OTHL - Monsters

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	Filip Chlapik	X				100.00	76	72	84	77	72	78	83	62	78	56	61	67	58	54	54	66	50	64	0
2	T.J. Tynan	X				100.00	67	58	88	62	58	83	87	67	80	74	56	61	53	44	44	65	50	63	0
3	Patrick Russell		X	X		100.00	77	75	82	62	75	76	79	63	50	59	64	65	61	44	44	65	50	62	0
4	Jack Rodewald			X		100.00	71	62	92	62	62	77	81	65	50	61	65	64	62	50	50	67	50	62	0
5	Frederick Gaudreau	X		X		100.00	65	41	98	74	65	53	82	61	68	52	57	59	25	58	58	60	50	60	0
6	Phillip Di Giuseppe (A)		X	X		100.00	73	73	72	79	73	43	40	60	50	55	58	66	55	56	56	60	50	59	0
7	Rourke Chartier (R)	X				100.00	76	43	94	71	69	54	55	58	65	50	58	69	25	45	45	61	50	59	0
8	Giovanni Fiore		X	X		100.00	77	71	91	63	71	72	77	55	50	47	57	65	54	50	50	61	50	59	0
9	Hunter Shinkaruk		X			100.00	72	65	88	70	65	68	74	49	50	45	47	61	45	52	52	55	50	56	0
10	Dion Phaneuf				X	100.00	84	76	78	80	86	66	90	64	25	47	47	69	25	87	89	61	50	70	0
11	Josh Brown				X	100.00	87	99	78	62	88	62	80	60	25	43	48	65	25	50	50	58	50	62	0
12	Tommy Cross				X	100.00	76	77	72	71	77	81	88	53	25	49	42	62	40	44	44	56	50	62	0
13	Erik Burgdoerfer				X	100.00	77	75	80	68	75	76	84	49	25	40	41	64	39	52	52	55	50	61	0
14	Evan McEneny				X	100.00	77	75	81	68	75	56	56	57	25	50	48	66	46	51	51	58	50	60	0
15	Sami Niku (R)				X	100.00	61	41	97	75	66	64	81	57	25	50	48	61	25	46	46	58	50	60	0
Rayé

	MOYENNE D'ÉQUIPE					100.00	74	67	85	70	72	67	76	59	46	52	53	64	43	52	52	60	50	61

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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	Brad Thiessen	100.00	59	50	63	58	61	62	61	65	65	64	30	44	44	60	50	59	0
2	Adam Wilcox	100.00	52	58	73	69	49	51	53	58	51	51	30	44	44	53	50	54	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	56	54	68	64	55	57	57	62	58	58	30	44	44	57	50	57

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Jim Montgomery	76	97	58	87	66	60	74	CAN	50	2	900,000$

