Ligue de hockey Simulée Ste-Flanelle

Colorado
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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	Âge	Contrat	Salaire Moyen
1	Colin White	X		X		100.00	69	49	83	81	67	70	84	70	64	70	61	57	53	64	64	0	50	64	0	22	6	3,000,000$
2	Matt Martin		X	X		100.00	99	68	78	74	83	56	78	59	37	56	58	70	25	75	77	0	50	63	0	30	4	850,000$
3	Nick Paul	X	X			100.00	85	65	89	71	87	62	78	63	62	61	65	67	25	56	56	0	50	63	0	24	4	925,000$
4	Brett Seney	X	X			100.00	60	56	70	77	56	82	87	63	79	60	62	58	59	49	49	0	50	61	0	23	3	782,500$
5	Jesper Boqvist (R)		X			100.00	76	43	92	86	63	57	70	66	35	50	60	61	75	47	47	0	50	60	0	20	4	925,001$
6	Carter Verhaeghe	X				100.00	70	50	91	64	68	60	81	58	48	59	67	62	25	59	59	0	50	60	0	24	1	850,000$
7	Dmytro Timashov		X			100.00	82	51	86	67	69	60	75	57	33	59	57	70	25	57	57	0	50	60	0	23	1	925,000$
8	Jordan Kyrou	X		X		100.00	61	41	90	81	65	57	71	64	36	64	66	52	25	48	48	0	50	60	0	21	2	742,500$
9	Otto Koivula		X	X		100.00	75	45	94	66	83	50	76	55	59	50	55	71	25	45	45	0	50	58	0	21	2	650,000$
10	Joona Koppanen	X	X			100.00	78	73	89	63	73	63	65	56	70	50	58	64	55	44	44	0	50	57	0	21	2	753,333$
11	Marian Studenic			X		100.00	69	62	84	60	62	65	68	57	50	50	60	60	57	44	44	0	50	56	0	20	2	775,833$
12	Haydn Fleury				X	100.00	77	50	94	72	82	63	64	58	28	52	48	72	75	58	59	0	50	63	0	23	2	2,000,000$
13	Caleb Jones				X	100.00	72	49	90	67	71	68	79	60	26	51	48	66	75	59	59	0	50	62	0	22	2	655,000$
14	Carl Dahlstrom				X	100.00	64	43	90	61	86	68	58	62	34	47	46	70	75	59	59	0	50	61	0	24	2	825,000$
15	Steven Kampfer				X	100.00	80	49	89	68	70	64	56	60	25	52	47	59	25	62	63	0	50	60	0	31	4	750,000$
16	Adam Clendening				X	100.00	73	73	58	63	71	64	68	56	25	56	44	62	44	55	56	0	50	59	0	26	2	900,000$
17	Teemu Kivihalme (R)				X	100.00	73	67	86	66	67	69	75	50	25	43	41	60	39	44	44	0	50	57	0	24	2	650,000$
Rayé
1	Oskar Steen (R)	X				100.00	69	65	77	68	65	66	70	54	68	54	50	59	48	44	44	0	50	56	0	21	4	809,168$
2	Grant Mismash (R)		X			100.00	51	45	85	66	67	59	75	47	55	43	42	46	44	54	54	0	50	51	0	20	4	825,000$
3	Linus Lindstrom (R)	X				100.00	36	40	40	40	49	35	35	36	40	36	36	40	38	32	30	0	50	37	0	21	2	650,000$
4	Lucas Carlsson				X	100.00	74	70	84	65	70	73	78	54	25	52	42	62	40	44	44	0	50	59	0	22	2	792,500$
5	Dominik Masin				X	100.00	72	73	66	60	73	75	81	49	25	40	39	63	38	52	52	0	50	58	0	23	1	725,000$
6	Connor Hobbs				X	100.00	71	68	75	59	69	58	58	49	25	35	40	62	39	53	53	0	50	55	0	22	2	730,000$
7	Niklas Hansson (R)				X	100.00	35	43	43	43	53	33	33	35	43	35	35	43	39	32	30	0	50	38	0	24	1	700,000$
8	Ziyat Paigin (R)				X	100.00	33	37	37	37	70	33	33	33	37	33	33	37	35	32	30	0	50	37	0	24	2	792,500$

	MOYENNE D'ÉQUIPE					100.00	68	55	78	65	70	60	68	55	42	50	50	60	44	51	51	0	50	57

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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	Calvin Petersen	100.00	59	77	96	70	56	64	50	61	57	56	30	44	44	0	50	59	0
2	Filip Larsson (R)	100.00	44	49	61	71	40	42	50	51	44	45	30	44	44	0	50	48	0
Rayé
1	Tyler Parsons (R)	100.00	42	45	44	67	42	41	41	41	41	41	39	32	30	0	50	43	0

