OTHL - Crunch

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	Tom Kuhnhackl		X	X		100.00	78	44	90	77	73	63	70	58	25	62	60	71	25	59	61	66	50	63	0
2	Alexandre Grenier			X		100.00	78	78	78	68	78	80	87	57	50	52	56	67	53	55	55	62	50	62	0
3	Miikka Salomaki		X	X		100.00	84	45	92	73	72	57	66	61	33	60	58	71	25	56	58	65	50	62	0
4	Liam O'Brien	X				100.00	72	76	63	68	76	82	89	55	69	48	58	61	55	45	45	61	50	60	0
5	Luca Sbisa				X	100.00	83	76	83	77	76	72	70	56	25	51	45	82	37	72	74	52	50	69	0
6	Justin Falk				X	100.00	99	99	70	70	87	61	59	57	25	40	47	79	25	64	65	59	50	67	0
7	Lawrence Pilut (R)				X	100.00	67	41	82	73	61	72	74	66	25	52	48	70	25	47	47	60	50	63	0
8	Radim Simek (R)				X	100.00	81	44	93	66	72	67	71	60	25	55	47	67	25	47	47	61	50	62	0
9	Seth Helgeson				X	100.00	72	80	52	66	80	82	91	47	25	38	39	60	37	48	48	52	50	60	0
10	Josh Teves (R)				X	100.00	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	49	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	76	63	75	69	73	69	73	57	35	51	51	68	36	54	55	59	50	62

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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	Matt Tomkins (R)	100.00	46	50	63	79	43	45	50	53	46	47	30	44	44	48	50	51	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	46	50	63	79	43	45	50	53	46	47	30	44	44	48	50	51

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Charlie Huddy	58	59	68	46	45	46	54	CAN	56	1	850,000$

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Astuces sur les Filtres (Anglais seulement)

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

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#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Alexandre Grenier	Crunch (TBL)	RW	82	71	72	143	90	84	0	219	163	439	129	315	16.17%	25	1615	19.70	8	13	21	45	148	1	1	2	8	197	8	0	65.34%	453	6	1.77	1	4	0	0	0	15	15	11
2	Liam O'Brien	Crunch (TBL)	C	82	55	67	122	107	75	5	167	197	316	76	197	17.41%	13	1581	19.29	5	9	14	33	139	1	0	1	7	209	10	3	71.66%	1870	6	1.54	1	2	0	0	1	13	9	6
3	Lawrence Pilut	Crunch (TBL)	D	80	28	84	112	69	24	0	88	84	132	42	93	21.21%	56	1590	19.88	12	12	24	63	131	0	3	3	1	167	5	2	50.00%	4	0	1.41	0	0	0	0	0	4	7	16
4	Luca Sbisa	Crunch (TBL)	D	76	15	40	55	58	67	5	150	70	110	29	89	13.64%	49	1405	18.49	5	6	11	37	141	0	2	2	3	182	3	2	0.00%	0	0	0.78	0	0	1	0	0	1	4	4
5	Radim Simek	Crunch (TBL)	D	54	17	37	54	34	29	5	90	41	66	31	35	25.76%	50	1064	19.72	8	8	16	33	113	1	2	3	1	170	2	3	0.00%	2	0	1.01	0	0	0	0	0	5	4	4
6	Seth Helgeson	Crunch (TBL)	D	82	14	34	48	59	150	10	286	55	107	42	56	13.08%	58	1559	19.02	4	7	11	32	130	2	1	3	2	125	5	0	50.00%	8	0	0.62	0	0	0	1	1	4	0	5
7	Vince Dunn	Lightning	D	18	6	25	31	17	4	0	15	12	27	14	17	22.22%	12	372	20.71	4	6	10	15	40	0	1	1	1	45	2	0	0.00%	0	0	1.66	0	0	0	0	0	2	1	4
8	Tom Kuhnhackl	Crunch (TBL)	LW/RW	23	18	13	31	18	12	0	27	48	65	15	43	27.69%	4	427	18.59	2	1	3	12	45	1	1	2	1	77	2	2	46.35%	192	1	1.45	0	3	0	0	0	4	2	1
9	Miikka Salomaki	Crunch (TBL)	LW/RW	23	5	21	26	19	14	0	29	24	60	9	51	8.33%	3	413	17.97	1	2	3	11	43	0	0	0	2	68	1	0	51.16%	43	1	1.26	2	3	0	0	0	1	1	2
10	Josh Teves	Crunch (TBL)	D	34	3	9	12	35	18	0	33	13	19	5	8	15.79%	15	567	16.68	0	1	1	0	10	0	0	0	1	46	0	0	0.00%	1	0	0.42	0	0	0	0	0	0	0	0
11	Justin Falk	Crunch (TBL)	D	14	2	5	7	5	39	5	53	7	26	6	14	7.69%	6	295	21.10	0	2	2	11	31	0	0	0	0	33	1	0	0.00%	0	0	0.47	0	0	0	0	1	0	1	1
Stats d'équipe Total ou en Moyenne				568	234	407	641	511	516	30	1157	714	1367	398	918	17.12%	291	10892	19.18	49	67	116	292	976	6	11	17	27	1325	39	12	68.13%	2573	14	1.18	4	12	1	1	3	49	44	54

