LHSM - Barracuda

Icefield
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Web Lines Editor

Show or Hide Column

Filter Tips

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

#	Player Name	#	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	Travis Konecny	0		X			100.00	60	29	58	29	39	70	78	78	37	82	85	65	75	45	57	0	44	73	0
2	Nikolay Goldobin	0		X			100.00	38	17	67	32	56	67	80	73	49	81	82	55	74	47	37	0	50	71	0
3	Barclay Goodrow	0			X		100.00	80	32	44	47	80	51	72	64	79	76	80	67	72	58	70	0	50	68	0
4	Devante Smith-Pelly	0		X	X		100.00	82	30	63	47	81	52	74	64	46	75	79	65	75	63	51	0	50	67	0
5	Tyler Pitlick	0			X		100.00	80	24	66	43	69	61	52	69	37	76	81	70	64	47	42	0	50	67	0
6	Tyler Ennis	0	X	X			100.00	69	24	73	35	34	49	59	74	38	78	82	51	87	69	56	0	50	67	0
7	Max Comtois (R)	0		X			100.00	52	33	76	57	69	68	82	59	31	68	77	69	68	34	46	0	50	65	0
8	Curtis Lazar	0	X		X		100.00	82	31	61	50	67	44	76	61	48	76	78	50	62	53	63	0	50	65	0
9	Teddy Blueger (R)	0	X				100.00	79	24	60	46	52	50	81	66	48	73	80	71	67	45	46	0	50	65	0
10	Joey Anderson (R)	0	X				100.00	69	24	71	49	50	59	65	63	50	74	79	69	67	33	39	0	50	65	0
11	Brett Seney	0	X				100.00	71	28	42	44	25	47	85	67	65	77	81	50	66	33	42	0	50	64	0
12	Nick Jensen	0				X	100.00	58	30	70	47	63	67	92	80	28	83	75	69	75	56	51	0	50	73	0
13	Derrick Pouliot	0				X	100.00	64	31	63	44	62	63	76	81	28	83	76	48	71	55	54	0	50	72	0
14	Samuel Girard	0				X	100.00	53	25	79	43	29	69	80	83	78	85	76	53	64	41	41	0	50	71	0
15	Connor Murphy	0				X	100.00	82	31	56	47	81	66	70	82	26	82	75	67	57	52	53	0	50	71	0
16	Slater Koekkoek	0				X	100.00	81	28	55	46	62	52	89	82	29	81	76	46	65	47	57	0	50	70	0
17	Jon Merrill	0				X	100.00	64	12	52	45	75	63	77	80	27	82	69	49	59	51	34	0	50	68	0
Scratches
1	Nikita Scherbak	0			X		100.00	47	24	70	51	59	56	67	60	58	67	77	57	67	39	40	0	50	63	0
2	Cory Conacher	0	X	X	X		100.00	51	29	49	52	46	44	91	61	33	68	78	53	73	60	56	0	50	62	0
3	Filip Chlapik	0	X				100.00	40	30	69	57	60	51	79	59	35	67	76	60	67	45	47	0	50	62	0
4	Kerby Rychel	0		X			100.00	41	42	81	66	80	46	84	56	31	65	72	65	66	53	60	0	50	62	0
5	Timothy Gettinger (R)	0		X			100.00	54	32	83	70	84	32	83	56	31	64	70	67	68	38	63	0	50	59	0
6	Blake Lizotte	0		X			100.00	26	22	82	68	30	51	84	56	31	64	70	70	68	30	43	0	50	56	0
7	Connor Clifton	0				X	100.00	83	29	46	47	43	56	80	80	30	80	75	33	67	40	48	0	50	68	0
8	Andreas Borgman	0				X	100.00	83	26	61	52	62	50	85	75	30	76	74	44	66	41	42	0	50	66	0