	MOYENNE D'ÉQUIPE	100.00	48	57	67	69	46	49	47	51	47	47	33	40	39	0	50	50

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire

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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	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Colin White	Monsters (Col)	C/RW	78	34	56	90	17	22	0	58	213	344	102	217	9.88%	11	1552	19.90	7	18	25	60	213	0	0	0	2	101	4	2	55.27%	2068	1	1	1.16	2	9	0	0	0	7	9	1
2	Brett Seney	Monsters (Col)	C/LW	82	20	58	78	13	13	5	37	196	231	78	212	8.66%	18	1509	18.41	1	15	16	47	207	0	1	1	5	83	2	2	60.31%	1867	0	0	1.03	2	2	0	0	1	3	3	3
3	Nick Paul	Monsters (Col)	C/LW	76	33	41	74	16	38	0	160	148	323	98	222	10.22%	17	1478	19.46	8	5	13	54	172	0	0	0	4	133	7	0	59.82%	443	0	0	1.00	4	7	0	0	0	3	7	4
4	Jesper Boqvist	Monsters (Col)	LW	82	35	37	72	28	18	0	82	188	338	103	260	10.36%	18	1662	20.27	2	0	2	6	54	0	0	0	2	19	5	2	33.06%	124	0	1	0.87	0	0	0	0	0	7	3	4
5	Matt Martin	Monsters (Col)	LW/RW	68	33	38	71	14	54	10	236	126	293	79	208	11.26%	27	1297	19.08	8	12	20	54	166	2	0	2	11	93	8	2	47.58%	124	0	1	1.09	0	0	1	1	0	5	6	7
6	Haydn Fleury	Monsters (Col)	D	81	14	54	68	22	50	0	203	88	162	48	104	8.64%	134	1886	23.29	6	13	19	78	203	0	0	0	2	170	1	1	0.00%	0	1	0	0.72	1	1	0	0	0	3	3	2
7	Dmytro Timashov	Monsters (Col)	LW	82	28	33	61	18	39	5	173	116	295	98	190	9.49%	25	1366	16.66	5	2	7	18	75	0	0	0	6	69	6	1	33.06%	121	0	0	0.89	0	0	0	0	1	3	3	4
8	Jordan Kyrou	Monsters (Col)	C/RW	82	27	30	57	11	6	0	37	117	293	89	202	9.22%	6	1363	16.63	5	6	11	47	204	0	0	0	0	0	3	1	38.74%	111	0	1	0.84	0	1	0	0	0	5	2	2
9	Carter Verhaeghe	Monsters (Col)	C	82	23	28	51	26	13	5	69	159	214	56	162	10.75%	17	1252	15.28	0	0	0	2	3	1	0	1	4	86	3	3	46.30%	1473	0	1	0.81	0	2	0	0	1	1	5	3
10	Otto Koivula	Monsters (Col)	LW/RW	82	26	25	51	22	14	0	72	105	252	69	153	10.32%	27	1538	18.76	6	4	10	41	200	0	1	1	1	112	7	4	47.95%	146	0	0	0.66	0	0	0	0	0	3	1	6
11	Steven Kampfer	Monsters (Col)	D	82	4	34	38	31	56	0	130	41	84	35	63	4.76%	91	1429	17.44	1	5	6	24	80	0	0	0	0	103	1	1	0.00%	0	0	0	0.53	0	0	0	0	0	1	2	3
12	Carl Dahlstrom	Monsters (Col)	D	82	8	26	34	20	26	0	86	89	120	50	81	6.67%	116	1835	22.38	3	5	8	38	126	1	1	2	4	184	1	1	0.00%	0	0	0	0.37	0	0	0	0	0	2	0	1
13	Lucas Carlsson	Monsters (Col)	D	75	5	26	31	7	47	15	135	54	89	28	63	5.62%	102	1510	20.14	1	7	8	30	189	0	0	0	0	70	0	0	0.00%	0	0	0	0.41	0	0	0	1	2	1	1	0
14	Adam Clendening	Monsters (Col)	D	54	3	26	29	4	52	10	153	38	60	17	48	5.00%	64	1095	20.29	0	6	6	11	124	0	0	0	1	98	0	1	0.00%	0	0	0	0.53	0	0	1	0	1	1	2	1
15	Caleb Jones	Monsters (Col)	D	37	7	17	24	7	8	0	44	40	62	16	44	11.29%	31	798	21.59	4	2	6	25	98	0	0	0	1	69	1	1	0.00%	0	0	0	0.60	0	0	0	0	0	1	0	2
16	Teemu Kivihalme	Monsters (Col)	D	67	3	20	23	38	29	5	64	23	48	7	36	6.25%	78	1040	15.53	0	0	0	0	13	0	0	0	0	33	0	1	0.00%	0	0	0	0.44	0	0	0	0	1	1	0	0
17	Joona Koppanen	Monsters (Col)	C/LW	82	10	10	20	12	22	10	43	70	96	25	76	10.42%	11	596	7.27	0	0	0	0	2	0	2	2	2	71	2	2	57.41%	634	0	0	0.67	1	8	2	0	0	1	0	1
18	Marian Studenic	Monsters (Col)	RW	82	9	10	19	16	24	0	40	29	81	24	77	11.11%	4	631	7.70	0	1	1	0	4	0	0	0	0	0	1	2	46.51%	43	0	0	0.60	0	0	0	0	0	0	0	1
19	Grant Mismash	Monsters (Col)	LW	20	0	3	3	4	0	0	4	6	8	2	8	0.00%	0	182	9.12	0	0	0	0	0	0	0	0	0	0	0	0	53.33%	15	0	0	0.33	0	0	0	0	0	0	0	0
20	Nate Thompson	Colorado	C/LW	2	0	2	2	1	0	0	7	3	7	1	4	0.00%	0	47	23.92	0	1	1	0	5	0	0	0	0	4	0	0	75.00%	4	0	0	0.84	0	1	0	0	0	0	1	0
21	Oskar Steen	Monsters (Col)	C	2	0	0	0	0	0	0	0	1	2	0	1	0.00%	0	8	4.10	0	0	0	0	1	0	0	0	0	0	0	0	54.55%	11	0	0	0.00	0	0	0	0	0	0	0	0
22	Dominik Masin	Monsters (Col)	D	1	0	0	0	0	2	0	0	0	1	0	2	0.00%	1	16	16.60	0	0	0	0	0	0	0	0	0	0	0	0	0.00%	0	0	0	0.00	0	0	0	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				1381	322	574	896	327	533	65	1833	1850	3403	1025	2433	9.46%	798	24100	17.45	57	102	159	535	2148	4	5	9	45	1507	52	27	53.87%	7184	2	5	0.74	10	31	4	2	7	48	48	45

