Ducks
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance

Game Center
	Moose 7-17-1, 15pts	2	FINAL	4	Gulls 20-5-1, 41pts

Team Stats
W1	Streak	W5
5-7-0	Home Record	10-3-0
2-10-1	Home Record	10-2-1
4-6-0	Last 10 Games	8-1-1
2.84	Goals Per Game	3.81
3.76	Goals Against Per Game	2.77
18.60%	Power Play Percentage	20.56%
73.64%	Penalty Kill Percentage	83.02%

Senators
8-12-4, 20pts

FINAL

Gulls
20-5-1, 41pts

Team Stats
L1	Streak	W5
6-5-2	Home Record	10-3-0
2-7-2	Home Record	10-2-1
4-5-1	Last 10 Games	8-1-1
3.38	Goals Per Game	3.81
3.83	Goals Against Per Game	2.77
12.79%	Power Play Percentage	20.56%
82.47%	Penalty Kill Percentage	83.02%

Gulls
20-5-1, 41pts

Day 59

Falcons
16-9-1, 33pts

Team Stats
W5	Streak	L1
10-3-0	Home Record	6-5-1
10-2-1	Away Record	10-4-0
8-1-1	Last 10 Games	5-4-1
3.81	Goals Per Game	3.62
2.77	Goals Against Per Game	3.62
20.56%	Power Play Percentage	22.68%
83.02%	Penalty Kill Percentage	77.27%

Gulls
20-5-1, 41pts

Day 61

Ice Hogs
11-13-2, 24pts

Team Stats
W5	Streak	W4
10-3-0	Home Record	4-6-2
10-2-1	Away Record	7-7-0
8-1-1	Last 10 Games	5-4-1
3.81	Goals Per Game	3.58
2.77	Goals Against Per Game	3.58
20.56%	Power Play Percentage	22.68%
83.02%	Penalty Kill Percentage	80.00%

Marlies
12-12-3, 27pts

Day 62

Gulls
20-5-1, 41pts

Team Stats
W3	Streak	W5
4-6-2	Home Record	10-3-0
8-6-1	Away Record	10-2-1
5-5-0	Last 10 Games	8-1-1
3.30	Goals Per Game	3.81
3.15	Goals Against Per Game	3.81
24.56%	Power Play Percentage	20.56%
82.02%	Penalty Kill Percentage	83.02%

Team Leaders
	Goals Pavel Dorofeyev 19	Assists Pavel Mintyukov 19

	Points Pavel Dorofeyev 33	Plus/Minus Riley Nash 14

	Wins Matt Murray 20	Save Percentage Matt Murray 0.907

Team Stats
Goals For 99 3.81 GFG	Shots For 930 35.77 Avg	Power Play Percentage 20.6% 22 GF	Offensive Zone Start 42.8%
Goals Against 72 2.77 GAA	Shots Against 757 29.12 Avg	Penalty Kill Percentage 83.0%% 18 GA	Defensive Zone Start 37.4%

Team Info
	General Manager	Mark Donais
	Coach	Steve Ott
	Division	Pacific
	Conference	American
	Captain
	Assistant #1	Riley Nash
	Assistant #2	Philippe Myers
Arena Info
	Capacity	3,000
	Attendance	2,122
	Season Tickets	0
Roster Info
	Pro Team	20
	Farm Team	20
	Contract Limit	40 / 250
	Prospects	49