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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	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Patrick Russell	Monsters (CLB)	LW/RW	82	84	75	159	124	108	10	249	100	424	119	309	19.81%	16	1545	18.85	10	11	21	42	171	0	1	1	1	90	16	3	63.03%	119	1	7	2.06	1	1	1	0	1	18	17	5
2	Jack Rodewald	Monsters (CLB)	RW	82	76	82	158	121	32	0	100	105	420	120	291	18.10%	20	1550	18.91	8	13	21	38	170	1	0	1	7	98	5	2	73.15%	149	0	7	2.04	1	3	0	0	0	14	12	5
3	Anthony Duclair	Blue Jackets	LW/RW	57	68	86	154	129	14	0	48	75	350	95	288	19.43%	9	1150	20.18	5	9	14	34	168	0	0	0	1	49	9	2	66.01%	203	0	11	2.68	0	1	0	0	0	17	6	4
4	T.J. Tynan	Monsters (CLB)	C	64	31	57	88	68	34	0	88	118	211	54	143	14.69%	17	1409	22.02	3	13	16	24	151	1	0	1	12	210	5	2	77.45%	1029	0	1	1.25	1	6	0	0	0	3	5	11
5	Phillip Di Giuseppe	Monsters (CLB)	LW/RW	82	37	44	81	65	66	0	121	95	240	67	171	15.42%	9	1152	14.06	6	8	14	24	127	0	0	0	0	6	8	0	68.92%	74	0	2	1.41	0	0	0	0	0	4	2	4
6	Rourke Chartier	Monsters (CLB)	C	57	51	30	81	76	34	0	50	70	248	77	164	20.56%	8	824	14.46	2	2	4	5	20	0	0	0	0	10	2	2	74.96%	603	0	7	1.97	0	0	0	0	0	4	7	6
7	Frederick Gaudreau	Monsters (CLB)	C/RW	82	26	54	80	75	6	0	27	104	172	56	144	15.12%	6	1186	14.47	2	6	8	22	122	0	0	0	1	70	5	1	74.25%	831	0	0	1.35	0	0	0	0	0	1	2	4
8	Filip Chlapik	Monsters (CLB)	C	57	24	53	77	57	20	0	56	125	172	58	129	13.95%	7	1075	18.86	2	15	17	23	159	1	4	5	10	208	1	1	75.25%	1386	0	1	1.43	2	4	0	0	0	4	5	3
9	Giovanni Fiore	Monsters (CLB)	LW/RW	82	29	45	74	86	48	0	93	51	221	66	158	13.12%	5	1081	13.19	0	2	2	4	29	0	0	0	1	44	4	2	65.82%	79	0	0	1.37	0	0	0	0	0	2	1	8
10	Tommy Cross	Monsters (CLB)	D	82	11	52	63	77	83	15	169	28	74	30	46	14.86%	40	1491	18.19	7	8	15	36	125	0	1	1	1	183	0	0	0.00%	0	0	0	0.84	0	0	1	1	1	0	3	4
11	Dion Phaneuf	Monsters (CLB)	D	49	11	51	62	44	66	0	153	37	84	34	56	13.10%	41	1097	22.40	6	5	11	41	122	0	1	1	0	160	1	0	0.00%	0	0	0	1.13	0	0	0	0	0	2	2	7
12	Philippe Myers	Blue Jackets	D	61	12	44	56	61	42	0	82	25	94	32	86	12.77%	39	1169	19.17	6	5	11	46	114	0	0	0	1	166	0	1	0.00%	0	0	0	0.96	0	0	0	0	0	1	0	2
13	Josh Brown	Monsters (CLB)	D	82	11	44	55	57	137	35	193	40	107	35	74	10.28%	47	1406	17.16	4	8	12	42	126	1	1	2	4	163	2	0	0.00%	1	0	0	0.78	0	0	2	2	2	0	3	4
14	Sonny Milano	Blue Jackets	LW/RW	36	22	26	48	25	15	5	20	22	107	37	77	20.56%	5	473	13.16	8	6	14	20	106	0	0	0	0	13	1	1	58.06%	31	0	0	2.03	0	0	1	0	0	3	2	3
15	Erik Burgdoerfer	Monsters (CLB)	D	78	9	38	47	77	61	5	98	25	56	17	38	16.07%	34	1329	17.04	4	6	10	22	122	1	0	1	2	144	1	0	0.00%	0	0	0	0.71	0	0	0	0	1	1	3	3
16	Evan McEneny	Monsters (CLB)	D	82	8	37	45	58	78	0	102	26	65	16	51	12.31%	42	1076	13.12	1	3	4	7	19	0	0	0	1	71	1	0	33.33%	3	0	0	0.84	0	0	0	0	0	2	0	1
17	Hunter Shinkaruk	Monsters (CLB)	LW	82	16	25	41	64	30	0	84	41	137	36	94	11.68%	9	945	11.54	0	0	0	1	0	0	0	0	0	4	3	0	64.46%	121	1	0	0.87	0	0	0	0	0	2	0	1
18	Sami Niku	Monsters (CLB)	D	71	6	30	36	35	12	0	29	37	43	9	27	13.95%	21	853	12.02	0	1	1	3	6	0	1	1	2	89	0	0	50.00%	4	0	0	0.84	0	0	0	0	0	1	0	1
19	Cory Conacher	Blue Jackets	LW/RW	18	17	13	30	26	16	0	28	37	66	17	51	25.76%	4	409	22.76	0	2	2	2	43	1	0	1	5	71	4	1	74.15%	147	0	2	1.46	0	2	0	0	0	6	1	0
20	Denis Malgin	Blue Jackets	C/RW	9	6	12	18	14	4	0	17	27	38	9	46	15.79%	3	208	23.20	0	2	2	5	24	0	0	0	1	41	2	0	61.83%	241	0	0	1.72	1	2	0	0	0	2	2	1
21	Greg McKegg	Blue Jackets	C/LW	13	4	11	15	20	4	0	9	13	42	11	26	9.52%	0	222	17.15	0	0	0	1	13	0	0	0	0	0	0	0	85.90%	78	0	0	1.35	0	0	0	0	0	0	1	0
Stats d'équipe Total ou en Moyenne				1308	559	909	1468	1359	910	70	1816	1201	3371	995	2469	16.58%	382	21660	16.56	74	125	199	442	1949	6	9	15	50	1898	70	18	73.62%	5099	2	38	1.36	6	19	5	3	5	87	74	77