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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	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Calvin Petersen	Monsters (Col)	70	49	15	4	0.916	2.98	4104	6	2	204	2425	2	0	0.760	25	70	0	7	2	3
2	Filip Larsson	Monsters (Col)	9	5	2	0	0.929	2.58	442	2	0	19	266	0	1	1.000	3	5	77	0	0	0
3	Jon Gillies	Colorado	7	4	2	1	0.910	3.22	428	0	0	23	255	0	0	0.500	2	7	0	0	1	1
Stats d'équipe Total ou en Moyenne			86	58	19	5	0.916	2.97	4975	8	2	246	2946	2	1	0.767	30	82	77	7	3	4

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Salaire Année 5	Salaire Année 6	Link
Adam Clendening	Monsters (Col)	D	26	1992-10-26	No	196 Lbs	6 ft0	No	No	No	2	Pro & Farm	900,000$	90,000$	0$	No	900,000$					Lien
Brett Seney	Monsters (Col)	C/LW	23	1996-02-28	No	156 Lbs	5 ft9	No	No	No	3	Pro & Farm	782,500$	78,250$	0$	No	782,500$	782,500$				Lien
Caleb Jones	Monsters (Col)	D	22	1997-06-06	No	192 Lbs	6 ft1	No	No	No	2	Pro & Farm	655,000$	655,000$	0$	No	655,000$					Lien
Calvin Petersen	Monsters (Col)	G	24	1994-10-19	No	185 Lbs	6 ft1	No	No	No	2	Pro & Farm	850,000$	85,000$	0$	No	850,000$					Lien
Carl Dahlstrom	Monsters (Col)	D	24	1995-01-28	No	231 Lbs	6 ft4	No	No	No	2	Pro & Farm	825,000$	82,500$	0$	No	825,000$					Lien
Carter Verhaeghe	Monsters (Col)	C	24	1995-08-14	No	181 Lbs	6 ft1	No	No	No	1	Pro & Farm	850,000$	85,000$	0$	No						Lien
Colin White	Monsters (Col)	C/RW	22	1997-01-30	No	183 Lbs	6 ft0	No	No	No	6	Pro & Farm	3,000,000$	300,000$	0$	No	3,000,000$	3,000,000$	3,000,000$	3,000,000$	3,000,000$	Lien
Connor Hobbs	Monsters (Col)	D	22	1997-01-04	No	187 Lbs	6 ft1	No	No	No	2	Pro & Farm	730,000$	73,000$	0$	No	730,000$					Lien
Dmytro Timashov	Monsters (Col)	LW	23	1996-09-30	No	195 Lbs	5 ft10	No	No	No	1	Pro & Farm	925,000$	92,500$	0$	No						Lien
Dominik Masin	Monsters (Col)	D	23	1996-01-31	No	198 Lbs	6 ft2	No	No	No	1	Pro & Farm	725,000$	72,500$	0$	No						Lien
Filip Larsson	Monsters (Col)	G	21	1998-08-17	Yes	181 Lbs	6 ft2	No	No	No	3	Pro & Farm	836,666$	83,667$	0$	No	836,666$	836,666$				Lien
Grant Mismash	Monsters (Col)	LW	20	1999-02-19	Yes	186 Lbs	6 ft0	No	No	No	4	Pro & Farm	825,000$	82,500$	0$	No	825,000$	825,000$	825,000$			Lien
Haydn Fleury	Monsters (Col)	D	23	1996-07-08	No	221 Lbs	6 ft3	No	No	No	2	Pro & Farm	2,000,000$	200,000$	0$	No	2,000,000$					Lien
Jesper Boqvist	Monsters (Col)	LW	20	1998-10-30	Yes	174 Lbs	5 ft11	No	No	No	4	Pro & Farm	925,001$	92,500$	0$	No	925,001$	925,001$	925,001$			Lien
Joona Koppanen	Monsters (Col)	C/LW	21	1998-02-25	No	192 Lbs	6 ft3	No	No	No	2	Pro & Farm	753,333$	75,333$	0$	No	753,333$					Lien
Jordan Kyrou	Monsters (Col)	C/RW	21	1998-05-05	No	175 Lbs	6 ft0	No	No	No	2	Pro & Farm	742,500$	74,250$	0$	No	742,500$					Lien
Linus Lindstrom	Monsters (Col)	C	21	1998-01-08	Yes	168 Lbs	5 ft11	No	No	No	2	Pro & Farm	650,000$	65,000$	0$	No	650,000$					Lien
Lucas Carlsson	Monsters (Col)	D	22	1997-07-05	No	190 Lbs	6 ft0	No	No	No	2	Pro & Farm	792,500$	79,250$	0$	No	792,500$					Lien
Marian Studenic	Monsters (Col)	RW	20	1998-10-28	No	163 Lbs	6 ft1	No	No	No	2	Pro & Farm	775,833$	77,583$	0$	No	775,833$					Lien
