OTHL - Penguins

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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	Tyson Jost	X	X			100.00	73	43	89	85	69	63	90	72	51	67	62	67	25	61	62	68	50	67	0
2	Michael Rasmussen	X	X	X		100.00	84	85	83	68	85	66	67	62	78	61	58	70	55	51	51	64	50	63	0
3	Steven Fogarty	X		X		100.00	79	77	83	62	77	81	86	63	79	62	60	67	57	45	45	65	50	63	0
4	Anthony Greco		X	X		100.00	70	62	90	67	62	75	79	60	50	55	59	64	56	55	55	62	50	61	0
5	Gabriel Gagne			X		100.00	78	73	90	64	73	61	62	61	50	56	61	67	58	50	50	64	50	60	0
6	Alexandre Fortin		X	X		100.00	73	65	91	61	65	71	75	57	50	49	57	65	54	57	57	61	50	59	0
7	Tomas Jurco		X	X		100.00	61	41	89	77	70	55	51	60	45	63	55	52	25	62	63	59	50	59	0
8	Dante Fabbro (R)				X	100.00	71	42	83	86	68	77	70	63	25	48	49	70	25	51	51	61	50	65	0
9	Roland McKeown				X	100.00	74	72	77	67	72	76	83	51	25	47	41	61	39	44	44	55	50	60	0
10	Aaron Ness				X	100.00	69	42	99	69	66	60	72	58	25	43	47	59	25	52	52	57	50	59	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	73	60	87	71	71	69	74	61	48	55	55	64	42	53	53	62	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	Tom McCollum	100.00	56	52	65	85	56	55	61	64	58	59	30	44	44	58	50	59	0
2	Matej Tomek (R)	100.00	55	55	55	55	55	55	55	55	55	55	55	55	55	55	50	55	1
Rayé

	MOYENNE D'ÉQUIPE	100.00	56	54	60	70	56	55	58	60	57	57	43	50	50	57	50	57

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Jay Leach	60	75	77	52	50	47	60	USA	40	2	800,000$

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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	S1	S2	S3
1	Gabriel Gagne	Penguins (PIT)	RW	30	25	24	49	34	10	0	30	28	130	40	84	19.23%	8	432	14.42	5	5	10	17	56	0	0	0	0	0	2	0	68.00%	25	1	2.26	1	1	0	3	6	3
2	Tyson Jost	Penguins (PIT)	C/LW	30	19	29	48	35	4	0	27	51	124	24	72	15.32%	2	506	16.88	2	5	7	11	58	0	2	2	4	39	0	1	60.00%	115	0	1.90	0	0	0	5	3	3
3	Michael Rasmussen	Penguins (PIT)	C/LW/RW	30	15	26	41	34	16	0	59	63	90	27	66	16.67%	7	532	17.73	4	8	12	22	58	2	0	2	4	63	0	0	78.44%	705	1	1.54	0	0	0	3	2	3
4	Steven Fogarty	Penguins (PIT)	C/RW	26	17	23	40	37	25	5	31	42	67	28	55	25.37%	4	355	13.66	0	0	0	0	0	2	0	2	5	34	7	0	77.53%	365	1	2.25	0	0	1	3	2	3
5	Anthony Greco	Penguins (PIT)	LW/RW	26	14	21	35	33	6	0	17	29	92	20	65	15.22%	2	379	14.60	1	1	2	13	65	0	0	0	0	0	1	0	76.92%	26	0	1.84	0	0	0	2	1	1
6	Alexandre Fortin	Penguins (PIT)	LW/RW	30	11	23	34	36	6	0	20	19	59	19	47	18.64%	4	540	18.02	2	8	10	12	58	0	0	0	0	66	2	1	66.67%	39	0	1.26	0	0	0	1	2	2
7	Aaron Ness	Penguins (PIT)	D	30	8	23	31	26	22	0	53	12	50	15	29	16.00%	21	511	17.04	4	3	7	22	72	0	1	1	1	49	2	0	0.00%	0	0	1.21	0	0	0	1	0	3
8	Dante Fabbro	Penguins (PIT)	D	30	5	26	31	31	16	0	36	24	22	16	27	22.73%	14	548	18.27	0	0	0	0	0	0	1	1	1	53	1	0	0.00%	0	0	1.13	0	0	0	0	1	1
9	Tomas Jurco	Penguins (PIT)	LW/RW	26	22	7	29	10	10	0	47	37	106	28	81	20.75%	15	370	14.26	1	4	5	8	62	0	0	0	0	0	1	1	47.06%	17	2	1.56	0	0	0	3	3	0
10	Roland McKeown	Penguins (PIT)	D	30	2	7	9	31	36	0	72	14	20	2	12	10.00%	13	545	18.18	0	0	0	0	0	0	1	1	1	51	1	0	0.00%	0	0	0.33	0	0	0	0	1	0
Stats d'équipe Total ou en Moyenne				288	138	209	347	307	151	5	392	319	760	219	538	18.16%	90	4721	16.40	19	34	53	105	432	4	5	9	16	359	17	3	75.54%	1292	5	1.47	1	1	1	21	21	19

