Ligue de hockey Simulée Ste-Flanelle

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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	Sheldon Dries	X	X			100.00	69	61	70	74	65	68	77	65	68	54	63	60	64	48	48	0	50	60	0	25	5	700,000$
2	Julien Gauthier			X		100.00	75	52	85	69	84	58	85	54	39	58	55	58	25	47	47	0	50	58	0	21	2	925,000$
3	Zach Senyshyn (R)			X		100.00	77	70	90	70	71	71	74	53	50	48	55	64	53	44	44	0	50	57	0	22	1	895,000$
4	Marcus Kruger	X	X	X		100.00	54	43	83	74	58	53	62	46	82	44	47	72	47	63	60	0	50	55	0	29	2	1,000,000$
5	Griffen Molino	X	X			100.00	71	64	89	60	63	54	52	52	65	50	46	62	45	51	51	0	50	53	0	25	3	900,000$
6	Tim Soderlund (R)		X	X		100.00	66	58	85	65	58	56	59	47	50	43	45	56	43	44	44	0	50	51	0	21	4	825,834$
7	Kasper Bjorkqvist (R)		X	X		100.00	77	72	89	50	72	49	49	50	50	38	56	62	53	44	44	0	50	51	0	22	2	700,000$
8	Teemu Pulkkinen		X	X		100.00	46	35	81	71	57	48	38	47	36	40	54	60	45	43	38	0	50	50	0	27	2	650,000$
9	Alexander Khokhlachev	X	X			100.00	41	35	91	71	55	40	27	30	40	30	30	61	48	39	36	0	50	43	0	26	1	600,000$
10	Scott Kosmachuk			X		100.00	41	35	84	68	57	40	27	31	35	33	30	64	44	35	32	0	50	43	0	25	1	560,000$
11	Adam Tambellini (R)	X	X			100.00	35	43	43	43	51	33	33	35	43	35	35	43	39	32	30	0	50	37	0	24	2	560,000$
12	Brian Lashoff				X	100.00	83	82	81	68	82	68	72	48	25	36	40	69	39	63	64	0	50	62	0	29	2	775,000$
13	Julian Melchiori				X	100.00	84	81	89	68	81	68	75	50	25	37	42	66	40	48	48	0	50	60	0	27	4	725,000$
14	Keaton Middleton				X	100.00	82	88	67	61	88	64	68	49	25	40	42	64	40	44	44	0	50	58	0	21	2	715,000$
15	Jimmy Schuldt (R)				X	100.00	76	75	77	61	75	61	64	53	25	46	45	62	43	44	44	0	50	57	0	24	2	825,000$
16	Johnathan Kovacevic (R)				X	100.00	78	78	79	63	78	55	58	48	25	38	42	62	40	44	44	0	50	56	0	22	3	792,500$
17	Jake Christiansen (R)				X	100.00	79	71	99	58	71	54	58	41	25	28	39	61	37	44	44	0	50	53	0	20	4	925,000$
Rayé
1	Todd Burgess (R)			X		100.00	36	40	40	40	55	35	35	36	40	36	36	40	38	32	30	0	50	37	0	23	2	650,000$
2	Kurtis MacDermid				X	100.00	87	88	70	66	82	59	69	59	28	48	48	65	25	51	51	0	50	61	0	25	4	650,000$
3	Jeremy Roy				X	100.00	70	71	81	61	68	58	64	47	25	39	40	62	38	47	47	0	50	55	0	22	1	825,000$
4	Reece Scarlett (R)				X	100.00	74	69	86	61	69	49	50	49	25	40	42	60	40	44	44	0	50	53	0	26	2	650,000$
5	Sergei Boikov (R)				X	100.00	33	37	37	37	66	33	33	33	37	33	33	37	35	32	30	0	50	37	0	23	2	705,000$

	MOYENNE D'ÉQUIPE					100.00	65	61	77	62	68	53	56	47	39	41	44	60	42	45	44	0	50	52

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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	Jake Oettinger	100.00	59	52	65	86	63	63	56	63	63	62	30	44	44	0	50	60	0
2	Michael Hutchinson	100.00	52	61	66	81	59	49	52	55	56	57	78	59	59	0	50	58	0
Rayé
1	Cory Schneider	100.00	57	61	65	80	58	52	53	59	63	57	72	69	70	0	50	60	0
2	Jean-Francois Berube	100.00	58	69	82	66	53	59	53	62	58	55	30	47	48	0	50	58	0