	TEAM AVERAGE					100.00	64	27	64	49	58	55	78	69	41	75	77	59	68	47	50	0	50	66

Show or Hide Column

Filter Tips

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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Malcolm Subban	100.00	89	83	86	82	87	92	89	89	90	61	88	58	70	0	50	82	0
2	Oscar Dansk	100.00	80	87	86	76	81	81	88	83	94	51	60	45	62	0	50	78	0
Scratches
1	Carter Hutton	100.00	94	89	92	85	95	94	93	93	93	76	89	58	71	0	17	86	0
2	Al Montoya	100.00	82	81	82	77	82	88	86	85	90	98	72	34	72	0	50	80	0
3	Scott Wedgewood	100.00	83	88	84	78	82	89	86	86	89	54	60	61	64	0	50	79	0
4	Landon Bow	100.00	80	89	80	77	81	80	87	83	93	51	60	54	61	0	50	78	0
5	Brandon Halverson	100.00	79	91	79	77	81	78	85	82	76	50	60	48	58	0	50	74	0

	TEAM AVERAGE	100.00	84	87	84	79	84	86	88	86	89	63	70	51	65	0	45	80

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary

Show or Hide Column

Filter Tips

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

#	Player Name	Team Name	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	GA	TA	EG	HT	P/20	PSG	PSS	S1	S2	S3
1	Barclay Goodrow	Barracuda (ICE)	RW	83	43	50	93	8	120	0	305	143	338	119	197	12.72%	76	1679	20.24	13	23	36	100	255	2	1	3	15	109	3	6	67.52%	157	84	37	2	1	1.11	1	1	6	7	3
2	Travis Konecny	Barracuda (ICE)	LW	62	40	45	85	26	60	0	105	110	259	111	135	15.44%	45	1359	21.92	6	17	23	27	143	0	2	2	0	43	6	1	40.43%	94	44	24	0	3	1.25	2	4	7	4	9
3	Nick Jensen	Barracuda (ICE)	D	83	21	57	78	22	46	0	107	243	244	85	113	8.61%	220	2485	29.94	10	12	22	62	250	2	4	6	14	308	5	1	0.00%	0	47	116	0	0	0.63	1	2	4	3	3
4	Tyler Ennis	Barracuda (ICE)	C/LW	83	34	40	74	3	16	0	108	63	213	78	122	15.96%	18	1307	15.75	13	12	25	49	192	0	0	0	0	0	7	2	44.01%	1327	37	10	0	0	1.13	0	1	5	2	3
5	Samuel Girard	Barracuda (ICE)	D	83	17	49	66	12	12	0	67	110	202	83	86	8.42%	92	2208	26.60	7	12	19	47	215	2	1	3	15	226	1	2	0.00%	0	57	38	0	0	0.60	0	1	0	3	3
6	Devante Smith-Pelly	Barracuda (ICE)	LW/RW	83	18	44	62	0	40	0	191	93	221	60	124	8.14%	61	1361	16.41	5	14	19	50	194	0	0	0	0	0	2	0	56.79%	81	54	32	0	1	0.91	0	1	2	2	5
7	Connor Murphy	Barracuda (ICE)	D	83	12	47	59	13	101	5	242	182	202	91	89	5.94%	181	1974	23.79	4	11	15	48	214	1	4	5	5	245	0	0	0.00%	0	35	66	0	0	0.60	0	0	3	3	4
8	Derrick Pouliot	Barracuda (ICE)	D	83	10	41	51	4	68	0	159	76	160	58	72	6.25%	99	2201	26.53	4	9	13	38	255	2	5	7	10	267	2	0	100.00%	1	56	35	0	0	0.46	0	0	2	0	2
9	Joey Anderson	Barracuda (ICE)	C	83	14	24	38	14	10	0	131	133	159	60	103	8.81%	45	1141	13.75	0	2	2	6	42	0	0	0	0	62	6	0	46.00%	713	37	26	1	0	0.67	0	1	2	1	1
10	Brett Seney	Barracuda (ICE)	C	83	13	20	33	10	28	0	167	51	174	51	101	7.47%	29	936	11.29	0	1	1	2	14	0	0	0	0	5	1	2	56.02%	457	37	5	0	0	0.70	0	0	0	1	1
11	Nikolay Goldobin	Barracuda (ICE)	LW	83	11	20	31	10	24	0	15	36	123	45	71	8.94%	20	822	9.91	0	0	0	0	0	1	2	3	3	53	0	2	44.74%	38	19	4	0	0	0.75	0	1	0	1	0
12	Slater Koekkoek	Barracuda (ICE)	D	83	6	19	25	15	60	0	154	45	86	36	30	6.98%	29	1149	13.85	1	1	2	5	9	0	0	0	3	38	1	0	50.00%	2	25	19	0	0	0.44	0	0	1	1	0
13	Filip Chlapik	Barracuda (ICE)	C	30	0	1	1	-3	2	0	9	20	14	2	11	0.00%	3	124	4.16	0	0	0	0	1	0	0	0	0	0	0	0	45.35%	86	8	3	0	0	0.16	0	0	0	0	0
Team Total or Average				1005	239	457	696	134	587	5	1760	1305	2395	879	1254	9.98%	918	18753	18.66	63	114	177	434	1790	10	19	29	65	1362	34	16	47.90%	2956	540	415	3	5	0.74	4	12	32	28	34

