LHSRSM - Flyers

Phantoms
Formation
Points
Informations Joueurs
Lignes
Statistiques d'Équipe
Calendrier
Finance
Profondeur
Actualité
Historique
Dernière Transactions
Blessure/Suspension
Stats d'équipe en Carrière
Client Web pour Formation
Client Web pour Lignes

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP
1	Claude Giroux (C)	X				100.00	57	39	84	83	69	85	89	86	87	84	75	72	86	80	93	0	50	74	0
2	Tom Wilson			X		100.00	97	75	56	72	90	83	84	71	58	73	77	84	69	71	76	0	50	72	0
3	Jeff Skinner (A)		X			100.00	56	39	84	82	71	83	86	79	73	71	80	56	82	75	69	0	50	70	0
4	Nazem Kadri	X				100.00	78	52	64	77	73	82	85	74	83	76	75	62	77	78	73	0	50	70	0
5	Micheal Ferland		X			100.00	90	62	69	72	81	79	77	71	58	73	70	62	69	75	68	0	50	69	0
6	Tyler Bertuzzi		X			100.00	74	56	78	73	77	83	87	72	56	75	74	59	67	69	66	0	50	69	0
7	Tyler Johnson	X				100.00	65	38	86	76	65	79	87	74	80	71	75	64	76	78	72	0	50	69	0
8	Cody Eakin	X				100.00	74	39	87	68	71	78	83	67	84	69	70	78	66	77	69	0	50	68	0
9	Evan Rodrigues	X				100.00	57	42	85	70	69	77	81	68	79	67	63	70	69	73	68	0	50	66	0
10	Matt Nieto		X			100.00	61	39	89	67	69	75	84	66	53	70	61	82	66	75	68	0	50	66	0
11	Riley Nash	X				100.00	72	43	89	61	77	69	86	62	80	63	59	73	62	79	72	0	50	65	0
12	Tobias Rieder	X				100.00	67	39	83	66	70	73	82	65	68	63	61	76	66	73	67	0	50	65	0
13	Jacob Trouba				X	100.00	84	51	69	73	85	89	91	72	30	80	67	85	56	71	67	0	50	71	0
14	Torey Krug				X	100.00	63	45	74	78	67	87	82	77	30	89	69	61	63	77	72	0	50	71	0
15	Brady Skjei				X	100.00	84	45	75	72	85	87	89	71	30	74	69	80	56	71	67	0	50	70	0
16	Jack Johnson				X	100.00	91	45	81	64	83	85	88	63	30	70	55	83	51	82	73	0	50	68	0
17	Justin Braun				X	100.00	81	42	83	63	81	85	87	62	30	73	56	77	49	82	73	0	50	67	0
Rayé
1	Joel Farabee		X			100.00	55	43	81	70	72	76	73	68	53	62	67	59	68	61	62	0	50	64	0
2	Christian Fischer			X		100.00	86	45	84	62	83	77	82	61	53	56	63	58	62	65	64	0	50	63	0

	MOYENNE D'ÉQUIPE					100.00	73	46	79	71	76	81	84	70	59	72	68	71	66	74	70	0	50	68

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Devan Dubnyk	100.00	91	95	93	97	90	89	91	90	89	91	90	84	88	0	50	76	0
2	Keith Kinkaid	100.00	73	76	74	81	72	71	73	72	71	73	72	79	85	0	50	65	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	82	86	84	89	81	80	82	81	80	82	81	82	87	0	50	71

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
John Tortorella	81	90	82	89	92	85	53	USA	61	1	100,000$

