	Thunderbirds	4	W		Sommaire du Match
11 - 2020-11-03	42	Thunderbirds	3	Bruins	4	L	X	Sommaire du Match
13 - 2020-11-05	50	Bruins	0	Thunderbirds	2	W		Sommaire du Match
15 - 2020-11-07	58	Monsters	2	Thunderbirds	6	W		Sommaire du Match
17 - 2020-11-09	62	Monsters	0	Thunderbirds	4	W		Sommaire du Match
19 - 2020-11-11	66	Thunderbirds	1	Monsters	2	L	X	Sommaire du Match
21 - 2020-11-13	70	Thunderbirds	2	Monsters	1	W	X	Sommaire du Match
23 - 2020-11-15	74	Monsters	3	Thunderbirds	0	L		Sommaire du Match
25 - 2020-11-17	78	Thunderbirds	2	Monsters	0	W		Sommaire du Match
29 - 2020-11-21	85	Thunderbirds	0	Comets	2	L		Sommaire du Match
31 - 2020-11-23	87	Thunderbirds	5	Comets	1	W		Sommaire du Match
33 - 2020-11-25	89	Comets	2	Thunderbirds	0	L		Sommaire du Match
35 - 2020-11-27	91	Comets	1	Thunderbirds	2	W		Sommaire du Match
37 - 2020-11-29	93	Thunderbirds	2	Comets	4	L		Sommaire du Match
39 - 2020-12-01	95	Comets	2	Thunderbirds	1	L	X	Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité	2000	1000
Prix des Billets	55	0
Assistance	20,000	10,000
Assistance PCT	100.00%	100.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité	Popularité de l'Équipe
31	3000 - 100.00%	97,200$	972,000$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
0$	2,357,771$	2,168,822$	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

	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
2020	82	52	20	0	2	2	4	2	351	160	191	41	30	7	0	1	1	1	1	187	72	115	41	22	13	0	1	1	3	1	164	88	76	120	351	624	975	0	16	143	112	90	9	2227	732	746	736	29	1437	396	909	1526	349	58	16.62%	376	46	87.77%	7	1503	2301	65.32%	1134	1856	61.10%	771	1229	62.73%	2511	1908	1526	476	922	516
Total Saison Régulière	82	52	20	0	2	2	4	2	351	160	191	41	30	7	0	1	1	1	1	187	72	115	41	22	13	0	1	1	3	1	164	88	76	120	351	624	975	0	16	143	112	90	9	2227	732	746	736	29	1437	396	909	1526	349	58	16.62%	376	46	87.77%	7	1503	2301	65.32%	1134	1856	61.10%	771	1229	62.73%	2511	1908	1526	476	922	516
2020	19	10	9	0	0	0	0	0	39	32	7	10	7	3	0	0	0	0	0	23	14	9	9	3	6	0	0	0	0	0	16	18	-2	20	39	72	111	1	3	18	11	9	1	419	152	111	131	25	367	113	182	349	125	17	13.60%	83	5	93.98%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124
Total Séries	19	10	9	0	0	0	0	0	39	32	7	10	7	3	0	0	0	0	0	23	14	9	9	3	6	0	0	0	0	0	16	18	-2	20	39	72	111	1	3	18	11	9	1	419	152	111	131	25	367	113	182	349	125	17	13.60%	83	5	93.98%	0	345	570	60.53%	271	497	54.53%	169	273	61.90%	514	367	437	141	243	124

	Thunderbirds
	GP: 19 \| W: 10 \| L: 9 GF: 39 \| GA: 32 \| PP%: 13.60% \| PK%: 93.98% DG: Scott \| Morale : 50 \| Moyenne d'Équipe : 62