Show or Hide Column

Filter Tips

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

#	Goalie Name	Team Name	GP	W	L	OTL	PCT	GAA	MP	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Malcolm Subban	Barracuda (ICE)	83	42	34	6	0.924	2.71	4959	2	224	2941	1561	4	3	0.636	22	83	0	8	8	9
2	Al Montoya	Barracuda (ICE)	1	1	0	0	0.750	7.74	31	0	4	16	7	0	0	0.000	0	0	43	0	0	0
Team Total or Average			84	43	34	6	0.923	2.74	4991	2	228	2957	1568	4	3	0.636	22	83	43	8	8	9

Show or Hide Column

Filter Tips

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

Player Name	Team Name	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Contract	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Link
Al Montoya	Barracuda (ICE)	G	34	1985-02-13	No	200 Lbs	188 CM	No	No	No	1	Pro & Farm	650,000$	0$	0$	No		Link / NHL Link
Andreas Borgman	Barracuda (ICE)	D	24	1995-06-18	No	199 Lbs	183 CM	No	No	No	1	Pro & Farm	1,775,000$	0$	0$	No		Link
Barclay Goodrow	Barracuda (ICE)	RW	26	1993-02-26	No	215 Lbs	188 CM	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$	Link / NHL Link
Blake Lizotte	Barracuda (ICE)	LW	21	1997-12-13	No	172 Lbs	170 CM	No	No	No	2	Pro & Farm	1,491,667$	0$	0$	No	1,491,667$	Link
Brandon Halverson	Barracuda (ICE)	G	23	1996-03-29	No	210 Lbs	193 CM	No	No	No	1	Pro & Farm	894,167$	0$	0$	No		Link
Brett Seney	Barracuda (ICE)	C	23	1996-02-28	No	156 Lbs	175 CM	No	No	No	2	Pro & Farm	825,000$	0$	0$	No	825,000$	Link
Carter Hutton	Barracuda (ICE)	G	33	1985-12-19	No	198 Lbs	183 CM	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$	Link / NHL Link
Connor Clifton	Barracuda (ICE)	D	24	1995-04-28	No	175 Lbs	180 CM	No	No	No	2	Pro & Farm	735,000$	0$	0$	No	735,000$	Link
Connor Murphy	Barracuda (ICE)	D	26	1993-03-26	No	212 Lbs	193 CM	No	No	No	2	Pro & Farm	3,850,000$	0$	0$	No	3,850,000$	Link / NHL Link
Cory Conacher	Barracuda (ICE)	C/LW/RW	29	1989-12-14	No	180 Lbs	173 CM	No	No	No	2	Pro & Farm	780,000$	0$	0$	No	780,000$	Link
Curtis Lazar	Barracuda (ICE)	C/RW	24	1995-02-02	No	205 Lbs	183 CM	No	No	No	1	Pro & Farm	950,000$	0$	0$	No		Link / NHL Link
Derrick Pouliot	Barracuda (ICE)	D	25	1994-01-16	No	196 Lbs	183 CM	No	No	No	2	Pro & Farm	1,100,000$	0$	0$	No	1,100,000$	Link / NHL Link
Devante Smith-Pelly	Barracuda (ICE)	LW/RW	27	1992-06-14	No	223 Lbs	183 CM	No	No	No	1	Pro & Farm	1,300,000$	0$	0$	No		Link / NHL Link
Filip Chlapik	Barracuda (ICE)	C	22	1997-06-03	No	196 Lbs	185 CM	No	No	No	1	Pro & Farm	894,167$	0$	0$	No		Link
Joey Anderson	Barracuda (ICE)	C	21	1998-06-19	Yes	190 Lbs	180 CM	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$	Link
Jon Merrill	Barracuda (ICE)	D	27	1992-02-03	No	205 Lbs	191 CM	No	No	No	1	Pro & Farm	1,137,500$	0$	0$	No		Link / NHL Link
Kerby Rychel	Barracuda (ICE)	LW	24	1994-10-07	No	213 Lbs	185 CM	No	No	No	2	Pro & Farm	725,000$	0$	0$	No	725,000$	Link
Landon Bow	Barracuda (ICE)	G	24	1995-08-24	No	210 Lbs	196 CM	No	No	No	2	Pro & Farm	697,500$	0$	0$	No	697,500$	Link
Malcolm Subban	Barracuda (ICE)	G	25	1993-12-21	No	200 Lbs	188 CM	No	No	No	1	Pro & Farm	650,000$	0$	0$	No		Link