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

#	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	GA	TA	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Salaire Année 5	Link
Brady Skjei	Flyers	D	24	1994-03-26	No	210 Lbs	6 ft3	No	No	No	5	Pro & Farm	5,250,000$	5,250,000$	5,250,000$	No	5,250,000$	5,250,000$	5,250,000$	5,250,000$	Lien / Lien NHL
Christian Fischer	Flyers	RW	21	1997-04-15	No	214 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,075,833$	1,075,833$	1,075,833$	No					Lien / Lien NHL
Claude Giroux	Flyers	C	30	1988-01-12	No	185 Lbs	5 ft11	No	No	No	3	Pro & Farm	8,275,000$	8,275,000$	8,275,000$	No	8,275,000$	8,275,000$			Lien / Lien NHL
Cody Eakin	Flyers	C	27	1991-05-24	No	183 Lbs	6 ft0	No	No	No	1	Pro & Farm	3,850,000$	3,850,000$	3,850,000$	No					Lien / Lien NHL
Devan Dubnyk	Flyers	G	32	1986-05-04	No	224 Lbs	6 ft6	No	No	No	2	Pro & Farm	4,333,000$	4,333,000$	4,333,000$	No	4,333,000$				Lien / Lien NHL
Evan Rodrigues	Flyers	C	25	1993-07-28	No	184 Lbs	5 ft11	No	No	No	0	Pro & Farm		0$	0$	No					Lien / Lien NHL
Jack Johnson	Flyers	D	31	1987-01-13	No	227 Lbs	6 ft1	No	No	No	4	Pro & Farm	3,250,000$	3,250,000$	3,250,000$	No	3,250,000$	3,250,000$	3,250,000$		Lien / Lien NHL
Jacob Trouba	Flyers	D	24	1994-02-26	No	209 Lbs	6 ft3	No	No	No	0	Pro & Farm		0$	0$	No					Lien / Lien NHL
Jeff Skinner	Flyers	LW	26	1992-05-16	No	195 Lbs	5 ft11	No	No	No	0	Pro & Farm		0$	0$	No					Lien / Lien NHL
Joel Farabee	Flyers	LW	18	2000-02-25	No	164 Lbs	6 ft0	No	No	No	1	Pro & Farm		0$	0$	No					Lien
Justin Braun	Flyers	D	31	1987-02-10	No	205 Lbs	6 ft2	No	No	No	1	Pro & Farm	3,800,000$	3,800,000$	3,800,000$	No					Lien / Lien NHL
Keith Kinkaid	Flyers	G	29	1989-07-04	No	186 Lbs	6 ft3	No	No	No	0	Pro & Farm		0$	0$	No					Lien / Lien NHL
Matt Nieto	Flyers	LW	25	1992-11-05	No	185 Lbs	5 ft11	No	No	No	1	Pro & Farm	1,975,000$	1,975,000$	1,975,000$	No					Lien / Lien NHL
Micheal Ferland	Flyers	LW	26	1992-04-20	No	217 Lbs	6 ft1	No	No	No	0	Pro & Farm		0$	0$	No					Lien / Lien NHL
Nazem Kadri	Flyers	C	28	1990-10-06	No	192 Lbs	6 ft0	No	No	No	3	Pro & Farm	4,500,000$	4,500,000$	4,500,000$	No	4,500,000$	4,500,000$			Lien / Lien NHL
Riley Nash	Flyers	C	29	1989-05-09	No	185 Lbs	6 ft2	No	No	No	2	Pro & Farm	2,750,000$	2,750,000$	2,750,000$	No	2,750,000$				Lien / Lien NHL
Tobias Rieder	Flyers	C	25	1993-01-10	No	186 Lbs	5 ft11	No	No	No	0	Pro & Farm		0$	0$	No					Lien / Lien NHL
Tom Wilson	Flyers	RW	24	1994-03-29	No	220 Lbs	6 ft4	No	No	No	5	Pro & Farm	5,166,666$	5,166,666$	5,166,666$	No	5,166,666$	5,166,666$	5,166,666$	5,166,666$	Lien / Lien NHL
Torey Krug	Flyers	D	27	1991-04-12	No	186 Lbs	5 ft9	No	No	No	1	Pro & Farm	5,250,000$	5,250,000$	5,250,000$	No					Lien / Lien NHL
Tyler Bertuzzi	Flyers	LW	23	1995-02-24	No	199 Lbs	6 ft1	No	No	No	1	Pro & Farm	1,400,000$	1,400,000$	1,400,000$	No					Lien / Lien NHL
Tyler Johnson	Flyers	C	28	1990-07-29	No	182 Lbs	5 ft8	No	No	No	5	Pro & Farm	5,000,000$	5,000,000$	5,000,000$	No	5,000,000$	5,000,000$	5,000,000$	5,000,000$	Lien / Lien NHL

