Stanley Cup Winners Hockey League

TORONTO Maple Leafs
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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	Casey Mittelstadt	0	X				100.00	62	39	88	66	77	81	83	64	71	63	62	65	66	63	65	87	98	69	0
2	Adam Erne	0		X			100.00	89	47	77	63	80	74	87	62	58	61	59	57	63	68	65	74	98	68	0
3	Henrik Borgstrom	0	X				100.00	64	43	86	64	83	80	77	66	73	61	59	63	66	65	62	81	98	68	0
4	Brett Ritchie	0			X		100.00	84	50	78	61	89	73	75	59	52	57	58	60	59	73	68	62	98	67	0
5	John Quenneville (R)	0	X				100.00	61	45	86	63	76	83	80	61	73	60	62	64	63	67	64	76	98	67	0
6	Paul Bittner (R)	0	X	X			100.00	73	51	87	59	90	85	82	58	55	57	56	60	59	67	64	73	98	67	0
7	Dominic Toninato (R)	0	X				100.00	63	47	82	61	79	80	78	59	65	58	60	62	59	71	66	57	98	67	0
8	Jordan Kyrou (R)	0	X				100.00	57	39	89	67	69	77	76	66	58	63	64	65	68	63	64	76	98	67	0
9	Lukas Vejdemo (R)	0	X				100.00	62	45	90	62	79	78	83	63	74	61	58	64	65	67	64	62	98	67	0
10	Rasmus Asplund (R)	0	X				100.00	58	39	82	66	70	81	78	63	72	64	61	65	63	65	66	77	99	67	0
11	Isaac Ratcliffe (R)	0		X			100.00	74	52	83	56	92	85	87	55	51	54	54	59	53	61	62	76	98	66	0
12	Nicholas Merkley (R)	0			X		100.00	58	42	89	60	69	77	73	62	57	61	59	58	62	65	63	78	98	64	0
13	Matthew Phillips (R)	0			X		100.00	53	39	87	61	57	83	78	62	58	60	59	57	62	63	61	64	98	64	0
14	Spencer Smallman (R)	0			X		100.00	63	45	84	60	77	75	73	61	55	59	52	58	61	67	63	52	98	64	0
15	Daniel O'Regan (R)	0	X				100.00	44	35	82	76	58	47	41	61	48	58	58	58	58	58	49	79	98	57	0
16	Ryan Collins (R)	0				X	100.00	77	49	87	55	93	76	78	56	30	53	54	60	45	67	64	70	98	65	0
17	Martin Fehervary (R)	0				X	100.00	64	45	87	60	81	84	74	58	30	57	57	59	46	61	63	74	98	65	0
18	Nick Ebert	0				X	100.00	62	47	86	57	74	82	81	56	30	55	54	58	46	71	66	59	98	64	0
19	Jesper Lindgren (R)	0				X	100.00	53	41	83	58	67	75	74	57	30	56	52	54	51	65	63	66	98	61	0
20	Andrew Welinski (R)	0				X	100.00	48	35	81	65	75	47	43	50	30	41	40	56	49	60	50	71	98	52	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	63	44	85	62	77	76	75	60	54	58	57	60	58	65	63	71	98	65

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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	Ville Husso (R)	100.00	74	84	82	84	73	72	74	73	72	74	73	69	73	61	98	68	0
2	Marcus Hogberg (R)	100.00	73	75	73	91	72	71	73	72	71	73	72	71	75	62	98	67	0
3	Veini Vehvilainen (R)	100.00	71	82	80	71	70	69	71	70	69	71	70	65	69	62	98	64	0
Rayé
1	Brandon Halverson (R)	100.00	60	58	56	88	59	58	60	59	58	60	59	67	71	67	98	57	0

	MOYENNE D'ÉQUIPE	100.00	70	75	73	84	69	68	70	69	68	70	69	68	72	63	98	64

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Eric Veilleux	66	66	66	66	70	70	74	CAN	48	3	300,000$

