Stanley Cup Winners Hockey League

TAMPA BAY Lightning
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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	Sean Kuraly	0	X				100.00	77	44	69	67	83	82	84	66	79	68	65	69	66	56	52	55	88	70	0
2	Rocco Grimaldi	0	X		X		100.00	60	35	75	67	62	71	76	66	70	66	64	60	65	54	51	73	88	64	0
3	Ryan Poehling (R)	0	X				100.00	62	35	77	62	77	78	50	62	70	60	61	66	62	50	55	83	89	63	0
4	Marko Dano	0	X	X			100.00	56	68	59	69	75	52	54	69	68	60	60	51	61	56	52	77	88	60	0
5	Mark McNeill	0	X		X		100.00	58	89	65	67	83	50	50	67	70	56	56	50	56	50	50	79	88	58	0
6	Jiri Sekac	0		X			100.00	59	38	80	69	70	46	47	58	31	55	50	59	53	60	49	71	88	57	0
7	Scott Kosmachuk	0			X		100.00	50	89	63	67	69	50	50	67	50	59	59	50	59	50	50	64	88	57	0
8	Lucas Lessio	0		X			100.00	57	37	74	67	75	45	45	57	31	51	54	61	59	58	47	79	88	56	0
9	Louis-Marc Aubry	0	X				100.00	54	41	66	64	84	50	45	54	43	50	51	51	52	64	61	55	88	56	0
10	Sebastian Collberg (R)	0			X		100.00	46	35	80	70	67	45	40	52	30	49	46	50	51	56	51	87	88	53	0
11	Gustav Olofsson	0				X	100.00	62	41	81	62	76	73	58	72	30	55	47	66	62	52	51	71	88	62	0
12	Justin Falk	0				X	100.00	74	62	71	59	94	53	55	59	30	49	46	57	56	61	53	47	88	59	0
13	Zach Trotman	0				X	100.00	64	35	70	59	87	59	56	59	30	50	46	58	56	53	51	40	88	58	0
14	Cody Goloubef	0				X	100.00	56	35	58	67	77	52	52	67	30	51	46	56	57	55	52	61	89	57	0
15	Jan Kostalek	0				X	100.00	56	41	75	50	72	52	54	53	30	39	39	60	56	50	50	44	89	52	0
Rayé
1	Andy Andreoff	0	X	X			100.00	76	44	67	69	74	74	62	64	40	65	63	60	66	56	55	55	41	65	0
2	Cal O'Reilly	0	X				100.00	50	89	70	70	72	50	50	70	70	60	60	50	60	56	52	29	41	59	0
3	Liam O'Brien	0		X			100.00	57	89	46	65	80	50	50	65	50	58	58	50	58	51	50	42	41	58	0

	MOYENNE D'ÉQUIPE					100.00	60	53	69	65	77	57	54	63	47	56	54	57	59	55	52	62	80	59

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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	Chad Johnson	100.00	71	71	70	83	70	70	71	70	70	70	66	64	68	35	89	64	0
2	Jonas Gustavsson	100.00	65	57	66	81	69	68	66	67	66	74	62	78	72	5	88	63	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	68	64	68	82	70	69	69	69	68	72	64	71	70	20	89	64

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Dale Hunter	65	65	65	65	76	76	54	CAN	60	2	82,000$

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Astuces sur les Filtres (Anglais seulement)