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
21	26.33	197 Lbs	6 ft1	1.71	2,660,738$

Somme Salaire 1e Année	Somme Salaire 2e Année	Somme Salaire 3e Année	Somme Salaire 4e Année	Somme Salaire 5e Année
55,875,499$	38,524,666$	31,441,666$	18,666,666$	15,416,666$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Jeff Skinner	Claude Giroux	Tyler Johnson	35	0	2	3
2	Micheal Ferland	Nazem Kadri	Tom Wilson	30	0	3	2
3	Evan Rodrigues	Cody Eakin	Tyler Bertuzzi	25	0	3	2
4	Tobias Rieder	Riley Nash	Matt Nieto	10	1	3	1

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Torey Krug	Jacob Trouba	32	1	2	2
2	Justin Braun	Brady Skjei	32	1	2	2
3	Jack Johnson		22	1	3	1
4	Torey Krug	Justin Braun	14	0	2	3

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Jeff Skinner	Claude Giroux	Tyler Johnson	60	1	1	3
2	Tyler Bertuzzi	Nazem Kadri	Micheal Ferland	40	1	1	3

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Torey Krug	Jacob Trouba	60	0	1	4
2	Brady Skjei	Justin Braun	40	0	1	4

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Cody Eakin	Tom Wilson	60	1	4	0
2	Riley Nash	Matt Nieto	40	1	4	0

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Justin Braun	Jacob Trouba	60	1	4	0
2	Jack Johnson		40	1	4	0

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Claude Giroux	60	0	5	0	Justin Braun	Jacob Trouba	60	0	5	0
2	Cody Eakin	40	0	5	0	Jack Johnson		40	0	5	0

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Claude Giroux	Jeff Skinner	60	0	2	3
2	Nazem Kadri	Tyler Johnson	40	0	2	3

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Torey Krug	Jacob Trouba	60	0	3	2
2	Brady Skjei	Justin Braun	40	0	3	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Jeff Skinner	Claude Giroux	Tyler Johnson	Torey Krug	Jacob Trouba

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Tyler Johnson	Claude Giroux	Tom Wilson	Jacob Trouba

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Micheal Ferland, Tyler Bertuzzi, Cody Eakin	Tom Wilson, Evan Rodrigues	Claude Giroux

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Brady Skjei, , Jack Johnson	Jack Johnson	Torey Krug, Brady Skjei

Tirs de Pénalité
Claude Giroux, Tyler Johnson, Micheal Ferland, Nazem Kadri, Tyler Bertuzzi

Gardien
#1 : Devan Dubnyk, #2 : Keith Kinkaid

Lignes d'Attaque Perso. en Prol.
Claude Giroux, Tyler Johnson, Nazem Kadri, Tom Wilson, Matt Nieto, Cody Eakin, Cody Eakin, Tyler Bertuzzi, Riley Nash, Tobias Rieder, Evan Rodrigues

Lignes de Défense Perso. en Prol.
Torey Krug, , Justin Braun, Brady Skjei, Jack Johnson

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
	Total	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00%	0	0	0	0.00%	0	0	0.00%	0	0	0.00%	0	0	0	0	0	0

			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
0	0	N/A	0	0	0	0	0	0	0	0	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
0	0	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
0	0	0.00%	0	0	0.00%	0

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
0	0	0	0	0	0	0	0

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
0	0	0.00%	0	0	0.00%	0	0	0.00%

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
0	0	0	0	0	0

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	RI	Lien

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2	Niveau 3	Niveau 4	Luxe
Capacité de l'Aréna	6000	5000	2000	4000	1000
Prix des Billets	100	60	35	25	200
Assistance	0.00%	0.00%	0.00%	0.00%	0.00%
Attendance PCT	0.00%	0.00%	0.00%	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
41	0 - 0.00%	0$	0$	18000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs	Valeur du Cap Salarial Spécial
0$	55,875,499$	54,799,666$	0$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Taxe de Luxe Totale	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	0$	0$	15	0

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

Total de l'Équipe Éstimé
Dépenses de la Saison Éstimées	Compte Bancaire Actuel	Compte Bancaire Projeté
0$	60,427,196$	60,427,196$