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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	PKS	PKM	GW	GT	FO%	FOT	EG	HT	P/20	PSG	PSS	FT	S1	S2	S3
1	Ryan Collins	Marlies de Toronto (TOR)	D	38	8	28	36	-12	56	0	117	58	93	28	67	8.60%	107	927	24.42	3	5	8	43	116	0	70	0	0	0.00%	0	0	0	0.78	0	0	0	3	2	0
2	Casey Mittelstadt	Marlies de Toronto (TOR)	C	38	19	16	35	-2	2	0	42	88	173	48	105	10.98%	29	719	18.93	6	8	14	36	113	5	116	2	2	56.87%	779	0	1	0.97	1	3	0	1	1	4
3	Brett Ritchie	Marlies de Toronto (TOR)	RW	38	12	17	29	-2	10	0	64	41	152	34	95	7.89%	18	684	18.02	3	5	8	35	113	0	0	1	0	47.83%	46	0	0	0.85	1	1	0	1	3	0
4	Adam Erne	Marlies de Toronto (TOR)	LW	38	11	17	28	-2	33	5	108	51	135	42	110	8.15%	21	734	19.34	2	4	6	20	113	3	47	0	0	59.52%	42	0	1	0.76	0	1	0	1	1	1
5	Paul Bittner	Marlies de Toronto (TOR)	C/LW	38	11	16	27	-3	14	0	70	39	143	35	81	7.69%	15	593	15.61	4	8	12	37	113	0	2	3	1	51.28%	39	0	0	0.91	0	0	0	1	2	2
6	Martin Fehervary	Marlies de Toronto (TOR)	D	38	5	16	21	-9	38	0	45	27	52	17	46	9.62%	49	765	20.15	2	6	8	25	106	0	75	0	0	0.00%	0	0	0	0.55	0	0	0	0	0	1
7	Henrik Borgstrom	Marlies de Toronto (TOR)	C	38	12	8	20	6	12	0	22	70	90	17	60	13.33%	9	472	12.45	0	0	0	0	0	4	50	3	0	56.46%	604	1	1	0.85	3	3	0	0	3	1
8	Nick Ebert	Marlies de Toronto (TOR)	D	38	7	11	18	-8	31	5	48	17	58	20	26	12.07%	37	744	19.59	4	2	6	32	105	0	61	1	1	0.00%	0	0	0	0.48	0	0	1	1	0	2
9	Isaac Ratcliffe	Marlies de Toronto (TOR)	LW	38	7	7	14	6	14	0	44	23	39	11	39	17.95%	9	416	10.97	0	0	0	0	0	0	0	1	1	52.50%	40	0	0	0.67	0	0	0	2	0	1
10	Jesper Lindgren	Marlies de Toronto (TOR)	D	38	1	11	12	9	16	0	43	23	29	9	10	3.45%	38	547	14.40	0	0	0	3	8	2	9	0	0	0.00%	0	0	0	0.44	0	0	0	0	1	1
11	Andrew Welinski	Marlies de Toronto (TOR)	D	38	2	9	11	9	14	0	40	17	16	6	7	12.50%	41	558	14.71	0	0	0	0	0	0	39	0	0	0.00%	0	0	0	0.39	0	0	0	0	1	0
12	John Quenneville	Marlies de Toronto (TOR)	C	38	5	5	10	-14	6	0	31	50	105	37	92	4.76%	10	377	9.93	0	0	0	0	0	0	0	1	0	57.86%	337	0	0	0.53	0	0	0	0	1	1
13	Matthew Phillips	Marlies de Toronto (TOR)	RW	38	3	7	10	6	0	0	7	42	77	17	51	3.90%	4	417	10.97	0	0	0	0	0	0	0	0	0	48.65%	37	0	0	0.48	0	0	0	0	0	0
14	Dominic Toninato	Marlies de Toronto (TOR)	C	38	0	0	0	0	0	0	2	0	3	0	1	0.00%	1	7	0.20	0	0	0	1	2	0	0	0	0	100.00%	1	0	0	0.00	0	0	0	0	0	0
15	Jordan Kyrou	Marlies de Toronto (TOR)	C	38	0	0	0	0	0	0	0	0	0	0	0	0.00%	0	2	0.06	0	0	0	0	2	0	0	0	0	0.00%	0	0	0	0.00	0	0	0	0	0	0
16	Lukas Vejdemo	Marlies de Toronto (TOR)	C	38	0	0	0	0	0	0	4	0	1	0	0	0.00%	2	33	0.89	0	0	0	0	0	1	26	0	0	20.00%	5	0	0	0.00	0	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				608	103	168	271	-16	246	10	687	546	1166	321	790	8.83%	390	8004	13.16	24	38	62	232	794	15	501	12	5	56.32%	1930	1	3	0.68	5	8	1	10	15	14

