LHSDB - Monsters

Columbus Blue Jackets
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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	Oscar Lindberg	X				100.00	84	55	76	78	80	78	82	71	81	63	70	76	55	71	68	1	77	69	0
2	David Kampf (R)	X				100.00	76	55	80	80	77	72	80	71	90	65	65	77	55	72	67	1	36	68	0
3	Dylan Strome (R)	X				100.00	63	55	70	82	75	70	70	79	73	70	70	65	55	73	72	1	71	68	0
4	Nick Cousins	X				100.00	77	55	73	75	69	66	67	76	68	65	73	78	55	65	64	1	78	68	0
5	Oskar Lindblom (R)		X			100.00	76	55	71	84	70	72	73	76	50	63	65	65	55	70	70	1	76	67	0
6	Chris Kelly	X				100.00	68	55	78	75	77	68	67	64	50	62	60	61	55	75	75	1	77	63	0
7	Nikita Soshnikov			X		100.00	84	55	78	81	67	62	64	65	50	60	61	62	55	52	52	1	76	62	0
8	Daniel Catenacci			X		100.00	59	55	72	62	66	62	69	55	50	55	55	60	55	75	74	1	76	58	0
9	Daniel Audette		X			100.00	58	55	72	67	62	59	69	55	50	55	55	59	55	71	75	1	76	58	0
10	Nick Tarnasky		X			100.00	74	55	58	63	81	79	64	55	50	55	55	55	55	64	65	1	76	58	0
11	Mike Brown			X		100.00	60	55	66	69	75	69	54	55	50	55	55	55	55	69	70	1	76	58	0
12	Zack Stortini	X				100.00	64	55	57	61	81	79	74	55	50	55	55	55	55	65	65	1	76	58	0
13	Ryan Haggerty			X		100.00	71	55	69	68	75	69	66	55	50	55	55	55	55	50	50	1	76	57	0
14	Colin White (R)	X				100.00	56	55	55	55	56	57	57	66	60	55	55	55	55	73	71	1	62	56	0
15	Madison Bowey				X	100.00	79	55	79	86	69	74	64	78	25	71	60	75	55	78	78	1	72	71	0
16	Mike Reilly				X	100.00	75	55	81	86	57	76	58	84	25	77	63	77	55	75	73	1	78	71	0
17	Ryan Sproul				X	100.00	68	55	66	76	67	79	64	74	25	64	62	68	55	70	70	1	76	66	0
18	Brian Lashoff				X	100.00	55	55	55	60	55	55	78	55	25	55	55	55	55	60	61	1	73	56	0
19	Ville Pokka				X	100.00	55	55	57	60	57	57	76	57	25	57	57	57	55	53	53	1	60	56	0
Rayé
1	Tim Bozon		X			100.00	74	55	67	62	74	68	64	55	50	55	55	55	55	50	50	1	26	56	0
2	Quinton Howden	X				100.00	56	55	63	65	68	66	69	55	50	55	55	55	55	56	56	1	25	56	0
3	Jayce Hawryluk (R)		X			100.00	60	55	70	63	67	62	69	55	50	55	55	55	55	50	50	1	25	55	0
4	Matt Buckles (R)	X				100.00	82	55	82	55	79	54	54	55	50	55	55	55	55	50	50	1	25	55	0
5	Akim Aliu (R)			X		100.00	56	55	55	55	58	59	60	55	50	55	55	55	55	75	75	1	25	55	0
6	Brandon Alderson			X		100.00	56	55	55	55	57	58	59	55	50	55	55	55	55	74	72	1	25	54	0
7	Buddy Robinson			X		100.00	56	55	55	55	57	58	58	55	50	55	55	55	55	74	72	1	25	54	0
8	Cody Kunyk (R)			X		100.00	56	55	55	55	57	58	58	55	50	55	55	55	55	73	71	1	25	54	0
9	Zach O'Brien (R)			X		100.00	70	55	68	55	54	61	68	55	50	55	55	55	55	50	50	1	25	54	0
10	Emil Pettersson (R)	X				100.00	55	55	55	55	55	55	55	55	50	55	55	55	55	75	71	1	25	54	0
11	Stephen MacAulay (R)	X				100.00	56	55	59	62	68	66	54	55	50	55	55	55	55	50	50	1	25	54	0
12	Lauri Korpikoski		X			100.00	55	55	55	55	55	55	55	55	50	55	55	55	55	74	73	1	25	54	0
13	Joshua Winquist		X			100.00	56	55	55	55	55	56	55	55	50	55	55	55	55	50	50	1	25	52	0
14	Olivier Archambault (R)		X			100.00	55	55	62	55	54	54	54	55	50	55	55	55	55	50	50	1	25	52	0
15	Mike Sislo			X		100.00	56	55	55	55	55	55	55	55	50	55	55	55	55	50	50	1	25	52	0
16	Mike Weber				X	100.00	55	55	55	60	55	55	62	55	25	55	55	55	55	69	70	1	28	55	0
17	Josiah Didier				X	100.00	55	55	55	60	55	55	59	55	25	55	55	55	55	53	53	1	25	54	0
18	Kyle Burroughs				X	100.00	56	55	56	60	56	56	63	56	25	56	56	56	55	53	53	1	25	54	0
19	Robin Norell (R)				X	100.00	55	55	55	60	55	55	67	55	25	55	55	55	55	53	53	1	25	54	0
20	Jeremy Roy (R)				X	100.00	56	55	55	60	55	55	56	55	25	55	55	55	55	62	62	1	25	54	0
21	Brennan Menell (R)				X	100.00	55	55	55	60	55	55	56	55	25	55	55	55	55	55	55	1	25	54	0
22	Will O'Neill				X	100.00	57	55	55	60	55	55	67	55	25	55	55	55	55	53	53	1	25	54	0
23	John Negrin				X	100.00	55	55	55	60	55	55	58	55	25	55	55	55	55	53	53	1	25	53	0