Show or Hide Columns

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	Age	Contract	Salary
1	Pavel Dorofeyev		X	X		100.00	64	43	81	66	72	66	64	64	56	62	71	69	63	56	57	0	50	66	0	24	2	1,800,000$
2	Sammy Blais		X			100.00	88	40	83	66	79	59	79	59	52	63	61	68	68	62	58	0	50	65	0	28	1	1,750,000$
3	Lukas Reichel		X	X		100.00	63	45	83	71	62	69	69	62	62	63	63	62	57	57	57	0	50	64	0	22	2	1,200,000$
4	Trey Fix-Wolansky			X		100.00	58	57	70	66	61	67	64	65	55	64	65	65	68	60	56	0	50	64	0	25	3	1,000,000$
5	Riley Tufte		X			100.00	59	56	64	65	67	69	67	63	56	60	62	66	55	62	57	0	50	63	0	26	3	1,200,000$
6	Riley Nash (A)	X	X	X		100.00	56	60	68	60	69	65	64	62	56	60	60	63	56	78	66	0	50	62	0	35	2	900,000$
7	Tanner Kero		X			100.00	55	58	67	61	65	67	65	61	60	60	59	64	58	72	63	0	50	62	0	32	1	1,000,000$
8	Greg McKegg	X	X			100.00	56	54	65	58	66	68	66	59	70	59	57	63	54	72	63	0	50	61	0	32	2	900,000$
9	Ivan Miroshnichenko		X			100.00	63	49	71	61	67	63	59	57	52	57	57	63	54	51	51	0	50	60	0	20	2	1,200,000$
10	Artem Anisimov	X	X			100.00	55	55	69	57	71	60	59	57	70	56	56	61	54	79	67	0	50	59	0	36	2	1,250,000$
11	Ryan Winterton	X				100.00	55	55	67	60	67	65	62	58	52	55	59	64	52	52	51	0	50	59	0	21	2	900,000$
12	Mark Liwiski	X				100.00	60	51	65	52	65	52	52	51	52	51	51	57	51	54	51	0	50	54	0	23	2	750,000$
13	Pavel Mintyukov				X	100.00	65	38	78	74	70	77	68	60	38	64	57	76	51	53	55	0	50	66	0	21	2	1,500,000$
14	Philippe Myers (A)				X	100.00	59	55	66	62	65	68	65	59	41	60	56	68	55	64	58	0	50	62	0	27	1	1,000,000$
15	Jacob Moverare				X	100.00	57	51	74	57	75	63	61	57	41	58	56	68	55	61	57	0	50	61	0	26	1	900,000$
16	Kevin Connauton				X	100.00	56	55	64	60	70	67	65	57	40	57	55	66	54	76	65	0	50	61	0	34	1	1,000,000$
17	Mason Geertsen				X	100.00	59	55	61	58	61	67	66	55	55	54	56	61	54	67	60	0	50	59	0	29	1	750,000$
18	Colin Felix				X	100.00	56	51	64	52	66	56	56	51	38	52	51	58	51	58	53	0	50	54	0	25	1	750,000$
Scratches

	TEAM AVERAGE					100.00	60	52	70	61	68	65	64	59	53	59	58	65	56	63	58	0	50	61

Show or Hide Columns

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	Age	Contract	Salary
1	Matt Murray	100.00	61	60	60	75	66	60	70	60	65	65	63	70	63	0	50	64	0	30	2	1,000,000$
2	Matt Tomkins	100.00	62	60	61	65	66	58	66	62	61	59	58	65	57	0	50	62	0	30	1	1,000,000$
Scratches

	TEAM AVERAGE	100.00	62	60	61	70	66	59	68	61	63	62	61	68	60	0	50	63

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Steve Ott	77	72	72	81	56	53	1	CAN	41	2	250,000$