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	A	PS %	PSA	ST	BG	S3
1	Brad Thiessen	Monsters (CLB)	69	51	8	5	0.882	2.05	3922	2	12	134	1136	5	0.308	13	68	22	2
2	Alex Lyon	Blue Jackets	14	13	1	0	0.888	1.73	834	0	3	24	214	0	0.750	4	14	0	0
3	Adam Wilcox	Monsters (CLB)	6	0	3	0	0.874	4.15	159	0	0	11	87	0	0.000	0	0	48	0
Stats d'équipe Total ou en Moyenne			89	64	12	5	0.882	2.06	4916	2	15	169	1437	5	0.412	17	82	70	2

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
Adam Wilcox	Monsters (CLB)	G	25	1994-04-25	No	187 Lbs	6 ft0	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$		Lien
Brad Thiessen	Monsters (CLB)	G	34	1986-03-18	No	171 Lbs	5 ft11	Yes	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$		Lien
Dion Phaneuf	Monsters (CLB)	D	35	1985-04-09	No	225 Lbs	6 ft4	No	No	No	3	Pro & Farm	5,500,000$	0$	0$	No	5,500,000$	5,500,000$	Lien
Erik Burgdoerfer	Monsters (CLB)	D	31	1988-12-11	No	207 Lbs	6 ft1	No	No	No	1	Pro & Farm	650,000$	0$	0$	No			Lien
Evan McEneny	Monsters (CLB)	D	25	1994-05-22	No	203 Lbs	6 ft2	No	No	No	1	Pro & Farm	800,000$	0$	0$	No			Lien
Filip Chlapik	Monsters (CLB)	C	22	1997-06-03	No	196 Lbs	6 ft1	No	No	No	2	Pro & Farm	894,167$	0$	0$	No	894,167$		Lien
Frederick Gaudreau	Monsters (CLB)	C/RW	26	1993-05-01	No	179 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,500,000$	0$	0$	No			Lien
Giovanni Fiore	Monsters (CLB)	LW/RW	23	1996-08-13	No	194 Lbs	6 ft1	No	No	No	3	Pro & Farm	900,000$	0$	0$	No	900,000$	900,000$	Lien
Hunter Shinkaruk	Monsters (CLB)	LW	25	1994-10-13	No	181 Lbs	5 ft10	No	No	No	1	Pro & Farm	1,075,833$	0$	0$	No			Lien
Jack Rodewald	Monsters (CLB)	RW	26	1994-02-14	No	169 Lbs	6 ft0	Yes	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$		Lien
Josh Brown	Monsters (CLB)	D	26	1994-01-21	No	225 Lbs	6 ft5	Yes	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$		Lien
Patrick Russell	Monsters (CLB)	LW/RW	27	1993-01-03	No	205 Lbs	6 ft1	Yes	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$		Lien
Phillip Di Giuseppe	Monsters (CLB)	LW/RW	26	1993-10-09	No	201 Lbs	6 ft0	No	No	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Rourke Chartier	Monsters (CLB)	C	24	1996-04-02	Yes	190 Lbs	5 ft11	No	No	No	3	Pro & Farm	863,333$	0$	0$	No	863,333$	863,333$	Lien
Sami Niku	Monsters (CLB)	D	23	1996-10-10	Yes	176 Lbs	6 ft1	No	No	No	3	Pro & Farm	916,666$	0$	0$	No	916,666$	916,666$	Lien
T.J. Tynan	Monsters (CLB)	C	28	1992-02-24	No	165 Lbs	5 ft8	No	No	No	1	Pro & Farm	925,000$	0$	0$	No			Lien
Tommy Cross	Monsters (CLB)	D	30	1989-09-12	No	205 Lbs	6 ft3	Yes	No	No	3	Pro & Farm	700,000$	0$	0$	No	700,000$	700,000$	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
17	26.82	193 Lbs	6 ft1	1.94	1,113,235$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Patrick Russell			40	1	2	2
2	Phillip Di Giuseppe	T.J. Tynan	Jack Rodewald	30	1	2	2
3	Giovanni Fiore	Frederick Gaudreau	Rourke Chartier	20	1	2	2
4	Hunter Shinkaruk	Rourke Chartier		10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dion Phaneuf	Josh Brown	40	1	2	2
2	Tommy Cross	Erik Burgdoerfer	30	1	2	2
3	Evan McEneny	Sami Niku	20	1	2	2
4	Dion Phaneuf	Josh Brown	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Patrick Russell			60	1	2	2
2	Phillip Di Giuseppe	T.J. Tynan	Jack Rodewald	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dion Phaneuf	Josh Brown	60	1	2	2
2	Tommy Cross	Erik Burgdoerfer	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2	T.J. Tynan	Jack Rodewald	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dion Phaneuf	Josh Brown	60	1	2	2
2	Tommy Cross	Erik Burgdoerfer	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		60	1	2	2	Dion Phaneuf	Josh Brown	60	1	2	2
2		40	1	2	2	Tommy Cross	Erik Burgdoerfer	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2	T.J. Tynan	Jack Rodewald	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Dion Phaneuf	Josh Brown	60	1	2	2
2	Tommy Cross	Erik Burgdoerfer	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Patrick Russell			Dion Phaneuf	Josh Brown