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

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	S1	S2	S3
1	Sean Kuraly	Syracruse Crunch (TBL)	C	56	45	39	84	66	48	0	167	124	384	100	241	11.72%	7	1370	24.48	4	7	11	32	122	2	1	3	12	179	10	2	64.71%	1734	5	1.23	0	3	12	6	4
2	Ryan Poehling	Syracruse Crunch (TBL)	C	56	43	38	81	54	10	0	88	104	290	88	193	14.83%	20	1130	20.19	3	8	11	33	117	1	3	4	8	145	6	2	60.25%	1215	3	1.43	1	3	7	10	4
3	Rocco Grimaldi	Syracruse Crunch (TBL)	C/RW	56	32	34	66	64	27	5	32	86	299	70	217	10.70%	11	1345	24.02	6	2	8	26	115	2	1	3	9	178	7	1	55.91%	93	0	0.98	0	3	4	8	2
4	Gustav Olofsson	Syracruse Crunch (TBL)	D	58	11	47	58	63	42	0	88	48	114	36	70	9.65%	59	1385	23.90	5	5	10	44	132	0	3	3	2	167	0	2	0.00%	0	0	0.84	0	2	0	4	6
5	Jiri Sekac	Syracruse Crunch (TBL)	LW	58	17	37	54	49	16	0	54	53	156	51	117	10.90%	7	971	16.74	3	4	7	20	113	0	0	0	0	4	5	0	47.17%	53	1	1.11	0	0	1	2	4
6	Mark McNeill	Syracruse Crunch (TBL)	C/RW	58	12	35	47	47	48	0	87	45	163	55	126	7.36%	9	978	16.87	0	5	5	13	116	0	0	0	0	0	2	0	51.06%	47	0	0.96	0	2	1	1	4
7	Scott Kosmachuk	Syracruse Crunch (TBL)	RW	58	22	24	46	34	22	0	56	49	198	59	158	11.11%	7	884	15.25	0	1	1	1	17	0	0	0	0	2	1	2	42.00%	50	1	1.04	0	1	3	3	2
8	Marko Dano	Syracruse Crunch (TBL)	C/LW	58	15	27	42	46	16	0	31	34	178	37	116	8.43%	4	942	16.25	1	5	6	22	117	1	2	3	6	130	3	1	64.62%	65	0	0.89	1	2	0	2	2
9	Lucas Lessio	Syracruse Crunch (TBL)	LW	58	15	25	40	35	28	0	38	44	181	61	112	8.29%	4	864	14.91	0	0	0	2	10	0	0	0	1	21	3	0	44.64%	56	1	0.93	1	1	2	1	1
10	Justin Falk	Syracruse Crunch (TBL)	D	58	5	33	38	54	60	0	107	28	66	21	35	7.58%	49	1123	19.37	1	2	3	17	103	0	2	2	3	141	0	0	100.00%	1	0	0.68	0	0	0	0	1
11	Brandon Manning	TAMPA BAY Lightning	D	43	7	25	32	43	40	0	81	35	72	35	61	9.72%	49	1016	23.65	2	1	3	25	97	0	0	0	1	121	1	0	0.00%	0	0	0.63	0	1	0	3	4
12	Cody Goloubef	Syracruse Crunch (TBL)	D	56	5	25	30	35	46	0	88	31	62	18	40	8.06%	36	908	16.22	0	2	2	8	19	0	0	0	0	30	1	1	100.00%	1	0	0.66	0	1	0	0	4
13	Mark Barberio	TAMPA BAY Lightning	D	52	6	24	30	39	22	0	37	38	83	24	64	7.23%	55	1156	22.25	0	0	0	4	13	0	0	0	3	202	2	0	0.00%	0	0	0.52	0	0	0	3	1
14	Zach Trotman	Syracruse Crunch (TBL)	D	58	7	22	29	54	28	0	50	35	61	23	51	11.48%	47	1120	19.31	0	3	3	20	105	1	1	2	3	146	1	0	0.00%	0	0	0.52	0	0	1	0	2
15	Louis-Marc Aubry	Syracruse Crunch (TBL)	C	58	10	14	24	38	12	0	68	63	108	17	80	9.26%	4	909	15.69	0	0	0	0	0	0	1	1	2	59	2	0	56.91%	875	0	0.53	0	0	1	0	2
16	Jan Kostalek	Syracruse Crunch (TBL)	D	56	2	15	17	35	24	0	50	20	44	23	30	4.55%	32	916	16.37	0	0	0	0	6	1	0	1	3	71	0	0	0.00%	0	0	0.37	1	1	0	1	0
17	Sebastian Collberg	Syracruse Crunch (TBL)	RW	58	2	11	13	21	4	0	4	12	50	17	29	4.00%	0	440	7.60	0	0	0	0	1	0	0	0	0	0	1	0	40.00%	25	0	0.59	0	0	0	0	0
18	Jordan Weal	TAMPA BAY Lightning	C	9	3	7	10	10	6	0	9	13	30	12	27	10.00%	0	126	14.03	1	0	1	2	4	0	0	0	0	0	0	1	76.85%	108	0	1.58	0	0	1	0	1
Stats d'équipe Total ou en Moyenne				964	259	482	741	787	499	5	1135	862	2539	747	1767	10.20%	400	17593	18.25	26	45	71	269	1215	8	14	22	53	1603	45	12	60.98%	4323	11	0.84	4	20	33	44	44

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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	S1	S2	S3
1	Chad Johnson	Syracruse Crunch (TBL)	58	44	12	2	0.923	1.66	3434	2	16	95	1230	1	0.750	20	58	0	9	1	1
2	Jonas Gustavsson	Syracruse Crunch (TBL)	3	0	0	0	0.921	2.65	68	2	0	3	38	0	0.000	0	0	58	0	0	0
Stats d'équipe Total ou en Moyenne			61	44	12	2	0.923	1.68	3502	4	16	98	1268	1	0.750	20	58	58	9	1	1