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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	Tom McCollum	Penguins (PIT)	30	21	9	0	0.892	2.31	1767	0	4	68	631	0	1	0	0.833	6	30	0	0	0	1
Stats d'équipe Total ou en Moyenne			30	21	9	0	0.892	2.31	1767	0	4	68	631	0	1	0	0.833	6	30	0	0	0	1

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
Aaron Ness	Penguins (PIT)	D	30	1990-05-18	No	184 Lbs	5 ft11	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Lien / Lien NHL
Alexandre Fortin	Penguins (PIT)	LW/RW	23	1997-02-25	No	175 Lbs	6 ft0	No	No	No	2	Pro & Farm	700,000$	700,000$	0$	0$	No	700,000$		Lien / Lien NHL
Anthony Greco	Penguins (PIT)	LW/RW	27	1993-09-30	No	172 Lbs	5 ft10	No	No	No	1	Pro & Farm	725,000$	725,000$	0$	0$	No			Lien / Lien NHL
Dante Fabbro	Penguins (PIT)	D	22	1998-06-20	Yes	189 Lbs	6 ft0	No	No	No	3	Pro & Farm	1,491,000$	1,491,000$	0$	0$	No	1,491,000$	1,491,000$	Lien / Lien NHL
Gabriel Gagne	Penguins (PIT)	RW	24	1996-11-11	No	186 Lbs	6 ft5	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$	Lien / Lien NHL
Matej Tomek	Penguins (PIT)	G	25	1995-02-10 11:42:25 AM	Yes	181 Lbs	6 ft3	No	No	No	2	Pro & Farm	700,000$	700,000$	0$	0$	No	700,000$
Michael Rasmussen	Penguins (PIT)	C/LW/RW	21	1999-04-17	No	220 Lbs	6 ft6	No	No	No	2	Pro & Farm	1,710,833$	1,710,833$	0$	0$	No	1,710,833$		Lien / Lien NHL
Roland McKeown	Penguins (PIT)	D	24	1996-01-19	No	195 Lbs	6 ft1	No	No	No	2	Pro & Farm	894,167$	894,167$	0$	0$	No	894,167$		Lien / Lien NHL
Steven Fogarty	Penguins (PIT)	C/RW	27	1993-04-18	No	206 Lbs	6 ft3	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Lien / Lien NHL
Tom McCollum	Penguins (PIT)	G	30	1989-12-06	No	226 Lbs	6 ft2	No	No	No	1	Pro & Farm	700,000$	700,000$	0$	0$	No			Lien / Lien NHL
Tomas Jurco	Penguins (PIT)	LW/RW	27	1992-12-27	No	188 Lbs	6 ft2	No	No	No	1	Pro & Farm	950,000$	950,000$	0$	0$	No			Lien / Lien NHL
Tyson Jost	Penguins (PIT)	C/LW	22	1998-03-14	No	191 Lbs	5 ft11	No	No	No	1	Pro & Farm	1,673,333$	1,673,333$	0$	0$	No			Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
12	25.17	193 Lbs	6 ft2	1.75	982,861$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Tyson Jost	Michael Rasmussen	Alexandre Fortin	40	1	2	2
2	Anthony Greco	Steven Fogarty	Gabriel Gagne	30	1	2	2
3	Tomas Jurco			20	1	2	2
4				10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dante Fabbro	Roland McKeown	40	1	2	2
2	Aaron Ness		30	1	2	2
3			20	1	2	2
4			10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Anthony Greco	Tyson Jost	Tomas Jurco	60	1	2	2
2	Alexandre Fortin	Michael Rasmussen	Gabriel Gagne	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Aaron Ness	60	1	2	2
2			40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Michael Rasmussen	Alexandre Fortin	60	1	2	2
2	Steven Fogarty	Tyson Jost	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Roland McKeown	Dante Fabbro	60	1	2	2
2		Aaron Ness	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Michael Rasmussen	60	1	2	2	Dante Fabbro	Aaron Ness	60	1	2	2
2	Gabriel Gagne	40	1	2	2	Roland McKeown		40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Steven Fogarty	Alexandre Fortin	60	1	2	2
2	Michael Rasmussen	Gabriel Gagne	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Roland McKeown	Dante Fabbro	60	1	2	2
2	Aaron Ness		40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Tyson Jost	Michael Rasmussen	Gabriel Gagne	Dante Fabbro	Roland McKeown