Max Comtois	Barracuda (ICE)	LW	20	1999-01-08	Yes	207 Lbs	188 CM	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$	Link
Nick Jensen	Barracuda (ICE)	D	29	1990-09-21	No	194 Lbs	183 CM	No	No	No	2	Pro & Farm	1,080,000$	0$	0$	No	1,080,000$	Link / NHL Link
Nikita Scherbak	Barracuda (ICE)	RW	23	1995-12-30	No	192 Lbs	188 CM	No	No	No	2	Pro & Farm	1,063,333$	0$	0$	No	1,063,333$	Link
Nikolay Goldobin	Barracuda (ICE)	LW	23	1995-10-07	No	196 Lbs	180 CM	No	No	No	1	Pro & Farm	1,137,500$	0$	0$	No		Link / NHL Link
Oscar Dansk	Barracuda (ICE)	G	25	1994-02-28	No	195 Lbs	191 CM	No	No	No	1	Pro & Farm	650,000$	0$	0$	No		Link
Samuel Girard	Barracuda (ICE)	D	21	1998-05-12	No	162 Lbs	178 CM	No	No	No	1	Pro & Farm	925,000$	0$	0$	No		Link
Scott Wedgewood	Barracuda (ICE)	G	27	1992-08-14	No	195 Lbs	188 CM	No	No	No	2	Pro & Farm	700,000$	0$	0$	No	700,000$	Link
Slater Koekkoek	Barracuda (ICE)	D	25	1994-02-18	No	193 Lbs	188 CM	No	No	No	1	Pro & Farm	865,000$	0$	0$	No		Link / NHL Link
Teddy Blueger	Barracuda (ICE)	C	25	1994-08-15	Yes	185 Lbs	183 CM	No	No	No	2	Pro & Farm	650,000$	0$	0$	No	650,000$	Link
Timothy Gettinger	Barracuda (ICE)	LW	21	1998-04-14	Yes	220 Lbs	198 CM	No	No	No	2	Pro & Farm	730,000$	0$	0$	No	730,000$	Link
Travis Konecny	Barracuda (ICE)	LW	22	1997-03-11	No	175 Lbs	178 CM	No	No	No	2	Pro & Farm	1,106,667$	0$	0$	No	1,106,667$	Link / NHL Link
Tyler Ennis	Barracuda (ICE)	C/LW	29	1989-10-06	No	161 Lbs	175 CM	No	No	No	2	Pro & Farm	780,000$	0$	0$	No	780,000$	Link / NHL Link
Tyler Pitlick	Barracuda (ICE)	RW	27	1991-11-01	No	200 Lbs	188 CM	No	No	No	2	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
32	24.97	195 Lbs	185 CM	1.63	1,009,141$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1			Barclay Goodrow	40	1	2	2
2		Tyler Ennis	Devante Smith-Pelly	30	1	2	2
3	Nikolay Goldobin	Joey Anderson		20	1	2	2
4		Brett Seney		10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Nick Jensen	Derrick Pouliot	40	1	2	2
2	Samuel Girard	Connor Murphy	30	1	2	2
3	Slater Koekkoek	Nick Jensen	20	1	2	2
4	Derrick Pouliot	Samuel Girard	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1			Barclay Goodrow	60	1	2	2
2		Tyler Ennis	Devante Smith-Pelly	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Nick Jensen	Derrick Pouliot	60	1	2	2
2	Samuel Girard	Connor Murphy	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			60	1	2	2
2		Nikolay Goldobin	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Nick Jensen	Derrick Pouliot	60	1	2	2
2	Samuel Girard	Connor Murphy	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1		60	1	2	2	Nick Jensen	Derrick Pouliot	60	1	2	2
2		40	1	2	2	Samuel Girard	Connor Murphy	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			60	1	2	2
2		Nikolay Goldobin	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Nick Jensen	Derrick Pouliot	60	1	2	2
2	Samuel Girard	Connor Murphy	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
		Barclay Goodrow	Nick Jensen	Derrick Pouliot