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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	A	PS %	PSA	ST	BG	S1	S2	S3
1	Francois Brassard	Lightning	76	40	32	3	0.902	2.87	4301	2	3	206	2109	7	0.600	10	76	0	3	3	4
2	Matt Tomkins	Crunch (TBL)	15	5	2	0	0.929	1.78	642	0	1	19	267	1	1.000	3	6	76	0	0	0
Stats d'équipe Total ou en Moyenne			91	45	34	3	0.905	2.73	4943	2	4	225	2376	8	0.692	13	82	76	3	3	4

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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	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Link
Alexandre Grenier	Crunch (TBL)	RW	28	1991-09-05	No	200 Lbs	6 ft5	No	No	No	1	Pro & Farm	725,000$	0$	0$	No		Lien
Josh Teves	Crunch (TBL)	D	25	1995-02-18 8:08:46 AM	Yes	170 Lbs	6 ft0	No	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$
Justin Falk	Crunch (TBL)	D	31	1988-10-11	No	223 Lbs	6 ft5	Yes	No	No	1	Pro & Farm	750,000$	0$	0$	No		Lien
Lawrence Pilut	Crunch (TBL)	D	24	1995-12-29	Yes	165 Lbs	5 ft11	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$	Lien
Liam O'Brien	Crunch (TBL)	C	25	1994-07-29	No	215 Lbs	5 ft11	No	No	No	1	Pro & Farm	750,000$	0$	0$	No		Lien
Luca Sbisa	Crunch (TBL)	D	30	1990-01-30	No	209 Lbs	6 ft2	No	No	No	1	Pro & Farm	3,600,000$	0$	0$	No		Lien
Matt Tomkins	Crunch (TBL)	G	25	1994-06-19	Yes	194 Lbs	6 ft3	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$	Lien
Miikka Salomaki	Crunch (TBL)	LW/RW	27	1993-03-08	No	203 Lbs	5 ft11	No	No	No	2	Pro & Farm	900,000$	0$	0$	No	900,000$	Lien
Radim Simek	Crunch (TBL)	D	27	1992-09-20	Yes	205 Lbs	5 ft10	No	No	No	1	Pro & Farm	625,000$	0$	0$	No		Lien
Seth Helgeson	Crunch (TBL)	D	29	1990-10-08	No	210 Lbs	6 ft4	No	No	No	2	Pro & Farm	900,000$	0$	0$	No	900,000$	Lien
Tom Kuhnhackl	Crunch (TBL)	LW/RW	28	1992-01-21	No	196 Lbs	6 ft2	No	No	No	1	Pro & Farm	850,000$	0$	0$	No		Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
11	27.18	199 Lbs	6 ft1	1.45	1,059,091$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Tom Kuhnhackl	Liam O'Brien	Miikka Salomaki	40	1	2	2
2			Alexandre Grenier	30	1	2	2
3				20	1	2	2
4	Miikka Salomaki	Tom Kuhnhackl	Alexandre Grenier	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Luca Sbisa	Justin Falk	40	1	2	2
2	Lawrence Pilut	Radim Simek	30	1	2	2
3	Seth Helgeson	Josh Teves	20	1	2	2
4	Luca Sbisa	Justin Falk	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Tom Kuhnhackl	Liam O'Brien	Miikka Salomaki	60	1	2	2
2			Alexandre Grenier	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Luca Sbisa	Justin Falk	60	1	2	2
2	Lawrence Pilut	Radim Simek	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Tom Kuhnhackl	Miikka Salomaki	60	1	2	2
2	Alexandre Grenier	Liam O'Brien	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Luca Sbisa	Justin Falk	60	1	2	2
2	Lawrence Pilut	Radim Simek	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Tom Kuhnhackl	60	1	2	2	Luca Sbisa	Justin Falk	60	1	2	2
2	Miikka Salomaki	40	1	2	2	Lawrence Pilut	Radim Simek	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Tom Kuhnhackl	Miikka Salomaki	60	1	2	2
2	Alexandre Grenier	Liam O'Brien	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Luca Sbisa	Justin Falk	60	1	2	2
2	Lawrence Pilut	Radim Simek	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Tom Kuhnhackl	Liam O'Brien	Miikka Salomaki	Luca Sbisa	Justin Falk

