Stanley Cup Winners Hockey League

TAMPA BAY Lightning
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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	Austin Czarnik	0	X		X		100.00	57	35	75	68	63	77	67	67	72	69	65	61	66	55	51	37	94	65	0
2	Greg McKegg	0	X				100.00	58	35	73	68	73	65	85	68	75	62	63	57	65	55	52	57	94	65	0
3	Vladislav Kamenev	0	X	X			100.00	64	35	71	65	79	71	61	64	74	63	62	65	63	51	50	74	94	64	0
4	Justin Bailey	0			X		100.00	62	35	67	68	89	53	55	67	50	62	62	52	63	53	51	69	94	62	0
5	Lee Stempniak	0			X		100.00	51	35	71	69	71	51	51	69	50	62	62	53	62	86	61	26	91	62	0
6	Matt Read	0			X		100.00	52	35	66	68	68	55	56	68	52	62	62	53	63	68	55	26	91	61	0
7	Byron Froese	0	X				100.00	54	89	62	70	78	50	50	70	70	63	63	52	63	54	51	49	91	60	0
8	Connor Brickley	0	X	X	X		100.00	58	53	68	64	75	54	57	64	50	59	58	52	59	53	51	61	91	59	0
9	Danick Martel	0		X			100.00	61	35	75	56	64	67	54	56	63	57	57	58	57	51	50	42	94	58	0
10	Jansen Harkins	0	X				100.00	50	89	64	67	74	50	50	67	50	59	59	50	59	50	50	44	94	58	0
11	Reid Boucher	0		X			100.00	50	35	69	70	69	1	50	70	70	70	70	50	70	55	52	60	97	57	0
12	Quinton Howden	0	X	X			100.00	54	37	80	68	73	45	45	58	40	50	54	63	55	62	54	63	88	57	0
13	Roman Lyubimov (R)	0	X	X			100.00	51	35	80	67	76	45	43	57	33	48	54	64	54	62	52	63	94	57	0
14	Jason Garrison	0				X	100.00	59	35	75	61	81	73	58	61	30	53	60	72	66	72	57	23	91	64	0
15	Juuso Valimaki (R)	0				X	100.00	61	35	75	66	83	65	62	66	30	56	53	66	64	51	50	84	91	62	0
16	Andrey Pedan	0				X	100.00	67	47	58	50	87	58	69	58	30	46	40	60	57	51	51	65	94	57	0
17	TJ Brennan	0				X	100.00	57	89	58	70	81	50	50	70	30	50	45	50	55	52	51	62	94	57	0
18	Seth Helgeson	0				X	100.00	64	89	42	58	91	46	50	58	30	46	41	50	51	52	51	48	90	54	0
Rayé
1	Frank Corrado	0				X	100.00	53	89	69	56	75	50	50	56	30	47	42	50	52	53	51	47	71	53	0
2	Ryan Lindgren (R)	0				X	100.00	53	35	53	58	75	47	52	58	30	45	41	51	51	50	50	73	68	52	0

	MOYENNE D'ÉQUIPE					100.00	57	50	68	64	76	54	56	64	48	56	56	56	60	57	52	54	90	59

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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	Philippe Desrosiers	100.00	74	62	81	76	73	73	74	73	73	73	68	50	62	43	94	64	0
2	Jean-Francois Berube	100.00	51	56	69	69	65	55	58	60	61	57	55	64	55	55	94	55	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	63	59	75	73	69	64	66	67	67	65	62	57	59	49	94	60

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Dean Evason	68	68	68	68	76	76	62	CAN	55	3	325,000$