Show or Hide Columns

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	EG	HT	P/20	PSS	FT	S1	S2	S3
1	Pavel Dorofeyev	Gulls (Anh)	LW/RW	26	19	14	33	11	10	0	40	62	123	32	81	15.45%	8	570	21.94	4	5	9	20	85	0	0	0	2	99	4	1	50.00%	80	1	0	1.16	1	0	5	4	0
2	Jack Quinn	Ducks	LW/RW	22	14	17	31	9	31	5	50	51	106	32	48	13.21%	5	407	18.54	1	7	8	24	72	0	0	0	1	14	5	1	22.22%	27	0	1	1.52	1	0	4	4	0
3	Pavel Mintyukov	Gulls (Anh)	D	26	5	19	24	5	14	0	57	58	66	18	48	7.58%	45	623	23.99	3	5	8	42	88	0	0	0	1	90	2	1	0%	0	0	0	0.77	0	0	1	0	3
4	Riley Tufte	Gulls (Anh)	LW	26	12	11	23	7	14	0	34	49	63	22	45	19.05%	7	356	13.71	1	0	1	3	10	0	1	1	0	35	1	1	31.43%	70	0	0	1.29	0	0	1	3	2
5	Riley Nash	Gulls (Anh)	C/LW/RW	26	4	18	22	14	14	0	31	69	57	18	44	7.02%	3	553	21.28	2	7	9	16	86	1	0	1	1	95	0	0	48.13%	667	0	0	0.80	0	0	1	1	1
6	Jacob Moverare	Gulls (Anh)	D	26	3	14	17	9	24	0	34	31	36	9	18	8.33%	23	541	20.84	1	3	4	10	85	0	0	0	0	73	1	0	0%	0	0	0	0.63	0	0	2	0	0
7	Trey Fix-Wolansky	Gulls (Anh)	RW	26	7	8	15	1	14	0	25	35	85	28	56	8.24%	6	376	14.48	3	3	6	22	88	0	0	0	0	0	1	0	47.22%	36	0	0	0.80	0	0	0	1	1
8	Lukas Reichel	Gulls (Anh)	LW/RW	26	6	8	14	1	4	0	45	45	68	28	51	8.82%	1	421	16.22	0	5	5	19	89	0	0	0	2	57	1	0	47.37%	38	0	0	0.66	1	0	1	1	2
9	Greg McKegg	Gulls (Anh)	C/LW	26	3	10	13	4	12	0	45	71	56	16	44	5.36%	6	499	19.19	1	4	5	11	86	0	0	0	1	62	1	0	53.88%	631	0	0	0.52	0	0	1	0	1
10	Tanner Kero	Gulls (Anh)	LW	26	3	10	13	7	8	0	25	29	49	11	48	6.12%	2	293	11.27	0	0	0	0	3	0	0	0	0	0	0	0	54.55%	22	0	0	0.89	0	0	1	0	1
11	Sammy Blais	Gulls (Anh)	LW	22	4	8	12	-2	24	0	60	26	50	17	43	8.00%	2	296	13.47	2	2	4	9	16	0	0	0	4	17	1	0	35.00%	20	0	0	0.81	0	0	0	1	2
12	Philippe Myers	Gulls (Anh)	D	26	3	8	11	2	16	0	49	25	31	9	22	9.68%	31	616	23.72	2	0	2	15	86	0	0	0	1	88	1	0	0%	0	0	0	0.36	0	0	0	0	1
13	Artem Anisimov	Gulls (Anh)	C/LW	26	1	9	10	11	9	5	19	38	42	14	24	2.38%	1	342	13.18	0	0	0	1	2	0	0	0	0	4	0	0	51.99%	377	0	0	0.58	0	1	0	1	0
14	Mason Geertsen	Gulls (Anh)	D	26	2	7	9	11	20	0	57	15	23	4	11	8.70%	19	410	15.80	0	0	0	1	8	0	0	0	0	30	0	0	0%	0	1	0	0.44	0	0	0	1	1
15	Mark Liwiski	Gulls (Anh)	C	26	3	6	9	-1	6	0	21	18	16	2	8	18.75%	3	200	7.71	0	0	0	0	0	0	0	0	0	0	1	0	43.29%	231	0	0	0.90	0	0	1	1	1
16	Kevin Connauton	Gulls (Anh)	D	26	3	5	8	8	10	0	50	23	32	10	20	9.38%	30	553	21.28	2	1	3	18	86	0	0	0	1	82	1	0	0%	0	0	0	0.29	0	0	0	0	0
17	Ivan Miroshnichenko	Gulls (Anh)	LW	26	4	3	7	4	16	0	25	29	32	5	26	12.50%	1	239	9.22	0	0	0	0	0	0	0	0	0	0	1	0	35.00%	20	0	0	0.58	0	0	0	0	0
18	Colin Felix	Gulls (Anh)	D	26	1	5	6	10	14	0	32	11	8	0	5	12.50%	14	383	14.74	0	0	0	0	4	0	0	0	0	5	0	0	0%	0	0	0	0.31	0	0	0	0	0
19	Ryan Winterton	Gulls (Anh)	C	26	3	1	4	0	10	0	12	16	22	8	15	13.64%	3	151	5.81	0	0	0	0	0	0	0	0	0	0	0	0	41.79%	67	0	0	0.53	0	0	1	0	0
Team Total or Average				486	100	181	281	111	270	10	711	701	965	283	657	10.36%	210	7837	16.13	22	42	64	211	900	1	1	2	14	757	21	4	48.73%	2286	2	1	0.72	3	1	19	18	16