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
		Barclay Goodrow	Nick Jensen	Derrick Pouliot

Extra Forwards
Normal	PowerPlay	Penalty Kill
Joey Anderson, Brett Seney, Barclay Goodrow	Joey Anderson, Brett Seney	Barclay Goodrow

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Slater Koekkoek, Connor Murphy, Nick Jensen	Slater Koekkoek	Connor Murphy, Nick Jensen

Penalty Shots
, , , Nikolay Goldobin, Barclay Goodrow

Goalie
#1 : Malcolm Subban, #2 :

Custom OT Lines Forwards
, , , , , , , , , ,

Custom OT Lines Defensemen
, , , ,

Show or Hide Column

Filter Tips

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

	Overall												Home											Visitor
#	VS Team	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	Benchwarmers	7	5	0	0	0	0	0	2	54	21	33	5	4	0	0	0	0	0	1	44	12	32	2	1	0	0	0	0	0	1	10	9	1	12	0.857	54	97	151	0	0	88	89	107	9	232	858	884	908	50	258	82	54	150	35	14	40.00%	27	9	66.67%	3	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
2	Caribou	5	1	2	0	0	1	0	1	10	13	-3	3	1	2	0	0	0	0	0	7	8	-1	2	0	0	0	0	1	0	1	3	5	-2	4	0.400	10	19	29	0	0	88	89	107	9	164	858	884	908	50	174	70	34	128	15	2	13.33%	17	2	88.24%	0	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
3	Casse-Tete	9	2	7	0	0	0	0	0	21	45	-24	3	1	2	0	0	0	0	0	7	10	-3	6	1	5	0	0	0	0	0	14	35	-21	4	0.222	21	35	56	2	0	88	89	107	9	266	858	884	908	50	325	126	72	174	21	5	23.81%	36	9	75.00%	0	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
4	Chevalier	4	0	4	0	0	0	0	0	9	14	-5	2	0	2	0	0	0	0	0	4	7	-3	2	0	2	0	0	0	0	0	5	7	-2	0	0.000	9	18	27	1	0	88	89	107	9	133	858	884	908	50	138	49	51	75	7	2	28.57%	19	6	68.42%	0	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
5	Diabotins	8	6	0	0	1	0	0	1	29	9	20	4	4	0	0	0	0	0	0	19	2	17	4	2	0	0	1	0	0	1	10	7	3	15	0.938	29	51	80	0	2	88	89	107	9	271	858	884	908	50	188	63	46	215	15	4	26.67%	23	3	86.96%	1	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
6	Hooligans	6	0	5	0	1	0	0	0	9	21	-12	2	0	1	0	1	0	0	0	3	3	0	4	0	4	0	0	0	0	0	6	18	-12	2	0.167	9	15	24	0	0	88	89	107	9	210	858	884	908	50	243	79	48	129	11	0	0.00%	24	7	70.83%	1	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
7	Hybrides	6	4	2	0	0	0	0	0	22	14	8	3	3	0	0	0	0	0	0	16	6	10	3	1	2	0	0	0	0	0	6	8	-2	8	0.667	22	33	55	0	0	88	89	107	9	205	858	884	908	50	207	58	50	149	32	9	28.13%	25	6	76.00%	2	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
8	Jardiniers	5	4	0	0	1	0	0	0	28	10	18	3	3	0	0	0	0	0	0	18	4	14	2	1	0	0	1	0	0	0	10	6	4	10	1.000	28	46	74	0	0	88	89	107	9	163	858	884	908	50	170	49	36	101	28	10	35.71%	19	4	78.95%	2	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
9	Leftovers	8	5	1	0	1	0	0	1	35	25	10	4	3	0	0	1	0	0	0	24	13	11	4	2	1	0	0	0	0	1	11	12	-1	13	0.813	35	59	94	0	0	88	89	107	9	252	858	884	908	50	288	98	64	187	48	14	29.17%	32	4	87.50%	0	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