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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	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	S1	S2	S3
1	Vladislav Kamenev	SYRACUSE Crunch (TB )	C/LW	15	11	17	28	22	6	25	21	75	21	66	14.67%	6	283	18.91	4	4	8	13	30	0	1	1	3	43	2	0	66.67%	36	1	1.97	4	0	0
2	Austin Czarnik	SYRACUSE Crunch (TB )	C/RW	15	8	16	24	20	4	12	39	76	26	55	10.53%	3	323	21.59	0	7	7	4	31	1	0	1	5	47	1	1	71.09%	377	1	1.48	1	1	0
3	Greg McKegg	SYRACUSE Crunch (TB )	C	15	6	18	24	14	6	14	23	67	21	56	8.96%	3	270	18.05	0	4	4	6	32	0	0	0	3	34	1	0	66.32%	288	0	1.77	0	1	2
4	Justin Bailey	SYRACUSE Crunch (TB )	RW	15	14	9	23	14	0	14	12	77	13	42	18.18%	3	232	15.50	1	1	2	8	31	0	0	0	0	0	1	0	55.56%	9	1	1.98	1	2	1
5	Jason Garrison	SYRACUSE Crunch (TB )	D	15	4	15	19	14	4	25	16	43	12	22	9.30%	20	365	24.36	2	2	4	11	29	0	0	0	2	39	1	0	0.00%	0	0	1.04	1	1	1
6	Connor Brickley	SYRACUSE Crunch (TB )	C/LW/RW	15	3	14	17	14	2	8	14	44	18	34	6.82%	2	256	17.13	0	1	1	2	31	0	0	0	1	24	0	0	68.75%	16	0	1.32	0	0	1
7	Lee Stempniak	SYRACUSE Crunch (TB )	RW	15	7	10	17	18	4	6	11	67	11	60	10.45%	3	213	14.21	1	0	1	4	11	0	0	0	1	18	0	0	38.89%	18	0	1.60	0	2	1
8	Juuso Valimaki	SYRACUSE Crunch (TB )	D	15	7	10	17	15	4	16	10	39	10	24	17.95%	15	339	22.62	5	5	10	20	30	0	1	1	0	38	1	1	0.00%	0	0	1.00	0	0	0
9	Matt Read	SYRACUSE Crunch (TB )	RW	15	5	11	16	12	6	17	16	42	21	32	11.90%	4	195	13.01	0	0	0	0	5	0	0	0	0	9	0	0	82.35%	17	0	1.64	0	0	2
10	Byron Froese	SYRACUSE Crunch (TB )	C	15	5	11	16	16	14	14	10	62	24	43	8.06%	6	186	12.43	0	1	1	0	0	0	0	0	0	1	1	0	66.34%	205	0	1.72	1	1	0
11	Jansen Harkins	SYRACUSE Crunch (TB )	C	15	5	10	15	9	8	12	14	33	9	31	15.15%	1	149	10.00	0	0	0	0	0	0	0	0	0	0	1	0	64.29%	168	0	2.00	1	0	1
12	Danick Martel	SYRACUSE Crunch (TB )	LW	15	7	4	11	16	2	12	10	45	17	23	15.56%	2	186	12.43	0	0	0	0	0	0	0	0	0	2	0	0	50.00%	16	0	1.18	1	0	0
13	TJ Brennan	SYRACUSE Crunch (TB )	D	15	1	9	10	21	10	11	1	10	10	12	10.00%	8	282	18.86	0	0	0	3	29	0	0	0	0	31	1	0	0.00%	0	0	0.71	1	0	0
14	Quinton Howden	SYRACUSE Crunch (TB )	C/LW	12	6	2	8	8	0	4	10	31	11	17	19.35%	1	160	13.37	0	0	0	3	14	0	0	0	0	0	1	0	42.86%	7	0	1.00	0	2	0
15	Roman Lyubimov	SYRACUSE Crunch (TB )	C/LW	15	3	4	7	8	2	6	7	32	8	21	9.38%	4	144	9.66	0	0	0	0	1	0	0	0	1	6	0	0	44.44%	9	0	0.97	0	1	0
16	Andrey Pedan	SYRACUSE Crunch (TB )	D	15	2	4	6	18	18	29	11	16	3	13	12.50%	12	297	19.86	0	0	0	4	29	0	0	0	1	29	0	0	0.00%	0	0	0.40	0	0	0
17	Reid Boucher	SYRACUSE Crunch (TB )	LW	4	0	0	0	1	0	0	0	2	0	0	0.00%	1	9	2.32	0	0	0	0	0	0	0	0	1	2	0	0	0.00%	0	0	0.00	0	0	0
18	Seth Helgeson	SYRACUSE Crunch (TB )	D	5	0	0	0	4	10	7	0	2	0	3	0.00%	6	91	18.36	0	0	0	0	3	0	0	0	0	7	0	0	0.00%	0	0	0.00	0	0	0
Stats d'équipe Total ou en Moyenne				246	94	164	258	244	100	232	225	763	235	554	12.32%	100	3991	16.22	13	25	38	78	312	1	2	3	18	338	11	2	66.81%	1166	3	1.29	11	11	9