	MOYENNE D'ÉQUIPE					100.00	63	55	63	64	64	63	64	60	45	58	57	59	55	63	62	1	46	58

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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	Jack Campbell	100.00	73	77	83	73	70	70	75	70	67	71	55	69	67	1	71	70	0
2	Eamon McAdam	100.00	62	70	70	79	65	65	65	65	68	64	55	60	62	1	76	65	0
Rayé
1	Jeff Malcolm	100.00	54	64	59	71	58	58	58	58	59	58	55	54	56	1	25	58	0
2	Marcus Hogberg	100.00	55	55	55	55	55	55	55	55	55	55	55	65	65	1	25	55	0
3	Filip Gustavsson (R)	100.00	55	55	55	55	55	55	55	55	55	55	55	50	60	1	25	54	0

	MOYENNE D'ÉQUIPE	100.00	60	64	64	67	61	61	62	61	61	61	55	60	62	1	44	60

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Randy Ladouceur	42	62	67	38	80	70	60	CAN	56	4	100,000$

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	EG	HT	P/20	FW	FL	FT	S1	S2	S3
1	Mike Reilly	Monsters (Clb)	D	24	5	23	28	3	26	0	29	36	45	12	26	11.11%	20	583	24.29	3	14	17	33	119	0	0	0	1	91	0	1	0.00%	0	0	0	0.96	0	0	0	0	1	3
2	Ryan Sproul	Monsters (Clb)	D	25	1	23	24	22	30	0	34	28	31	12	29	3.23%	24	581	23.27	1	5	6	22	99	0	1	1	0	95	0	0	0.00%	0	0	0	0.83	0	0	0	0	3	3
3	Oscar Lindberg	Monsters (Clb)	C	25	8	13	21	7	31	5	37	66	81	22	44	9.88%	6	540	21.60	4	8	12	25	122	1	1	2	4	118	3	0	66.73%	571	0	1	0.78	0	0	1	2	2	0
4	Oskar Lindblom	Monsters (Clb)	LW	25	7	12	19	2	6	0	16	26	67	15	42	10.45%	1	413	16.52	3	7	10	25	125	0	0	0	2	20	0	0	40.63%	32	0	0	0.92	0	0	0	0	0	1
5	Nick Cousins	Monsters (Clb)	C	24	10	8	18	10	10	0	27	48	64	15	54	15.63%	5	395	16.46	2	2	4	9	57	0	1	1	2	76	3	0	61.42%	324	0	0	0.91	0	0	0	2	3	2
6	Madison Bowey	Monsters (Clb)	D	23	6	9	15	0	23	5	47	19	32	6	16	18.75%	17	559	24.32	5	3	8	21	100	0	0	0	1	90	2	0	0.00%	0	0	0	0.54	1	0	0	2	0	1
7	Jake Virtanen	Columbus Blue Jackets	RW	16	6	8	14	6	30	0	29	38	64	15	57	9.38%	5	375	23.49	3	2	5	18	78	0	1	1	7	64	1	0	59.63%	161	1	0	0.75	0	0	0	1	3	0
8	Nikita Soshnikov	Monsters (Clb)	RW	25	6	8	14	5	28	0	33	24	43	8	30	13.95%	2	415	16.62	2	2	4	12	116	0	0	0	0	0	1	0	76.19%	21	0	0	0.67	0	0	0	1	1	1
9	David Kampf	Monsters (Clb)	C	9	5	7	12	6	2	0	7	11	24	8	18	20.83%	8	134	14.99	0	3	3	1	12	1	0	1	4	30	0	0	65.52%	87	0	0	1.78	0	0	0	3	0	0
10	Dylan Strome	Monsters (Clb)	C	21	9	2	11	3	6	0	13	58	57	13	44	15.79%	2	343	16.34	2	0	2	8	31	1	0	1	2	58	4	1	64.42%	326	0	0	0.64	0	0	0	1	2	1
11	Daniel Audette	Monsters (Clb)	LW	25	4	6	10	6	4	0	16	15	26	10	21	15.38%	3	388	15.53	2	1	3	5	81	0	0	0	0	0	2	0	47.06%	17	0	0	0.52	0	0	0	0	1	1
12	Daniel Catenacci	Monsters (Clb)	RW	25	3	3	6	9	12	0	17	18	27	13	13	11.11%	4	344	13.78	0	0	0	1	18	0	0	0	0	15	0	1	27.27%	22	0	0	0.35	0	0	0	0	1	1
13	Brian Lashoff	Monsters (Clb)	D	25	1	5	6	6	35	5	25	8	6	0	3	16.67%	18	397	15.90	1	2	3	2	30	0	0	0	0	44	0	0	0.00%	0	0	0	0.30	0	1	0	0	1	1
14	Nick Tarnasky	Monsters (Clb)	LW	25	1	2	3	9	26	0	31	10	12	4	11	8.33%	0	326	13.05	0	1	1	0	27	0	0	0	0	1	1	0	54.55%	11	0	0	0.18	0	0	0	0	0	0
15	Mike Brown	Monsters (Clb)	RW	25	1	2	3	-1	6	0	10	7	8	2	5	12.50%	0	175	7.04	1	0	1	1	26	0	0	0	0	0	0	0	25.00%	8	0	0	0.34	0	0	0	0	0	0
16	Chris Kelly	Monsters (Clb)	C	25	0	2	2	0	11	5	9	14	13	7	19	0.00%	2	145	5.83	0	0	0	0	5	0	0	0	1	24	0	0	50.53%	95	0	0	0.27	0	0	1	0	0	0
17	Colin White	Monsters (Clb)	C	13	1	1	2	2	2	0	3	6	2	1	4	50.00%	0	68	5.23	0	1	1	0	4	0	0	0	0	32	1	0	69.57%	23	0	0	0.59	0	0	0	0	0	0
18	Ryan Haggerty	Monsters (Clb)	RW	25	1	1	2	3	6	0	3	1	9	4	9	11.11%	0	102	4.11	0	0	0	1	1	0	0	0	0	3	0	0	42.86%	7	0	0	0.39	0	0	0	0	0	0
19	Ville Pokka	Monsters (Clb)	D	20	2	0	2	7	17	5	25	2	10	3	6	20.00%	15	395	19.80	1	0	1	5	57	0	0	0	0	59	0	0	0.00%	0	0	0	0.10	0	1	0	0	0	0
20	Tim Bozon	Monsters (Clb)	LW	5	0	1	1	1	4	0	3	0	4	1	1	0.00%	0	36	7.24	0	0	0	0	0	0	0	0	0	0	0	0	25.00%	4	0	0	0.55	0	0	0	0	0	0
21	Brendan Leipsic	Columbus Blue Jackets	C/LW	1	0	1	1	0	0	0	0	3	2	0	0	0.00%	0	20	20.08	0	1	1	1	7	0	0	0	0	3	0	0	73.33%	15	0	0	1.00	0	0	0	0	0	0
22	Zack Stortini	Monsters (Clb)	C	25	0	0	0	-1	2	0	3	1	1	0	1	0.00%	1	58	2.34	0	0	0	0	8	0	0	0	0	14	0	0	62.50%	32	0	0	0.00	0	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				456	77	137	214	105	317	25	417	439	628	171	453	12.26%	133	6801	14.91	30	52	82	190	1134	3	4	7	24	847	18	3	62.24%	1756	1	1	0.63	1	2	2	12	18	15