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Astuces sur les Filtres (Anglais seulement)

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

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Salaire Moyen	Salaire Moyen Restant	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
Andy Andreoff	Syracruse Crunch (TBL)	C/LW	28	1991-05-17	No	92 Kg	185 CM	No	No	No	6	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$	750,000$	750,000$	750,000$	Lien
Cal O'Reilly	Syracruse Crunch (TBL)	C	32	1986-09-30	No	85 Kg	183 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$				Lien
Chad Johnson	Syracruse Crunch (TBL)	G	33	1986-06-10	No	90 Kg	191 CM	No	No	No	3	Pro & Farm	1,500,000$	459,459$	1,500,000$	459,459$	0$	0$	No	1,500,000$	1,500,000$				Lien
Cody Goloubef	Syracruse Crunch (TBL)	D	29	1989-11-30	No	91 Kg	185 CM	No	No	No	6	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$	750,000$	750,000$	750,000$	Lien
Gustav Olofsson	Syracruse Crunch (TBL)	D	24	1994-12-01	No	89 Kg	191 CM	No	No	No	5	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$	750,000$	750,000$		Lien
Jan Kostalek	Syracruse Crunch (TBL)	D	24	1995-02-17	No	89 Kg	185 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$				Lien
Jiri Sekac	Syracruse Crunch (TBL)	LW	27	1992-06-10	No	84 Kg	185 CM	No	No	No	1	Pro & Farm	1,000,000$	306,306$	1,000,000$	306,306$	0$	0$	No
Jonas Gustavsson	Syracruse Crunch (TBL)	G	34	1984-10-24	No	91 Kg	193 CM	No	No	No	4	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$	750,000$
Justin Falk	Syracruse Crunch (TBL)	D	30	1988-10-11	No	101 Kg	196 CM	No	No	No	2	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$					Lien
Liam O'Brien	Syracruse Crunch (TBL)	LW	25	1994-07-29	No	98 Kg	185 CM	No	No	No	0	Pro & Farm					0$	0$	No						Lien
Louis-Marc Aubry	Syracruse Crunch (TBL)	C	27	1991-11-11	No	99 Kg	193 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$
Lucas Lessio	Syracruse Crunch (TBL)	LW	26	1993-01-23	No	96 Kg	185 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$
Mark McNeill	Syracruse Crunch (TBL)	C/RW	26	1993-02-22	No	97 Kg	188 CM	No	No	No	4	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$	750,000$			Lien
Marko Dano	Syracruse Crunch (TBL)	C/LW	24	1994-11-30	No	96 Kg	180 CM	No	No	No	3	Pro & Farm	950,000$	290,990$	950,000$	290,990$	0$	0$	No	950,000$	950,000$				Lien
Rocco Grimaldi	Syracruse Crunch (TBL)	C/RW	26	1993-02-08	No	82 Kg	168 CM	No	No	No	1	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No						Lien
Ryan Poehling	Syracruse Crunch (TBL)	C	20	1999-01-03	Yes	83 Kg	188 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$				Lien
Scott Kosmachuk	Syracruse Crunch (TBL)	RW	25	1994-01-24	No	84 Kg	180 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$				Lien
Sean Kuraly	Syracruse Crunch (TBL)	C	26	1993-01-20	No	97 Kg	188 CM	No	No	No	3	Pro & Farm	1,275,000$	390,540$	1,275,000$	390,540$	0$	0$	No	1,275,000$	1,275,000$				Lien
Sebastian Collberg	Syracruse Crunch (TBL)	RW	25	1994-02-23	Yes	89 Kg	180 CM	No	No	No	5	Pro & Farm	925,000$	283,333$	925,000$	283,333$	0$	0$	No	925,000$	925,000$	925,000$	925,000$
Zach Trotman	Syracruse Crunch (TBL)	D	29	1990-08-26	No	99 Kg	191 CM	No	No	No	3	Pro & Farm	750,000$	229,729$	750,000$	229,729$	0$	0$	No	750,000$	750,000$				Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
20	27.00	92 Kg	185 CM	3.20	807,500$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Marko Dano	Sean Kuraly	Rocco Grimaldi	31	1	2	2
2	Jiri Sekac	Ryan Poehling	Mark McNeill	26	1	2	2
3	Lucas Lessio	Louis-Marc Aubry	Scott Kosmachuk	23	1	2	2
4	Rocco Grimaldi	Sean Kuraly	Sebastian Collberg	20	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Gustav Olofsson	31	1	2	2
2	Justin Falk	Zach Trotman	26	1	2	2
3	Cody Goloubef	Jan Kostalek	23	1	2	2
4		Gustav Olofsson	20	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Marko Dano	Sean Kuraly	Rocco Grimaldi	55	1	2	2
2	Jiri Sekac	Ryan Poehling	Mark McNeill	45	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Gustav Olofsson	55	1	2	2
2	Justin Falk	Zach Trotman	45	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Sean Kuraly	Rocco Grimaldi	55	1	2	2
2	Ryan Poehling	Marko Dano	45	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Gustav Olofsson	55	1	2	2
2	Justin Falk	Zach Trotman	45	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Sean Kuraly	55	1	2	2		Gustav Olofsson	55	1	2	2
2	Rocco Grimaldi	45	1	2	2	Justin Falk	Zach Trotman	45	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Sean Kuraly	Rocco Grimaldi	55	1	2	2
2	Ryan Poehling	Marko Dano	45	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1		Gustav Olofsson	55	1	2	2
2	Justin Falk	Zach Trotman	45	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Marko Dano	Sean Kuraly	Rocco Grimaldi		Gustav Olofsson