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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	Philippe Desrosiers	SYRACUSE Crunch (TB )	15	11	3	1	0.918	1.79	904	0	2	27	329	0	0	0	0.000	0	15	0	0	0	0
Stats d'équipe Total ou en Moyenne			15	11	3	1	0.918	1.79	904	0	2	27	329	0	0	0	0.000	0	15	0	0	0	0

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Status	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Link
Andrey Pedan	SYRACUSE Crunch (TB )	D	26	1993-07-03	No	97 Kg	196 CM	No	No	No	1	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Austin Czarnik	SYRACUSE Crunch (TB )	C/RW	26	1992-12-12	No	77 Kg	175 CM	No	No	No	4	Avec Restriction	Pro & Farm	1,250,000$	0$	0$	No	Lien
Byron Froese	SYRACUSE Crunch (TB )	C	28	1991-03-12	No	92 Kg	185 CM	No	No	No	3	Sans Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Connor Brickley	SYRACUSE Crunch (TB )	C/LW/RW	27	1992-02-25	No	92 Kg	183 CM	No	No	No	3	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Danick Martel	SYRACUSE Crunch (TB )	LW	24	1994-12-12	No	80 Kg	173 CM	No	No	No	1	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Frank Corrado	SYRACUSE Crunch (TB )	D	26	1993-03-26	No	93 Kg	183 CM	No	No	No	4	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Greg McKegg	SYRACUSE Crunch (TB )	C	27	1992-06-17	No	88 Kg	183 CM	No	No	No	2	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Jansen Harkins	SYRACUSE Crunch (TB )	C	22	1997-05-23	No	83 Kg	185 CM	No	No	No	2	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Jason Garrison	SYRACUSE Crunch (TB )	D	34	1984-11-13	No	99 Kg	185 CM	No	No	No	2	Sans Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Jean-Francois Berube	SYRACUSE Crunch (TB )	G	28	1991-07-13	No	80 Kg	185 CM	No	No	No	3	Sans Restriction	Pro & Farm	750,000$	0$	0$	No
Justin Bailey	SYRACUSE Crunch (TB )	RW	24	1995-07-01	No	97 Kg	193 CM	No	No	No	1	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Juuso Valimaki	SYRACUSE Crunch (TB )	D	20	1998-10-06	Yes	96 Kg	188 CM	No	No	No	3	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Lee Stempniak	SYRACUSE Crunch (TB )	RW	36	1983-02-04	No	89 Kg	180 CM	No	No	No	1	Sans Restriction	Pro & Farm	2,500,000$	0$	0$	No	Lien
Matt Read	SYRACUSE Crunch (TB )	RW	33	1986-06-14	No	85 Kg	178 CM	No	No	No	5	Sans Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Philippe Desrosiers	SYRACUSE Crunch (TB )	G	24	1995-08-16	No	89 Kg	185 CM	No	No	No	4	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Quinton Howden	SYRACUSE Crunch (TB )	C/LW	27	1992-01-21	No	86 Kg	188 CM	No	No	No	6	Avec Restriction	Pro & Farm	750,000$	0$	0$	No
Reid Boucher	SYRACUSE Crunch (TB )	LW	25	1993-09-08	No	89 Kg	178 CM	No	No	No	2	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Roman Lyubimov	SYRACUSE Crunch (TB )	C/LW	27	1992-01-06	Yes	94 Kg	188 CM	No	No	No	2	Avec Restriction	Pro & Farm	950,000$	0$	0$	No
Ryan Lindgren	SYRACUSE Crunch (TB )	D	21	1998-02-11	Yes	91 Kg	183 CM	No	No	No	3	Avec Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Seth Helgeson	SYRACUSE Crunch (TB )	D	28	1990-10-08	No	100 Kg	193 CM	No	No	No	4	Sans Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
TJ Brennan	SYRACUSE Crunch (TB )	D	30	1989-04-03	No	98 Kg	185 CM	No	No	No	5	Sans Restriction	Pro & Farm	750,000$	0$	0$	No	Lien
Vladislav Kamenev	SYRACUSE Crunch (TB )	C/LW	23	1996-08-12	No	88 Kg	188 CM	No	No	No	2	Avec Restriction	Pro & Farm	830,000$	0$	0$	No	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
22	26.64	90 Kg	185 CM	2.86	865,000$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Vladislav Kamenev	Austin Czarnik	Lee Stempniak	31	1	2	2
2	Connor Brickley	Greg McKegg	Justin Bailey	26	1	2	2
3	Danick Martel	Byron Froese	Matt Read	23	1	2	2
4	Quinton Howden	Jansen Harkins	Austin Czarnik	20	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jason Garrison	Juuso Valimaki	31	1	2	2
2	TJ Brennan	Andrey Pedan	26	1	2	2
3	Seth Helgeson	Jason Garrison	23	1	2	2
4	Juuso Valimaki	TJ Brennan	20	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Vladislav Kamenev	Austin Czarnik	Lee Stempniak	55	1	2	2
2	Connor Brickley	Greg McKegg	Justin Bailey	45	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jason Garrison	Juuso Valimaki	55	1	2	2
2	TJ Brennan	Andrey Pedan	45	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Austin Czarnik	Greg McKegg	55	1	2	2
2	Vladislav Kamenev	Lee Stempniak	45	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jason Garrison	Juuso Valimaki	55	1	2	2
2	TJ Brennan	Andrey Pedan	45	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Austin Czarnik	55	1	2	2	Jason Garrison	Juuso Valimaki	55	1	2	2
2	Greg McKegg	45	1	2	2	TJ Brennan	Andrey Pedan	45	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Austin Czarnik	Greg McKegg	55	1	2	2
2	Vladislav Kamenev	Lee Stempniak	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jason Garrison	Juuso Valimaki	55	1	2	2
2	TJ Brennan	Andrey Pedan	45	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Vladislav Kamenev	Austin Czarnik	Lee Stempniak	Jason Garrison	Juuso Valimaki