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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	Ville Husso	Marlies de Toronto (TOR)	38	16	17	5	0.918	2.96	2308	4	1	114	1388	0	4	1	0.818	11	38	0	8	1	0
Stats d'équipe Total ou en Moyenne			38	16	17	5	0.918	2.96	2308	4	1	114	1388	0	4	1	0.818	11	38	0	8	1	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	Salaire Moyen	Salaire Moyen Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Adam Erne	Marlies de Toronto (TOR)	LW	25	1995-04-20	No	96 Kg	185 CM	No	No	No	4	Pro & Farm	800,000$	418,348$	800,000$	418,348$	0$	0$	No	800,000$	800,000$	800,000$	Lien
Andrew Welinski	Marlies de Toronto (TOR)	D	27	1993-04-27	Yes	91 Kg	188 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Brandon Halverson	Marlies de Toronto (TOR)	G	24	1996-03-29	Yes	95 Kg	193 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Brett Ritchie	Marlies de Toronto (TOR)	RW	27	1993-07-01	No	100 Kg	193 CM	No	No	No	2	Pro & Farm	1,750,000$	915,137$	1,750,000$	915,137$	0$	0$	No	1,750,000$			Lien
Casey Mittelstadt	Marlies de Toronto (TOR)	C	21	1998-11-22	No	90 Kg	185 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No				Lien
Daniel O'Regan	Marlies de Toronto (TOR)	C	26	1994-01-30	Yes	82 Kg	175 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Dominic Toninato	Marlies de Toronto (TOR)	C	26	1994-03-09	Yes	87 Kg	188 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Henrik Borgstrom	Marlies de Toronto (TOR)	C	23	1997-08-06	No	90 Kg	191 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No				Lien
Isaac Ratcliffe	Marlies de Toronto (TOR)	LW	21	1999-02-15	Yes	91 Kg	198 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Jesper Lindgren	Marlies de Toronto (TOR)	D	23	1997-05-19	Yes	75 Kg	183 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
John Quenneville	Marlies de Toronto (TOR)	C	24	1996-04-16	Yes	89 Kg	185 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Jordan Kyrou	Marlies de Toronto (TOR)	C	22	1998-05-05	Yes	80 Kg	183 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Lukas Vejdemo	Marlies de Toronto (TOR)	C	24	1996-01-25	Yes	88 Kg	188 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Marcus Hogberg	Marlies de Toronto (TOR)	G	25	1994-11-25	Yes	95 Kg	196 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Martin Fehervary	Marlies de Toronto (TOR)	D	20	1999-10-06	Yes	91 Kg	188 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No
Matthew Phillips	Marlies de Toronto (TOR)	RW	22	1998-04-06	Yes	64 Kg	170 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Nicholas Merkley	Marlies de Toronto (TOR)	RW	23	1997-05-23	Yes	88 Kg	178 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Nick Ebert	Marlies de Toronto (TOR)	D	26	1994-05-11	No	91 Kg	183 CM	No	No	No	1	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No				Lien
Paul Bittner	Marlies de Toronto (TOR)	C/LW	23	1996-11-04	Yes	93 Kg	196 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Rasmus Asplund	Marlies de Toronto (TOR)	C	22	1997-12-03	Yes	86 Kg	180 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Ryan Collins	Marlies de Toronto (TOR)	D	24	1996-05-06	Yes	98 Kg	196 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Spencer Smallman	Marlies de Toronto (TOR)	RW	23	1996-09-09	Yes	90 Kg	185 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Veini Vehvilainen	Marlies de Toronto (TOR)	G	23	1997-02-13	Yes	82 Kg	183 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$
Ville Husso	Marlies de Toronto (TOR)	G	25	1995-02-06	Yes	93 Kg	191 CM	No	No	No	4	Pro & Farm	750,000$	392,201$	750,000$	392,201$	0$	0$	No	750,000$	750,000$	750,000$