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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	SO	GA	SA	A	ST	BG	S1	S2	S3
1	Jack Campbell	Monsters (Clb)	25	20	4	1	0.913	1.55	1468	8	38	437	4	25	0	5	1	1
2	Eamon McAdam	Monsters (Clb)	1	0	0	0	1.000	0.00	32	0	0	10	0	0	25	0	0	0
Stats d'équipe Total ou en Moyenne			26	20	4	1	0.915	1.52	1501	8	38	447	4	25	25	5	1	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	Status	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4
Akim Aliu	Monsters (Clb)	RW	27	1989-04-23	Yes	225 Lbs	6 ft4	No	No	No	3	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$
Brandon Alderson	Monsters (Clb)	RW	24	1992-01-21	No	202 Lbs	6 ft4	No	No	No	1	Avec Restriction	Pro & Farm	300,000$	0$	0$	No
Brennan Menell	Monsters (Clb)	D	19	1997-05-24	Yes	183 Lbs	5 ft11	No	No	No	4	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Brian Lashoff	Monsters (Clb)	D	26	1990-07-15	No	212 Lbs	6 ft3	No	No	No	4	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Buddy Robinson	Monsters (Clb)	RW	25	1991-09-30	No	235 Lbs	6 ft5	No	No	No	2	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$
Chris Kelly	Monsters (Clb)	C	36	1980-11-11	No	198 Lbs	6 ft0	No	No	No	2	Sans Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$
Cody Kunyk	Monsters (Clb)	RW	26	1990-05-20	Yes	195 Lbs	5 ft11	No	No	No	3	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$
Colin White	Monsters (Clb)	C	19	1997-01-30	Yes	190 Lbs	6 ft1	No	No	No	4	Contrat d'Entrée	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$	900,000$
Daniel Audette	Monsters (Clb)	LW	20	1996-05-06	No	176 Lbs	5 ft8	No	No	No	3	Contrat d'Entrée	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$
Daniel Catenacci	Monsters (Clb)	RW	23	1993-03-09	No	186 Lbs	5 ft9	No	No	No	2	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$
David Kampf	Monsters (Clb)	C	22	1995-01-12	Yes	191 Lbs	6 ft2	No	No	No	4	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Dylan Strome	Monsters (Clb)	C	19	1997-03-07	Yes	185 Lbs	6 ft3	No	No	No	4	Contrat d'Entrée	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$	900,000$
Eamon McAdam	Monsters (Clb)	G	22	1994-09-23	No	199 Lbs	6 ft3	No	No	No	3	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Emil Pettersson	Monsters (Clb)	C	22	1994-01-14	Yes	176 Lbs	6 ft2	No	No	No	4	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Filip Gustavsson	Monsters (Clb)	G	18	1998-06-07	Yes	185 Lbs	6 ft2	No	No	No	4	Contrat d'Entrée	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
Jack Campbell	Monsters (Clb)	G	25	1992-01-09	No	195 Lbs	6 ft3	No	No	No	1	Avec Restriction	Pro & Farm	300,000$	0$	0$	No
Jayce Hawryluk	Monsters (Clb)	LW	21	1996-01-01	Yes	188 Lbs	5 ft10	No	No	No	3	Contrat d'Entrée	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$
Jeff Malcolm	Monsters (Clb)	G	27	1989-04-13	No	179 Lbs	6 ft2	No	No	No	2	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$
Jeremy Roy	Monsters (Clb)	D	19	1997-05-14	Yes	187 Lbs	6 ft0	No	No	No	4	Contrat d'Entrée	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
John Negrin	Monsters (Clb)	D	27	1989-03-25	No	194 Lbs	6 ft2	No	No	No	3	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$