	MOYENNE D'ÉQUIPE	100.00	57	61	70	78	58	56	54	60	60	58	53	55	55	0	50	59

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	Sheldon Dries	Monsters (Clb)	C/LW	72	43	47	90	25	24	0	212	262	392	105	287	10.97%	20	1716	23.84	4	10	14	56	170	1	1	2	11	200	7	0	56.71%	2093	1	1	1.05	0	4	0	0	0	12	4	5
2	Zach Senyshyn	Monsters (Clb)	RW	82	33	41	74	-4	42	0	187	138	379	98	254	8.71%	22	1614	19.69	6	18	24	67	196	0	1	1	3	115	7	2	46.63%	163	0	1	0.92	1	4	0	0	0	7	6	1
3	Kurtis MacDermid	Monsters (Clb)	D	79	15	48	63	17	137	35	235	110	162	66	106	9.26%	113	1645	20.82	6	10	16	67	167	0	0	0	3	172	1	1	0.00%	0	0	0	0.77	0	0	4	1	2	3	3	6
4	Marcus Kruger	Monsters (Clb)	C/LW/RW	77	22	37	59	1	18	0	33	231	252	82	189	8.73%	29	1483	19.27	7	7	14	38	179	1	1	2	6	131	5	2	62.07%	1371	0	0	0.80	0	3	0	0	0	1	4	4
5	Griffen Molino	Monsters (Clb)	C/LW	82	20	34	54	-8	28	0	113	137	232	59	145	8.62%	10	1490	18.18	5	10	15	51	190	0	0	0	2	37	2	3	53.56%	870	0	0	0.72	0	1	0	0	0	1	2	6
6	Gustav Lindstrom	Columbus	D	57	7	42	49	0	16	0	86	95	158	45	111	4.43%	110	1350	23.69	6	9	15	74	147	0	1	1	0	158	0	0	0.00%	0	0	0	0.73	0	0	0	0	0	0	2	3
7	Brian Lashoff	Monsters (Clb)	D	70	19	27	46	1	45	5	142	64	124	31	78	15.32%	106	1640	23.43	7	8	15	45	179	1	0	1	2	174	3	1	0.00%	0	0	0	0.56	0	0	0	0	1	2	3	4
8	Julien Gauthier	Monsters (Clb)	RW	82	18	28	46	14	30	0	131	123	229	53	169	7.86%	10	1481	18.07	4	7	11	46	200	0	0	0	1	126	2	1	43.92%	255	0	0	0.62	3	6	0	0	0	2	5	3
9	Julian Melchiori	Monsters (Clb)	D	82	13	32	45	15	22	0	109	80	137	39	94	9.49%	125	1688	20.59	6	7	13	45	144	0	0	0	3	205	5	2	0.00%	0	0	0	0.53	0	0	0	0	0	1	3	3
10	Teemu Pulkkinen	Monsters (Clb)	LW/RW	82	20	19	39	8	8	0	17	92	173	62	134	11.56%	11	1144	13.96	1	1	2	6	63	0	0	0	2	74	2	1	35.38%	130	0	0	0.68	0	0	0	0	0	2	3	1
11	Jimmy Schuldt	Monsters (Clb)	D	61	8	30	38	14	65	5	138	45	89	17	69	8.99%	79	1023	16.78	4	7	11	24	114	0	0	0	1	41	1	1	0.00%	0	0	0	0.74	0	0	0	1	0	1	1	2
12	Kasper Bjorkqvist	Monsters (Clb)	LW/RW	82	20	18	38	-3	64	0	199	80	194	47	127	10.31%	23	1289	15.72	1	3	4	12	51	0	0	0	1	58	2	0	50.36%	139	1	0	0.59	0	1	0	0	0	3	1	1
13	Tim Soderlund	Monsters (Clb)	LW/RW	82	15	20	35	-17	26	0	89	82	162	45	112	9.26%	13	1324	16.15	1	3	4	11	81	0	0	0	0	10	0	2	44.17%	120	0	0	0.53	0	2	0	0	0	1	1	0
14	Keaton Middleton	Monsters (Clb)	D	82	9	25	34	-8	63	15	233	81	85	24	47	10.59%	103	1418	17.30	2	3	5	27	71	0	0	0	1	71	4	0	0.00%	0	0	0	0.48	0	0	0	2	1	1	4	1
15	Jeremy Roy	Monsters (Clb)	D	58	4	18	22	-10	18	0	39	47	36	14	25	11.11%	73	946	16.32	0	1	1	1	11	0	0	0	1	59	0	0	0.00%	0	0	0	0.46	0	0	0	0	0	0	0	2
16	Alexander Khokhlachev	Monsters (Clb)	C/LW	80	4	15	19	-4	0	0	3	128	63	16	44	6.35%	17	1167	14.60	0	0	0	0	17	0	0	0	0	8	0	1	35.04%	1347	0	0	0.33	1	1	0	0	0	0	0	0
17	William Carrier	Columbus	LW	19	3	8	11	4	14	0	76	36	55	21	52	5.45%	4	355	18.69	0	2	2	9	52	0	0	0	1	53	0	1	39.57%	139	0	0	0.62	0	1	0	0	0	1	0	1
18	Johnathan Kovacevic	Monsters (Clb)	D	56	0	10	10	3	54	10	79	10	28	8	21	0.00%	35	479	8.56	0	3	3	4	33	0	0	0	0	16	0	0	0.00%	1	0	0	0.42	0	0	1	0	1	0	0	0
19	Scott Kosmachuk	Monsters (Clb)	RW	80	5	5	10	-1	0	0	6	28	51	11	39	9.80%	13	584	7.31	0	0	0	1	5	0	0	0	0	44	1	1	27.63%	76	0	0	0.34	0	0	0	0	0	0	0	0
20	Adam Tambellini	Monsters (Clb)	C/LW	61	1	3	4	-5	8	0	14	21	7	2	9	14.29%	0	454	7.45	0	0	0	0	0	0	0	0	0	1	0	0	34.29%	347	0	0	0.18	0	0	0	0	0	0	0	0
21	Jake Christiansen	Monsters (Clb)	D	12	1	2	3	0	8	0	8	3	12	1	6	8.33%	7	217	18.15	0	1	1	1	4	0	0	0	0	7	0	1	0.00%	0	0	0	0.28	0	0	0	0	0	0	0	0
22	Adam Erne	Columbus	LW/RW	1	1	0	1	0	2	0	4	3	3	1	3	33.33%	0	22	22.32	0	0	0	1	2	0	0	0	0	0	1	0	100.00%	3	0	0	0.90	0	0	0	0	0	0	0	0
23	Reece Scarlett	Monsters (Clb)	D	4	0	1	1	-3	0	0	5	1	0	0	2	0.00%	7	67	16.96	0	0	0	0	2	0	0	0	0	1	0	0	0.00%	0	0	0	0.29	0	0	0	0	0	0	0	0
24	Sergei Boikov	Monsters (Clb)	D	2	0	0	0	-1	0	0	1	0	0	0	0	0.00%	2	33	16.63	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				1445	281	510	791	38	692	70	2159	1897	3023	847	2123	9.30%	932	24641	17.05	60	110	170	586	2089	3	4	7	38	1773	43	20	50.06%	7054	2	2	0.64	5	23	5	4	5	38	42	43