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Marko Dano	Sean Kuraly	Rocco Grimaldi		Gustav Olofsson

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Scott Kosmachuk, Lucas Lessio, Louis-Marc Aubry	Scott Kosmachuk, Lucas Lessio	Louis-Marc Aubry

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Cody Goloubef, Jan Kostalek, Justin Falk	Cody Goloubef	Jan Kostalek, Justin Falk

Tirs de Pénalité
Sean Kuraly, Rocco Grimaldi, Ryan Poehling, Marko Dano, Mark McNeill

Gardien
#1 : Chad Johnson, #2 : Jonas Gustavsson

Lignes d'Attaque Perso. en Prol.
Sean Kuraly, Rocco Grimaldi, Ryan Poehling, Marko Dano, Mark McNeill, Scott Kosmachuk, Scott Kosmachuk, Jiri Sekac, Lucas Lessio, Louis-Marc Aubry, Sebastian Collberg

Lignes de Défense Perso. en Prol.
, Gustav Olofsson, Justin Falk, Zach Trotman, Cody Goloubef

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	BELLEVILLE Senators	2	2	0	0	0	0	0	0	7	0	7	2	2	0	0	0	0	0	0	7	0	7	0	0	0	0	0	0	0	0	0	0	0	4	1.000	7	13	20	0	2	102	79	66	7	175	766	807	836	42	11	6	6	34	0	0	0.00%	3	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
2	BRIDGEPORT Sound Tigers	3	2	0	0	1	0	0	0	13	7	6	2	1	0	0	1	0	0	0	7	3	4	1	1	0	0	0	0	0	0	6	4	2	6	1.000	13	24	37	0	0	102	79	66	7	108	766	807	836	42	76	30	28	57	17	6	35.29%	14	2	85.71%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
3	BROOKLYN Wolfpack	1	1	0	0	0	0	0	0	11	0	11	1	1	0	0	0	0	0	0	11	0	11	0	0	0	0	0	0	0	0	0	0	0	2	1.000	11	20	31	0	1	102	79	66	7	76	766	807	836	42	7	2	2	21	0	0	0.00%	1	0	100.00%	2	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
4	Binghampton Devils	4	4	0	0	0	0	0	0	18	8	10	1	1	0	0	0	0	0	0	5	2	3	3	3	0	0	0	0	0	0	13	6	7	8	1.000	18	34	52	0	0	102	79	66	7	211	766	807	836	42	71	20	26	86	8	3	37.50%	13	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
5	CHICAGO Wolves	1	0	1	0	0	0	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	2	3	5	0	0	102	79	66	7	31	766	807	836	42	20	6	10	23	2	0	0.00%	5	1	80.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
6	COLORADO Eagles	2	1	1	0	0	0	0	0	5	6	-1	1	1	0	0	0	0	0	0	4	3	1	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	5	9	14	0	0	102	79	66	7	58	766	807	836	42	43	8	18	29	5	2	40.00%	8	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
7	CORNWALL Aces	4	4	0	0	0	0	0	0	37	0	37	2	2	0	0	0	0	0	0	24	0	24	2	2	0	0	0	0	0	0	13	0	13	8	1.000	37	70	107	0	4	102	79	66	7	280	766	807	836	42	10	3	10	54	1	0	0.00%	5	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
8	HERSEY Bears	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	8	12	0	0	102	79	66	7	29	766	807	836	42	26	3	8	17	4	2	50.00%	4	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
9	HOLLYWOOD Oscar	3	3	0	0	0	0	0	0	32	0	32	1	1	0	0	0	0	0	0	12	0	12	2	2	0	0	0	0	0	0	20	0	20	6	1.000	32	61	93	0	3	102	79	66	7	252	766	807	836	42	13	3	4	61	0	0	0.00%	2	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
10	IOWA Wild	2	1	1	0	0	0	0	0	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	4	5	-1	2	0.500	4	8	12	0	0	102	79	66	7	70	766	807	836	42	37	15	14	54	11	1	9.09%	5	2	60.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
11	LAVAL Rockets	2	2	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	2	0	2	1	1	0	0	0	0	0	0	5	2	3	4	1.000	7	11	18	0	1	102	79	66	7	55	766	807	836	42	48	14	24	54	8	2	25.00%	10	1	90.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
12	LEHIGH VALLEY Phantoms	3	1	1	0	0	0	1	0	8	6	2	2	0	1	0	0	0	1	0	4	4	0	1	1	0	0	0	0	0	0	4	2	2	4	0.667	8	14	22	0	0	102	79	66	7	98	766	807	836	42	79	22	49	47	11	1	9.09%	17	4	76.47%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
13	MILWAUKEE Admirals	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	3	5	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	4	7	0	0	102	79	66	7	20	766	807	836	42	36	8	14	25	0	0	0.00%	7	2	71.43%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