Joshua Winquist	Monsters (Clb)	LW	23	1993-09-06	No	185 Lbs	5 ft11	No	No	No	3	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Josiah Didier	Monsters (Clb)	D	23	1993-04-08	No	203 Lbs	6 ft2	No	No	No	3	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Kyle Burroughs	Monsters (Clb)	D	21	1995-07-12	No	198 Lbs	6 ft0	No	No	No	1	Contrat d'Entrée	Pro & Farm	630,000$	0$	0$	No
Lauri Korpikoski	Monsters (Clb)	LW	30	1986-07-28	No	205 Lbs	6 ft1	No	No	No	1	Sans Restriction	Pro & Farm	976,882$	0$	0$	No
Madison Bowey	Monsters (Clb)	D	21	1995-04-22	No	194 Lbs	6 ft1	No	No	No	1	Contrat d'Entrée	Pro & Farm	894,000$	0$	0$	No
Marcus Hogberg	Monsters (Clb)	G	22	1994-11-25	No	209 Lbs	6 ft5	No	No	No	4	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Matt Buckles	Monsters (Clb)	C	21	1995-05-05	Yes	218 Lbs	6 ft3	No	No	No	4	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Mike Brown	Monsters (Clb)	RW	31	1985-06-23	No	205 Lbs	5 ft11	No	No	No	1	Sans Restriction	Pro & Farm	300,000$	0$	0$	No
Mike Reilly	Monsters (Clb)	D	23	1993-07-13	No	156 Lbs	5 ft11	No	No	No	4	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
Mike Sislo	Monsters (Clb)	RW	28	1988-01-19	No	195 Lbs	5 ft11	No	No	No	1	Sans Restriction	Pro & Farm	300,000$	0$	0$	No
Mike Weber	Monsters (Clb)	D	29	1987-12-15	No	212 Lbs	6 ft2	No	No	No	1	Sans Restriction	Pro & Farm	300,000$	0$	0$	No
Nick Cousins	Monsters (Clb)	C	23	1993-07-19	No	169 Lbs	5 ft10	No	No	No	3	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Nick Tarnasky	Monsters (Clb)	LW	32	1984-11-24	No	230 Lbs	6 ft2	No	No	No	4	Sans Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$	300,000$
Nikita Soshnikov	Monsters (Clb)	RW	23	1993-10-14	No	183 Lbs	5 ft11	No	No	No	2	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$
Olivier Archambault	Monsters (Clb)	LW	23	1993-02-15	Yes	176 Lbs	5 ft11	No	No	No	4	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
Oscar Lindberg	Monsters (Clb)	C	25	1991-10-28	No	195 Lbs	6 ft1	No	No	No	3	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$
Oskar Lindblom	Monsters (Clb)	LW	20	1996-08-15	Yes	192 Lbs	6 ft1	No	No	No	4	Contrat d'Entrée	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
Quinton Howden	Monsters (Clb)	C	24	1992-01-20	No	189 Lbs	6 ft2	No	No	No	3	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Robin Norell	Monsters (Clb)	D	21	1995-02-17	Yes	195 Lbs	5 ft11	No	No	No	3	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Ryan Haggerty	Monsters (Clb)	RW	23	1993-03-03	No	201 Lbs	6 ft0	No	No	No	1	Avec Restriction	Pro & Farm	300,000$	0$	0$	No
Ryan Sproul	Monsters (Clb)	D	24	1993-01-12	No	206 Lbs	6 ft4	No	No	No	3	Avec Restriction	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Stephen MacAulay	Monsters (Clb)	C	24	1992-04-20	Yes	182 Lbs	6 ft2	No	No	No	4	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
Tim Bozon	Monsters (Clb)	LW	22	1994-03-24	No	196 Lbs	6 ft1	No	No	No	3	Contrat d'Entrée	Pro & Farm	300,000$	0$	0$	No	300,000$	300,000$
Ville Pokka	Monsters (Clb)	D	22	1994-06-02	No	214 Lbs	6 ft0	No	No	No	1	Contrat d'Entrée	Pro & Farm	500,000$	0$	0$	No
Will O'Neill	Monsters (Clb)	D	28	1988-04-27	No	190 Lbs	6 ft1	No	No	No	1	Sans Restriction	Pro & Farm	500,000$	0$	0$	No
Zach O'Brien	Monsters (Clb)	RW	24	1992-06-29	Yes	192 Lbs	5 ft11	No	No	No	4	Avec Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$	500,000$
Zack Stortini	Monsters (Clb)	C	31	1985-09-10	No	230 Lbs	6 ft4	No	No	No	3	Sans Restriction	Pro & Farm	500,000$	0$	0$	No	500,000$	500,000$