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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	Jake Oettinger	Monsters (Clb)	79	42	30	4	0.913	3.12	4464	10	2	232	2678	0	1	0.800	20	78	4	6	8	2
2	Michael Hutchinson	Monsters (Clb)	12	3	2	1	0.909	3.33	487	0	0	27	297	2	0	0.667	3	4	78	0	0	0
Stats d'équipe Total ou en Moyenne			91	45	32	5	0.913	3.14	4951	10	2	259	2975	2	1	0.783	23	82	82	6	8	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	Salaire Année 4	Salaire Année 5	Link
Adam Tambellini	Monsters (Clb)	C/LW	24	1994-11-01	Yes	169 Lbs	6 ft2	No	No	No	2	Pro & Farm	560,000$	56,000$	0$	No	560,000$				Lien
Alexander Khokhlachev	Monsters (Clb)	C/LW	26	1993-09-09	No	181 Lbs	5 ft10	No	No	No	1	Pro & Farm	600,000$	60,000$	0$	No					Lien
Brian Lashoff	Monsters (Clb)	D	29	1990-07-15	No	221 Lbs	6 ft3	No	No	No	2	Pro & Farm	775,000$	77,500$	0$	No	775,000$				Lien
Cory Schneider	Monsters (Clb)	G	33	1986-03-18	No	200 Lbs	6 ft3	No	No	No	3	Pro & Farm	5,250,000$	525,000$	0$	No	5,250,000$	5,250,000$			Lien
Griffen Molino	Monsters (Clb)	C/LW	25	1994-01-21	No	171 Lbs	5 ft11	No	No	No	3	Pro & Farm	900,000$	90,000$	0$	No	900,000$	900,000$			Lien
Jake Christiansen	Monsters (Clb)	D	20	1999-09-12	Yes	194 Lbs	6 ft1	No	No	No	4	Pro & Farm	925,000$	92,500$	0$	No	925,000$	925,000$	925,000$
Jake Oettinger	Monsters (Clb)	G	20	1998-12-18	No	212 Lbs	6 ft4	No	No	No	3	Pro & Farm	925,000$	92,500$	0$	No	925,000$	925,000$			Lien
Jean-Francois Berube	Monsters (Clb)	G	28	1991-07-13	No	177 Lbs	6 ft1	No	No	No	2	Pro & Farm	999,999$	100,000$	0$	No	999,999$				Lien
Jeremy Roy	Monsters (Clb)	D	22	1997-05-14	No	185 Lbs	6 ft0	No	No	No	1	Pro & Farm	825,000$	82,500$	0$	No					Lien
Jimmy Schuldt	Monsters (Clb)	D	24	1995-05-11	Yes	205 Lbs	6 ft1	No	No	No	2	Pro & Farm	825,000$	82,500$	0$	No	825,000$				Lien
Johnathan Kovacevic	Monsters (Clb)	D	22	1997-07-12	Yes	207 Lbs	6 ft4	No	No	No	3	Pro & Farm	792,500$	79,250$	0$	No	792,500$	792,500$			Lien
Julian Melchiori	Monsters (Clb)	D	27	1991-12-06	No	214 Lbs	6 ft5	No	No	No	4	Pro & Farm	725,000$	72,500$	0$	No	725,000$	725,000$	725,000$		Lien
Julien Gauthier	Monsters (Clb)	RW	21	1997-10-15	No	225 Lbs	6 ft4	No	No	No	2	Pro & Farm	925,000$	92,500$	0$	No	925,000$				Lien
Kasper Bjorkqvist	Monsters (Clb)	LW/RW	22	1997-07-10	Yes	198 Lbs	6 ft1	No	No	No	2	Pro & Farm	700,000$	70,000$	0$	No	700,000$				Lien
Keaton Middleton	Monsters (Clb)	D	21	1998-02-10	No	233 Lbs	6 ft6	No	No	No	2	Pro & Farm	715,000$	71,500$	0$	No	715,000$				Lien
Kurtis MacDermid	Monsters (Clb)	D	25	1994-03-25	No	208 Lbs	6 ft5	No	No	No	4	Pro & Farm	650,000$	65,000$	0$	No	650,000$	650,000$	650,000$		Lien
Marcus Kruger	Monsters (Clb)	C/LW/RW	29	1990-05-27	No	186 Lbs	6 ft0	No	No	No	2	Pro & Farm	1,000,000$	100,000$	0$	No	1,000,000$				Lien
Michael Hutchinson	Monsters (Clb)	G	29	1990-03-01	No	202 Lbs	6 ft3	Yes	No	No	2	Pro & Farm	560,000$	56,000$	0$	No	560,000$				Lien
Reece Scarlett	Monsters (Clb)	D	26	1993-05-31	Yes	185 Lbs	6 ft1	No	No	No	2	Pro & Farm	650,000$	65,000$	0$	No	650,000$				Lien
Scott Kosmachuk	Monsters (Clb)	RW	25	1994-01-24	No	185 Lbs	5 ft11	No	No	No	1	Pro & Farm	560,000$	56,000$	0$	No					Lien
Sergei Boikov	Monsters (Clb)	D	23	1996-01-24	Yes	200 Lbs	6 ft2	No	No	No	2	Pro & Farm	705,000$	70,500$	0$	No	705,000$				Lien
Sheldon Dries	Monsters (Clb)	C/LW	25	1994-04-23	No	185 Lbs	5 ft9	No	No	No	5	Pro & Farm	700,000$	70,000$	0$	No	700,000$	700,000$	700,000$	700,000$	Lien
Teemu Pulkkinen	Monsters (Clb)	LW/RW	27	1992-01-02	No	185 Lbs	5 ft10	No	No	No	2	Pro & Farm	650,000$	65,000$	0$	No	650,000$				Lien
Tim Soderlund	Monsters (Clb)	LW/RW	21	1998-01-23	Yes	163 Lbs	5 ft9	No	No	No	4	Pro & Farm	825,834$	82,583$	0$	No	825,834$	825,834$	825,834$		Lien
Todd Burgess	Monsters (Clb)	RW	23	1996-04-03	Yes	178 Lbs	6 ft2	No	No	No	2	Pro & Farm	650,000$	65,000$	0$	No	650,000$				Lien
Zach Senyshyn	Monsters (Clb)	RW	22	1997-03-30	Yes	192 Lbs	6 ft1	No	No	No	1	Pro & Farm	895,000$	89,500$	0$	No					Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
26	24.58	195 Lbs	6 ft1	2.42	934,167$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Teemu Pulkkinen	Sheldon Dries	Julien Gauthier	40	0	2	3
2	Griffen Molino	Marcus Kruger	Zach Senyshyn	30	0	2	3
3	Kasper Bjorkqvist	Alexander Khokhlachev	Tim Soderlund	20	0	3	2
4	Adam Tambellini	Sheldon Dries	Scott Kosmachuk	10	0	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jake Christiansen	Brian Lashoff	40	0	2	3
2	Keaton Middleton	Julian Melchiori	30	0	2	3
3	Johnathan Kovacevic	Jimmy Schuldt	20	1	2	2
4	Brian Lashoff	Jake Christiansen	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Teemu Pulkkinen	Sheldon Dries	Julien Gauthier	60	0	1	4
2	Griffen Molino	Marcus Kruger	Zach Senyshyn	40	0	1	4