10	Pirates	4	2	2	0	0	0	0	0	11	10	1	2	1	1	0	0	0	0	0	4	5	-1	2	1	1	0	0	0	0	0	7	5	2	4	0.500	11	22	33	0	0	88	89	107	9	125	858	884	908	50	179	84	45	90	5	1	20.00%	16	3	81.25%	0	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
11	Requins	5	2	2	0	1	0	0	0	13	14	-1	2	1	1	0	0	0	0	0	4	6	-2	3	1	1	0	1	0	0	0	9	8	1	6	0.600	13	24	37	0	0	88	89	107	9	162	858	884	908	50	175	58	46	116	19	4	21.05%	23	4	82.61%	0	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
12	Royaux	5	3	1	0	0	0	1	0	18	18	0	3	2	1	0	0	0	0	0	10	14	-4	2	1	0	0	0	0	1	0	8	4	4	8	0.800	18	30	48	0	0	88	89	107	9	152	858	884	908	50	193	57	48	115	16	3	18.75%	24	3	87.50%	1	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
	Total	84	37	35	0	5	1	1	5	290	255	35	42	26	13	0	2	0	0	1	177	107	70	42	11	22	0	3	1	1	4	113	148	-35	92	0.548	290	508	798	3	2	88	89	107	9	2677	858	884	908	50	2981	1025	684	1922	293	76	25.94%	332	66	80.12%	13	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
14	Vagabond	6	2	4	0	0	0	0	0	13	15	-2	3	2	1	0	0	0	0	0	7	4	3	3	0	3	0	0	0	0	0	6	11	-5	4	0.333	13	25	38	0	0	88	89	107	9	174	858	884	908	50	213	67	38	130	26	2	7.69%	20	2	90.00%	1	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
15	Werewolves	6	1	5	0	0	0	0	0	18	26	-8	3	1	2	0	0	0	0	0	10	13	-3	3	0	3	0	0	0	0	0	8	13	-5	2	0.167	18	34	52	0	0	88	89	107	9	168	858	884	908	50	230	85	52	163	15	6	40.00%	27	4	85.19%	2	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
	_Since Last GM Reset	84	37	35	0	5	1	1	5	290	255	35	42	26	13	0	2	0	0	1	177	107	70	42	11	22	0	3	1	1	4	113	148	-35	92	0.548	290	508	798	3	2	88	89	107	9	2677	858	884	908	50	2981	1025	684	1922	293	76	25.94%	332	66	80.12%	13	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
	_Vs Conference	35	11	21	0	0	1	1	1	98	130	-32	17	8	9	0	0	0	0	0	54	58	-4	18	3	12	0	0	1	1	1	44	72	-28	26	0.371	98	169	267	3	0	88	89	107	9	1088	858	884	908	50	1267	445	307	804	106	27	25.47%	148	30	79.73%	5	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
	_Vs Division	20	3	12	0	0	0	0	0	69	49	20	10	2	4	0	0	0	0	0	45	21	24	10	1	8	0	0	0	0	0	24	28	-4	6	0.150	69	118	187	0	2	88	89	107	9	644	858	884	908	50	625	206	148	527	62	19	30.65%	75	13	82.67%	5	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
84	92	L1	290	508	798	2677	2981	1025	684	1922	3	2

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
84	37	35	5	1	1	5	290	255

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
42	26	13	2	0	0	1	177	107

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
42	11	22	3	1	1	4	113	148

Last 10 Games
W	L	OTW	OTL	SOW	SOL
5	4	0	0	0	1

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
293	76	25.94%	332	66	80.12%	13

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
858	884	908	50	88	89	107	9

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
851	1787	47.62%	874	1924	45.43%	526	1113	47.26%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
1815	1143	2030	665	1232	614