Show or Hide Columns

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	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Matt Murray	Gulls (Anh)	26	20	5	1	0.907	2.70	1536	2	69	738	1	0.500	2	26	0	1	1	1
2	Matt Tomkins	Gulls (Anh)	1	0	0	0	1.000	0	31	0	0	17	1	0	0	0	26	0	0	0
Team Total or Average			27	20	5	1	0.909	2.64	1568	2	69	755	2		2	26	26	1	1	1

Show or Hide Columns

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	Country	Rookie	Weight	Height	No Trade	Available For Trade	Acquired By	Last Trade Date	Force Waivers	Waiver Possible	Contract	Contract Signature Date	Force UFA	Emergency Recall	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Salary Year 5	Salary Year 6	Salary Year 7	Salary Year 8	Salary Year 9	Salary Year 10	Salary Cap Year 2	Salary Cap Year 3	Salary Cap Year 4	Salary Cap Year 5	Salary Cap Year 6	Salary Cap Year 7	Salary Cap Year 8	Salary Cap Year 9	Salary Cap Year 10	No Trade Year 2	No Trade Year 3	No Trade Year 4	No Trade Year 5	No Trade Year 6	No Trade Year 7	No Trade Year 8	No Trade Year 9	No Trade Year 10	Link
Artem Anisimov	Gulls (Anh)	C/LW	36	1988-05-24	RUS	No	198 Lbs	6 ft4	No	No	Free Agent		No	No	2	2024-07-27	False	False	Pro & Farm	1,250,000$	0$	0$	No	1,250,000$	-	-	-	-	-	-	-	-	1,250,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Colin Felix	Gulls (Anh)	D	25	1999-01-07	USA	No	202 Lbs	6 ft1	No	No	Free Agent		No	No	1	2024-10-07	False	False	Pro & Farm	750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Greg McKegg	Gulls (Anh)	C/LW	32	1992-06-17	CAN	No	194 Lbs	6 ft0	No	No	N/A		No	No	2		False	False	Pro & Farm	900,000$	0$	0$	No	900,000$	-	-	-	-	-	-	-	-	900,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Ivan Miroshnichenko	Gulls (Anh)	LW	20	2004-02-04	RUS	No	184 Lbs	6 ft0	No	No	N/A		No	No	2		False	False	Pro & Farm	1,200,000$	0$	0$	No	1,200,000$	-	-	-	-	-	-	-	-	1,200,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Jacob Moverare	Gulls (Anh)	D	26	1998-08-31	SWE	No	209 Lbs	6 ft3	No	No	N/A		No	No	1		False	False	Pro & Farm	900,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Kevin Connauton	Gulls (Anh)	D	34	1990-02-23	CAN	No	205 Lbs	6 ft2	No	No	Free Agent		No	No	1	2024-08-11	False	False	Pro & Farm	1,000,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Lukas Reichel	Gulls (Anh)	LW/RW	22	2002-05-17	GER	No	169 Lbs	6 ft0	No	No	N/A		No	No	2		False	False	Pro & Farm	1,200,000$	0$	0$	No	1,200,000$	-	-	-	-	-	-	-	-	1,200,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Mark Liwiski	Gulls (Anh)	C	23	2001-08-08	CAN	No	194 Lbs	6 ft1	No	No	Free Agent		No	No	2	2024-10-07	False	False	Pro & Farm	750,000$	0$	0$	No	750,000$	-	-	-	-	-	-	-	-	750,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Mason Geertsen	Gulls (Anh)	D	29	1995-04-19	CAN	