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Brian Lashoff	Keaton Middleton	60	0	1	4
2	Johnathan Kovacevic	Jimmy Schuldt	40	0	1	4

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Marcus Kruger	Sheldon Dries	60	0	4	1
2	Kasper Bjorkqvist	Zach Senyshyn	40	0	4	1

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Keaton Middleton	Brian Lashoff	60	0	5	0
2	Johnathan Kovacevic	Julian Melchiori	40	0	5	0

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Marcus Kruger	60	0	5	0	Keaton Middleton	Brian Lashoff	60	0	5	0
2	Sheldon Dries	40	0	5	0	Jimmy Schuldt	Julian Melchiori	40	0	5	0

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Marcus Kruger	Sheldon Dries	60	1	2	2
2	Julien Gauthier	Zach Senyshyn	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Brian Lashoff	Keaton Middleton	60	1	2	2
2	Johnathan Kovacevic	Julian Melchiori	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Teemu Pulkkinen	Sheldon Dries	Julien Gauthier	Brian Lashoff	Keaton Middleton

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Marcus Kruger	Sheldon Dries	Zach Senyshyn	Brian Lashoff	Jimmy Schuldt

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Kasper Bjorkqvist, Tim Soderlund, Teemu Pulkkinen	Kasper Bjorkqvist, Tim Soderlund	Teemu Pulkkinen

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Keaton Middleton, Jimmy Schuldt,	Keaton Middleton	Jimmy Schuldt,

Tirs de Pénalité
Tim Soderlund, Sheldon Dries, Julien Gauthier, Zach Senyshyn, Marcus Kruger