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Tom Kuhnhackl	Liam O'Brien	Miikka Salomaki	Luca Sbisa	Justin Falk

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Liam O'Brien, Alexandre Grenier, Tom Kuhnhackl	Liam O'Brien, Alexandre Grenier	Tom Kuhnhackl

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Seth Helgeson, Josh Teves, Lawrence Pilut	Seth Helgeson	Josh Teves, Lawrence Pilut

Tirs de Pénalité
Tom Kuhnhackl, Miikka Salomaki, Alexandre Grenier, Liam O'Brien,

Gardien
#1 : Matt Tomkins, #2 :

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

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

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Americans	4	0	4	0	0	0	0	0	8	15	-7	2	0	2	0	0	0	0	0	5	7	-2	2	0	2	0	0	0	0	0	3	8	-5	0	0.000	8	15	23	0	0	109	89	82	6	52	771	770	772	20	126	36	45	57	14	0	0.00%	18	1	94.44%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
2	Admirals	2	1	1	0	0	0	0	0	8	7	1	1	1	0	0	0	0	0	0	5	3	2	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	8	16	24	0	0	109	89	82	6	67	771	770	772	20	83	20	12	51	4	0	0.00%	6	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
3	Barracudas	2	1	1	0	0	0	0	0	7	6	1	1	1	0	0	0	0	0	0	7	2	5	1	0	1	0	0	0	0	0	0	4	-4	2	0.500	7	13	20	0	0	109	89	82	6	73	771	770	772	20	55	15	6	52	7	3	42.86%	3	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
4	Bears	3	2	1	0	0	0	0	0	9	7	2	1	1	0	0	0	0	0	0	2	1	1	2	1	1	0	0	0	0	0	7	6	1	4	0.667	9	15	24	0	0	109	89	82	6	80	771	770	772	20	92	25	18	48	15	0	0.00%	9	0	100.00%	1	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
5	Bruins	4	1	2	0	0	0	0	1	7	8	-1	2	1	1	0	0	0	0	0	4	3	1	2	0	1	0	0	0	0	1	3	5	-2	3	0.375	7	14	21	0	1	109	89	82	6	83	771	770	772	20	77	23	43	58	19	1	5.26%	17	2	88.24%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
6	Checkers	3	0	3	0	0	0	0	0	4	14	-10	1	0	1	0	0	0	0	0	0	3	-3	2	0	2	0	0	0	0	0	4	11	-7	0	0.000	4	6	10	0	0	109	89	82	6	51	771	770	772	20	119	37	22	48	13	1	7.69%	11	1	90.91%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
7	Comets	2	0	2	0	0	0	0	0	4	11	-7	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	1	6	-5	0	0.000	4	6	10	0	0	109	89	82	6	15	771	770	772	20	76	22	22	32	4	1	25.00%	11	3	72.73%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
8	Condors	2	0	2	0	0	0	0	0	1	9	-8	1	0	1	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	0	4	-4	0	0.000	1	2	3	0	0	109	89	82	6	16	771	770	772	20	55	17	28	35	6	0	0.00%	13	2	84.62%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
9	Devils	3	3	0	0	0	0	0	0	20	3	17	1	1	0	0	0	0	0	0	7	0	7	2	2	0	0	0	0	0	0	13	3	10	6	1.000	20	38	58	0	1	109	89	82	6	160	771	770	772	20	63	20	14	83	3	0	0.00%	5	0	100.00%	1	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