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Patrick Russell			Dion Phaneuf	Josh Brown

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Frederick Gaudreau, Giovanni Fiore, Hunter Shinkaruk	Frederick Gaudreau, Giovanni Fiore	Hunter Shinkaruk

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Evan McEneny, Sami Niku, Tommy Cross	Evan McEneny	Sami Niku, Tommy Cross

Tirs de Pénalité
, , T.J. Tynan, Jack Rodewald, Patrick Russell

Gardien
#1 : Brad Thiessen, #2 : Adam Wilcox

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

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

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Americans	3	2	1	0	0	0	0	0	10	8	2	1	0	1	0	0	0	0	0	2	5	-3	2	2	0	0	0	0	0	0	8	3	5	4	0.667	10	18	28	0	0	179	153	124	5	73	884	1008	985	27	58	17	26	49	13	1	7.69%	12	2	83.33%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
2	Admirals	2	2	0	0	0	0	0	0	18	2	16	1	1	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	11	1	10	4	1.000	18	31	49	0	0	179	153	124	5	99	884	1008	985	27	27	4	20	59	4	1	25.00%	9	0	100.00%	1	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
3	Barracudas	2	2	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	3	0	3	4	1.000	9	13	22	0	1	179	153	124	5	82	884	1008	985	27	33	8	23	47	6	1	16.67%	9	1	88.89%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
4	Bears	4	3	0	0	0	0	0	1	11	5	6	2	1	0	0	0	0	0	1	4	4	0	2	2	0	0	0	0	0	0	7	1	6	7	0.875	11	20	31	0	1	179	153	124	5	104	884	1008	985	27	73	14	32	82	17	2	11.76%	16	2	87.50%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
5	Bruins	3	3	0	0	0	0	0	0	19	5	14	1	1	0	0	0	0	0	0	4	2	2	2	2	0	0	0	0	0	0	15	3	12	6	1.000	19	31	50	0	0	179	153	124	5	91	884	1008	985	27	70	13	50	46	22	11	50.00%	14	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
6	Checkers	4	1	3	0	0	0	0	0	15	19	-4	2	1	1	0	0	0	0	0	7	6	1	2	0	2	0	0	0	0	0	8	13	-5	2	0.250	15	28	43	0	1	179	153	124	5	92	884	1008	985	27	108	33	57	93	19	5	26.32%	25	1	96.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
7	Comets	2	0	1	0	0	0	0	1	4	9	-5	1	0	0	0	0	0	0	1	2	3	-1	1	0	1	0	0	0	0	0	2	6	-4	1	0.250	4	6	10	0	0	179	153	124	5	34	884	1008	985	27	39	17	24	38	9	0	0.00%	12	0	100.00%	1	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
8	Condors	2	1	0	0	0	0	0	1	5	5	0	1	1	0	0	0	0	0	0	3	2	1	1	0	0	0	0	0	0	1	2	3	-1	3	0.750	5	8	13	0	0	179	153	124	5	40	884	1008	985	27	38	14	28	41	11	2	18.18%	14	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
9	Crunch	3	2	0	0	0	0	0	1	10	8	2	2	2	0	0	0	0	0	0	7	4	3	1	0	0	0	0	0	0	1	3	4	-1	5	0.833	10	17	27	0	0	179	153	124	5	77	884	1008	985	27	53	17	38	40	12	2	16.67%	18	2	88.89%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
10	Devils	4	4	0	0	0	0	0	0	34	4	30	2	2	0	0	0	0	0	0	20	1	19	2	2	0	0	0	0	0	0	14	3	11	8	1.000	34	56	90	0	1	179	153	124	5	220	884	1008	985	27	54	20	8	68	10	3	30.00%	4	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