Gardien
#1 : Jake Oettinger, #2 : Michael Hutchinson

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	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	18	27	0	0	101	99	76	6	67	942	997	987	50	68	11	18	48	5	0	0.00%	9	1	88.89%	1	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
2	Baby Hawks	2	0	2	0	0	0	0	0	2	5	-3	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	0	2	-2	0	0.000	2	3	5	0	0	101	99	76	6	49	942	997	987	50	72	22	18	68	6	0	0.00%	9	1	88.89%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
3	Bears	4	2	1	0	0	1	0	0	14	11	3	2	0	1	0	0	1	0	0	6	9	-3	2	2	0	0	0	0	0	0	8	2	6	5	0.625	14	26	40	0	0	101	99	76	6	134	942	997	987	50	158	57	24	89	18	6	33.33%	11	1	90.91%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
4	Bruins	3	1	1	0	0	0	1	0	10	9	1	1	0	0	0	0	0	1	0	4	3	1	2	1	1	0	0	0	0	0	6	6	0	4	0.667	10	16	26	0	0	101	99	76	6	105	942	997	987	50	108	24	22	77	10	4	40.00%	10	2	80.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
5	Cabaret Lady Mary Ann	3	3	0	0	0	0	0	0	18	9	9	2	2	0	0	0	0	0	0	12	6	6	1	1	0	0	0	0	0	0	6	3	3	6	1.000	18	31	49	0	0	101	99	76	6	190	942	997	987	50	87	15	14	98	4	3	75.00%	7	1	85.71%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
6	Caroline	4	2	2	0	0	0	0	0	12	13	-1	2	2	0	0	0	0	0	0	8	6	2	2	0	2	0	0	0	0	0	4	7	-3	4	0.500	12	20	32	0	0	101	99	76	6	125	942	997	987	50	154	51	29	99	14	3	21.43%	11	0	100.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
7	Chiefs	2	1	0	0	0	0	1	0	6	4	2	1	1	0	0	0	0	0	0	2	1	1	1	0	0	0	0	0	1	0	4	3	1	4	1.000	6	8	14	0	0	101	99	76	6	73	942	997	987	50	74	28	8	35	10	1	10.00%	4	1	75.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
8	Chill	2	0	2	0	0	0	0	0	8	11	-3	1	0	1	0	0	0	0	0	4	6	-2	1	0	1	0	0	0	0	0	4	5	-1	0	0.000	8	14	22	1	0	101	99	76	6	66	942	997	987	50	100	20	16	61	5	2	40.00%	8	3	62.50%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
9	Comets	2	1	0	0	0	0	0	1	8	7	1	1	0	0	0	0	0	0	1	4	5	-1	1	1	0	0	0	0	0	0	4	2	2	3	0.750	8	14	22	0	0	101	99	76	6	60	942	997	987	50	80	16	54	41	7	2	28.57%	10	1	90.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
10	Cougars	3	2	1	0	0	0	0	0	10	8	2	2	2	0	0	0	0	0	0	9	5	4	1	0	1	0	0	0	0	0	1	3	-2	4	0.667	10	19	29	0	0	101	99	76	6	75	942	997	987	50	103	28	19	83	10	4	40.00%	7	0	100.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
11	Crunch	3	2	1	0	0	0	0	0	10	11	-1	1	1	0	0	0	0	0	0	6	3	3	2	1	1	0	0	0	0	0	4	8	-4	4	0.667	10	17	27	0	0	101	99	76	6	105	942	997	987	50	118	36	36	73	10	1	10.00%	12	2	83.33%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
12	Heat	2	1	1	0	0	0	0	0	8	8	0	1	0	1	0	0	0	0	0	2	3	-1	1	1	0	0	0	0	0	0	6	5	1	2	0.500	8	14	22	0	0	101	99	76	6	74	942	997	987	50	73	20	12	48	3	0	0.00%	6	0	100.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
13	Jayhawks	2	0	2	0	0	0	0	0	4	8	-4	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	4	7	11	0	0	101	99	76	6	65	942	997	987	50	94	17	20	53	5	1	20.00%	10	1	90.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
14	Las Vegas	2	0	1	0	0	0	0	1	8	11	-3	1	0	0	0	0	0	0	1	6	7	-1	1	0	1	0	0	0	0	0	2	4	-2	1	0.250	8	13	21	0	0	101	99	76	6	81	942	997	987	50	70	20	12	55	6	2	33.33%	6	2	66.67%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
15	Manchots	4	3	0	0	1	0	0	0	16	10	6	2	1	0	0	1	0	0	0	8	6	2	2	2	0	0	0	0	0	0	8	4	4	8	1.000	16	27	43	0	0	101	99	76	6	179	942	997	987	50	143	42	35	93	15	6	40.00%	9	1	88.89%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
16	Marlies	3	1	2	0	0	0	0	0	9	14	-5	1	0	1	0	0	0	0	0	2	5	-3	2	1	1	0	0	0	0	0	7	9	-2	2	0.333	9	14	23	0	0	101	99	76	6	84	942	997	987	50	145	41	35	74	9	3	33.33%	10	3	70.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
17	Minnesota	2	2	0	0	0	0	0	0	11	6	5	1	1	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	5	2	3	4	1.000	11	20	31	0	0	101	99	76	6	105	942	997	987	50	64	23	29	59	2	1	50.00%	7	0	100.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