Last Played Games Show or Hide Column

Filter Tips

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

Day	Game	Visitor Team	Score	Home Team	Score	ST	OT	SO	Link
1 - 2019-10-06	4	Barracuda	0	Casse-Tete	9	L			BoxScore
3 - 2019-10-08	13	Diabotins	1	Barracuda	2	W			BoxScore
6 - 2019-10-11	21	Leftovers	1	Barracuda	3	W			BoxScore
7 - 2019-10-12	29	Benchwarmers	3	Barracuda	16	W			BoxScore
10 - 2019-10-15	41	Barracuda	4	Casse-Tete	2	W			BoxScore
11 - 2019-10-16	44	Barracuda	3	Diabotins	2	W	X		BoxScore
13 - 2019-10-18	49	Barracuda	4	Leftovers	3	W			BoxScore
15 - 2019-10-20	58	Hybrides	2	Barracuda	4	W			BoxScore
16 - 2019-10-21	64	Barracuda	4	Requins	3	W			BoxScore
18 - 2019-10-23	72	Benchwarmers	1	Barracuda	12	W			BoxScore
21 - 2019-10-26	82	Barracuda	3	Hybrides	4	L			BoxScore
22 - 2019-10-27	85	Vagabond	2	Barracuda	5	W			BoxScore
24 - 2019-10-29	93	Barracuda	4	Vagabond	5	L			BoxScore
26 - 2019-10-31	101	Caribou	2	Barracuda	1	L			BoxScore
27 - 2019-11-01	103	Barracuda	3	Casse-Tete	5	L			BoxScore
29 - 2019-11-03	113	Barracuda	3	Hooligans	4	L			BoxScore
31 - 2019-11-05	120	Benchwarmers	3	Barracuda	2	L	X	X	BoxScore
32 - 2019-11-06	128	Barracuda	2	Hybrides	1	W			BoxScore
34 - 2019-11-08	134	Royaux	2	Barracuda	3	W			BoxScore
36 - 2019-11-10	145	Diabotins	0	Barracuda	2	W			BoxScore
37 - 2019-11-11	151	Barracuda	2	Chevalier	3	L			BoxScore
41 - 2019-11-15	160	Barracuda	2	Requins	1	W	X		BoxScore
42 - 2019-11-16	166	Benchwarmers	3	Barracuda	5	W			BoxScore
43 - 2019-11-17	174	Diabotins	0	Barracuda	13	W			BoxScore
45 - 2019-11-19	183	Barracuda	6	Royaux	3	W			BoxScore
47 - 2019-11-21	190	Chevalier	3	Barracuda	1	L			BoxScore
49 - 2019-11-23	199	Royaux	2	Barracuda	4	W			BoxScore
52 - 2019-11-26	212	Leftovers	9	Barracuda	10	W	X		BoxScore
55 - 2019-11-29	221	Barracuda	7	Benchwarmers	5	W			BoxScore
56 - 2019-11-30	227	Requins	1	Barracuda	3	W			BoxScore
59 - 2019-12-03	237	Barracuda	2	Leftovers	4	L			BoxScore
61 - 2019-12-05	243	Casse-Tete	4	Barracuda	2	L			BoxScore
62 - 2019-12-06	251	Barracuda	2	Leftovers	3	L	X	X	BoxScore
64 - 2019-12-08	258	Casse-Tete	2	Barracuda	0	L			BoxScore
66 - 2019-12-10	267	Barracuda	1	Hooligans	4	L			BoxScore
68 - 2019-12-12	274	Vagabond	1	Barracuda	0	L			BoxScore
70 - 2019-12-14	283	Barracuda	1	Caribou	2	L	X	X	BoxScore
71 - 2019-12-15	289	Hooligans	1	Barracuda	2	W	X		BoxScore
73 - 2019-12-17	299	Barracuda	4	Jardiniers	3	W	X		BoxScore
75 - 2019-12-19	303	Leftovers	2	Barracuda	6	W			BoxScore
77 - 2019-12-21	311	Barracuda	1	Hooligans	4	L			BoxScore
79 - 2019-12-23	317	Barracuda	3	Casse-Tete	4	L			BoxScore
80 - 2019-12-24	323	Hybrides	2	Barracuda	4	W			BoxScore
82 - 2019-12-26	332	Caribou	3	Barracuda	4	W			BoxScore
85 - 2019-12-29	341	Barracuda	3	Diabotins	1	W			BoxScore
86 - 2019-12-30	347	Jardiniers	1	Barracuda	6	W			BoxScore
88 - 2020-01-01	355	Barracuda	6	Jardiniers	3	W			BoxScore
89 - 2020-01-02	360	Barracuda	2	Pirates	3	L			BoxScore
91 - 2020-01-04	369	Jardiniers	2	Barracuda	5	W			BoxScore
93 - 2020-01-06	377	Barracuda	3	Chevalier	4	L			BoxScore