Cap Salarial de la Saison Éstimé	Cap Salarial Disponible	Cap Salarial Maximum	Au dessus du Plafond Salarial Minimum
0$	79,500,000$	79,500,000$	-58,800,000$

Charte de Profondeur

Ailier Gauche

Centre

Ailier Droit

Jeff Skinner	AGE:26	PO:0	OV:70
Micheal Ferland	AGE:26	PO:0	OV:69
Tyler Bertuzzi	AGE:23	PO:0	OV:69
Matt Nieto	AGE:25	PO:0	OV:66
Joel Farabee	AGE:18	PO:0	OV:64

Claude Giroux	AGE:30	PO:0	OV:74
Nazem Kadri	AGE:28	PO:0	OV:70
Tyler Johnson	AGE:28	PO:0	OV:69
Cody Eakin	AGE:27	PO:0	OV:68
Evan Rodrigues	AGE:25	PO:0	OV:66
Martin Necas	AGE:19	PO:0	OV:66
Riley Nash	AGE:29	PO:0	OV:65
Tobias Rieder	AGE:25	PO:0	OV:65
Christoffer Ehn	AGE:22	PO:0	OV:64
Mikhail Vorobyev	AGE:21	PO:0	OV:62
J.C. Beaudin	AGE:21	PO:0	OV:61

Tom Wilson	AGE:24	PO:0	OV:72
Martin Frk	AGE:25	PO:0	OV:65
Christian Fischer	AGE:21	PO:0	OV:63
Keegan Kolesar	AGE:21	PO:0	OV:62

Défense #1

Défense #2

Gardien

Jacob Trouba	AGE:24	PO:0	OV:71
Torey Krug	AGE:27	PO:0	OV:71
Brady Skjei	AGE:24	PO:0	OV:70
Jack Johnson	AGE:31	PO:0	OV:68
Justin Braun	AGE:31	PO:0	OV:67

Devon Toews	AGE:24	PO:0	OV:67
Dan Hamhuis	AGE:35	PO:0	OV:64
Luke Schenn	AGE:28	PO:0	OV:64
Dillon Heatherington	AGE:23	PO:0	OV:62
Michael Paliotta	AGE:25	PO:0	OV:59

Devan Dubnyk	AGE:32	PO:0	OV:76
Hunter Miska	AGE:23	PO:0	OV:65
Keith Kinkaid	AGE:29	PO:0	OV:65
Jon Gillies	AGE:24	PO:0	OV:64

Éspoirs

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

Éspoir	Nom de l'Équipe	Année de Repêchage	Choix Total	Lien
Andrei Markov	Flyers
Arvid Costmar	Flyers	9	124
Bobby Butler	Flyers			Lien
Clinston Franklin	Flyers	4	122
Conner Hurley	Flyers	3	77
Dan Girardi	Flyers			Lien
Dennis Rasmussen	Flyers
Greg Rallo	Flyers
Gustav Possler	Flyers	3	68
Hayden Hawkey	Flyers	4	153
Henri Ikonen	Flyers
Jakub Kindl	Flyers
Joel Vermin	Flyers
Michael Houser	Flyers
Viktor Loov	Flyers

Choix au Repêchage

Année	R1	R2	R3	R4	R5	R6
10			PHI	DET
11	PHI	PHI	WIN PHI	PHI	PHI	PHI
12	PHI	PHI	PHI	PHI	PHI	PHI
13	PHI	PHI	PHI	PHI	PHI	PHI