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
24	23.71	89 Kg	188 CM	3.42	793,750$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Adam Erne		Brett Ritchie	31	1	2	2
2	Paul Bittner	Casey Mittelstadt		26	1	2	2
3	Isaac Ratcliffe	Henrik Borgstrom	Matthew Phillips	23	1	2	2
4		John Quenneville		20	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Ryan Collins	31	1	2	2
2	Martin Fehervary	Nick Ebert	26	1	2	2
3	Jesper Lindgren	Andrew Welinski	23	1	2	2
4		Ryan Collins	20	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Adam Erne		Brett Ritchie	55	1	2	2
2	Paul Bittner	Casey Mittelstadt		45	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Ryan Collins	55	1	2	2
2	Martin Fehervary	Nick Ebert	45	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Casey Mittelstadt	55	1	2	2
2	Henrik Borgstrom	Adam Erne	45	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Ryan Collins	55	1	2	2
2	Martin Fehervary	Nick Ebert	45	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		55	1	2	2		Ryan Collins	55	1	2	2
2	Casey Mittelstadt	45	1	2	2	Martin Fehervary	Nick Ebert	45	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1		Casey Mittelstadt	55	1	2	2
2	Henrik Borgstrom	Adam Erne	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Ryan Collins	55	1	2	2
2	Martin Fehervary	Nick Ebert	45	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Adam Erne		Brett Ritchie		Ryan Collins

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Adam Erne		Brett Ritchie		Ryan Collins

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Dominic Toninato, Jordan Kyrou, Lukas Vejdemo	Dominic Toninato, Jordan Kyrou	Lukas Vejdemo

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Jesper Lindgren, Andrew Welinski, Martin Fehervary	Jesper Lindgren	Andrew Welinski, Martin Fehervary

Tirs de Pénalité
, Casey Mittelstadt, Henrik Borgstrom, Adam Erne, Brett Ritchie

Gardien
#1 : Ville Husso, #2 : Marcus Hogberg

Lignes d'Attaque Perso. en Prol.
, Casey Mittelstadt, Henrik Borgstrom, Adam Erne, Brett Ritchie, John Quenneville, John Quenneville, Paul Bittner, Dominic Toninato, Jordan Kyrou, Lukas Vejdemo

Lignes de Défense Perso. en Prol.
, Ryan Collins, Martin Fehervary, Nick Ebert, Jesper Lindgren