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
47	24.00	196 Lbs	6 ft1	2.77	427,678$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Oskar Lindblom	Oscar Lindberg	Nikita Soshnikov	40	1	2	2
2	Daniel Audette	Nick Cousins	Mike Brown	30	1	2	2
3	Nick Tarnasky	Dylan Strome	Daniel Catenacci	20	1	2	2
4		David Kampf	Ryan Haggerty	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Madison Bowey	Mike Reilly	40	1	2	2
2	Ryan Sproul	Ville Pokka	30	1	2	2
3	Brian Lashoff	Madison Bowey	20	1	2	2
4	Mike Reilly	Ryan Sproul	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Oskar Lindblom	Oscar Lindberg	Nikita Soshnikov	60	1	2	2
2	Daniel Audette	Nick Cousins	Mike Brown	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Madison Bowey	Mike Reilly	60	1	2	2
2	Ryan Sproul	Ville Pokka	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Oscar Lindberg	Nick Cousins	60	1	2	2
2	Dylan Strome	David Kampf	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Madison Bowey	Mike Reilly	60	1	2	2
2	Ryan Sproul	Ville Pokka	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Oscar Lindberg	60	1	2	2	Madison Bowey	Mike Reilly	60	1	2	2
2	Nick Cousins	40	1	2	2	Ryan Sproul	Ville Pokka	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Oscar Lindberg	Nick Cousins	60	1	2	2
2	Dylan Strome	David Kampf	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Madison Bowey	Mike Reilly	60	1	2	2
2	Ryan Sproul	Ville Pokka	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Oskar Lindblom	Oscar Lindberg	Nikita Soshnikov	Madison Bowey	Mike Reilly