11	Eagles	2	2	0	0	0	0	0	0	13	2	11	1	1	0	0	0	0	0	0	10	1	9	1	1	0	0	0	0	0	0	3	1	2	4	1.000	13	20	33	0	0	179	153	124	5	113	884	1008	985	27	28	9	14	35	2	0	0.00%	7	1	85.71%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
12	Griffins	3	3	0	0	0	0	0	0	22	5	17	2	2	0	0	0	0	0	0	15	1	14	1	1	0	0	0	0	0	0	7	4	3	6	1.000	22	36	58	0	1	179	153	124	5	128	884	1008	985	27	61	19	18	71	6	1	16.67%	9	2	77.78%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
13	Gulls	2	1	1	0	0	0	0	0	6	7	-1	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	4	3	1	2	0.500	6	10	16	0	0	179	153	124	5	63	884	1008	985	27	38	12	16	38	11	2	18.18%	8	2	75.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
14	Heat	2	1	1	0	0	0	0	0	9	10	-1	1	0	1	0	0	0	0	0	6	8	-2	1	1	0	0	0	0	0	0	3	2	1	2	0.500	9	13	22	0	0	179	153	124	5	50	884	1008	985	27	45	18	23	38	16	6	37.50%	9	4	55.56%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
15	IceHogs	2	1	1	0	0	0	0	0	9	7	2	1	1	0	0	0	0	0	0	7	2	5	1	0	1	0	0	0	0	0	2	5	-3	2	0.500	9	14	23	0	0	179	153	124	5	42	884	1008	985	27	54	16	17	48	4	2	50.00%	6	2	66.67%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
16	Marlies	3	2	1	0	0	0	0	0	16	10	6	1	1	0	0	0	0	0	0	7	2	5	2	1	1	0	0	0	0	0	9	8	1	4	0.667	16	25	41	0	0	179	153	124	5	55	884	1008	985	27	72	16	37	68	3	2	66.67%	16	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
17	Moose	2	2	0	0	0	0	0	0	6	0	6	1	1	0	0	0	0	0	0	4	0	4	1	1	0	0	0	0	0	0	2	0	2	4	1.000	6	12	18	0	2	179	153	124	5	58	884	1008	985	27	23	8	19	30	10	1	10.00%	7	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
18	Penguins	4	4	0	0	0	0	0	0	19	4	15	2	2	0	0	0	0	0	0	10	3	7	2	2	0	0	0	0	0	0	9	1	8	8	1.000	19	37	56	0	1	179	153	124	5	139	884	1008	985	27	71	19	46	94	17	3	17.65%	23	2	91.30%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
19	Phantoms	4	3	1	0	0	0	0	0	22	14	8	2	1	1	0	0	0	0	0	8	5	3	2	2	0	0	0	0	0	0	14	9	5	6	0.750	22	39	61	0	0	179	153	124	5	114	884	1008	985	27	80	17	55	104	9	3	33.33%	25	5	80.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
20	Rampage	2	2	0	0	0	0	0	0	12	2	10	1	1	0	0	0	0	0	0	7	0	7	1	1	0	0	0	0	0	0	5	2	3	4	1.000	12	22	34	0	1	179	153	124	5	96	884	1008	985	27	29	6	23	49	4	3	75.00%	9	1	88.89%	1	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
21	Reign	2	2	0	0	0	0	0	0	20	1	19	1	1	0	0	0	0	0	0	10	0	10	1	1	0	0	0	0	0	0	10	1	9	4	1.000	20	34	54	0	1	179	153	124	5	118	884	1008	985	27	17	7	4	38	1	0	0.00%	2	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
22	Roadrunners	2	2	0	0	0	0	0	0	19	1	18	1	1	0	0	0	0	0	0	9	1	8	1	1	0	0	0	0	0	0	10	0	10	4	1.000	19	36	55	0	1	179	153	124	5	97	884	1008	985	27	24	9	18	47	6	4	66.67%	8	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