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	BRIDGEPORT Sound Tigers	2	0	1	0	1	0	0	0	5	6	-1	1	0	1	0	0	0	0	0	2	4	-2	1	0	0	0	1	0	0	0	3	2	1	2	0.500	5	9	14	0	0	35	39	28	7	43	377	392	406	14	85	25	14	33	3	1	33.33%	5	3	40.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
2	BROOKLYN Wolfpack	1	0	0	0	0	0	1	0	4	3	1	1	0	0	0	0	0	1	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	5	9	0	0	35	39	28	7	30	377	392	406	14	28	5	17	18	5	0	0.00%	2	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
3	Binghampton Devils	2	0	1	0	0	1	0	0	3	6	-3	2	0	1	0	0	1	0	0	3	6	-3	0	0	0	0	0	0	0	0	0	0	0	1	0.250	3	6	9	0	0	35	39	28	7	67	377	392	406	14	90	13	11	47	4	1	25.00%	3	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
4	CHICAGO Wolves	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	5	8	0	0	35	39	28	7	35	377	392	406	14	33	10	4	22	3	1	33.33%	2	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
5	COLORADO Eagles	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	3	5	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	6	9	1	0	35	39	28	7	16	377	392	406	14	41	15	10	20	4	1	25.00%	5	2	60.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
6	HERSEY Bears	2	2	0	0	0	0	0	0	8	4	4	0	0	0	0	0	0	0	0	0	0	0	2	2	0	0	0	0	0	0	8	4	4	4	1.000	8	14	22	0	1	35	39	28	7	44	377	392	406	14	60	13	6	26	6	3	50.00%	3	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
7	HOLLYWOOD Oscar	1	0	1	0	0	0	0	0	0	3	-3	1	0	1	0	0	0	0	0	0	3	-3	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	35	39	28	7	24	377	392	406	14	43	14	10	14	7	0	0.00%	5	1	80.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
8	IOWA Wild	2	0	2	0	0	0	0	0	3	8	-5	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	1	4	-3	0	0.000	3	6	9	0	0	35	39	28	7	66	377	392	406	14	70	29	12	42	8	1	12.50%	6	2	66.67%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
9	LAVAL Rockets	2	0	2	0	0	0	0	0	0	11	-11	0	0	0	0	0	0	0	0	0	0	0	2	0	2	0	0	0	0	0	0	11	-11	0	0.000	0	0	0	0	0	35	39	28	7	42	377	392	406	14	80	21	18	28	6	0	0.00%	7	1	85.71%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
10	LEHIGH VALLEY Phantoms	1	1	0	0	0	0	0	0	4	2	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	2	2	2	1.000	4	6	10	0	0	35	39	28	7	36	377	392	406	14	31	6	4	11	4	2	50.00%	2	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
11	MONT-LAURIER Sommet	1	1	0	0	0	0	0	0	5	3	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	5	3	2	2	1.000	5	7	12	0	0	35	39	28	7	39	377	392	406	14	19	8	4	17	4	1	25.00%	2	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
12	Manitoba Moose	2	0	2	0	0	0	0	0	3	7	-4	0	0	0	0	0	0	0	0	0	0	0	2	0	2	0	0	0	0	0	3	7	-4	0	0.000	3	6	9	0	0	35	39	28	7	46	377	392	406	14	72	25	14	39	6	1	16.67%	7	2	71.43%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
13	PROVIDENCE Bruins	1	0	1	0	0	0	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	4	-1	0	0.000	3	6	9	0	0	35	39	28	7	18	377	392	406	14	53	17	4	18	5	2	40.00%	2	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
14	PV Sharapovas	2	0	2	0	0	0	0	0	4	8	-4	1	0	1	0	0	0	0	0	2	5	-3	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	4	6	10	0	0	35	39	28	7	57	377	392	406	14	98	28	14	38	5	0	0.00%	7	1	85.71%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
15	Quebec Nordiques	2	1	0	0	0	0	1	0	7	4	3	2	1	0	0	0	0	1	0	7	4	3	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	10	17	0	0	35	39	28	7	73	377	392	406	14	52	10	20	42	4	1	25.00%	9	1	88.89%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
16	ROCKFORD IceHogs	1	1	0	0	0	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	3	2	1	2	1.000	3	4	7	0	0	35	39	28	7	33	377	392	406	14	29	12	4	12	4	0	0.00%	2	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
17	SAN DIEGO Gulls	2	2	0	0	0	0	0	0	19	4	15	1	1	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	10	2	8	4	1.000	19	32	51	0	0	35	39	28	7	135	377	392	406	14	38	13	10	44	2	1	50.00%	5	1	80.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
18	STOCKTON Flames	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	4	8	0	0	35	39	28	7	59	377	392	406	14	77	21	10	36	12	2	16.67%	5	1	80.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
19	Syracruse Crunch	3	1	1	0	1	0	0	0	10	5	5	2	0	1	0	1	0	0	0	4	4	0	1	1	0	0	0	0	0	0	6	1	5	4	0.667	10	17	27	0	0	35	39	28	7	98	377	392	406	14	110	21	22	48	10	1	10.00%	8	1	87.50%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
20	TUSCON Roadrunners	1	0	0	0	0	1	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	2	3	-1	1	0.500	2	3	5	0	0	35	39	28	7	23	377	392	406	14	26	7	6	18	4	0	0.00%	3	1	66.67%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
	Total	38	11	17	0	2	5	3	0	107	115	-8	20	4	8	0	1	4	3	0	53	59	-6	18	7	9	0	1	1	0	0	54	56	-2	37	0.487	107	174	281	1	1	35	39	28	7	1178	377	392	406	14	1389	405	250	687	135	24	17.78%	107	19	82.24%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
22	UTICA Comets	1	0	1	0	0	0	0	0	3	5	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	5	-2	0	0.000	3	4	7	0	0	35	39	28	7	31	377	392	406	14	39	16	8	26	5	1	20.00%	4	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
23	VICTORIAVILLE Tigres	2	0	1	0	0	1	0	0	2	5	-3	2	0	1	0	0	1	0	0	2	5	-3	0	0	0	0	0	0	0	0	0	0	0	1	0.250	2	4	6	0	0	35	39	28	7	62	377	392	406	14	78	18	10	31	8	1	12.50%	5	0	100.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
24	WILKIES-BARRIE Penguins	3	1	0	0	0	2	0	0	9	10	-1	3	1	0	0	0	2	0	0	9	10	-1	0	0	0	0	0	0	0	0	0	0	0	4	0.667	9	14	23	0	0	35	39	28	7	101	377	392	406	14	137	58	18	57	16	3	18.75%	8	2	75.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
	_Since Last GM Reset	38	11	17	0	2	5	3	0	107	115	-8	20	4	8	0	1	4	3	0	53	59	-6	18	7	9	0	1	1	0	0	54	56	-2	37	0.487	107	174	281	1	1	35	39	28	7	1178	377	392	406	14	1389	405	250	687	135	24	17.78%	107	19	82.24%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
	_Vs Conference	15	6	6	0	1	1	1	0	53	49	4	5	2	2	0	0	0	1	0	21	16	5	10	4	4	0	1	1	0	0	32	33	-1	17	0.567	53	88	141	1	1	35	39	28	7	450	377	392	406	14	512	154	101	265	47	10	21.28%	40	8	80.00%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
	_Vs Division	8	2	2	0	0	1	1	0	33	23	10	3	0	0	0	0	0	1	0	12	7	5	5	2	2	0	0	1	0	0	21	16	5	7	0.438	33	50	83	0	0	35	39	28	7	311	377	392	406	14	242	79	48	155	34	5	14.71%	24	4	83.33%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214