14	MONT-LAURIER Sommet	4	2	1	0	0	0	0	1	10	8	2	2	0	1	0	0	0	0	1	3	6	-3	2	2	0	0	0	0	0	0	7	2	5	5	0.625	10	19	29	0	0	102	79	66	7	86	766	807	836	42	111	26	38	62	10	1	10.00%	16	3	81.25%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
15	Manitoba Moose	1	1	0	0	0	0	0	0	3	0	3	1	1	0	0	0	0	0	0	3	0	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	5	8	0	1	102	79	66	7	30	766	807	836	42	26	8	14	19	4	1	25.00%	7	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
16	Marlies de Toronto	1	1	0	0	0	0	0	0	10	0	10	1	1	0	0	0	0	0	0	10	0	10	0	0	0	0	0	0	0	0	0	0	0	2	1.000	10	18	28	0	1	102	79	66	7	95	766	807	836	42	5	3	4	15	1	0	0.00%	2	0	100.00%	3	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
17	PROVIDENCE Bruins	2	1	1	0	0	0	0	0	5	2	3	1	0	1	0	0	0	0	0	0	1	-1	1	1	0	0	0	0	0	0	5	1	4	2	0.500	5	7	12	0	0	102	79	66	7	71	766	807	836	42	62	19	26	33	5	0	0.00%	13	1	92.31%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
18	PV Sharapovas	4	3	1	0	0	0	0	0	11	11	0	2	2	0	0	0	0	0	0	7	3	4	2	1	1	0	0	0	0	0	4	8	-4	6	0.750	11	17	28	0	1	102	79	66	7	107	766	807	836	42	117	42	40	79	14	1	7.14%	15	2	86.67%	1	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
19	ROCKFORD IceHogs	2	1	0	0	1	0	0	0	6	4	2	1	1	0	0	0	0	0	0	2	1	1	1	0	0	0	1	0	0	0	4	3	1	4	1.000	6	12	18	0	0	102	79	66	7	71	766	807	836	42	47	9	18	43	6	1	16.67%	9	1	88.89%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
20	SAN DIEGO Gulls	1	1	0	0	0	0	0	0	6	0	6	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	6	0	6	2	1.000	6	9	15	0	1	102	79	66	7	70	766	807	836	42	9	4	4	28	0	0	0.00%	2	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
21	STOCKTON Flames	4	1	2	0	0	0	0	1	8	13	-5	2	1	0	0	0	0	0	1	5	4	1	2	0	2	0	0	0	0	0	3	9	-6	3	0.375	8	13	21	0	0	102	79	66	7	112	766	807	836	42	107	26	26	73	6	1	16.67%	12	2	83.33%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
22	TUSCON Roadrunners	2	2	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	3	1	2	4	1.000	6	11	17	0	0	102	79	66	7	50	766	807	836	42	60	11	20	33	7	0	0.00%	9	2	77.78%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
	Total	58	40	12	0	3	0	1	2	251	100	151	29	20	4	0	2	0	1	2	130	43	87	29	20	8	0	1	0	0	0	121	57	64	90	0.776	251	453	704	0	16	102	79	66	7	2431	766	807	836	42	1268	358	479	1096	150	25	16.67%	212	27	87.26%	8	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
24	UTICA Comets	2	1	0	0	1	0	0	0	5	3	2	1	0	0	0	1	0	0	0	3	2	1	1	1	0	0	0	0	0	0	2	1	1	4	1.000	5	9	14	0	0	102	79	66	7	47	766	807	836	42	48	15	10	29	13	0	0.00%	5	0	100.00%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
25	VICTORIAVILLE Tigres	4	3	1	0	0	0	0	0	23	9	14	2	2	0	0	0	0	0	0	11	5	6	2	1	1	0	0	0	0	0	12	4	8	6	0.750	23	41	64	0	0	102	79	66	7	122	766	807	836	42	160	46	58	90	12	3	25.00%	25	3	88.00%	2	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
26	WILKIES-BARRIE Penguins	2	1	1	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	7	3	4	2	0.500	7	13	20	0	1	102	79	66	7	107	766	807	836	42	39	9	8	30	5	0	0.00%	3	1	66.67%	0	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
	_Since Last GM Reset	58	40	12	0	3	0	1	2	251	100	151	29	20	4	0	2	0	1	2	130	43	87	29	20	8	0	1	0	0	0	121	57	64	90	0.776	251	453	704	0	16	102	79	66	7	2431	766	807	836	42	1268	358	479	1096	150	25	16.67%	212	27	87.26%	8	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
	_Vs Conference	37	26	8	0	0	0	1	2	168	63	105	17	12	2	0	0	0	1	2	81	24	57	20	14	6	0	0	0	0	0	87	39	48	56	0.757	168	308	476	0	12	102	79	66	7	1650	766	807	836	42	781	226	293	689	82	12	14.63%	123	17	86.18%	3	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
	_Vs Division	15	11	6	0	0	0	1	2	77	15	62	9	4	2	0	0	0	1	2	50	4	46	6	7	4	0	0	0	0	0	27	11	16	26	0.867	77	136	213	0	9	102	79	66	7	783	766	807	836	42	253	87	110	269	29	3	10.34%	48	4	91.67%	4	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354