10	Eagles	2	2	0	0	0	0	0	0	13	4	9	1	1	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	6	2	4	4	1.000	13	24	37	0	0	109	89	82	6	105	771	770	772	20	38	10	6	54	1	0	0.00%	3	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
11	Griffins	4	2	1	0	1	0	0	0	10	7	3	2	2	0	0	0	0	0	0	8	4	4	2	0	1	0	1	0	0	0	2	3	-1	6	0.750	10	19	29	0	0	109	89	82	6	107	771	770	772	20	94	23	38	89	10	2	20.00%	14	2	85.71%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
12	Gulls	2	1	1	0	0	0	0	0	5	6	-1	1	1	0	0	0	0	0	0	4	3	1	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	5	7	12	0	0	109	89	82	6	62	771	770	772	20	65	26	14	44	10	3	30.00%	7	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
13	Heat	2	0	1	0	0	0	0	1	2	5	-3	1	0	1	0	0	0	0	0	0	2	-2	1	0	0	0	0	0	0	1	2	3	-1	1	0.250	2	3	5	0	0	109	89	82	6	29	771	770	772	20	66	29	20	49	6	2	33.33%	10	1	90.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
14	IceHogs	2	0	2	0	0	0	0	0	2	8	-6	1	0	1	0	0	0	0	0	1	2	-1	1	0	1	0	0	0	0	0	1	6	-5	0	0.000	2	3	5	0	0	109	89	82	6	26	771	770	772	20	83	27	23	27	6	1	16.67%	9	1	88.89%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
15	Marlies	4	3	0	0	1	0	0	0	17	9	8	2	2	0	0	0	0	0	0	8	2	6	2	1	0	0	1	0	0	0	9	7	2	8	1.000	17	30	47	0	0	109	89	82	6	93	771	770	772	20	76	29	22	108	15	8	53.33%	10	1	90.00%	2	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
16	Monsters	3	0	2	0	0	0	1	0	8	10	-2	1	0	0	0	0	0	1	0	4	3	1	2	0	2	0	0	0	0	0	4	7	-3	2	0.333	8	14	22	0	0	109	89	82	6	53	771	770	772	20	77	20	26	58	18	2	11.11%	12	2	83.33%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
17	Moose	2	0	2	0	0	0	0	0	1	6	-5	1	0	1	0	0	0	0	0	1	2	-1	1	0	1	0	0	0	0	0	0	4	-4	0	0.000	1	2	3	0	0	109	89	82	6	44	771	770	772	20	72	18	17	34	2	0	0.00%	7	1	85.71%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
18	Penguins	3	1	2	0	0	0	0	0	8	9	-1	2	1	1	0	0	0	0	0	5	5	0	1	0	1	0	0	0	0	0	3	4	-1	2	0.333	8	15	23	0	0	109	89	82	6	92	771	770	772	20	77	26	18	50	12	1	8.33%	8	2	75.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
19	Phantoms	3	2	1	0	0	0	0	0	6	5	1	2	2	0	0	0	0	0	0	4	2	2	1	0	1	0	0	0	0	0	2	3	-1	4	0.667	6	10	16	0	0	109	89	82	6	77	771	770	772	20	75	16	14	61	12	0	0.00%	5	1	80.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
20	Rampage	2	2	0	0	0	0	0	0	14	4	10	1	1	0	0	0	0	0	0	8	1	7	1	1	0	0	0	0	0	0	6	3	3	4	1.000	14	24	38	0	0	109	89	82	6	98	771	770	772	20	64	10	6	43	6	1	16.67%	3	1	66.67%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
21	Reign	2	2	0	0	0	0	0	0	16	5	11	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	11	3	8	4	1.000	16	28	44	0	0	109	89	82	6	120	771	770	772	20	53	10	2	50	3	1	33.33%	1	0	100.00%	1	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
22	Roadrunners	2	1	0	0	0	0	1	0	11	7	4	1	1	0	0	0	0	0	0	5	2	3	1	0	0	0	0	0	1	0	6	5	1	4	1.000	11	16	27	0	0	109	89	82	6	57	771	770	772	20	65	16	19	39	7	3	42.86%	7	2	71.43%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