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Oskar Lindblom	Oscar Lindberg	Nikita Soshnikov	Madison Bowey	Mike Reilly

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Chris Kelly, Zack Stortini, Daniel Catenacci	Chris Kelly, Zack Stortini	Daniel Catenacci

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Brian Lashoff, Ville Pokka, Madison Bowey	Brian Lashoff	Ville Pokka, Madison Bowey

Tirs de Pénalité
Oscar Lindberg, Nick Cousins, Dylan Strome, David Kampf, Oskar Lindblom

Gardien
#1 : Jack Campbell, #2 : Eamon McAdam

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	Admirals	1	1	0	0	0	0	0	0	4	0	4	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	0	4	2	1.000	4	7	11	0	1	29	29	23	0	25	227	199	223	0	8	1	6	12	12	3	25.00%	3	0	100.00%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
2	Americans	1	1	0	0	0	0	0	0	2	1	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	2	1	1	2	1.000	2	4	6	0	0	29	29	23	0	29	227	199	223	0	11	6	16	15	12	1	8.33%	8	1	87.50%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
3	Bears	2	2	0	0	0	0	0	0	6	0	6	0	0	0	0	0	0	0	0	0	0	0	2	2	0	0	0	0	0	0	6	0	6	4	1.000	6	11	17	0	2	29	29	23	0	42	227	199	223	0	35	17	18	34	14	4	28.57%	9	0	100.00%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
4	Comets	1	1	0	0	0	0	0	0	3	0	3	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	3	0	3	2	1.000	3	4	7	0	1	29	29	23	0	22	227	199	223	0	16	7	19	23	7	1	14.29%	7	0	100.00%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
5	Crunch	2	0	1	0	0	1	0	0	5	7	-2	2	0	1	0	0	1	0	0	5	7	-2	0	0	0	0	0	0	0	0	0	0	0	1	0.250	5	8	13	0	0	29	29	23	0	58	227	199	223	0	33	9	26	36	13	2	15.38%	13	2	84.62%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
6	Devils	1	1	0	0	0	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	3	2	1	2	1.000	3	6	9	0	0	29	29	23	0	19	227	199	223	0	24	7	8	20	6	2	33.33%	4	1	75.00%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
7	Griffins	1	1	0	0	0	0	0	0	4	0	4	1	1	0	0	0	0	0	0	4	0	4	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	8	12	0	1	29	29	23	0	26	227	199	223	0	31	8	26	24	5	0	0.00%	6	0	100.00%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
8	IceHogs	3	3	0	0	0	0	0	0	8	1	7	2	2	0	0	0	0	0	0	6	0	6	1	1	0	0	0	0	0	0	2	1	1	6	1.000	8	11	19	0	2	29	29	23	0	73	227	199	223	0	64	18	40	47	11	4	36.36%	18	1	94.44%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
9	Marlies	5	5	0	0	0	0	0	0	26	7	19	3	3	0	0	0	0	0	0	14	4	10	2	2	0	0	0	0	0	0	12	3	9	10	1.000	26	45	71	0	1	29	29	23	0	183	227	199	223	0	80	26	48	94	22	8	36.36%	22	0	100.00%	2	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
10	Penguins	3	1	2	0	0	0	0	0	7	9	-2	2	1	1	0	0	0	0	0	4	5	-1	1	0	1	0	0	0	0	0	3	4	-1	2	0.333	7	13	20	0	0	29	29	23	0	58	227	199	223	0	50	20	39	40	22	3	13.64%	17	2	88.24%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
11	Phantoms	2	1	1	0	0	0	0	0	3	7	-4	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	3	7	-4	2	0.500	3	5	8	0	0	29	29	23	0	42	227	199	223	0	51	13	43	30	17	1	5.88%	19	4	78.95%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
12	Rocket	2	2	0	0	0	0	0	0	8	3	5	2	2	0	0	0	0	0	0	8	3	5	0	0	0	0	0	0	0	0	0	0	0	4	1.000	8	15	23	0	0	29	29	23	0	55	227	199	223	0	25	8	24	42	18	3	16.67%	12	1	91.67%	1	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
	Total	25	20	4	0	0	1	0	0	81	38	43	12	9	2	0	0	1	0	0	41	19	22	13	11	2	0	0	0	0	0	40	19	21	41	0.820	81	140	221	0	8	29	29	23	0	649	227	199	223	0	447	144	321	434	162	32	19.75%	142	12	91.55%	3	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
14	Wolves	1	1	0	0	0	0	0	0	2	1	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	2	1	1	2	1.000	2	3	5	0	0	29	29	23	0	17	227	199	223	0	19	4	8	17	3	0	0.00%	4	0	100.00%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
	_Since Last GM Reset	25	20	4	0	0	1	0	0	81	38	43	12	9	2	0	0	1	0	0	41	19	22	13	11	2	0	0	0	0	0	40	19	21	41	0.820	81	140	221	0	8	29	29	23	0	649	227	199	223	0	447	144	321	434	162	32	19.75%	142	12	91.55%	3	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
	_Vs Conference	10	5	4	0	0	1	0	0	24	25	-1	4	1	2	0	0	1	0	0	9	12	-3	6	4	2	0	0	0	0	0	15	13	2	11	0.550	24	43	67	0	2	29	29	23	0	219	227	199	223	0	193	66	134	160	72	12	16.67%	62	9	85.48%	0	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
	_Vs Division	11	2	1	0	0	0	0	0	45	18	27	8	0	0	0	0	0	0	0	31	14	17	3	2	1	0	0	0	0	0	14	4	10	4	0.182	45	80	125	0	2	29	29	23	0	351	227	199	223	0	180	57	140	211	70	14	20.00%	61	4	93.44%	3	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159