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Vladislav Kamenev	Austin Czarnik	Lee Stempniak	Jason Garrison	Juuso Valimaki

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Roman Lyubimov, Reid Boucher, Matt Read	Roman Lyubimov, Reid Boucher	Matt Read

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Seth Helgeson, Andrey Pedan, Jason Garrison	Seth Helgeson	Andrey Pedan, Jason Garrison

Tirs de Pénalité
Austin Czarnik, Greg McKegg, Vladislav Kamenev, Lee Stempniak, Justin Bailey

Gardien
#1 : Philippe Desrosiers, #2 : Jean-Francois Berube

Lignes d'Attaque Perso. en Prol.
Austin Czarnik, Greg McKegg, Vladislav Kamenev, Lee Stempniak, Justin Bailey, Matt Read, Matt Read, Byron Froese, Connor Brickley, Jansen Harkins, Danick Martel

Lignes de Défense Perso. en Prol.
Jason Garrison, Juuso Valimaki, TJ Brennan, Andrey Pedan, Seth Helgeson

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	1	0	1	0	0	0	0	0	1	12	-11	1	0	1	0	0	0	0	0	1	12	-11	0	0	0	0	0	0	0	0	0	0	0	0	0.000	1	2	3	0	0	54	39	30	1	3	293	327	351	10	93	21	2	0	1	0	0.00%	0	0	0.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
2	BROOKLYN Wolfpack	1	0	0	0	0	0	0	1	5	6	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	5	6	-1	1	0.500	5	10	15	0	0	54	39	30	1	42	293	327	351	10	71	18	2	23	0	0	0.00%	1	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
3	CHICAGO Wolves	1	1	0	0	0	0	0	0	13	0	13	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	13	0	13	2	1.000	13	26	39	0	1	54	39	30	1	100	293	327	351	10	6	2	6	12	0	0	0.00%	3	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
4	CLEVELAND Monsters	1	1	0	0	0	0	0	0	12	1	11	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	12	1	11	2	1.000	12	19	31	0	0	54	39	30	1	88	293	327	351	10	10	6	4	11	3	2	66.67%	2	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
5	HERSEY Bears	1	1	0	0	0	0	0	0	7	0	7	1	1	0	0	0	0	0	0	7	0	7	0	0	0	0	0	0	0	0	0	0	0	2	1.000	7	12	19	0	1	54	39	30	1	48	293	327	351	10	22	5	2	16	1	0	0.00%	1	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
6	HOLLYWOOD Oscar	2	1	1	0	0	0	0	0	9	13	-4	1	0	1	0	0	0	0	0	4	9	-5	1	1	0	0	0	0	0	0	5	4	1	2	0.500	9	18	27	0	0	54	39	30	1	67	293	327	351	10	96	31	0	58	1	0	0.00%	0	0	0.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
7	IOWA Wild	1	0	1	0	0	0	0	0	4	11	-7	1	0	1	0	0	0	0	0	4	11	-7	0	0	0	0	0	0	0	0	0	0	0	0	0.000	4	8	12	0	0	54	39	30	1	29	293	327	351	10	133	39	0	16	0	0	0.00%	0	0	0.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
8	LAVAL Rockets	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	54	39	30	1	22	293	327	351	10	22	8	10	19	6	1	16.67%	5	1	80.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
9	LEHIGH VALLEY Phantoms	2	1	1	0	0	0	0	0	9	12	-3	2	1	1	0	0	0	0	0	9	12	-3	0	0	0	0	0	0	0	0	0	0	0	2	0.500	9	15	24	0	0	54	39	30	1	38	293	327	351	10	121	46	2	11	4	1	25.00%	1	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
10	MANITOBA Moose	1	0	1	0	0	0	0	0	0	12	-12	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	12	-12	0	0.000	0	0	0	0	0	54	39	30	1	0	293	327	351	10	112	27	0	5	0	0	0.00%	0	0	0.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