23	Rocket	4	1	3	0	0	0	0	0	6	19	-13	2	0	2	0	0	0	0	0	1	9	-8	2	1	1	0	0	0	0	0	5	10	-5	2	0.250	6	8	14	0	0	109	89	82	6	47	771	770	772	20	151	32	28	52	21	4	19.05%	14	2	85.71%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
24	Senators	4	4	0	0	0	0	0	0	18	7	11	2	2	0	0	0	0	0	0	9	5	4	2	2	0	0	0	0	0	0	9	2	7	8	1.000	18	30	48	0	1	109	89	82	6	149	771	770	772	20	100	33	24	77	8	4	50.00%	11	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
25	Sound Tigers	3	3	0	0	0	0	0	0	17	5	12	2	2	0	0	0	0	0	0	12	3	9	1	1	0	0	0	0	0	0	5	2	3	6	1.000	17	30	47	0	0	109	89	82	6	125	771	770	772	20	70	23	24	73	5	2	40.00%	10	1	90.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
26	Stars	2	2	0	0	0	0	0	0	6	3	3	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	2	1	1	4	1.000	6	12	18	0	0	109	89	82	6	73	771	770	772	20	73	5	16	30	4	0	0.00%	6	1	83.33%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
27	Thunderbirds	4	2	1	0	0	0	0	1	15	14	1	2	1	0	0	0	0	0	1	8	8	0	2	1	1	0	0	0	0	0	7	6	1	5	0.625	15	30	45	0	0	109	89	82	6	74	771	770	772	20	158	31	28	79	11	3	27.27%	14	1	92.86%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
	Total	82	41	34	0	2	0	2	3	284	228	56	41	26	13	0	0	0	1	1	154	94	60	41	15	21	0	2	0	1	2	130	134	-4	93	0.567	284	501	785	0	4	109	89	82	6	2319	771	770	772	20	2376	638	593	1629	253	44	17.39%	262	29	88.93%	5	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
29	Wild	2	2	0	0	0	0	0	0	14	4	10	1	1	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	7	3	4	4	1.000	14	24	38	0	0	109	89	82	6	120	771	770	772	20	57	9	12	53	3	0	0.00%	6	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
30	Wolf Pack	3	3	0	0	0	0	0	0	23	4	19	2	2	0	0	0	0	0	0	17	2	15	1	1	0	0	0	0	0	0	6	2	4	6	1.000	23	40	63	0	1	109	89	82	6	134	771	770	772	20	61	17	12	52	5	1	20.00%	5	1	80.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
31	Wolves	2	0	2	0	0	0	0	0	4	7	-3	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	4	7	11	0	0	109	89	82	6	37	771	770	772	20	55	13	14	43	3	0	0.00%	7	0	100.00%	0	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
	_Since Last GM Reset	82	41	34	0	2	0	2	3	284	228	56	41	26	13	0	0	0	1	1	154	94	60	41	15	21	0	2	0	1	2	130	134	-4	93	0.567	284	501	785	0	4	109	89	82	6	2319	771	770	772	20	2376	638	593	1629	253	44	17.39%	262	29	88.93%	5	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
	_Vs Conference	50	27	18	0	2	0	1	2	172	132	40	25	17	6	0	0	0	1	1	92	55	37	25	10	12	0	2	0	0	1	80	77	3	62	0.620	172	305	477	0	4	109	89	82	6	1340	771	770	772	20	1366	377	353	968	171	30	17.54%	156	19	87.82%	4	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
	_Vs Division	28	13	8	0	2	0	1	0	81	79	2	14	7	3	0	0	0	1	0	43	38	5	14	6	5	0	2	0	0	0	38	41	-3	32	0.571	81	146	227	0	2	109	89	82	6	605	771	770	772	20	782	207	228	520	98	22	22.45%	98	9	90.82%	2	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443