			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
38	37	L1	107	174	281	1178	1389	405	250	687	1	1

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
38	11	17	2	5	3	0	107	115

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
20	4	8	1	4	3	0	53	59

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
18	7	9	1	1	0	0	54	56

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
5	5	0	0	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
135	24	17.78%	107	19	82.24%	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
377	392	406	14	35	39	28	7

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
502	1206	41.63%	520	1401	37.12%	217	558	38.89%

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
862	614	1002	264	446	214

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	RI	Lien
1 - 2020-08-02	4	Marlies de Toronto	1	IOWA Wild	4	L				Sommaire du Match
2 - 2020-08-03	19	Quebec Nordiques	1	Marlies de Toronto	3	W				Sommaire du Match
4 - 2020-08-05	33	VICTORIAVILLE Tigres	2	Marlies de Toronto	1	L	X			Sommaire du Match
6 - 2020-08-07	56	WILKIES-BARRIE Penguins	4	Marlies de Toronto	3	L	X			Sommaire du Match
7 - 2020-08-08	66	Marlies de Toronto	3	BRIDGEPORT Sound Tigers	2	W	X			Sommaire du Match
9 - 2020-08-10	83	WILKIES-BARRIE Penguins	3	Marlies de Toronto	2	L	X			Sommaire du Match
11 - 2020-08-12	99	Marlies de Toronto	5	MONT-LAURIER Sommet	3	W				Sommaire du Match
12 - 2020-08-13	112	Marlies de Toronto	3	PROVIDENCE Bruins	4	L				Sommaire du Match
13 - 2020-08-14	125	Binghampton Devils	2	Marlies de Toronto	1	L	X			Sommaire du Match
14 - 2020-08-15	135	Syracruse Crunch	2	Marlies de Toronto	1	L				Sommaire du Match
16 - 2020-08-17	150	Marlies de Toronto	3	HERSEY Bears	0	W				Sommaire du Match
17 - 2020-08-18	162	Quebec Nordiques	3	Marlies de Toronto	4	W	X	X		Sommaire du Match
18 - 2020-08-19	175	Marlies de Toronto	2	TUSCON Roadrunners	3	L	X			Sommaire du Match
19 - 2020-08-20	180	Marlies de Toronto	2	Manitoba Moose	4	L				Sommaire du Match
20 - 2020-08-21	193	PV Sharapovas	5	Marlies de Toronto	2	L				Sommaire du Match
22 - 2020-08-23	212	STOCKTON Flames	2	Marlies de Toronto	3	W	X	X		Sommaire du Match
24 - 2020-08-25	235	VICTORIAVILLE Tigres	3	Marlies de Toronto	1	L				Sommaire du Match
25 - 2020-08-26	250	Marlies de Toronto	3	ROCKFORD IceHogs	2	W				Sommaire du Match
26 - 2020-08-27	261	COLORADO Eagles	5	Marlies de Toronto	3	L				Sommaire du Match
28 - 2020-08-29	277	Marlies de Toronto	6	Syracruse Crunch	1	W				Sommaire du Match
29 - 2020-08-30	285	Marlies de Toronto	0	LAVAL Rockets	7	L				Sommaire du Match
30 - 2020-08-31	292	SAN DIEGO Gulls	2	Marlies de Toronto	9	W				Sommaire du Match
32 - 2020-09-02	313	CHICAGO Wolves	2	Marlies de Toronto	3	W				Sommaire du Match
33 - 2020-09-03	324	Marlies de Toronto	0	LAVAL Rockets	4	L				Sommaire du Match
34 - 2020-09-04	332	Marlies de Toronto	1	Manitoba Moose	3	L				Sommaire du Match
35 - 2020-09-05	345	IOWA Wild	4	Marlies de Toronto	2	L				Sommaire du Match
37 - 2020-09-07	364	WILKIES-BARRIE Penguins	3	Marlies de Toronto	4	W				Sommaire du Match
38 - 2020-09-08	373	Marlies de Toronto	3	UTICA Comets	5	L			R3	Sommaire du Match
40 - 2020-09-10	389	BROOKLYN Wolfpack	3	Marlies de Toronto	4	W	X	X		Sommaire du Match
41 - 2020-09-11	402	Marlies de Toronto	4	LEHIGH VALLEY Phantoms	2	W				Sommaire du Match
43 - 2020-09-13	418	Marlies de Toronto	1	STOCKTON Flames	3	L				Sommaire du Match
44 - 2020-09-14	421	Binghampton Devils	4	Marlies de Toronto	2	L				Sommaire du Match