11	MONT-LAURIER Sommet	2	2	0	0	0	0	0	0	14	4	10	1	1	0	0	0	0	0	0	10	1	9	1	1	0	0	0	0	0	0	4	3	1	4	1.000	14	27	41	0	0	54	39	30	1	90	293	327	351	10	46	10	12	28	8	3	37.50%	6	1	83.33%	1	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
12	PV Sharapovas	3	0	3	0	0	0	0	0	4	20	-16	1	0	1	0	0	0	0	0	0	13	-13	2	0	2	0	0	0	0	0	4	7	-3	0	0.000	4	8	12	0	0	54	39	30	1	46	293	327	351	10	158	37	18	36	7	0	0.00%	8	2	75.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
13	ROCKFORD IceHogs	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	2	4	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	2	4	6	0	0	54	39	30	1	34	293	327	351	10	41	13	16	13	4	2	50.00%	8	2	75.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
14	SAN DIEGO Gulls	2	2	0	0	0	0	0	0	14	2	12	1	1	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	7	1	6	4	1.000	14	22	36	0	0	54	39	30	1	175	293	327	351	10	21	13	12	46	0	0	0.00%	5	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
15	STOCKTON Flames	1	0	1	0	0	0	0	0	0	11	-11	1	0	1	0	0	0	0	0	0	11	-11	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	54	39	30	1	6	293	327	351	10	91	15	0	4	0	0	0.00%	0	0	0.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
16	TORONTO Marlies	1	0	0	0	0	1	0	0	1	2	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	1	2	-1	1	0.500	1	2	3	0	0	54	39	30	1	31	293	327	351	10	27	7	10	19	2	0	0.00%	5	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
17	TUSCON Roadrunners	1	1	0	0	0	0	0	0	11	1	10	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	11	1	10	2	1.000	11	19	30	0	0	54	39	30	1	69	293	327	351	10	12	2	14	20	9	4	44.44%	6	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
	Total	28	11	14	0	1	1	0	1	124	156	-32	14	5	8	0	1	0	0	0	56	85	-29	14	6	6	0	0	1	0	1	68	71	-3	26	0.464	124	224	348	0	2	54	39	30	1	978	293	327	351	10	1473	411	112	396	50	13	26.00%	52	6	88.46%	1	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
19	VICTORIAVILLE Tigres	3	0	2	0	1	0	0	0	6	28	-22	1	0	0	0	1	0	0	0	4	3	1	2	0	2	0	0	0	0	0	2	25	-23	2	0.333	6	9	15	0	0	54	39	30	1	39	293	327	351	10	251	61	2	19	4	0	0.00%	1	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
20	WILKIES-BARRIE Penguins	2	0	2	0	0	0	0	0	9	15	-6	1	0	1	0	0	0	0	0	5	6	-1	1	0	1	0	0	0	0	0	4	9	-5	0	0.000	9	18	27	0	0	54	39	30	1	51	293	327	351	10	140	50	0	40	0	0	0.00%	0	0	0.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
	_Since Last GM Reset	28	11	14	0	1	1	0	1	124	156	-32	14	5	8	0	1	0	0	0	56	85	-29	14	6	6	0	0	1	0	1	68	71	-3	26	0.464	124	224	348	0	2	54	39	30	1	978	293	327	351	10	1473	411	112	396	50	13	26.00%	52	6	88.46%	1	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
	_Vs Conference	18	4	12	0	1	1	0	0	56	128	-72	9	2	6	0	1	0	0	0	36	66	-30	9	2	6	0	0	1	0	0	20	62	-42	11	0.306	56	105	161	0	0	54	39	30	1	397	293	327	351	10	1175	323	44	236	26	4	15.38%	21	3	85.71%	1	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
	_Vs Division	8	0	5	0	0	1	0	0	43	46	-3	5	0	2	0	0	0	0	0	22	30	-8	3	0	3	0	0	1	0	0	21	16	5	1	0.063	43	76	119	0	1	54	39	30	1	270	293	327	351	10	457	146	12	101	9	3	33.33%	5	0	100.00%	0	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127