			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
82	93	L2	284	501	785	2319	2376	638	593	1629	0	4

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	41	34	2	0	2	3	284	228

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	26	13	0	0	1	1	154	94

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	15	21	2	0	1	2	130	134

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	4	0	0	1	1

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
253	44	17.39%	262	29	88.93%	5

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
771	770	772	20	109	89	82	6

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
1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%

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
2016	1511	2035	513	897	443

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
2 - 2019-10-05	12	Thunderbirds	6	Crunch	7	W			Sommaire du Match
4 - 2019-10-07	24	Crunch	5	Thunderbirds	2	W			Sommaire du Match
5 - 2019-10-08	31	Crunch	1	Checkers	5	L			Sommaire du Match
9 - 2019-10-12	56	Crunch	6	Marlies	5	W			Sommaire du Match
11 - 2019-10-14	72	Crunch	3	Senators	2	W			Sommaire du Match
14 - 2019-10-17	87	Crunch	1	Rocket	7	L			Sommaire du Match
16 - 2019-10-19	99	Crunch	1	Bruins	2	L	X	X	Sommaire du Match
18 - 2019-10-21	122	Eagles	2	Crunch	7	W			Sommaire du Match
22 - 2019-10-25	144	Penguins	2	Crunch	4	W			Sommaire du Match
25 - 2019-10-28	168	Admirals	3	Crunch	5	W			Sommaire du Match
28 - 2019-10-31	185	Crunch	6	Wolf Pack	2	W			Sommaire du Match
29 - 2019-11-01	192	Crunch	6	Devils	2	W			Sommaire du Match
31 - 2019-11-03	200	Crunch	5	Sound Tigers	2	W			Sommaire du Match
38 - 2019-11-10	248	Crunch	2	Americans	4	L			Sommaire du Match
39 - 2019-11-11	260	Americans	3	Crunch	2	L			Sommaire du Match
44 - 2019-11-16	292	Wolf Pack	2	Crunch	9	W			Sommaire du Match
46 - 2019-11-18	312	Moose	2	Crunch	1	L			Sommaire du Match
49 - 2019-11-21	329	Crunch	6	Rampage	3	W			Sommaire du Match
51 - 2019-11-23	337	Crunch	1	IceHogs	6	L			Sommaire du Match
53 - 2019-11-25	358	Gulls	3	Crunch	4	W			Sommaire du Match
55 - 2019-11-27	373	Americans	4	Crunch	3	L			Sommaire du Match
57 - 2019-11-29	388	Rampage	1	Crunch	8	W			Sommaire du Match
59 - 2019-12-01	402	Crunch	5	Bears	1	W			Sommaire du Match
60 - 2019-12-02	414	Checkers	3	Crunch	0	L			Sommaire du Match
63 - 2019-12-05	429	Crunch	3	Admirals	4	L			Sommaire du Match
65 - 2019-12-07	446	Wild	1	Crunch	7	W			Sommaire du Match
67 - 2019-12-09	461	Barracudas	2	Crunch	7	W			Sommaire du Match
69 - 2019-12-11	470	Sound Tigers	2	Crunch	7	W			Sommaire du Match
70 - 2019-12-12	476	Crunch	2	Thunderbirds	4	L			Sommaire du Match
72 - 2019-12-14	497	Bruins	3	Crunch	1	L			Sommaire du Match
74 - 2019-12-16	514	Bears	1	Crunch	2	W			Sommaire du Match
77 - 2019-12-19	530	Senators	2	Crunch	4	W			Sommaire du Match
79 - 2019-12-21	545	Stars	2	Crunch	4	W			Sommaire du Match
81 - 2019-12-23	564	Crunch	2	Bears	5	L			Sommaire du Match
83 - 2019-12-25	577	Thunderbirds	2	Crunch	1	L	X	X	Sommaire du Match
88 - 2019-12-30	598	Rocket	6	Crunch	0	L			Sommaire du Match
89 - 2019-12-31	611	Griffins	3	Crunch	5	W			Sommaire du Match
91 - 2020-01-02	614	Crunch	1	Americans	4	L			Sommaire du Match
93 - 2020-01-04	632	Crunch	4	Rocket	3	W			Sommaire du Match
95 - 2020-01-06	649	Crunch	6	Senators	0	W			Sommaire du Match
96 - 2020-01-07	654	Crunch	3	Checkers	6	L			Sommaire du Match
98 - 2020-01-09	672	Comets	5	Crunch	3	L			Sommaire du Match
100 - 2020-01-11	687	Roadrunners	2	Crunch	5	W			Sommaire du Match