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Tyson Jost	Michael Rasmussen	Gabriel Gagne	Dante Fabbro	Roland McKeown

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,	,

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,		,

Tirs de Pénalité
, , , ,

Gardien
#1 : Tom McCollum, #2 :

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

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

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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

	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	Bears	2	2	0	0	0	0	0	0	13	6	7	1	1	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	7	2	5	4	1.000	13	21	34	0	0	64	45	44	2	94	360	342	297	6	49	12	8	38	6	1	16.67%	3	0	100.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
2	Bruins	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	9	14	0	0	64	45	44	2	37	360	342	297	6	43	11	12	22	15	1	6.67%	6	1	83.33%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
3	Checkers	2	2	0	0	0	0	0	0	12	2	10	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	8	0	8	4	1.000	12	21	33	0	1	64	45	44	2	61	360	342	297	6	40	9	10	35	5	1	20.00%	4	1	75.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
4	Comets	2	0	2	0	0	0	0	0	1	4	-3	1	0	1	0	0	0	0	0	1	3	-2	1	0	1	0	0	0	0	0	0	1	-1	0	0.000	1	1	2	0	0	64	45	44	2	38	360	342	297	6	46	17	14	32	8	0	0.00%	6	0	100.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
5	Crunch	2	1	1	0	0	0	0	0	9	6	3	1	1	0	0	0	0	0	0	6	2	4	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	9	14	23	0	0	64	45	44	2	71	360	342	297	6	52	16	14	27	9	2	22.22%	7	1	85.71%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
6	Devils	2	2	0	0	0	0	0	0	19	2	17	1	1	0	0	0	0	0	0	8	1	7	1	1	0	0	0	0	0	0	11	1	10	4	1.000	19	35	54	0	0	64	45	44	2	89	360	342	297	6	24	7	17	44	2	2	100.00%	6	0	100.00%	1	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
7	Griffins	2	2	0	0	0	0	0	0	14	7	7	1	1	0	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	7	3	4	4	1.000	14	26	40	0	0	64	45	44	2	81	360	342	297	6	63	9	8	50	7	2	28.57%	4	2	50.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
8	Marlies	2	2	0	0	0	0	0	0	13	4	9	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	8	2	6	4	1.000	13	24	37	0	0	64	45	44	2	92	360	342	297	6	36	9	12	45	2	1	50.00%	6	0	100.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
9	Monsters	2	0	2	0	0	0	0	0	2	7	-5	1	0	1	0	0	0	0	0	0	3	-3	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	2	4	6	0	0	64	45	44	2	20	360	342	297	6	51	11	16	18	8	1	12.50%	8	2	75.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
10	Phantoms	2	2	0	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	5	2	3	4	1.000	8	13	21	0	0	64	45	44	2	78	360	342	297	6	46	12	16	38	2	0	0.00%	8	0	100.00%	2	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