23	Rocket	3	0	2	0	0	0	0	1	4	10	-6	1	0	0	0	0	0	0	1	2	3	-1	2	0	2	0	0	0	0	0	2	7	-5	1	0.167	4	8	12	0	0	179	153	124	5	69	884	1008	985	27	61	16	47	65	18	1	5.56%	20	3	85.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
24	Senators	3	3	0	0	0	0	0	0	20	3	17	2	2	0	0	0	0	0	0	12	3	9	1	1	0	0	0	0	0	0	8	0	8	6	1.000	20	39	59	0	1	179	153	124	5	145	884	1008	985	27	38	14	38	87	3	1	33.33%	19	1	94.74%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
25	Sound Tigers	4	4	0	0	0	0	0	0	34	5	29	2	2	0	0	0	0	0	0	16	3	13	2	2	0	0	0	0	0	0	18	2	16	8	1.000	34	60	94	0	2	179	153	124	5	194	884	1008	985	27	56	11	30	90	5	2	40.00%	14	2	85.71%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
26	Stars	2	2	0	0	0	0	0	0	10	5	5	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	5	3	2	4	1.000	10	17	27	0	0	179	153	124	5	79	884	1008	985	27	31	7	31	53	3	1	33.33%	9	2	77.78%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
27	Thunderbirds	3	1	0	0	1	0	1	0	18	9	9	2	1	0	0	0	0	1	0	14	6	8	1	0	0	0	1	0	0	0	4	3	1	6	1.000	18	29	47	0	0	179	153	124	5	65	884	1008	985	27	53	19	52	75	14	5	35.71%	23	6	73.91%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
	Total	82	62	13	0	1	0	1	5	458	175	283	41	32	5	0	0	0	1	3	237	78	159	41	30	8	0	1	0	0	2	221	97	124	133	0.811	458	799	1257	0	15	179	153	124	5	2886	884	1008	985	27	1457	406	862	1775	272	67	24.63%	379	46	87.86%	4	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
29	Wild	2	2	0	0	0	0	0	0	23	2	21	1	1	0	0	0	0	0	0	12	1	11	1	1	0	0	0	0	0	0	11	1	10	4	1.000	23	42	65	0	0	179	153	124	5	133	884	1008	985	27	21	2	8	50	7	1	14.29%	4	0	100.00%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
30	Wolf Pack	4	4	0	0	0	0	0	0	33	5	28	2	2	0	0	0	0	0	0	15	2	13	2	2	0	0	0	0	0	0	18	3	15	8	1.000	33	64	97	0	1	179	153	124	5	166	884	1008	985	27	49	11	24	101	6	1	16.67%	11	2	81.82%	1	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
31	Wolves	2	1	1	0	0	0	0	0	8	6	2	1	1	0	0	0	0	0	0	4	1	3	1	0	1	0	0	0	0	0	4	5	-1	2	0.500	8	14	22	0	0	179	153	124	5	50	884	1008	985	27	53	13	36	31	4	0	0.00%	17	3	82.35%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
	_Since Last GM Reset	82	62	13	0	1	0	1	5	458	175	283	41	32	5	0	0	0	1	3	237	78	159	41	30	8	0	1	0	0	2	221	97	124	133	0.811	458	799	1257	0	15	179	153	124	5	2886	884	1008	985	27	1457	406	862	1775	272	67	24.63%	379	46	87.86%	4	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
	_Vs Conference	51	37	8	0	1	0	1	4	281	115	166	26	20	2	0	0	0	1	3	143	48	95	25	17	6	0	1	0	0	1	138	67	71	82	0.804	281	495	776	0	9	179	153	124	5	1693	884	1008	985	27	938	256	556	1122	170	42	24.71%	249	28	88.76%	2	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
	_Vs Division	24	14	4	0	0	0	0	3	119	58	61	12	9	0	0	0	0	0	2	63	26	37	12	5	4	0	0	0	0	1	56	32	24	31	0.646	119	203	322	0	2	179	153	124	5	703	884	1008	985	27	466	131	306	501	91	24	26.37%	131	16	87.79%	0	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519