No	220 Lbs	6 ft4	No	No	Free Agent		No	No	1	2024-08-11	False	False	Pro & Farm	750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Matt Murray	Gulls (Anh)	G	30	1994-05-25	CAN	No	178 Lbs	6 ft4	No	No	Free Agent		No	No	2	2024-08-26	False	False	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$	-	-	-	-	-	-	-	-	1,000,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Matt Tomkins	Gulls (Anh)	G	30	1994-06-19	CAN	No	191 Lbs	6 ft4	No	No	Free Agent		No	No	1	2024-08-26	False	False	Pro & Farm	1,000,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Pavel Dorofeyev	Gulls (Anh)	LW/RW	24	2000-10-26	RUS	No	193 Lbs	6 ft0	No	No	N/A		No	No	2		False	False	Pro & Farm	1,800,000$	0$	0$	No	1,800,000$	-	-	-	-	-	-	-	-	1,800,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Pavel Mintyukov	Gulls (Anh)	D	21	2003-11-25	RUS	No	193 Lbs	6 ft0	No	No	N/A		No	No	2		False	False	Pro & Farm	1,500,000$	0$	0$	No	1,500,000$	-	-	-	-	-	-	-	-	1,500,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Philippe Myers	Gulls (Anh)	D	27	1997-01-25	CAN	No	213 Lbs	6 ft5	No	No	N/A		No	No	1		False	False	Pro & Farm	1,000,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Riley Nash	Gulls (Anh)	C/LW/RW	35	1989-05-09	CAN	No	187 Lbs	6 ft2	No	No	N/A		No	No	2		False	False	Pro & Farm	900,000$	0$	0$	No	900,000$	-	-	-	-	-	-	-	-	900,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Riley Tufte	Gulls (Anh)	LW	26	1998-04-10	USA	No	228 Lbs	6 ft5	No	No	N/A		No	No	3		False	False	Pro & Farm	1,200,000$	0$	0$	No	1,200,000$	1,200,000$	-	-	-	-	-	-	-	1,200,000$	1,200,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link / NHL Link
Ryan Winterton	Gulls (Anh)	C	21	2003-09-04	CAN	No	173 Lbs	6 ft2	No	No	N/A		No	No	2		False	False	Pro & Farm	900,000$	0$	0$	No	900,000$	-	-	-	-	-	-	-	-	900,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Sammy Blais	Gulls (Anh)	LW	28	1996-06-17	CAN	No	204 Lbs	6 ft2	No	No	Trade	2024-11-23	No	No	1		False	False	Pro & Farm	1,750,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Tanner Kero	Gulls (Anh)	LW	32	1992-07-24	USA	No	185 Lbs	6 ft0	No	No	Free Agent		No	No	1	2024-07-27	False	False	Pro & Farm	1,000,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Trey Fix-Wolansky	Gulls (Anh)	RW	25	1999-05-26	CAN	No	191 Lbs	5 ft6	No	No	N/A		No	No	3		False	False	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$	1,000,000$	-	-	-	-	-	-	-	1,000,000$	1,000,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
20	27.30	196 Lbs	6 ft2	1.70	1,087,500$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Sammy Blais	Riley Nash	Pavel Dorofeyev	40	0	0	5
2	Lukas Reichel	Greg McKegg	Trey Fix-Wolansky	30	0	2	3
3	Riley Tufte	Artem Anisimov	Tanner Kero	25	2	2	1
4	Ivan Miroshnichenko	Mark Liwiski	Ryan Winterton	5	0	2	3