11	Rocket	2	0	1	0	0	0	1	0	5	6	-1	1	0	0	0	0	0	1	0	4	3	1	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	5	8	13	0	0	64	45	44	2	32	360	342	297	6	48	12	24	36	7	2	28.57%	12	0	100.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
12	Senators	2	2	0	0	0	0	0	0	13	2	11	1	1	0	0	0	0	0	0	6	0	6	1	1	0	0	0	0	0	0	7	2	5	4	1.000	13	21	34	0	1	64	45	44	2	77	360	342	297	6	44	11	8	34	9	5	55.56%	4	0	100.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
13	Sound Tigers	2	2	0	0	0	0	0	0	19	0	19	1	1	0	0	0	0	0	0	9	0	9	1	1	0	0	0	0	0	0	10	0	10	4	1.000	19	35	54	0	2	64	45	44	2	95	360	342	297	6	26	7	2	48	3	1	33.33%	1	0	100.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
14	Thunderbirds	2	0	2	0	0	0	0	0	5	9	-4	1	0	1	0	0	0	0	0	2	5	-3	1	0	1	0	0	0	0	0	3	4	-1	0	0.000	5	9	14	0	0	64	45	44	2	38	360	342	297	6	38	12	10	20	14	1	7.14%	5	3	40.00%	0	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
	Total	30	20	9	0	0	0	1	0	154	69	85	15	11	3	0	0	0	1	0	74	35	39	15	9	6	0	0	0	0	0	80	34	46	42	0.700	154	270	424	0	4	64	45	44	2	999	360	342	297	6	644	162	185	524	99	20	20.20%	87	10	88.51%	4	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
16	Wolf Pack	2	2	0	0	0	0	0	0	16	4	12	1	1	0	0	0	0	0	0	9	1	8	1	1	0	0	0	0	0	0	7	3	4	4	1.000	16	29	45	0	0	64	45	44	2	96	360	342	297	6	38	7	14	37	2	0	0.00%	7	0	100.00%	1	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
	_Since Last GM Reset	30	20	9	0	0	0	1	0	154	69	85	15	11	3	0	0	0	1	0	74	35	39	15	9	6	0	0	0	0	0	80	34	46	42	0.700	154	270	424	0	4	64	45	44	2	999	360	342	297	6	644	162	185	524	99	20	20.20%	87	10	88.51%	4	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
	_Vs Conference	30	20	9	0	0	0	1	0	154	69	85	15	11	3	0	0	0	1	0	74	35	39	15	9	6	0	0	0	0	0	80	34	46	42	0.700	154	270	424	0	4	64	45	44	2	999	360	342	297	6	644	162	185	524	99	20	20.20%	87	10	88.51%	4	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
	_Vs Division	14	11	3	0	0	0	0	0	89	25	64	7	6	1	0	0	0	0	0	39	13	26	7	5	2	0	0	0	0	0	50	12	38	22	0.786	89	158	247	0	3	64	45	44	2	533	360	342	297	6	274	65	83	258	28	6	21.43%	37	3	91.89%	4	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169

			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
30	42	W2	154	270	424	999	644	162	185	524	0	4

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
30	20	9	0	0	1	0	154	69

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	11	3	0	0	1	0	74	35

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	9	6	0	0	0	0	80	34

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
7	3	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
99	20	20.20%	87	10	88.51%	4

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
360	342	297	6	64	45	44	2

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
497	793	62.67%	438	703	62.30%	331	494	67.00%