Matt Martin	Monsters (Col)	LW/RW	30	1989-05-08	No	220 Lbs	6 ft3	No	No	No	4	Pro & Farm	850,000$	85,000$	0$	No	850,000$	850,000$	850,000$			Lien
Nick Paul	Monsters (Col)	C/LW	24	1995-03-20	No	230 Lbs	6 ft4	Yes	No	No	4	Pro & Farm	925,000$	92,500$	0$	No	925,000$	925,000$	925,000$			Lien
Niklas Hansson	Monsters (Col)	D	24	1995-01-08	Yes	175 Lbs	6 ft0	No	No	No	1	Pro & Farm	700,000$	70,000$	0$	No						Lien
Oskar Steen	Monsters (Col)	C	21	1998-03-09	Yes	187 Lbs	5 ft9	No	No	No	4	Pro & Farm	809,168$	80,917$	0$	No	809,168$	809,168$	809,168$			Lien
Otto Koivula	Monsters (Col)	LW/RW	21	1998-10-01	No	220 Lbs	6 ft4	No	No	No	2	Pro & Farm	650,000$	65,000$	0$	No	650,000$					Lien
Steven Kampfer	Monsters (Col)	D	31	1988-09-24	No	195 Lbs	5 ft11	No	No	No	4	Pro & Farm	750,000$	75,000$	0$	No	750,000$	750,000$	750,000$			Lien
Teemu Kivihalme	Monsters (Col)	D	24	1995-06-14	Yes	181 Lbs	6 ft0	Yes	No	No	2	Pro & Farm	650,000$	65,000$	0$	No	650,000$					Lien
Tyler Parsons	Monsters (Col)	G	22	1997-09-18	Yes	185 Lbs	6 ft1	No	No	No	2	Pro & Farm	742,500$	74,250$	0$	No	742,500$					Lien
Ziyat Paigin	Monsters (Col)	D	24	1995-02-08	Yes	209 Lbs	6 ft6	No	No	No	2	Pro & Farm	792,500$	79,250$	0$	No	792,500$					Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
28	22.96	191 Lbs	6 ft1	2.50	907,589$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Jesper Boqvist	Brett Seney	Matt Martin	40	1	2	2
2	Nick Paul	Colin White	Otto Koivula	30	1	2	2
3	Dmytro Timashov	Carter Verhaeghe	Jordan Kyrou	20	1	2	2
4	Jesper Boqvist	Joona Koppanen	Marian Studenic	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Carl Dahlstrom	Haydn Fleury	40	1	2	2
2	Adam Clendening	Caleb Jones	30	1	2	2
3	Steven Kampfer	Teemu Kivihalme	20	1	2	2
4	Carl Dahlstrom	Haydn Fleury	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Matt Martin	Colin White	Otto Koivula	60	1	2	2
2	Jesper Boqvist	Brett Seney	Jordan Kyrou	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Haydn Fleury	Carl Dahlstrom	60	1	2	2
2	Adam Clendening	Caleb Jones	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Brett Seney	Otto Koivula	60	1	2	2
2	Joona Koppanen	Matt Martin	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Carl Dahlstrom	Haydn Fleury	60	1	2	2
2	Adam Clendening	Steven Kampfer	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Colin White	60	1	2	2	Carl Dahlstrom	Haydn Fleury	60	1	2	2
2	Joona Koppanen	40	1	2	2	Adam Clendening	Steven Kampfer	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Colin White	Matt Martin	60	1	2	2
2	Brett Seney	Jesper Boqvist	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Carl Dahlstrom	Haydn Fleury	60	1	2	2
2	Adam Clendening	Steven Kampfer	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Brett Seney	Colin White	Matt Martin	Carl Dahlstrom	Steven Kampfer

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Dmytro Timashov	Colin White	Matt Martin	Carl Dahlstrom	Haydn Fleury

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Dmytro Timashov, Jesper Boqvist, Carter Verhaeghe	Dmytro Timashov, Jesper Boqvist	Carter Verhaeghe