			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
58	90	W4	251	453	704	2431	1268	358	479	1096	0	16

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
58	40	12	3	0	1	2	251	100

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
29	20	4	2	0	1	2	130	43

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
29	20	8	1	0	0	0	121	57

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
8	2	0	0	0	0

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
150	25	16.67%	212	27	87.26%	8

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
766	807	836	42	102	79	66	7

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
1247	1968	63.36%	779	1470	52.99%	543	860	63.14%

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
1785	1378	1072	334	646	354

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

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

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	RI	Lien
1 - 2020-01-15	2	VICTORIAVILLE Tigres	3	Syracruse Crunch	4	W			R3	Sommaire du Match
2 - 2020-01-16	15	Syracruse Crunch	2	VICTORIAVILLE Tigres	3	L				Sommaire du Match
3 - 2020-01-17	38	Syracruse Crunch	5	Binghampton Devils	2	W				Sommaire du Match
4 - 2020-01-18	46	Syracruse Crunch	2	PV Sharapovas	7	L				Sommaire du Match
5 - 2020-01-19	53	HOLLYWOOD Oscar	0	Syracruse Crunch	12	W				Sommaire du Match
7 - 2020-01-21	69	STOCKTON Flames	2	Syracruse Crunch	1	L	X	X		Sommaire du Match
9 - 2020-01-23	82	MONT-LAURIER Sommet	3	Syracruse Crunch	2	L	X	X		Sommaire du Match
11 - 2020-01-25	102	Syracruse Crunch	6	CORNWALL Aces	0	W				Sommaire du Match
13 - 2020-01-27	110	Syracruse Crunch	7	WILKIES-BARRIE Penguins	0	W				Sommaire du Match
15 - 2020-01-29	122	Syracruse Crunch	0	IOWA Wild	3	L				Sommaire du Match
17 - 2020-01-31	132	BRIDGEPORT Sound Tigers	2	Syracruse Crunch	3	W	X			Sommaire du Match
19 - 2020-02-02	148	BELLEVILLE Senators	0	Syracruse Crunch	4	W				Sommaire du Match
21 - 2020-02-04	165	Syracruse Crunch	4	MONT-LAURIER Sommet	1	W				Sommaire du Match
22 - 2020-02-05	171	Binghampton Devils	2	Syracruse Crunch	5	W				Sommaire du Match
23 - 2020-02-06	188	PV Sharapovas	3	Syracruse Crunch	4	W				Sommaire du Match
24 - 2020-02-07	197	Syracruse Crunch	5	Binghampton Devils	2	W				Sommaire du Match
26 - 2020-02-09	215	MILWAUKEE Admirals	5	Syracruse Crunch	3	L				Sommaire du Match
27 - 2020-02-10	224	Syracruse Crunch	3	TUSCON Roadrunners	1	W				Sommaire du Match
28 - 2020-02-11	238	Syracruse Crunch	3	MONT-LAURIER Sommet	1	W				Sommaire du Match
29 - 2020-02-12	251	VICTORIAVILLE Tigres	2	Syracruse Crunch	7	W			R3	Sommaire du Match
31 - 2020-02-14	267	Syracruse Crunch	2	STOCKTON Flames	6	L				Sommaire du Match
32 - 2020-02-15	270	Syracruse Crunch	2	CHICAGO Wolves	3	L				Sommaire du Match
33 - 2020-02-16	282	MONT-LAURIER Sommet	3	Syracruse Crunch	1	L				Sommaire du Match
34 - 2020-02-17	302	Syracruse Crunch	4	ROCKFORD IceHogs	3	W	X			Sommaire du Match
35 - 2020-02-18	312	UTICA Comets	2	Syracruse Crunch	3	W	X			Sommaire du Match
37 - 2020-02-20	328	Syracruse Crunch	2	PV Sharapovas	1	W				Sommaire du Match
38 - 2020-02-21	333	Syracruse Crunch	1	STOCKTON Flames	3	L				Sommaire du Match
39 - 2020-02-22	347	PV Sharapovas	0	Syracruse Crunch	3	W				Sommaire du Match
40 - 2020-02-23	360	COLORADO Eagles	3	Syracruse Crunch	4	W				Sommaire du Match
42 - 2020-02-25	375	Syracruse Crunch	7	CORNWALL Aces	0	W				Sommaire du Match
43 - 2020-02-26	386	ROCKFORD IceHogs	1	Syracruse Crunch	2	W				Sommaire du Match
44 - 2020-02-27	402	PROVIDENCE Bruins	1	Syracruse Crunch	0	L				Sommaire du Match
45 - 2020-02-28	417	Syracruse Crunch	4	IOWA Wild	2	W				Sommaire du Match
46 - 2020-02-29	424	Syracruse Crunch	10	VICTORIAVILLE Tigres	1	W			R3	Sommaire du Match
47 - 2020-03-01	438	CORNWALL Aces	0	Syracruse Crunch	13	W				Sommaire du Match