			Total Pour les Joueurs
Matchs Joués	Points	Séquence	Buts	Passes	Points	Tirs Pour	Tirs Contre	Tirs Bloqués	Minutes de Pénalité	Mises en Échec	Buts en Filet Désert	Blanchissage
82	133	L1	458	799	1257	2886	1457	406	862	1775	0	15

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	62	13	1	0	1	5	458	175

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	32	5	0	0	1	3	237	78

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	30	8	1	0	0	2	221	97

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
8	1	0	0	0	1

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
272	67	24.63%	379	46	87.86%	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
884	1008	985	27	179	153	124	5

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
1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%

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
2676	2092	1384	424	889	519

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
3 - 2019-10-06	13	Marlies	2	Monsters	7	W			Sommaire du Match
4 - 2019-10-07	27	Monsters	6	Penguins	0	W			Sommaire du Match
6 - 2019-10-09	34	Americans	5	Monsters	2	L			Sommaire du Match
10 - 2019-10-13	60	Gulls	4	Monsters	2	L			Sommaire du Match
11 - 2019-10-14	62	Monsters	3	Checkers	5	L			Sommaire du Match
15 - 2019-10-18	93	Stars	2	Monsters	5	W			Sommaire du Match
17 - 2019-10-20	108	Monsters	2	IceHogs	5	L			Sommaire du Match
18 - 2019-10-21	114	Sound Tigers	3	Monsters	8	W			Sommaire du Match
20 - 2019-10-23	132	Monsters	5	Marlies	6	L			Sommaire du Match
23 - 2019-10-26	145	Checkers	6	Monsters	4	L			Sommaire du Match
25 - 2019-10-28	167	Monsters	8	Phantoms	4	W			Sommaire du Match
29 - 2019-11-01	189	Condors	2	Monsters	3	W			Sommaire du Match
31 - 2019-11-03	202	Monsters	5	Rampage	2	W			Sommaire du Match
32 - 2019-11-04	208	Heat	8	Monsters	6	L			Sommaire du Match
35 - 2019-11-07	225	Wolves	1	Monsters	4	W			Sommaire du Match
37 - 2019-11-09	237	Monsters	10	Roadrunners	0	W			Sommaire du Match
39 - 2019-11-11	254	Monsters	3	Eagles	1	W			Sommaire du Match
42 - 2019-11-14	275	Monsters	1	Rocket	3	L			Sommaire du Match
45 - 2019-11-17	294	Rampage	0	Monsters	7	W			Sommaire du Match
49 - 2019-11-21	319	Rocket	3	Monsters	2	L	X	X	Sommaire du Match
51 - 2019-11-23	336	Griffins	0	Monsters	4	W			Sommaire du Match
53 - 2019-11-25	360	Monsters	2	Moose	0	W			Sommaire du Match
55 - 2019-11-27	366	Senators	1	Monsters	5	W			Sommaire du Match
57 - 2019-11-29	379	Phantoms	2	Monsters	6	W			Sommaire du Match
59 - 2019-12-01	395	Penguins	1	Monsters	4	W			Sommaire du Match
60 - 2019-12-02	412	Monsters	6	Sound Tigers	0	W			Sommaire du Match
63 - 2019-12-05	425	Roadrunners	1	Monsters	9	W			Sommaire du Match
65 - 2019-12-07	440	Wolf Pack	0	Monsters	7	W			Sommaire du Match
67 - 2019-12-09	457	Monsters	4	Thunderbirds	3	W	X		Sommaire du Match
69 - 2019-12-11	471	Monsters	3	Bears	0	W			Sommaire du Match
72 - 2019-12-14	494	Monsters	3	Penguins	1	W			Sommaire du Match
74 - 2019-12-16	510	Monsters	8	Senators	0	W			Sommaire du Match
76 - 2019-12-18	519	Bears	2	Monsters	1	L	X	X	Sommaire du Match
77 - 2019-12-19	526	Monsters	7	Griffins	4	W			Sommaire du Match
79 - 2019-12-21	540	Reign	0	Monsters	10	W			Sommaire du Match
81 - 2019-12-23	555	Devils	1	Monsters	10	W			Sommaire du Match
83 - 2019-12-25	574	Monsters	12	Sound Tigers	2	W			Sommaire du Match
87 - 2019-12-29	592	Monsters	4	Bears	1	W			Sommaire du Match
89 - 2019-12-31	605	IceHogs	2	Monsters	7	W			Sommaire du Match
91 - 2020-01-02	616	Thunderbirds	1	Monsters	8	W			Sommaire du Match
93 - 2020-01-04	628	Monsters	9	Bruins	2	W			Sommaire du Match
95 - 2020-01-06	644	Barracudas	2	Monsters	6	W			Sommaire du Match
97 - 2020-01-08	659	Monsters	10	Reign	1	W			Sommaire du Match