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Haydn Fleury, Steven Kampfer, Adam Clendening	Haydn Fleury	Steven Kampfer, Adam Clendening

Tirs de Pénalité
Colin White, Joona Koppanen, Carter Verhaeghe, Brett Seney, Jordan Kyrou

Gardien
#1 : Calvin Petersen, #2 : Filip Larsson

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	3	1	1	0	0	0	1	0	16	13	3	2	1	1	0	0	0	0	0	8	6	2	1	0	0	0	0	0	1	0	8	7	1	4	0.667	16	28	44	0	0	114	115	92	12	125	1059	1124	1192	55	118	30	18	79	11	4	36.36%	9	1	88.89%	1	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
2	Baby Hawks	4	1	2	0	0	0	1	0	12	12	0	2	0	2	0	0	0	0	0	4	7	-3	2	1	0	0	0	0	1	0	8	5	3	4	0.500	12	20	32	0	0	114	115	92	12	153	1059	1124	1192	55	122	38	27	93	20	2	10.00%	11	4	63.64%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
3	Bears	2	1	1	0	0	0	0	0	4	9	-5	1	1	0	0	0	0	0	0	3	2	1	1	0	1	0	0	0	0	0	1	7	-6	2	0.500	4	7	11	0	0	114	115	92	12	61	1059	1124	1192	55	92	25	10	39	6	0	0.00%	4	2	50.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
4	Bruins	2	2	0	0	0	0	0	0	8	6	2	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	4	3	1	4	1.000	8	14	22	0	0	114	115	92	12	66	1059	1124	1192	55	67	15	10	29	2	0	0.00%	5	0	100.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
5	Cabaret Lady Mary Ann	2	2	0	0	0	0	0	0	11	4	7	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	7	1	6	4	1.000	11	21	32	0	0	114	115	92	12	163	1059	1124	1192	55	69	17	10	58	2	1	50.00%	5	0	100.00%	1	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
6	Caroline	2	1	1	0	0	0	0	0	5	7	-2	1	0	1	0	0	0	0	0	2	6	-4	1	1	0	0	0	0	0	0	3	1	2	2	0.500	5	8	13	0	0	114	115	92	12	58	1059	1124	1192	55	79	34	40	47	4	1	25.00%	14	2	85.71%	1	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
7	Chiefs	5	4	0	0	0	0	1	0	18	10	8	3	3	0	0	0	0	0	0	9	4	5	2	1	0	0	0	0	1	0	9	6	3	10	1.000	18	27	45	0	0	114	115	92	12	180	1059	1124	1192	55	163	37	25	117	25	6	24.00%	8	1	87.50%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
8	Chill	4	3	1	0	0	0	0	0	18	12	6	2	1	1	0	0	0	0	0	9	5	4	2	2	0	0	0	0	0	0	9	7	2	6	0.750	18	33	51	0	0	114	115	92	12	145	1059	1124	1192	55	133	35	16	91	17	2	11.76%	8	1	87.50%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
9	Comets	3	2	1	0	0	0	0	0	9	8	1	1	1	0	0	0	0	0	0	5	2	3	2	1	1	0	0	0	0	0	4	6	-2	4	0.667	9	17	26	0	0	114	115	92	12	117	1059	1124	1192	55	99	28	8	61	9	2	22.22%	4	2	50.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
10	Cougars	2	1	1	0	0	0	0	0	8	9	-1	1	1	0	0	0	0	0	0	5	3	2	1	0	1	0	0	0	0	0	3	6	-3	2	0.500	8	12	20	0	0	114	115	92	12	77	1059	1124	1192	55	82	15	12	50	3	0	0.00%	5	2	60.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
11	Crunch	2	1	0	0	0	1	0	0	8	7	1	1	1	0	0	0	0	0	0	5	3	2	1	0	0	0	0	1	0	0	3	4	-1	3	0.750	8	14	22	0	0	114	115	92	12	93	1059	1124	1192	55	79	24	10	39	5	2	40.00%	5	2	60.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
12	Heat	3	1	1	0	1	0	0	0	12	11	1	2	0	1	0	1	0	0	0	8	10	-2	1	1	0	0	0	0	0	0	4	1	3	4	0.667	12	22	34	0	0	114	115	92	12	151	1059	1124	1192	55	110	24	14	66	10	3	30.00%	7	2	71.43%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
13	Jayhawks	3	1	1	0	0	0	1	0	12	11	1	2	1	0	0	0	0	1	0	7	3	4	1	0	1	0	0	0	0	0	5	8	-3	4	0.667	12	20	32	0	0	114	115	92	12	133	1059	1124	1192	55	97	28	10	85	13	5	38.46%	5	1	80.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
14	Las Vegas	3	2	1	0	0	0	0	0	10	8	2	1	1	0	0	0	0	0	0	4	2	2	2	1	1	0	0	0	0	0	6	6	0	4	0.667	10	16	26	0	0	114	115	92	12	109	1059	1124	1192	55	97	33	22	53	10	0	0.00%	10	1	90.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
15	Manchots	2	1	1	0	0	0	0	0	8	7	1	1	1	0	0	0	0	0	0	4	2	2	1	0	1	0	0	0	0	0	4	5	-1	2	0.500	8	15	23	0	0	114	115	92	12	66	1059	1124	1192	55	86	22	23	53	2	0	0.00%	9	1	88.89%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
16	Marlies	2	0	1	0	0	0	1	0	6	6	0	1	0	1	0	0	0	0	0	4	5	-1	1	0	0	0	0	0	1	0	2	1	1	2	0.500	6	9	15	0	0	114	115	92	12	91	1059	1124	1192	55	53	11	6	45	10	1	10.00%	3	0	100.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
17	Minnesota	5	5	0	0	0	0	0	0	37	17	20	2	2	0	0	0	0	0	0	14	10	4	3	3	0	0	0	0	0	0	23	7	16	10	1.000	37	68	105	0	1	114	115	92	12	334	1059	1124	1192	55	215	46	36	99	12	5	41.67%	17	6	64.71%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