49 - 2020-03-03	455	LAVAL Rockets	0	Syracruse Crunch	2	W				Sommaire du Match
50 - 2020-03-04	471	Syracruse Crunch	4	LEHIGH VALLEY Phantoms	2	W				Sommaire du Match
52 - 2020-03-06	481	Syracruse Crunch	3	Binghampton Devils	2	W				Sommaire du Match
53 - 2020-03-07	489	Syracruse Crunch	2	UTICA Comets	1	W				Sommaire du Match
54 - 2020-03-08	498	Marlies de Toronto	0	Syracruse Crunch	10	W				Sommaire du Match
56 - 2020-03-10	514	Syracruse Crunch	5	LAVAL Rockets	2	W				Sommaire du Match
57 - 2020-03-11	523	BROOKLYN Wolfpack	0	Syracruse Crunch	11	W				Sommaire du Match
59 - 2020-03-13	539	Manitoba Moose	0	Syracruse Crunch	3	W				Sommaire du Match
60 - 2020-03-14	562	Syracruse Crunch	1	COLORADO Eagles	3	L				Sommaire du Match
61 - 2020-03-15	570	CORNWALL Aces	0	Syracruse Crunch	11	W				Sommaire du Match
62 - 2020-03-16	583	Syracruse Crunch	6	SAN DIEGO Gulls	0	W				Sommaire du Match
63 - 2020-03-17	595	LEHIGH VALLEY Phantoms	1	Syracruse Crunch	2	W	X	X		Sommaire du Match
65 - 2020-03-19	611	Syracruse Crunch	6	BRIDGEPORT Sound Tigers	4	W				Sommaire du Match
66 - 2020-03-20	623	BELLEVILLE Senators	0	Syracruse Crunch	3	W				Sommaire du Match
67 - 2020-03-21	637	Syracruse Crunch	0	WILKIES-BARRIE Penguins	3	L				Sommaire du Match
68 - 2020-03-22	646	BRIDGEPORT Sound Tigers	1	Syracruse Crunch	4	W				Sommaire du Match
69 - 2020-03-23	664	TUSCON Roadrunners	1	Syracruse Crunch	3	W				Sommaire du Match
70 - 2020-03-24	671	Syracruse Crunch	11	HOLLYWOOD Oscar	0	W				Sommaire du Match
72 - 2020-03-26	692	LEHIGH VALLEY Phantoms	3	Syracruse Crunch	2	L				Sommaire du Match
73 - 2020-03-27	705	Syracruse Crunch	5	PROVIDENCE Bruins	1	W				Sommaire du Match
74 - 2020-03-28	713	Syracruse Crunch	9	HOLLYWOOD Oscar	0	W				Sommaire du Match
75 - 2020-03-29	726	STOCKTON Flames	2	Syracruse Crunch	4	W				Sommaire du Match
77 - 2020-03-31	742	HERSEY Bears	3	Syracruse Crunch	4	W				Sommaire du Match
78 - 2020-04-01	754	Syracruse Crunch	-	MILWAUKEE Admirals	-
79 - 2020-04-02	768	VICTORIAVILLE Tigres	-	Syracruse Crunch	-
80 - 2020-04-03	780	Syracruse Crunch	-	MILWAUKEE Admirals	-
81 - 2020-04-04	791	Syracruse Crunch	-	PV Sharapovas	-
82 - 2020-04-05	804	CHICAGO Wolves	-	Syracruse Crunch	-
84 - 2020-04-07	822	HERSEY Bears	-	Syracruse Crunch	-
85 - 2020-04-08	837	Syracruse Crunch	-	BROOKLYN Wolfpack	-
86 - 2020-04-09	845	WILKIES-BARRIE Penguins	-	Syracruse Crunch	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
88 - 2020-04-11	857	Syracruse Crunch	-	VICTORIAVILLE Tigres	-
89 - 2020-04-12	869	Syracruse Crunch	-	Marlies de Toronto	-
90 - 2020-04-13	880	IOWA Wild	-	Syracruse Crunch	-
91 - 2020-04-14	899	HOLLYWOOD Oscar	-	Syracruse Crunch	-
93 - 2020-04-16	913	Syracruse Crunch	-	HERSEY Bears	-
94 - 2020-04-17	925	SAN DIEGO Gulls	-	Syracruse Crunch	-
95 - 2020-04-18	945	Manitoba Moose	-	Syracruse Crunch	-
97 - 2020-04-20	964	IOWA Wild	-	Syracruse Crunch	-
98 - 2020-04-21	971	Syracruse Crunch	-	BELLEVILLE Senators	-
99 - 2020-04-22	981	Syracruse Crunch	-	Manitoba Moose	-
100 - 2020-04-23	997	Syracruse Crunch	-	Manitoba Moose	-
101 - 2020-04-24	1000	Syracruse Crunch	-	BELLEVILLE Senators	-
102 - 2020-04-25	1007	Syracruse Crunch	-	LEHIGH VALLEY Phantoms	-
103 - 2020-04-26	1014	WILKIES-BARRIE Penguins	-	Syracruse Crunch	-
106 - 2020-04-29	1038	HOLLYWOOD Oscar	-	Syracruse Crunch	-
110 - 2020-05-03	1066	Binghampton Devils	-	Syracruse Crunch	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	15
Assistance	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
12	0 - 0.00%	0$	0$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
1,297,851$	1,615,000$	1,615,000$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	1,241,023$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	34	15,288$	519,792$