			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
25	41	L2	81	140	221	649	447	144	321	434	0	8

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
25	20	4	0	1	0	0	81	38

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
12	9	2	0	1	0	0	41	19

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
13	11	2	0	0	0	0	40	19

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
162	32	19.75%	142	12	91.55%	3

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
227	199	223	0	29	29	23	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
509	805	63.23%	384	636	60.38%	202	331	61.03%

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
721	529	487	167	293	159

Derniers Match Joués

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	Lien
3 - 2018-09-07	2	IceHogs	0	Monsters	5	W		Sommaire du Match
4 - 2018-09-08	10	IceHogs	0	Monsters	1	W		Sommaire du Match
6 - 2018-09-10	27	Monsters	7	Marlies	2	W		Sommaire du Match
9 - 2018-09-13	32	Penguins	3	Monsters	1	L		Sommaire du Match
11 - 2018-09-15	47	Penguins	2	Monsters	3	W		Sommaire du Match
17 - 2018-09-21	72	Monsters	3	Comets	0	W		Sommaire du Match
18 - 2018-09-22	90	Monsters	3	Devils	2	W		Sommaire du Match
22 - 2018-09-26	102	Monsters	2	Wolves	1	W		Sommaire du Match
24 - 2018-09-28	112	Monsters	4	Admirals	0	W		Sommaire du Match
25 - 2018-09-29	120	Monsters	2	IceHogs	1	W		Sommaire du Match
31 - 2018-10-05	141	Marlies	2	Monsters	3	W		Sommaire du Match
33 - 2018-10-07	169	Griffins	0	Monsters	4	W		Sommaire du Match
36 - 2018-10-10	177	Monsters	2	Americans	1	W		Sommaire du Match
38 - 2018-10-12	182	Rocket	1	Monsters	4	W		Sommaire du Match
39 - 2018-10-13	192	Rocket	2	Monsters	4	W		Sommaire du Match
45 - 2018-10-19	223	Monsters	4	Bears	0	W		Sommaire du Match
46 - 2018-10-20	238	Monsters	2	Bears	0	W		Sommaire du Match
52 - 2018-10-26	266	Marlies	0	Monsters	7	W		Sommaire du Match
54 - 2018-10-28	293	Monsters	5	Marlies	1	W		Sommaire du Match
57 - 2018-10-31	301	Marlies	2	Monsters	4	W		Sommaire du Match
59 - 2018-11-02	308	Crunch	4	Monsters	3	L	X	Sommaire du Match
60 - 2018-11-03	318	Crunch	3	Monsters	2	L		Sommaire du Match
64 - 2018-11-07	343	Monsters	3	Phantoms	2	W		Sommaire du Match
66 - 2018-11-09	354	Monsters	3	Penguins	4	L		Sommaire du Match
67 - 2018-11-10	369	Monsters	0	Phantoms	5	L		Sommaire du Match
73 - 2018-11-16	395	Monsters	-	Americans	-
74 - 2018-11-17	404	Americans	-	Monsters	-
78 - 2018-11-21	426	Monsters	-	Griffins	-
80 - 2018-11-23	436	Senators	-	Monsters	-
81 - 2018-11-24	449	Senators	-	Monsters	-
85 - 2018-11-28	465	Americans	-	Monsters	-
87 - 2018-11-30	477	Griffins	-	Monsters	-
88 - 2018-12-01	492	Monsters	-	Griffins	-
95 - 2018-12-08	531	Comets	-	Monsters	-
96 - 2018-12-09	545	Comets	-	Monsters	-
99 - 2018-12-12	553	Monsters	-	Senators	-
101 - 2018-12-14	563	Monsters	-	Crunch	-
102 - 2018-12-15	582	Monsters	-	Devils	-
108 - 2018-12-21	610	Monsters	-	Rocket	-
109 - 2018-12-22	616	Monsters	-	Rocket	-
113 - 2018-12-26	647	Marlies	-	Monsters	-
114 - 2018-12-27	653	Wolves	-	Monsters	-
116 - 2018-12-29	675	Wolves	-	Monsters	-
122 - 2019-01-04	688	Monsters	-	Penguins	-
123 - 2019-01-05	702	Monsters	-	Crunch	-
126 - 2019-01-08	713	Monsters	-	Checkers	-
127 - 2019-01-09	716	Monsters	-	Checkers	-
130 - 2019-01-12	739	Devils	-	Monsters	-
131 - 2019-01-13	749	Devils	-	Monsters	-
133 - 2019-01-15	757	Americans	-	Monsters	-
136 - 2019-01-18	769	Monsters	-	Senators	-
137 - 2019-01-19	783	Monsters	-	Senators	-
138 - 2019-01-20	799	Monsters	-	Comets	-
141 - 2019-01-23	811	Monsters	-	Americans	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25	819	Admirals	-	Monsters	-
145 - 2019-01-27	841	Admirals	-	Monsters	-
149 - 2019-01-31	855	Rocket	-	Monsters	-
150 - 2019-02-01	859	Rocket	-	Monsters	-
151 - 2019-02-02	868	Monsters	-	Marlies	-
156 - 2019-02-07	896	Senators	-	Monsters	-
157 - 2019-02-08	898	Senators	-	Monsters	-
162 - 2019-02-13	934	Monsters	-	Admirals	-
164 - 2019-02-15	950	Monsters	-	Wolves	-
165 - 2019-02-16	960	Monsters	-	IceHogs	-
171 - 2019-02-22	988	Monsters	-	Americans	-
172 - 2019-02-23	996	Bears	-	Monsters	-
173 - 2019-02-24	1009	Bears	-	Monsters	-
178 - 2019-03-01	1028	Phantoms	-	Monsters	-
179 - 2019-03-02	1041	Phantoms	-	Monsters	-
183 - 2019-03-06	1066	Monsters	-	Rocket	-
185 - 2019-03-08	1078	Monsters	-	Rocket	-
186 - 2019-03-09	1090	Monsters	-	Senators	-
188 - 2019-03-11	1104	Americans	-	Monsters	-
191 - 2019-03-14	1114	Checkers	-	Monsters	-
192 - 2019-03-15	1115	Checkers	-	Monsters	-
194 - 2019-03-17	1145	Monsters	-	Marlies	-