18	Monarchs	3	1	1	0	0	0	0	1	10	11	-1	1	0	1	0	0	0	0	0	3	4	-1	2	1	0	0	0	0	0	1	7	7	0	3	0.500	10	18	28	0	0	114	115	92	12	124	1059	1124	1192	55	150	39	28	64	9	2	22.22%	14	2	85.71%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
19	Monsters	2	1	1	0	0	0	0	0	9	7	2	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	7	3	4	2	0.500	9	17	26	0	0	114	115	92	12	65	1059	1124	1192	55	74	18	10	48	12	3	25.00%	5	2	60.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
20	Oceanics	4	2	1	0	0	0	0	1	13	11	2	2	0	1	0	0	0	0	1	6	8	-2	2	2	0	0	0	0	0	0	7	3	4	5	0.625	13	24	37	0	0	114	115	92	12	133	1059	1124	1192	55	152	51	28	89	11	2	18.18%	13	1	92.31%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
21	Oil Kings	3	2	1	0	0	0	0	0	12	12	0	1	0	1	0	0	0	0	0	2	5	-3	2	2	0	0	0	0	0	0	10	7	3	4	0.667	12	21	33	0	0	114	115	92	12	104	1059	1124	1192	55	115	35	21	64	5	1	20.00%	8	3	62.50%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
22	Phantoms	2	2	0	0	0	0	0	0	8	5	3	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	3	1	4	1.000	8	14	22	0	0	114	115	92	12	66	1059	1124	1192	55	73	29	14	64	4	1	25.00%	7	1	85.71%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
23	Rocket	2	1	1	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	5	1	4	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	7	12	19	0	0	114	115	92	12	74	1059	1124	1192	55	65	23	6	45	6	1	16.67%	3	1	66.67%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
24	Senators	2	1	0	0	0	0	1	0	10	6	4	1	1	0	0	0	0	0	0	6	3	3	1	0	0	0	0	0	1	0	4	3	1	4	1.000	10	15	25	0	0	114	115	92	12	92	1059	1124	1192	55	85	27	15	44	9	2	22.22%	5	1	80.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
25	Sharks	3	2	0	0	0	0	0	1	9	6	3	2	2	0	0	0	0	0	0	8	4	4	1	0	0	0	0	0	0	1	1	2	-1	5	0.833	9	16	25	0	0	114	115	92	12	128	1059	1124	1192	55	78	28	36	60	3	1	33.33%	7	2	71.43%	1	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
26	Sound Tigers	2	1	0	0	1	0	0	0	8	4	4	1	0	0	0	1	0	0	0	3	2	1	1	1	0	0	0	0	0	0	5	2	3	4	1.000	8	14	22	0	0	114	115	92	12	78	1059	1124	1192	55	67	13	9	37	6	2	33.33%	2	0	100.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
27	Spiders	2	2	0	0	0	0	0	0	7	5	2	1	1	0	0	0	0	0	0	2	1	1	1	1	0	0	0	0	0	0	5	4	1	4	1.000	7	12	19	0	0	114	115	92	12	64	1059	1124	1192	55	67	15	12	41	6	1	16.67%	5	2	60.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
28	Stars	4	2	1	0	0	1	0	0	15	15	0	2	1	1	0	0	0	0	0	6	7	-1	2	1	0	0	0	1	0	0	9	8	1	5	0.625	15	25	40	1	0	114	115	92	12	176	1059	1124	1192	55	138	35	30	81	18	6	33.33%	14	6	57.14%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
29	Thunder	2	2	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	3	0	3	1	1	0	0	0	0	0	0	4	2	2	4	1.000	7	12	19	0	1	114	115	92	12	73	1059	1124	1192	55	59	15	18	47	3	0	0.00%	9	0	100.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
	Total	82	50	19	0	2	2	6	3	329	250	79	41	25	12	0	2	0	1	1	156	120	36	41	25	7	0	0	2	5	2	173	130	43	121	0.738	329	573	902	1	2	114	115	92	12	3400	1059	1124	1192	55	2947	805	545	1833	256	57	22.27%	224	49	78.13%	4	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
30	Wolf Pack	2	2	0	0	0	0	0	0	12	5	7	1	1	0	0	0	0	0	0	7	3	4	1	1	0	0	0	0	0	0	5	2	3	4	1.000	12	22	34	0	0	114	115	92	12	101	1059	1124	1192	55	63	15	21	45	3	1	33.33%	3	0	100.00%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
	_Since Last GM Reset	82	50	19	0	2	2	6	3	329	250	79	41	25	12	0	2	0	1	1	156	120	36	41	25	7	0	0	2	5	2	173	130	43	121	0.738	329	573	902	1	2	114	115	92	12	3400	1059	1124	1192	55	2947	805	545	1833	256	57	22.27%	224	49	78.13%	4	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
	_Vs Conference	43	26	11	0	1	2	3	0	176	135	41	21	13	6	0	1	0	1	0	80	66	14	22	13	5	0	0	2	2	0	96	69	27	62	0.721	176	303	479	1	1	114	115	92	12	1922	1059	1124	1192	55	1530	417	271	958	142	35	24.65%	116	33	71.55%	2	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555
	_Vs Division	26	6	3	0	0	2	0	0	113	77	36	13	4	1	0	0	0	0	0	48	41	7	13	2	2	0	0	2	0	0	65	36	29	14	0.269	113	197	310	1	1	114	115	92	12	1121	1059	1124	1192	55	923	242	162	570	103	23	22.33%	71	19	73.24%	0	1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%	2126	1508	1780	572	1073	555