45 - 2020-09-15	438	Marlies de Toronto	2	PV Sharapovas	3	L				Sommaire du Match
46 - 2020-09-16	447	Syracruse Crunch	2	Marlies de Toronto	3	W	X			Sommaire du Match
48 - 2020-09-18	462	Marlies de Toronto	10	SAN DIEGO Gulls	2	W				Sommaire du Match
49 - 2020-09-19	474	HOLLYWOOD Oscar	3	Marlies de Toronto	0	L				Sommaire du Match
50 - 2020-09-20	488	Marlies de Toronto	5	HERSEY Bears	4	W				Sommaire du Match
51 - 2020-09-21	497	BRIDGEPORT Sound Tigers	4	Marlies de Toronto	2	L				Sommaire du Match
53 - 2020-09-23	511	Marlies de Toronto	-	IOWA Wild	-
54 - 2020-09-24	525	BELLEVILLE Senators	-	Marlies de Toronto	-
56 - 2020-09-26	541	Marlies de Toronto	-	Quebec Nordiques	-
57 - 2020-09-27	551	Quebec Nordiques	-	Marlies de Toronto	-
58 - 2020-09-28	560	Marlies de Toronto	-	WILKIES-BARRIE Penguins	-
59 - 2020-09-29	579	PV Sharapovas	-	Marlies de Toronto	-
60 - 2020-09-30	586	Marlies de Toronto	-	BRIDGEPORT Sound Tigers	-
61 - 2020-10-01	602	LAVAL Rockets	-	Marlies de Toronto	-
63 - 2020-10-03	616	Marlies de Toronto	-	Manitoba Moose	-
64 - 2020-10-04	629	HERSEY Bears	-	Marlies de Toronto	-
65 - 2020-10-05	644	Marlies de Toronto	-	STOCKTON Flames	-
66 - 2020-10-06	653	Marlies de Toronto	-	Binghampton Devils	-
67 - 2020-10-07	660	MONT-LAURIER Sommet	-	Marlies de Toronto	-
69 - 2020-10-09	677	Marlies de Toronto	-	MILWAUKEE Admirals	-
70 - 2020-10-10	688	TUSCON Roadrunners	-	Marlies de Toronto	-
71 - 2020-10-11	704	Marlies de Toronto	-	PROVIDENCE Bruins	-
72 - 2020-10-12	713	ROCKFORD IceHogs	-	Marlies de Toronto	-
74 - 2020-10-14	728	Marlies de Toronto	-	SAN DIEGO Gulls	-
75 - 2020-10-15	738	Manitoba Moose	-	Marlies de Toronto	-
77 - 2020-10-17	757	VICTORIAVILLE Tigres	-	Marlies de Toronto	-
78 - 2020-10-18	766	Marlies de Toronto	-	CHICAGO Wolves	-
79 - 2020-10-19	782	UTICA Comets	-	Marlies de Toronto	-
81 - 2020-10-21	796	Marlies de Toronto	-	TUSCON Roadrunners	-
82 - 2020-10-22	811	CHICAGO Wolves	-	Marlies de Toronto	-
83 - 2020-10-23	823	Marlies de Toronto	-	COLORADO Eagles	-
84 - 2020-10-24	829	Marlies de Toronto	-	VICTORIAVILLE Tigres	-
85 - 2020-10-25	843	ROCKFORD IceHogs	-	Marlies de Toronto	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
87 - 2020-10-27	856	Marlies de Toronto	-	HOLLYWOOD Oscar	-
88 - 2020-10-28	866	Marlies de Toronto	-	BROOKLYN Wolfpack	-
89 - 2020-10-29	874	LEHIGH VALLEY Phantoms	-	Marlies de Toronto	-
90 - 2020-10-30	891	Marlies de Toronto	-	BELLEVILLE Senators	-
91 - 2020-10-31	899	Marlies de Toronto	-	MONT-LAURIER Sommet	-
92 - 2020-11-01	908	LEHIGH VALLEY Phantoms	-	Marlies de Toronto	-
93 - 2020-11-02	922	Marlies de Toronto	-	BELLEVILLE Senators	-
94 - 2020-11-03	933	PROVIDENCE Bruins	-	Marlies de Toronto	-
95 - 2020-11-04	947	Marlies de Toronto	-	BROOKLYN Wolfpack	-
97 - 2020-11-06	959	LAVAL Rockets	-	Marlies de Toronto	-
99 - 2020-11-08	977	COLORADO Eagles	-	Marlies de Toronto	-
102 - 2020-11-11	997	MILWAUKEE Admirals	-	Marlies de Toronto	-
103 - 2020-11-12	1007	Marlies de Toronto	-	UTICA Comets	-
106 - 2020-11-15	1027	MILWAUKEE Admirals	-	Marlies de Toronto	-
107 - 2020-11-16	1032	Marlies de Toronto	-	HOLLYWOOD Oscar	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	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
20	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
911,556$	1,905,000$	1,905,000$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	908,804$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	57	17,477$	996,189$