102 - 2020-01-13	698	Crunch	2	Phantoms	3	L			Sommaire du Match
103 - 2020-01-14	707	Crunch	7	Devils	1	W			Sommaire du Match
105 - 2020-01-16	722	Reign	2	Crunch	5	W			Sommaire du Match
107 - 2020-01-18	732	Crunch	7	Wild	3	W			Sommaire du Match
108 - 2020-01-19	742	Crunch	0	Moose	4	L			Sommaire du Match
118 - 2020-01-29	768	Crunch	2	Stars	1	W			Sommaire du Match
120 - 2020-01-31	779	Crunch	11	Reign	3	W			Sommaire du Match
122 - 2020-02-02	785	Crunch	1	Gulls	3	L			Sommaire du Match
123 - 2020-02-03	803	Crunch	0	Barracudas	4	L			Sommaire du Match
126 - 2020-02-06	822	Wolves	3	Crunch	2	L			Sommaire du Match
128 - 2020-02-08	838	Penguins	3	Crunch	1	L			Sommaire du Match
130 - 2020-02-10	850	Sound Tigers	1	Crunch	5	W			Sommaire du Match
132 - 2020-02-12	860	Crunch	2	Monsters	4	L			Sommaire du Match
133 - 2020-02-13	874	Crunch	3	Penguins	4	L			Sommaire du Match
135 - 2020-02-15	888	Condors	5	Crunch	1	L			Sommaire du Match
137 - 2020-02-17	904	Phantoms	1	Crunch	2	W			Sommaire du Match
139 - 2020-02-19	917	Crunch	6	Eagles	2	W			Sommaire du Match
142 - 2020-02-22	939	Crunch	2	Wolves	4	L			Sommaire du Match
144 - 2020-02-24	948	Crunch	6	Roadrunners	5	W	X	X	Sommaire du Match
147 - 2020-02-27	979	Marlies	1	Crunch	5	W			Sommaire du Match
149 - 2020-02-29	992	IceHogs	2	Crunch	1	L			Sommaire du Match
151 - 2020-03-02	1009	Heat	2	Crunch	0	L			Sommaire du Match
154 - 2020-03-05	1026	Bruins	0	Crunch	3	W			Sommaire du Match
156 - 2020-03-07	1042	Rocket	3	Crunch	1	L			Sommaire du Match
158 - 2020-03-09	1049	Crunch	2	Bruins	3	L			Sommaire du Match
159 - 2020-03-10	1062	Crunch	2	Griffins	1	W	X		Sommaire du Match
161 - 2020-03-12	1077	Crunch	3	Marlies	2	W	X		Sommaire du Match
163 - 2020-03-14	1090	Phantoms	1	Crunch	2	W			Sommaire du Match
165 - 2020-03-16	1107	Griffins	1	Crunch	3	W			Sommaire du Match
166 - 2020-03-17	1116	Devils	0	Crunch	7	W			Sommaire du Match
169 - 2020-03-20	1136	Crunch	1	Comets	6	L			Sommaire du Match
171 - 2020-03-22	1149	Crunch	0	Condors	4	L			Sommaire du Match
172 - 2020-03-23	1155	Crunch	2	Heat	3	L	X	X	Sommaire du Match
176 - 2020-03-27	1188	Marlies	1	Crunch	3	W			Sommaire du Match
178 - 2020-03-29	1202	Monsters	3	Crunch	4	W	X	X	Sommaire du Match
179 - 2020-03-30	1214	Wolf Pack	0	Crunch	8	W			Sommaire du Match
182 - 2020-04-02	1237	Senators	3	Crunch	5	W			Sommaire du Match
184 - 2020-04-04	1243	Crunch	2	Monsters	3	L			Sommaire du Match
186 - 2020-04-06	1262	Crunch	0	Griffins	2	L			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	30	0
Assistance	76,394	36,315
Assistance PCT	93.16%	88.57%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
0	2749 - 91.63%	55,898$	2,291,820$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,564,513$	1,165,000$	1,155,000$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
6,263$	714,500$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	10,833$	0$

Afficher ou Masquer des Colonnes

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	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
2019	82	41	34	0	2	0	2	3	284	228	56	41	26	13	0	0	0	1	1	154	94	60	41	15	21	0	2	0	1	2	130	134	-4	93	284	501	785	0	4	109	89	82	6	2319	771	770	772	20	2376	638	593	1629	253	44	17.39%	262	29	88.93%	5	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443
Total Saison Régulière	82	41	34	0	2	0	2	3	284	228	56	41	26	13	0	0	0	1	1	154	94	60	41	15	21	0	2	0	1	2	130	134	-4	93	284	501	785	0	4	109	89	82	6	2319	771	770	772	20	2376	638	593	1629	253	44	17.39%	262	29	88.93%	5	1081	2054	52.63%	1187	2398	49.50%	618	1261	49.01%	2016	1511	2035	513	897	443