			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	121	W1	329	573	902	3400	2947	805	545	1833	1	2

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	50	19	2	2	6	3	329	250

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	25	12	2	0	1	1	156	120

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	25	7	0	2	5	2	173	130

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
9	0	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
256	57	22.27%	224	49	78.13%	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
1059	1124	1192	55	114	115	92	12

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
1673	3028	55.25%	1453	2815	51.62%	759	1388	54.68%

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
2126	1508	1780	572	1073	555

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	Lien
2 - 2020-10-23	11	Heat	5	Monsters	6	W	X		Sommaire du Match
4 - 2020-10-25	27	Minnesota	6	Monsters	7	W			Sommaire du Match
9 - 2020-10-30	56	Bruins	3	Monsters	4	W			Sommaire du Match
11 - 2020-11-01	72	Jayhawks	2	Monsters	3	W	X	X	Sommaire du Match
13 - 2020-11-03	83	Monsters	1	Bears	7	L			Sommaire du Match
15 - 2020-11-05	92	Monsters	4	Manchots	5	L			Sommaire du Match
17 - 2020-11-07	107	Monsters	7	Cabaret Lady Mary Ann	1	W			Sommaire du Match
18 - 2020-11-08	117	Monsters	4	Thunder	2	W			Sommaire du Match
20 - 2020-11-10	131	Monsters	4	Chiefs	2	W			Sommaire du Match
24 - 2020-11-14	155	Monsters	1	Las Vegas	3	L			Sommaire du Match
25 - 2020-11-15	168	Admirals	2	Monsters	5	W			Sommaire du Match
29 - 2020-11-19	192	Cabaret Lady Mary Ann	3	Monsters	4	W			Sommaire du Match
31 - 2020-11-21	201	Stars	3	Monsters	4	W			Sommaire du Match
32 - 2020-11-22	214	Monsters	5	Jayhawks	8	L			Sommaire du Match
35 - 2020-11-25	230	Monsters	4	Stars	5	L	X		Sommaire du Match
37 - 2020-11-27	244	Chill	4	Monsters	2	L			Sommaire du Match
39 - 2020-11-29	260	Monsters	4	Monsters	2	L			Sommaire du Match
42 - 2020-12-02	276	Monsters	4	Oceanics	2	W			Sommaire du Match
44 - 2020-12-04	290	Monsters	5	Oil Kings	3	W			Sommaire du Match
46 - 2020-12-06	311	Monsters	2	Comets	5	L			Sommaire du Match
49 - 2020-12-09	328	Monsters	4	Heat	1	W			Sommaire du Match
51 - 2020-12-11	340	Monsters	9	Minnesota	4	W			Sommaire du Match
53 - 2020-12-13	351	Marlies	5	Monsters	4	L			Sommaire du Match
57 - 2020-12-17	387	Oil Kings	5	Monsters	2	L			Sommaire du Match
59 - 2020-12-19	394	Monsters	4	Baby Hawks	2	W			Sommaire du Match
60 - 2020-12-20	413	Baby Hawks	4	Monsters	2	L			Sommaire du Match
64 - 2020-12-24	434	Monsters	2	Marlies	1	W	X	X	Sommaire du Match
65 - 2020-12-25	439	Monsters	2	Rocket	3	L			Sommaire du Match
67 - 2020-12-27	455	Monsters	4	Bruins	3	W			Sommaire du Match
69 - 2020-12-29	471	Heat	5	Monsters	2	L			Sommaire du Match
71 - 2020-12-31	486	Phantoms	2	Monsters	4	W			Sommaire du Match
73 - 2021-01-02	500	Spiders	1	Monsters	2	W			Sommaire du Match
76 - 2021-01-05	522	Monsters	5	Chiefs	4	W	X	X	Sommaire du Match
78 - 2021-01-07	536	Monsters	4	Baby Hawks	3	W	X	X	Sommaire du Match
79 - 2021-01-08	544	Caroline	6	Monsters	2	L			Sommaire du Match
81 - 2021-01-10	562	Baby Hawks	3	Monsters	2	L			Sommaire du Match
83 - 2021-01-12	580	Monsters	5	Las Vegas	3	W			Sommaire du Match
87 - 2021-01-16	586	Minnesota	4	Monsters	7	W			Sommaire du Match
88 - 2021-01-17	593	Monsters	5	Stars	3	W			Sommaire du Match