18	Monarchs	2	0	1	0	0	0	0	1	5	9	-4	1	0	1	0	0	0	0	0	2	5	-3	1	0	0	0	0	0	0	1	3	4	-1	1	0.250	5	9	14	0	0	101	99	76	6	65	942	997	987	50	106	33	18	58	6	2	33.33%	9	3	66.67%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
19	Monsters	2	1	1	0	0	0	0	0	7	9	-2	1	0	1	0	0	0	0	0	3	7	-4	1	1	0	0	0	0	0	0	4	2	2	2	0.500	7	13	20	0	0	101	99	76	6	74	942	997	987	50	65	27	24	46	5	2	40.00%	12	3	75.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
20	Oceanics	2	1	1	0	0	0	0	0	8	8	0	1	0	1	0	0	0	0	0	3	5	-2	1	1	0	0	0	0	0	0	5	3	2	2	0.500	8	13	21	0	0	101	99	76	6	73	942	997	987	50	61	19	8	51	12	1	8.33%	4	0	100.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
21	Oil Kings	2	2	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	3	0	3	1	1	0	0	0	0	0	0	3	2	1	4	1.000	6	10	16	0	1	101	99	76	6	82	942	997	987	50	75	23	12	55	6	2	33.33%	6	1	83.33%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
22	Phantoms	4	0	3	0	0	0	1	0	8	13	-5	2	0	1	0	0	0	1	0	5	5	0	2	0	2	0	0	0	0	0	3	8	-5	2	0.250	8	14	22	0	0	101	99	76	6	116	942	997	987	50	136	39	25	72	10	1	10.00%	10	2	80.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
23	Rocket	3	1	2	0	0	0	0	0	6	11	-5	1	0	1	0	0	0	0	0	2	5	-3	2	1	1	0	0	0	0	0	4	6	-2	2	0.333	6	11	17	0	0	101	99	76	6	74	942	997	987	50	115	34	20	58	5	1	20.00%	10	2	80.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
24	Senators	3	2	1	0	0	0	0	0	15	10	5	2	1	1	0	0	0	0	0	9	8	1	1	1	0	0	0	0	0	0	6	2	4	4	0.667	15	26	41	0	0	101	99	76	6	118	942	997	987	50	105	37	18	70	9	1	11.11%	7	2	71.43%	1	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
25	Sharks	2	2	0	0	0	0	0	0	6	3	3	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	1	2	4	1.000	6	10	16	0	0	101	99	76	6	77	942	997	987	50	56	8	15	54	5	1	20.00%	5	1	80.00%	1	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
26	Sound Tigers	4	3	1	0	0	0	0	0	11	9	2	2	1	1	0	0	0	0	0	5	7	-2	2	2	0	0	0	0	0	0	6	2	4	6	0.750	11	21	32	0	0	101	99	76	6	149	942	997	987	50	117	37	20	114	15	4	26.67%	10	1	90.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
27	Spiders	4	1	3	0	0	0	0	0	11	14	-3	2	1	1	0	0	0	0	0	8	8	0	2	0	2	0	0	0	0	0	3	6	-3	2	0.250	11	18	29	0	0	101	99	76	6	117	942	997	987	50	168	75	40	93	6	0	0.00%	20	4	80.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
28	Stars	2	1	1	0	0	0	0	0	8	9	-1	1	0	1	0	0	0	0	0	2	5	-3	1	1	0	0	0	0	0	0	6	4	2	2	0.500	8	16	24	0	0	101	99	76	6	92	942	997	987	50	65	14	25	56	10	2	20.00%	5	2	60.00%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
29	Thunder	3	2	1	0	0	0	0	0	9	4	5	2	1	1	0	0	0	0	0	4	4	0	1	1	0	0	0	0	0	0	5	0	5	4	0.667	9	16	25	0	1	101	99	76	6	122	942	997	987	50	76	24	16	50	7	0	0.00%	7	1	85.71%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
	Total	82	41	32	0	1	2	3	3	280	263	17	41	19	16	0	1	1	2	2	148	142	6	41	22	16	0	0	1	1	1	132	121	11	95	0.579	280	490	770	1	2	101	99	76	6	2953	942	997	987	50	2976	875	666	2026	245	57	23.27%	262	43	83.59%	3	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
30	Wolf Pack	4	3	0	0	0	1	0	0	17	10	7	2	2	0	0	0	0	0	0	9	3	6	2	1	0	0	0	1	0	0	8	7	1	7	0.875	17	32	49	0	0	101	99	76	6	157	942	997	987	50	120	34	24	95	10	1	10.00%	11	1	90.91%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
	_Since Last GM Reset	82	41	32	0	1	2	3	3	280	263	17	41	19	16	0	1	1	2	2	148	142	6	41	22	16	0	0	1	1	1	132	121	11	95	0.579	280	490	770	1	2	101	99	76	6	2953	942	997	987	50	2976	875	666	2026	245	57	23.27%	262	43	83.59%	3	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
	_Vs Conference	46	22	18	0	1	2	2	1	156	142	14	23	9	10	0	1	1	2	0	79	78	1	23	13	8	0	0	1	0	1	77	64	13	53	0.576	156	274	430	1	1	101	99	76	6	1629	942	997	987	50	1667	501	334	1099	142	32	22.54%	140	26	81.43%	3	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551
	_Vs Division	28	7	7	0	0	1	1	0	89	80	9	14	4	3	0	0	0	1	0	49	44	5	14	3	4	0	0	1	0	0	40	36	4	17	0.304	89	158	247	0	0	101	99	76	6	977	942	997	987	50	996	335	197	655	88	21	23.86%	82	10	87.80%	0	1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%	1932	1306	1923	614	1099	551