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Pavel Mintyukov	Philippe Myers	40	1	2	2
2	Jacob Moverare	Kevin Connauton	35	1	2	2
3	Mason Geertsen	Colin Felix	25	1	2	2
4	Pavel Mintyukov	Philippe Myers	0	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Sammy Blais	Riley Nash	Pavel Dorofeyev	60	0	0	5
2	Lukas Reichel	Greg McKegg	Trey Fix-Wolansky	40	0	2	3

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Pavel Mintyukov	Philippe Myers	60	0	0	5
2	Jacob Moverare	Kevin Connauton	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Riley Nash	Pavel Dorofeyev	60	1	3	1
2	Greg McKegg	Lukas Reichel	40	0	2	3

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Pavel Mintyukov	Philippe Myers	60	1	3	1
2	Jacob Moverare	Kevin Connauton	40	0	2	3

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Riley Nash	60	0	4	1	Pavel Mintyukov	Philippe Myers	60	0	4	1
2	Greg McKegg	40	0	4	1	Jacob Moverare	Kevin Connauton	40	0	4	1

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Pavel Dorofeyev	Sammy Blais	60	1	2	2
2	Riley Nash	Lukas Reichel	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Pavel Mintyukov	Philippe Myers	60	1	2	2
2	Jacob Moverare	Kevin Connauton	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Pavel Dorofeyev	Riley Nash	Sammy Blais	Pavel Mintyukov	Philippe Myers

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Sammy Blais	Riley Nash	Pavel Dorofeyev	Pavel Mintyukov	Philippe Myers

Extra Forwards
Normal	PowerPlay	Penalty Kill
Riley Tufte, Riley Nash, Tanner Kero	Riley Tufte, Riley Nash	Riley Tufte

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Kevin Connauton, Mason Geertsen, Colin Felix	Kevin Connauton	Kevin Connauton, Mason Geertsen

Penalty Shots
Sammy Blais, Pavel Dorofeyev, Lukas Reichel, Trey Fix-Wolansky, Riley Tufte

Goalie
#1 : Matt Murray, #2 : Matt Tomkins

Custom OT Lines Forwards
Sammy Blais, Pavel Dorofeyev, Lukas Reichel, Trey Fix-Wolansky, Riley Tufte, Riley Nash, Riley Nash, Tanner Kero, Greg McKegg, Ivan Miroshnichenko, Artem Anisimov

Custom OT Lines Defensemen
Pavel Mintyukov, Philippe Myers, Jacob Moverare, Kevin Connauton, Mason Geertsen