			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
28	26	W1	124	224	348	978	1473	411	112	396	0	2

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
28	11	14	1	1	0	1	124	156

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
14	5	8	1	0	0	0	56	85

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
14	6	6	0	1	0	1	68	71

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
6	3	0	1	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
50	13	26.00%	52	6	88.46%	1

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
293	327	351	10	54	39	30	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
422	656	64.33%	291	744	39.11%	234	517	45.26%

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
574	433	829	168	285	127

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
1 - 2019-07-30	2	LEHIGH VALLEY Phantoms	11	SYRACUSE Crunch	0	L			Sommaire du Match
2 - 2019-07-31	24	HOLLYWOOD Oscar	9	SYRACUSE Crunch	4	L			Sommaire du Match
3 - 2019-08-01	39	SYRACUSE Crunch	0	MANITOBA Moose	12	L			Sommaire du Match
4 - 2019-08-02	46	SYRACUSE Crunch	2	VICTORIAVILLE Tigres	11	L			Sommaire du Match
6 - 2019-08-04	65	IOWA Wild	11	SYRACUSE Crunch	4	L			Sommaire du Match
7 - 2019-08-05	69	SYRACUSE Crunch	4	WILKIES-BARRIE Penguins	9	L			Sommaire du Match
9 - 2019-08-07	88	SYRACUSE Crunch	0	VICTORIAVILLE Tigres	14	L			Sommaire du Match
10 - 2019-08-08	92	SYRACUSE Crunch	5	BROOKLYN Wolfpack	6	L	X	X	Sommaire du Match
11 - 2019-08-09	106	PV Sharapovas	13	SYRACUSE Crunch	0	L			Sommaire du Match
12 - 2019-08-10	124	STOCKTON Flames	11	SYRACUSE Crunch	0	L			Sommaire du Match
14 - 2019-08-12	133	SYRACUSE Crunch	5	HOLLYWOOD Oscar	4	W			Sommaire du Match
15 - 2019-08-13	147	BRIDGEPORT Sound Tigers	12	SYRACUSE Crunch	1	L			Sommaire du Match
17 - 2019-08-15	161	WILKIES-BARRIE Penguins	6	SYRACUSE Crunch	5	L			Sommaire du Match
19 - 2019-08-17	184	SYRACUSE Crunch	4	MONT-LAURIER Sommet	3	W			Sommaire du Match
20 - 2019-08-18	190	LEHIGH VALLEY Phantoms	1	SYRACUSE Crunch	9	W			Sommaire du Match
22 - 2019-08-20	206	SYRACUSE Crunch	11	TUSCON Roadrunners	1	W			Sommaire du Match
23 - 2019-08-21	219	SAN DIEGO Gulls	1	SYRACUSE Crunch	7	W			Sommaire du Match
24 - 2019-08-22	233	SYRACUSE Crunch	13	CHICAGO Wolves	0	W			Sommaire du Match