98 - 2020-01-09	663	Monsters	4	Gulls	3	W			Sommaire du Match
100 - 2020-01-11	685	Monsters	3	Barracudas	0	W			Sommaire du Match
102 - 2020-01-13	701	Monsters	4	Wolves	5	L			Sommaire du Match
105 - 2020-01-16	716	Bruins	2	Monsters	4	W			Sommaire du Match
107 - 2020-01-18	728	Checkers	0	Monsters	3	W			Sommaire du Match
109 - 2020-01-20	743	Devils	0	Monsters	10	W			Sommaire du Match
110 - 2020-01-21	757	Monsters	11	Wolf Pack	2	W			Sommaire du Match
113 - 2020-01-24	766	Moose	0	Monsters	4	W			Sommaire du Match
123 - 2020-02-03	793	Monsters	3	Americans	1	W			Sommaire du Match
124 - 2020-02-04	807	Monsters	1	Rocket	4	L			Sommaire du Match
126 - 2020-02-06	815	Thunderbirds	5	Monsters	6	W	X	X	Sommaire du Match
129 - 2020-02-09	839	Griffins	1	Monsters	11	W			Sommaire du Match
130 - 2020-02-10	844	Eagles	1	Monsters	10	W			Sommaire du Match
132 - 2020-02-12	860	Crunch	2	Monsters	4	W			Sommaire du Match
135 - 2020-02-15	881	Monsters	5	Americans	2	W			Sommaire du Match
136 - 2020-02-16	892	Wolf Pack	2	Monsters	8	W			Sommaire du Match
138 - 2020-02-18	908	Monsters	8	Devils	2	W			Sommaire du Match
140 - 2020-02-20	923	Monsters	6	Phantoms	5	W			Sommaire du Match
142 - 2020-02-22	933	Phantoms	3	Monsters	2	L			Sommaire du Match
144 - 2020-02-24	951	Monsters	11	Admirals	1	W			Sommaire du Match
146 - 2020-02-26	967	Senators	2	Monsters	7	W			Sommaire du Match
147 - 2020-02-27	973	Monsters	11	Wild	1	W			Sommaire du Match
150 - 2020-03-01	996	Wild	1	Monsters	12	W			Sommaire du Match
152 - 2020-03-03	1012	Comets	3	Monsters	2	L	X	X	Sommaire du Match
155 - 2020-03-06	1029	Monsters	3	Heat	2	W			Sommaire du Match
158 - 2020-03-09	1051	Monsters	2	Condors	3	L	X	X	Sommaire du Match
159 - 2020-03-10	1065	Monsters	2	Comets	6	L			Sommaire du Match
163 - 2020-03-14	1084	Penguins	2	Monsters	6	W			Sommaire du Match
165 - 2020-03-16	1101	Admirals	1	Monsters	7	W			Sommaire du Match
167 - 2020-03-18	1119	Monsters	6	Bruins	1	W			Sommaire du Match
170 - 2020-03-21	1138	Bears	2	Monsters	3	W			Sommaire du Match
172 - 2020-03-23	1162	Monsters	4	Marlies	2	W			Sommaire du Match
174 - 2020-03-25	1173	Monsters	6	Devils	1	W			Sommaire du Match
175 - 2020-03-26	1180	Monsters	7	Wolf Pack	1	W			Sommaire du Match
178 - 2020-03-29	1202	Monsters	3	Crunch	4	L	X	X	Sommaire du Match
179 - 2020-03-30	1208	Monsters	5	Stars	3	W			Sommaire du Match
181 - 2020-04-01	1225	Sound Tigers	0	Monsters	8	W			Sommaire du Match
184 - 2020-04-04	1243	Crunch	2	Monsters	3	W			Sommaire du Match
185 - 2020-04-05	1256	Monsters	5	Checkers	8	L			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	100	0
Assistance	46,669	35,502
Assistance PCT	56.91%	86.59%

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	2004 - 66.81%	113,827$	4,666,900$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
2,780,852$	1,892,500$	1,892,500$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
10,175$	1,880,809$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	15,013$	0$

Afficher ou Masquer des Colonnes

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	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
2019	82	62	13	0	1	0	1	5	458	175	283	41	32	5	0	0	0	1	3	237	78	159	41	30	8	0	1	0	0	2	221	97	124	133	458	799	1257	0	15	179	153	124	5	2886	884	1008	985	27	1457	406	862	1775	272	67	24.63%	379	46	87.86%	4	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519
Total Saison Régulière	82	62	13	0	1	0	1	5	458	175	283	41	32	5	0	0	0	1	3	237	78	159	41	30	8	0	1	0	0	2	221	97	124	133	458	799	1257	0	15	179	153	124	5	2886	884	1008	985	27	1457	406	862	1775	272	67	24.63%	379	46	87.86%	4	1484	2303	64.44%	951	1765	53.88%	793	1324	59.89%	2676	2092	1384	424	889	519