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
33	58	40	12	0	3	0	1	2	251	100	151	29	20	4	0	2	0	1	2	130	43	87	29	20	8	0	1	0	0	0	121	57	64	90	251	453	704	0	16	102	79	66	7	2431	766	807	836	42	1268	358	479	1096	150	25	16.67%	212	27	87.26%	8	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
Total Saison Régulière	58	40	12	0	3	0	1	2	251	100	151	29	20	4	0	2	0	1	2	130	43	87	29	20	8	0	1	0	0	0	121	57	64	90	251	453	704	0	16	102	79	66	7	2431	766	807	836	42	1268	358	479	1096	150	25	16.67%	212	27	87.26%	8	1247	1968	63.36%	779	1470	52.99%	543	860	63.14%	1785	1378	1072	334	646	354
Séries
32	4	0	4	0	0	0	0	0	8	15	-7	2	0	2	0	0	0	0	0	4	7	-3	2	0	2	0	0	0	0	0	4	8	-4	0	8	13	21	0	0	1	6	1	0	125	35	33	34	23	175	62	32	92	16	0	0.00%	15	3	80.00%	0	34	148	22.97%	45	169	26.63%	27	68	39.71%	106	76	124	31	50	24
Total Séries	4	0	4	0	0	0	0	0	8	15	-7	2	0	2	0	0	0	0	0	4	7	-3	2	0	2	0	0	0	0	0	4	8	-4	0	8	13	21	0	0	1	6	1	0	125	35	33	34	23	175	62	32	92	16	0	0.00%	15	3	80.00%	0	34	148	22.97%	45	169	26.63%	27	68	39.71%	106	76	124	31	50	24