91 - 2021-01-20	621	Oceanics	3	Monsters	2	L			Sommaire du Match
93 - 2021-01-22	634	Chiefs	2	Monsters	3	W			Sommaire du Match
95 - 2021-01-24	649	Monsters	5	Spiders	4	W			Sommaire du Match
97 - 2021-01-26	661	Monsters	5	Sound Tigers	2	W			Sommaire du Match
98 - 2021-01-27	666	Monsters	5	Wolf Pack	2	W			Sommaire du Match
101 - 2021-01-30	691	Manchots	2	Monsters	4	W			Sommaire du Match
105 - 2021-02-03	722	Stars	4	Monsters	2	L			Sommaire du Match
107 - 2021-02-05	738	Sharks	2	Monsters	4	W			Sommaire du Match
109 - 2021-02-07	744	Chiefs	1	Monsters	4	W			Sommaire du Match
111 - 2021-02-09	759	Cougars	3	Monsters	5	W			Sommaire du Match
123 - 2021-02-21	799	Monsters	4	Phantoms	3	W			Sommaire du Match
126 - 2021-02-24	813	Monsters	3	Crunch	4	L	X		Sommaire du Match
128 - 2021-02-26	833	Monsters	4	Senators	3	W	X	X	Sommaire du Match
130 - 2021-02-28	851	Monsters	7	Monsters	3	W			Sommaire du Match
131 - 2021-03-01	859	Monsters	8	Minnesota	0	W			Sommaire du Match
133 - 2021-03-03	874	Senators	3	Monsters	6	W			Sommaire du Match
135 - 2021-03-05	888	Bears	2	Monsters	3	W			Sommaire du Match
137 - 2021-03-07	902	Monarchs	4	Monsters	3	L			Sommaire du Match
139 - 2021-03-09	919	Thunder	0	Monsters	3	W			Sommaire du Match
141 - 2021-03-11	931	Sound Tigers	2	Monsters	3	W	X		Sommaire du Match
143 - 2021-03-13	947	Monsters	8	Admirals	7	W	X	X	Sommaire du Match
144 - 2021-03-14	958	Monsters	3	Monarchs	4	L	X	X	Sommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
148 - 2021-03-18	981	Crunch	3	Monsters	5	W			Sommaire du Match
150 - 2021-03-20	996	Monsters	3	Caroline	1	W			Sommaire du Match
151 - 2021-03-21	1008	Monsters	4	Chill	3	W			Sommaire du Match
153 - 2021-03-23	1017	Monsters	3	Cougars	6	L			Sommaire du Match
155 - 2021-03-25	1031	Admirals	4	Monsters	3	L			Sommaire du Match
157 - 2021-03-27	1047	Monsters	2	Comets	1	W			Sommaire du Match
159 - 2021-03-29	1065	Monsters	1	Sharks	2	L	X	X	Sommaire du Match
160 - 2021-03-30	1069	Monsters	4	Monarchs	3	W			Sommaire du Match
162 - 2021-04-01	1081	Wolf Pack	3	Monsters	7	W			Sommaire du Match
164 - 2021-04-03	1096	Comets	2	Monsters	5	W			Sommaire du Match
166 - 2021-04-05	1110	Las Vegas	2	Monsters	4	W			Sommaire du Match
168 - 2021-04-07	1129	Sharks	2	Monsters	4	W			Sommaire du Match
170 - 2021-04-09	1142	Monsters	5	Chill	4	W			Sommaire du Match
172 - 2021-04-11	1154	Rocket	1	Monsters	5	W			Sommaire du Match
174 - 2021-04-13	1172	Monsters	6	Minnesota	3	W			Sommaire du Match
176 - 2021-04-15	1188	Monsters	5	Oil Kings	4	W			Sommaire du Match
178 - 2021-04-17	1201	Monsters	3	Oceanics	1	W			Sommaire du Match
180 - 2021-04-19	1222	Chill	1	Monsters	7	W			Sommaire du Match
182 - 2021-04-21	1235	Jayhawks	1	Monsters	4	W			Sommaire du Match
184 - 2021-04-23	1251	Oceanics	5	Monsters	4	L	X	X	Sommaire du Match
186 - 2021-04-25	1258	Chiefs	1	Monsters	2	W			Sommaire du Match

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	15
Assistance	78,831	39,392
Assistance PCT	96.14%	96.08%

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	2883 - 96.12%	81,706$	3,349,965$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
2,514,211$	3,130,750$	3,130,750$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
16,832$	2,514,413$	28	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	16,832$	0$

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