			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	95	L1	280	490	770	2953	2976	875	666	2026	1	2

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	41	32	1	2	3	3	280	263

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	19	16	1	1	2	2	148	142

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	22	16	0	1	1	1	132	121

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
4	4	0	2	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
245	57	23.27%	262	43	83.59%	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
942	997	987	50	101	99	76	6

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
1494	2887	51.75%	1412	2977	47.43%	674	1363	49.45%

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
1932	1306	1923	614	1099	551

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 - 2020-10-24	16	Marlies	5	Monsters	2	L			Sommaire du Match
4 - 2020-10-25	22	Monsters	4	Manchots	3	W			Sommaire du Match
6 - 2020-10-27	35	Crunch	3	Monsters	6	W			Sommaire du Match
10 - 2020-10-31	59	Admirals	2	Monsters	7	W			Sommaire du Match
11 - 2020-11-01	69	Monsters	1	Caroline	2	L			Sommaire du Match
15 - 2020-11-05	94	Stars	5	Monsters	2	L			Sommaire du Match
17 - 2020-11-07	110	Monsters	0	Baby Hawks	2	L			Sommaire du Match
18 - 2020-11-08	120	Sound Tigers	1	Monsters	2	W			Sommaire du Match
20 - 2020-11-10	129	Monsters	2	Marlies	5	L			Sommaire du Match
23 - 2020-11-13	148	Caroline	2	Monsters	3	W			Sommaire du Match
25 - 2020-11-15	165	Monsters	1	Phantoms	3	L			Sommaire du Match
29 - 2020-11-19	189	Oil Kings	0	Monsters	3	W			Sommaire du Match
31 - 2020-11-21	200	Monsters	4	Chiefs	3	W	X	X	Sommaire du Match
32 - 2020-11-22	212	Heat	3	Monsters	2	L			Sommaire du Match
35 - 2020-11-25	227	Las Vegas	7	Monsters	6	L	X	X	Sommaire du Match
37 - 2020-11-27	246	Monsters	2	Jayhawks	4	L			Sommaire du Match
39 - 2020-11-29	260	Monsters	4	Monsters	2	W			Sommaire du Match
42 - 2020-12-02	273	Monsters	0	Rocket	3	L			Sommaire du Match
45 - 2020-12-05	297	Chiefs	1	Monsters	2	W			Sommaire du Match
49 - 2020-12-09	322	Rocket	5	Monsters	2	L			Sommaire du Match
51 - 2020-12-11	337	Cougars	2	Monsters	4	W			Sommaire du Match
53 - 2020-12-13	352	Monsters	5	Oceanics	3	W			Sommaire du Match
55 - 2020-12-15	369	Senators	3	Monsters	2	L			Sommaire du Match
57 - 2020-12-17	384	Phantoms	2	Monsters	1	L			Sommaire du Match
59 - 2020-12-19	400	Manchots	3	Monsters	4	W			Sommaire du Match
60 - 2020-12-20	410	Monsters	3	Sound Tigers	1	W			Sommaire du Match
63 - 2020-12-23	429	Jayhawks	4	Monsters	2	L			Sommaire du Match
65 - 2020-12-25	444	Wolf Pack	1	Monsters	5	W			Sommaire du Match
67 - 2020-12-27	458	Monsters	6	Cabaret Lady Mary Ann	3	W			Sommaire du Match
69 - 2020-12-29	469	Monsters	5	Bears	1	W			Sommaire du Match
72 - 2021-01-01	490	Monsters	4	Manchots	1	W			Sommaire du Match
74 - 2021-01-03	501	Monsters	6	Senators	2	W			Sommaire du Match
76 - 2021-01-05	521	Bears	3	Monsters	1	L			Sommaire du Match
77 - 2021-01-06	529	Monsters	1	Cougars	3	L			Sommaire du Match
79 - 2021-01-08	541	Monarchs	5	Monsters	2	L			Sommaire du Match
81 - 2021-01-10	561	Spiders	5	Monsters	3	L			Sommaire du Match
83 - 2021-01-12	573	Monsters	3	Sound Tigers	1	W			Sommaire du Match
87 - 2021-01-16	585	Monsters	3	Bears	1	W			Sommaire du Match
89 - 2021-01-18	604	Baby Hawks	3	Monsters	2	L			Sommaire du Match