La base de donnée des nouvelles n'a pas été trouvée

[2020-07-12 2:48:03 PM] - Flyers drafts Arvid Costmar as the #124 overall pick in the Entry Draft of year 9.
[2020-05-30 7:52:43 PM] - Joel Farabee was added to Flyers.
[2020-05-29 10:11:47 PM] - Flyers are the Playoffs Champions for year 8!
[2020-04-05 11:48:32 AM] - Phantoms didn't make playoff for year 8.
[2020-03-15 10:00:42 PM] - TRADE : From Hurricanes to Flyers : Luke Schenn (64).
[2020-01-27 5:56:05 PM] - Jeff Skinner has been selected as assistant for Flyers.
[2020-01-27 5:56:05 PM] - Tyler Johnson is no longer as assistant for Flyers.
[2020-01-27 5:56:05 PM] - Dan Girardi has been selected as assistant for Flyers.
[2020-01-27 5:56:05 PM] - Unknown Player is no longer as assistant for Flyers.
[2020-01-20 8:05:08 PM] - TRADE : From Flyers to Senateurs : Mike Green (69), Shayne Gostisbehere (71), Filip Chytil (65), Y:9-RND:1-PHI, Y:9-RND:3-PHI.
[2020-01-20 8:05:08 PM] - TRADE : From Senateurs to Flyers : Nazem Kadri (70), Torey Krug (72), Dan Girardi (68), Evan Rodrigues (66).
[2019-12-22 11:12:04 PM] - TRADE : From Red Wings to Flyers : Tobias Rieder (66), Y:10-RND:4-DET.
[2019-12-22 11:12:04 PM] - TRADE : From Flyers to Red Wings : Kevan Miller (66), Y:10-RND:2-WSH.

[2020-07-12 2:48:03 PM] Flyers drafts Arvid Costmar as the #124 overall pick in the Entry Draft of year 9.
[2020-05-30 7:52:43 PM] Joel Farabee was added to Flyers.

Pas de Blessure ou de Suspension.