25 - 2019-08-23	244	SYRACUSE Crunch	12	CLEVELAND Monsters	1	W			Sommaire du Match
26 - 2019-08-24	256	HERSEY Bears	0	SYRACUSE Crunch	7	W			Sommaire du Match
28 - 2019-08-26	272	SYRACUSE Crunch	2	PV Sharapovas	4	L			Sommaire du Match
29 - 2019-08-27	284	VICTORIAVILLE Tigres	3	SYRACUSE Crunch	4	W	X		Sommaire du Match
30 - 2019-08-28	299	ROCKFORD IceHogs	4	SYRACUSE Crunch	2	L			Sommaire du Match
31 - 2019-08-29	310	SYRACUSE Crunch	2	PV Sharapovas	3	L			Sommaire du Match
32 - 2019-08-30	326	MONT-LAURIER Sommet	1	SYRACUSE Crunch	10	W			Sommaire du Match
33 - 2019-08-31	339	SYRACUSE Crunch	7	SAN DIEGO Gulls	1	W			Sommaire du Match
34 - 2019-09-01	350	SYRACUSE Crunch	1	TORONTO Marlies	2	L	X		Sommaire du Match
35 - 2019-09-02	361	LAVAL Rockets	2	SYRACUSE Crunch	3	W			Sommaire du Match
37 - 2019-09-04	376	SYRACUSE Crunch	-	VICTORIAVILLE Tigres	-
38 - 2019-09-05	387	HOLLYWOOD Oscar	-	SYRACUSE Crunch	-
39 - 2019-09-06	402	CHICAGO Wolves	-	SYRACUSE Crunch	-
41 - 2019-09-08	418	VICTORIAVILLE Tigres	-	SYRACUSE Crunch	-
43 - 2019-09-10	430	SYRACUSE Crunch	-	BRIDGEPORT Sound Tigers	-
44 - 2019-09-11	443	SYRACUSE Crunch	-	TORONTO Marlies	-
46 - 2019-09-13	456	LEHIGH VALLEY Phantoms	-	SYRACUSE Crunch	-
47 - 2019-09-14	468	SYRACUSE Crunch	-	ROCKFORD IceHogs	-
48 - 2019-09-15	480	PV Sharapovas	-	SYRACUSE Crunch	-
49 - 2019-09-16	495	SYRACUSE Crunch	-	MANITOBA Moose	-
50 - 2019-09-17	509	WILKIES-BARRIE Penguins	-	SYRACUSE Crunch	-
51 - 2019-09-18	521	SYRACUSE Crunch	-	WILKIES-BARRIE Penguins	-
52 - 2019-09-19	531	SYRACUSE Crunch	-	BROOKLYN Wolfpack	-
53 - 2019-09-20	542	BROOKLYN Wolfpack	-	SYRACUSE Crunch	-
55 - 2019-09-22	558	TORONTO Marlies	-	SYRACUSE Crunch	-
57 - 2019-09-24	577	PROVIDENCE Bruins	-	SYRACUSE Crunch	-
59 - 2019-09-26	585	SYRACUSE Crunch	-	MILWAUKEE Admirals	-
60 - 2019-09-27	602	MILWAUKEE Admirals	-	SYRACUSE Crunch	-
62 - 2019-09-29	614	SYRACUSE Crunch	-	UTICA Comets	-
64 - 2019-10-01	626	MANITOBA Moose	-	SYRACUSE Crunch	-
66 - 2019-10-03	642	SYRACUSE Crunch	-	MONT-LAURIER Sommet	-
67 - 2019-10-04	655	COLORADO Eagles	-	SYRACUSE Crunch	-
68 - 2019-10-05	667	SYRACUSE Crunch	-	LAVAL Rockets	-
70 - 2019-10-07	678	STOCKTON Flames	-	SYRACUSE Crunch	-
73 - 2019-10-10	703	TORONTO Marlies	-	SYRACUSE Crunch	-
74 - 2019-10-11	719	SYRACUSE Crunch	-	PROVIDENCE Bruins	-