95 - 2020-01-08	380	Pirates	4	Barracuda	2	L			BoxScore
96 - 2020-01-09	386	Barracuda	2	Diabotins	1	W			BoxScore
98 - 2020-01-11	397	Chevalier	4	Barracuda	3	L			BoxScore
101 - 2020-01-14	403	Barracuda	1	Vagabond	3	L			BoxScore
103 - 2020-01-16	412	Benchwarmers	2	Barracuda	9	W			BoxScore
104 - 2020-01-17	418	Barracuda	2	Royaux	1	W	X	X	BoxScore
107 - 2020-01-20	427	Werewolves	4	Barracuda	2	L			BoxScore
108 - 2020-01-21	432	Barracuda	1	Werewolves	3	L			BoxScore
110 - 2020-01-23	442	Hooligans	2	Barracuda	1	L			BoxScore
114 - 2020-01-27	453	Casse-Tete	4	Barracuda	5	W			BoxScore
115 - 2020-01-28	458	Barracuda	3	Requins	4	L			BoxScore
117 - 2020-01-30	468	Jardiniers	1	Barracuda	7	W			BoxScore
120 - 2020-02-02	478	Barracuda	1	Vagabond	3	L			BoxScore
121 - 2020-02-03	484	Hybrides	2	Barracuda	8	W			BoxScore
122 - 2020-02-04	492	Barracuda	1	Hooligans	6	L			BoxScore
125 - 2020-02-07	498	Caribou	3	Barracuda	2	L			BoxScore
126 - 2020-02-08	504	Barracuda	2	Caribou	3	L	X		BoxScore
129 - 2020-02-11	512	Barracuda	4	Werewolves	5	L			BoxScore
130 - 2020-02-12	516	Requins	5	Barracuda	1	L			BoxScore
134 - 2020-02-16	528	Barracuda	2	Diabotins	3	L	X	X	BoxScore
135 - 2020-02-17	533	Royaux	10	Barracuda	3	L			BoxScore
138 - 2020-02-20	543	Diabotins	1	Barracuda	2	W			BoxScore
140 - 2020-02-22	551	Barracuda	3	Casse-Tete	10	L			BoxScore
141 - 2020-02-23	556	Leftovers	1	Barracuda	5	W			BoxScore
142 - 2020-02-24	561	Barracuda	1	Hybrides	3	L			BoxScore
145 - 2020-02-27	572	Barracuda	5	Pirates	2	W			BoxScore
146 - 2020-02-28	579	Werewolves	7	Barracuda	5	L			BoxScore
149 - 2020-03-02	588	Pirates	1	Barracuda	2	W			BoxScore
150 - 2020-03-03	594	Barracuda	3	Leftovers	2	W			BoxScore
153 - 2020-03-06	603	Vagabond	1	Barracuda	2	W			BoxScore
154 - 2020-03-07	605	Barracuda	3	Werewolves	5	L			BoxScore
159 - 2020-03-12	619	Werewolves	2	Barracuda	3	W			BoxScore
160 - 2020-03-13	621	Barracuda	3	Benchwarmers	4	L	X	X	BoxScore
162 - 2020-03-15	628	Barracuda	1	Casse-Tete	5	L			BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	0	0
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
0	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
3,199,891$	4,225,251$	3,551,617$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
25,922$	3,199,891$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	25,922$	0$

Show or Hide Column

	Overall											Home											Visitor
Year	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
2019	84	37	35	0	5	1	1	5	290	255	35	42	26	13	0	2	0	0	1	177	107	70	42	11	22	0	3	1	1	4	113	148	-35	92	290	508	798	3	2	88	89	107	9	2677	858	884	908	50	2981	1025	684	1922	293	76	25.94%	332	66	80.12%	13	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614
Total Regular Season	84	37	35	0	5	1	1	5	290	255	35	42	26	13	0	2	0	0	1	177	107	70	42	11	22	0	3	1	1	4	113	148	-35	92	290	508	798	3	2	88	89	107	9	2677	858	884	908	50	2981	1025	684	1922	293	76	25.94%	332	66	80.12%	13	851	1787	47.62%	874	1924	45.43%	526	1113	47.26%	1815	1143	2030	665	1232	614