91 - 2021-01-20	619	Cabaret Lady Mary Ann	2	Monsters	6	W			Sommaire du Match
93 - 2021-01-22	627	Monsters	4	Bruins	3	W			Sommaire du Match
95 - 2021-01-24	642	Sharks	2	Monsters	3	W			Sommaire du Match
97 - 2021-01-26	662	Monsters	3	Monarchs	4	L	X	X	Sommaire du Match
98 - 2021-01-27	674	Monsters	2	Admirals	5	L			Sommaire du Match
100 - 2021-01-29	688	Monsters	3	Sharks	1	W			Sommaire du Match
102 - 2021-01-31	700	Monsters	2	Las Vegas	4	L			Sommaire du Match
105 - 2021-02-03	719	Bruins	3	Monsters	4	W	X	X	Sommaire du Match
107 - 2021-02-05	733	Caroline	4	Monsters	5	W			Sommaire du Match
109 - 2021-02-07	751	Spiders	3	Monsters	5	W			Sommaire du Match
110 - 2021-02-08	758	Monsters	5	Wolf Pack	3	W			Sommaire du Match
113 - 2021-02-11	766	Oceanics	5	Monsters	3	L			Sommaire du Match
123 - 2021-02-21	792	Monsters	3	Crunch	2	W			Sommaire du Match
124 - 2021-02-22	807	Monsters	4	Rocket	3	W			Sommaire du Match
126 - 2021-02-24	818	Cabaret Lady Mary Ann	4	Monsters	6	W			Sommaire du Match
129 - 2021-02-27	841	Cougars	3	Monsters	5	W			Sommaire du Match
130 - 2021-02-28	851	Monsters	7	Monsters	3	L			Sommaire du Match
132 - 2021-03-02	863	Thunder	3	Monsters	1	L			Sommaire du Match
135 - 2021-03-05	880	Monsters	1	Crunch	6	L			Sommaire du Match
136 - 2021-03-06	893	Wolf Pack	2	Monsters	4	W			Sommaire du Match
138 - 2021-03-08	912	Monsters	1	Spiders	3	L			Sommaire du Match
140 - 2021-03-10	920	Monsters	2	Phantoms	5	L			Sommaire du Match
142 - 2021-03-12	936	Phantoms	3	Monsters	4	W	X	X	Sommaire du Match
144 - 2021-03-14	955	Monsters	4	Chill	5	L			Sommaire du Match
146 - 2021-03-16	967	Senators	5	Monsters	7	W			Sommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17	978	Monsters	5	Minnesota	2	W			Sommaire du Match
150 - 2021-03-20	995	Minnesota	4	Monsters	6	W			Sommaire du Match
152 - 2021-03-22	1013	Comets	5	Monsters	4	L	X	X	Sommaire du Match
155 - 2021-03-25	1030	Monsters	6	Heat	5	W			Sommaire du Match
158 - 2021-03-28	1058	Monsters	3	Oil Kings	2	W			Sommaire du Match
159 - 2021-03-29	1064	Monsters	4	Comets	2	W			Sommaire du Match
163 - 2021-04-02	1088	Manchots	3	Monsters	4	W	X		Sommaire du Match
165 - 2021-04-04	1105	Chill	6	Monsters	4	L			Sommaire du Match
167 - 2021-04-06	1118	Monsters	2	Bruins	3	L			Sommaire du Match
170 - 2021-04-09	1141	Bears	6	Monsters	5	L	X		Sommaire du Match
172 - 2021-04-11	1155	Monsters	5	Marlies	4	W			Sommaire du Match
174 - 2021-04-13	1171	Monsters	2	Spiders	3	L			Sommaire du Match
175 - 2021-04-14	1178	Monsters	3	Wolf Pack	4	L	X		Sommaire du Match
178 - 2021-04-17	1199	Monsters	5	Thunder	0	W			Sommaire du Match
179 - 2021-04-18	1212	Monsters	6	Stars	4	W			Sommaire du Match
181 - 2021-04-20	1226	Sound Tigers	6	Monsters	3	L			Sommaire du Match
184 - 2021-04-23	1249	Thunder	1	Monsters	3	W			Sommaire du Match
185 - 2021-04-24	1255	Monsters	3	Caroline	5	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	35	15
Assistance	78,607	39,228
Assistance PCT	95.86%	95.68%

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	2874 - 95.80%	81,455$	3,339,665$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
2,281,450$	2,428,833$	2,428,833$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
13,058$	2,281,450$	26	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	0	13,058$	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