75 - 2019-10-12	726	SYRACUSE Crunch	-	STOCKTON Flames	-
76 - 2019-10-13	734	UTICA Comets	-	SYRACUSE Crunch	-
77 - 2019-10-14	754	SYRACUSE Crunch	-	CORNWALL Aces	-
78 - 2019-10-15	760	TUSCON Roadrunners	-	SYRACUSE Crunch	-
80 - 2019-10-17	780	BELLEVILLE Senators	-	SYRACUSE Crunch	-
81 - 2019-10-18	790	SYRACUSE Crunch	-	HOLLYWOOD Oscar	-
83 - 2019-10-20	807	SYRACUSE Crunch	-	HERSEY Bears	-
84 - 2019-10-21	814	BELLEVILLE Senators	-	SYRACUSE Crunch	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
85 - 2019-10-22	828	SYRACUSE Crunch	-	COLORADO Eagles	-
86 - 2019-10-23	839	IOWA Wild	-	SYRACUSE Crunch	-
88 - 2019-10-25	859	MONT-LAURIER Sommet	-	SYRACUSE Crunch	-
89 - 2019-10-26	868	SYRACUSE Crunch	-	LEHIGH VALLEY Phantoms	-
90 - 2019-10-27	880	SYRACUSE Crunch	-	STOCKTON Flames	-
91 - 2019-10-28	887	BRIDGEPORT Sound Tigers	-	SYRACUSE Crunch	-
92 - 2019-10-29	899	SYRACUSE Crunch	-	IOWA Wild	-
94 - 2019-10-31	918	CLEVELAND Monsters	-	SYRACUSE Crunch	-
96 - 2019-11-02	934	SYRACUSE Crunch	-	LEHIGH VALLEY Phantoms	-
97 - 2019-11-03	941	SYRACUSE Crunch	-	BELLEVILLE Senators	-
98 - 2019-11-04	948	SYRACUSE Crunch	-	IOWA Wild	-
99 - 2019-11-05	957	CORNWALL Aces	-	SYRACUSE Crunch	-
100 - 2019-11-06	965	SYRACUSE Crunch	-	BELLEVILLE Senators	-
102 - 2019-11-08	979	CORNWALL Aces	-	SYRACUSE Crunch	-
104 - 2019-11-10	996	MANITOBA Moose	-	SYRACUSE Crunch	-
105 - 2019-11-11	1001	SYRACUSE Crunch	-	CORNWALL Aces	-

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
25	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
730,220$	1,903,000$	1,903,000$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	623,923$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	72	20,822$	1,499,184$

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
32	28	11	14	0	1	1	0	1	124	156	-32	14	5	8	0	1	0	0	0	56	85	-29	14	6	6	0	0	1	0	1	68	71	-3	26	124	224	348	0	2	54	39	30	1	978	293	327	351	10	1473	411	112	396	50	13	26.00%	52	6	88.46%	1	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127
Total Saison Régulière	28	11	14	0	1	1	0	1	124	156	-32	14	5	8	0	1	0	0	0	56	85	-29	14	6	6	0	0	1	0	1	68	71	-3	26	124	224	348	0	2	54	39	30	1	978	293	327	351	10	1473	411	112	396	50	13	26.00%	52	6	88.46%	1	422	656	64.33%	291	744	39.11%	234	517	45.26%	574	433	829	168	285	127