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
26	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
115,801$	201,009$	133,818$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	78,664$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	122	1,552$	189,344$

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
13	25	20	4	0	0	1	0	0	81	38	43	12	9	2	0	0	1	0	0	41	19	22	13	11	2	0	0	0	0	0	40	19	21	41	81	140	221	0	8	29	29	23	0	649	227	199	223	0	447	144	321	434	162	32	19.75%	142	12	91.55%	3	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
Total Saison Régulière	25	20	4	0	0	1	0	0	81	38	43	12	9	2	0	0	1	0	0	41	19	22	13	11	2	0	0	0	0	0	40	19	21	41	81	140	221	0	8	29	29	23	0	649	227	199	223	0	447	144	321	434	162	32	19.75%	142	12	91.55%	3	509	805	63.23%	384	636	60.38%	202	331	61.03%	721	529	487	167	293	159
Séries
12	20	16	4	0	0	0	0	0	49	19	30	10	8	2	0	0	0	0	0	21	9	12	10	8	2	0	0	0	0	0	28	10	18	32	49	83	132	0	6	12	18	17	2	466	143	156	146	21	405	121	392	396	141	14	9.93%	166	10	93.98%	3	381	673	56.61%	365	666	54.80%	168	279	60.22%	509	338	516	168	266	131
12	20	16	4	0	0	0	0	0	49	19	30	10	8	2	0	0	0	0	0	21	9	12	10	8	2	0	0	0	0	0	28	10	18	32	49	83	132	0	6	12	18	17	2	466	143	156	146	21	405	121	392	396	141	14	9.93%	166	10	93.98%	3	381	673	56.61%	365	666	54.80%	168	279	60.22%	509	338	516	168	266	131
Total Séries	40	32	8	0	0	0	0	0	98	38	60	20	16	4	0	0	0	0	0	42	18	24	20	16	4	0	0	0	0	0	56	20	36	64	98	166	264	0	12	24	36	34	4	932	286	312	292	42	810	242	784	792	282	28	9.93%	332	20	93.98%	6	762	1346	56.61%	730	1332	54.80%	336	558	60.22%	1018	677	1032	337	533	263

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