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
888	695	601	171	315	169

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
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
1 - 2020-10-24	14	Griffins	4	Penguins	7	W			Sommaire du Match
2 - 2020-10-25	30	Monsters	3	Penguins	0	L			Sommaire du Match
3 - 2020-10-26	31	Penguins	8	Checkers	0	W			Sommaire du Match
4 - 2020-10-27	59	Penguins	1	Bruins	3	L			Sommaire du Match
5 - 2020-10-28	66	Bruins	3	Penguins	4	W			Sommaire du Match
6 - 2020-10-29	90	Devils	1	Penguins	8	W			Sommaire du Match
7 - 2020-10-30	104	Penguins	11	Devils	1	W			Sommaire du Match
9 - 2020-11-01	117	Penguins	7	Bears	2	W			Sommaire du Match
10 - 2020-11-02	128	Penguins	3	Crunch	4	L			Sommaire du Match
11 - 2020-11-03	141	Penguins	8	Marlies	2	W			Sommaire du Match
12 - 2020-11-04	154	Crunch	2	Penguins	6	W			Sommaire du Match
13 - 2020-11-05	174	Rocket	3	Penguins	4	W	X	X	Sommaire du Match
15 - 2020-11-07	192	Penguins	5	Phantoms	2	W			Sommaire du Match
16 - 2020-11-08	204	Penguins	10	Sound Tigers	0	W			Sommaire du Match
17 - 2020-11-09	216	Thunderbirds	5	Penguins	2	L			Sommaire du Match
18 - 2020-11-10	235	Penguins	3	Thunderbirds	4	L			Sommaire du Match
19 - 2020-11-11	244	Phantoms	2	Penguins	3	W			Sommaire du Match
20 - 2020-11-12	260	Penguins	7	Griffins	3	W			Sommaire du Match
21 - 2020-11-13	273	Wolf Pack	1	Penguins	9	W			Sommaire du Match
23 - 2020-11-15	294	Penguins	1	Rocket	3	L			Sommaire du Match
24 - 2020-11-16	310	Checkers	2	Penguins	4	W			Sommaire du Match
25 - 2020-11-17	327	Penguins	7	Senators	2	W			Sommaire du Match
26 - 2020-11-18	337	Comets	3	Penguins	1	L			Sommaire du Match
27 - 2020-11-19	356	Penguins	7	Wolf Pack	3	W			Sommaire du Match
28 - 2020-11-20	366	Penguins	0	Comets	1	L			Sommaire du Match
29 - 2020-11-21	372	Sound Tigers	0	Penguins	9	W			Sommaire du Match
31 - 2020-11-23	398	Senators	0	Penguins	6	W			Sommaire du Match
32 - 2020-11-24	404	Penguins	2	Monsters	4	L			Sommaire du Match
34 - 2020-11-26	432	Bears	4	Penguins	6	W			Sommaire du Match
38 - 2020-11-30	452	Marlies	2	Penguins	5	W			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	100	0
Assistance	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
0	0 - 0.00%	0$	0$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
0$	1,179,433$	1,122,833$	0$

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

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

Afficher ou Masquer des Colonnes

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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
2020	30	20	9	0	0	0	1	0	154	69	85	15	11	3	0	0	0	1	0	74	35	39	15	9	6	0	0	0	0	0	80	34	46	42	154	270	424	0	4	64	45	44	2	999	360	342	297	6	644	162	185	524	99	20	20.20%	87	10	88.51%	4	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169
Total Saison Régulière	30	20	9	0	0	0	1	0	154	69	85	15	11	3	0	0	0	1	0	74	35	39	15	9	6	0	0	0	0	0	80	34	46	42	154	270	424	0	4	64	45	44	2	999	360	342	297	6	644	162	185	524	99	20	20.20%	87	10	88.51%	4	497	793	62.67%	438	703	62.30%	331	494	67.00%	888	695	601	171	315	169