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
33	82	9	73	0	0	0	0	0	136	849	-713	41	5	36	0	0	0	0	0	73	422	-349	41	4	37	0	0	0	0	0	63	427	-364	18	136	237	373	0	0	73	35	28	0	1514	571	506	437	0	6619	1965	76	1103	99	5	5.05%	31	10	67.74%	0	363	1196	30.35%	342	2229	15.34%	310	1600	19.38%	717	459	3438	496	764	249
34	38	11	17	0	2	5	3	0	107	115	-8	20	4	8	0	1	4	3	0	53	59	-6	18	7	9	0	1	1	0	0	54	56	-2	37	107	174	281	1	1	35	39	28	7	1178	377	392	406	14	1389	405	250	687	135	24	17.78%	107	19	82.24%	0	502	1206	41.63%	520	1401	37.12%	217	558	38.89%	862	614	1002	264	446	214
Total Saison Régulière	120	20	90	0	2	5	3	0	243	964	-721	61	9	44	0	1	4	3	0	126	481	-355	59	11	46	0	1	1	0	0	117	483	-366	55	243	411	654	1	1	108	74	56	7	2692	948	898	843	14	8008	2370	326	1790	234	29	12.39%	138	29	78.99%	0	865	2402	36.01%	862	3630	23.75%	527	2158	24.42%	1579	1074	4440	760	1210	464