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
Saison Régulière
2	82	41	31	0	2	2	3	3	257	214	43	41	20	17	0	0	1	3	0	134	112	22	41	21	14	0	2	1	0	3	123	102	21	82	257	456	713	4	6	84	81	87	8	2799	919	940	920	38	2275	623	834	1518	368	71	19.29%	324	46	85.80%	2	1663	3164	52.56%	1426	2710	52.62%	664	1268	52.37%	2085	1472	1849	596	1027	525
3	82	35	25	0	7	1	6	8	230	195	35	41	17	11	0	4	0	3	6	117	103	14	41	18	14	0	3	1	3	2	113	92	21	70	230	404	634	3	8	83	66	68	22	2578	851	836	848	93	2105	621	1031	1623	345	46	13.33%	349	46	86.82%	3	1555	3015	51.58%	1336	2661	50.21%	601	1203	49.96%	2089	1470	1886	591	1030	522
4	82	40	30	0	0	7	3	2	226	207	19	41	26	11	0	0	3	0	1	128	99	29	41	14	19	0	0	4	3	1	98	108	-10	80	226	398	624	0	5	77	78	68	4	2354	746	764	834	25	2150	623	672	1796	247	38	15.38%	256	55	78.52%	1	1411	2758	51.16%	1384	2662	51.99%	657	1231	53.37%	1998	1397	1903	605	1055	521
5	82	47	23	0	4	3	3	2	285	205	80	41	24	12	0	2	2	1	0	138	91	47	41	23	11	0	2	1	2	2	147	114	33	94	285	510	795	3	6	92	90	96	8	2677	855	863	940	39	2221	600	564	1634	273	56	20.51%	229	40	82.53%	0	1593	2974	53.56%	1396	2636	52.96%	741	1346	55.05%	2054	1432	1831	597	1072	547
6	82	31	34	0	6	6	3	2	231	252	-21	41	14	16	0	5	3	3	0	125	128	-3	41	17	18	0	1	3	0	2	106	124	-18	62	231	425	656	2	0	79	74	69	11	2631	865	880	855	54	2586	688	683	1935	306	54	17.65%	273	58	78.75%	1	1568	2973	52.74%	1538	2901	53.02%	679	1263	53.76%	2037	1447	1916	581	1013	510
7	82	46	19	0	5	8	2	2	258	206	52	41	27	6	0	2	3	2	1	136	94	42	41	19	13	0	3	5	0	1	122	112	10	92	258	460	718	1	4	84	84	83	8	2649	843	894	888	41	2392	689	624	1874	281	54	19.22%	260	52	80.00%	2	1540	2930	52.56%	1447	2736	52.89%	614	1196	51.34%	2070	1484	1888	568	1000	509
8	82	37	27	0	4	2	6	6	224	194	30	41	22	11	0	2	2	2	2	119	90	29	41	15	16	0	2	0	4	4	105	104	1	102	224	405	629	1	3	75	59	80	16	2315	792	732	735	102	2251	658	813	1940	376	70	18.62%	347	49	85.88%	2	1531	2840	53.91%	1428	2793	51.13%	614	1194	51.42%	2036	1411	1913	615	1046	525
8	82	37	27	0	4	2	6	6	224	194	30	41	22	11	0	2	2	2	2	119	90	29	41	15	16	0	2	0	4	4	105	104	1	102	224	405	629	1	3	75	59	80	16	2315	792	732	735	102	2251	658	813	1940	376	70	18.62%	347	49	85.88%	2	1531	2840	53.91%	1428	2793	51.13%	614	1194	51.42%	2036	1411	1913	615	1046	525
Total Saison Régulière	656	314	216	0	32	31	32	31	1935	1667	268	328	172	95	0	17	16	16	12	1016	807	209	328	142	121	0	15	15	16	19	919	860	59	684	1935	3463	5398	15	35	649	591	631	93	20318	6663	6641	6755	494	18231	5160	6034	14260	2572	459	17.85%	2385	395	83.44%	13	12392	23494	52.75%	11383	21892	52.00%	5184	9895	52.39%	16410	11525	15102	4772	8293	4188
Séries
2	25	16	9	0	0	0	0	0	80	69	11	11	7	4	0	0	0	0	0	38	27	11	14	9	5	0	0	0	0	0	42	42	0	32	80	146	226	0	2	23	32	22	3	849	208	289	254	98	864	268	297	520	115	22	19.13%	101	16	84.16%	0	501	980	51.12%	508	1000	50.80%	194	391	49.62%	678	476	645	204	346	172
3	17	10	7	0	0	0	0	0	44	41	3	9	4	5	0	0	0	0	0	24	28	-4	8	6	2	0	0	0	0	0	20	13	7	20	44	80	124	0	1	8	20	15	1	462	122	159	165	16	428	122	242	369	90	8	8.89%	66	8	87.88%	0	307	616	49.84%	272	559	48.66%	115	239	48.12%	447	312	383	124	213	110
4	4	0	4	0	0	0	0	0	9	13	-4	2	0	2	0	0	0	0	0	5	7	-2	2	0	2	0	0	0	0	0	4	6	-2	0	9	16	25	0	0	4	1	4	0	101	28	33	39	1	116	28	30	101	8	1	12.50%	9	1	88.89%	0	64	127	50.39%	59	122	48.36%	38	65	58.46%	98	69	93	28	54	27
5	24	13	11	0	0	0	0	0	63	59	4	13	6	7	0	0	0	0	0	36	39	-3	11	7	4	0	0	0	0	0	27	20	7	26	63	112	175	1	4	24	19	19	1	828	251	246	244	87	819	236	188	462	77	15	19.48%	78	13	83.33%	0	519	956	54.29%	483	942	51.27%	197	379	51.98%	611	426	614	190	328	160
7	20	16	4	0	0	0	0	0	77	46	31	11	8	3	0	0	0	0	0	40	26	14	9	8	1	0	0	0	0	0	37	20	17	32	77	139	216	0	0	30	21	23	3	701	221	209	256	15	588	157	138	447	63	11	17.46%	56	11	80.36%	0	384	723	53.11%	363	700	51.86%	175	337	51.93%	503	360	473	142	253	129
8	27	16	11	0	0	0	0	0	77	75	2	13	8	5	0	0	0	0	0	36	30	6	14	8	6	0	0	0	0	0	41	45	-4	32	77	137	214	1	0	28	20	23	6	830	248	250	272	60	822	227	332	634	115	22	19.13%	131	22	83.21%	1	521	1050	49.62%	566	1033	54.79%	224	432	51.85%	691	471	687	220	373	184
Total Séries	117	71	46	0	0	0	0	0	350	303	47	59	33	26	0	0	0	0	0	179	157	22	58	38	20	0	0	0	0	0	171	146	25	142	350	630	980	2	7	117	113	106	14	3771	1078	1186	1230	277	3637	1038	1227	2533	468	79	16.88%	441	71	83.90%	1	2296	4452	51.57%	2251	4356	51.68%	943	1843	51.17%	3030	2116	2897	911	1569	785