Show or Hide Columns

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	Barracuda	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	7	11	0	0	33	38	25	3	40	335	312	270	16	30	12	6	24	9	0	0.00%	3	0	100.00%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
2	Bruins	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	2	2	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	7	11	0	0	33	38	25	3	34	335	312	270	16	32	12	8	26	2	1	50.00%	4	1	75.00%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
3	Crunch	1	0	0	0	1	0	0	0	4	3	1	1	0	0	0	1	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	33	38	25	3	31	335	312	270	16	26	3	8	25	5	0	0.00%	1	0	100.00%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
4	Heat	2	2	0	0	0	0	0	0	7	3	4	0	0	0	0	0	0	0	0	0	0	0	2	2	0	0	0	0	0	0	7	3	4	4	1.000	7	12	19	0	0	33	38	25	3	60	335	312	270	16	69	12	18	50	7	1	14.29%	8	0	100.00%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
5	Marlies	3	2	0	0	1	0	0	0	13	5	8	1	1	0	0	0	0	0	0	6	2	4	2	1	0	0	1	0	0	0	7	3	4	6	1.000	13	23	36	0	1	33	38	25	3	116	335	312	270	16	64	16	24	71	10	1	10.00%	12	2	83.33%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
6	Monsters	3	3	0	0	0	0	0	0	16	9	7	0	0	0	0	0	0	0	0	0	0	0	3	3	0	0	0	0	0	0	16	9	7	6	1.000	16	27	43	0	0	33	38	25	3	117	335	312	270	16	91	25	16	66	19	5	26.32%	8	3	62.50%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
7	Moose	3	2	0	0	1	0	0	0	12	7	5	2	1	0	0	1	0	0	0	8	5	3	1	1	0	0	0	0	0	0	4	2	2	6	1.000	12	22	34	0	0	33	38	25	3	102	335	312	270	16	82	32	28	73	9	2	22.22%	13	1	92.31%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
8	Rampage	4	2	1	0	0	0	0	1	10	7	3	3	2	1	0	0	0	0	0	10	6	4	1	0	0	0	0	0	0	1	0	1	-1	5	0.625	10	17	27	1	0	33	38	25	3	129	335	312	270	16	97	24	40	105	10	2	20.00%	18	3	83.33%	1	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
9	Reign	3	0	3	0	0	0	0	0	9	17	-8	1	0	1	0	0	0	0	0	4	5	-1	2	0	2	0	0	0	0	0	5	12	-7	0	0.000	9	14	23	1	0	33	38	25	3	110	335	312	270	16	106	37	48	91	11	3	27.27%	15	4	73.33%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
10	Rocket	2	1	1	0	0	0	0	0	6	9	-3	2	1	1	0	0	0	0	0	6	9	-3	0	0	0	0	0	0	0	0	0	0	0	2	0.500	6	11	17	0	0	33	38	25	3	72	335	312	270	16	65	10	20	59	8	1	12.50%	9	2	77.78%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
11	Senators	1	1	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	6	2	4	0	0	0	0	0	0	0	0	0	0	0	2	1.000	6	12	18	0	0	33	38	25	3	38	335	312	270	16	24	4	8	26	7	2	28.57%	3	0	100.00%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
12	Sound Tigers	1	1	0	0	0	0	0	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	3	1	2	1.000	4	8	12	0	0	33	38	25	3	38	335	312	270	16	34	9	12	21	6	3	50.00%	5	1	80.00%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
13	Wheat Kings	1	1	0	0	0	0	0	0	4	2	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	2	2	2	1.000	4	8	12	0	0	33	38	25	3	43	335	312	270	16	37	12	14	29	4	1	25.00%	7	1	85.71%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
	Total	26	17	5	0	3	0	0	1	99	72	27	13	8	3	0	2	0	0	0	52	37	15	13	9	2	0	1	0	0	1	47	35	12	41	0.788	99	176	275	2	1	33	38	25	3	930	335	312	270	16	757	208	250	666	107	22	20.56%	106	18	83.02%	1	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
	_Since Last GM Reset	26	17	5	0	3	0	0	1	99	72	27	13	8	3	0	2	0	0	0	52	37	15	13	9	2	0	1	0	0	1	47	35	12	41	0.788	99	176	275	2	1	33	38	25	3	930	335	312	270	16	757	208	250	666	107	22	20.56%	106	18	83.02%	1	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
	_Vs Conference	20	13	4	0	2	0	0	1	75	53	22	8	5	2	0	1	0	0	0	32	21	11	12	8	2	0	1	0	0	1	43	32	11	31	0.775	75	130	205	2	1	33	38	25	3	717	335	312	270	16	576	170	194	509	79	15	18.99%	84	14	83.33%	1	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173
	_Vs Division	6	10	4	0	1	0	0	1	20	23	-3	2	4	2	0	0	0	0	0	8	8	0	4	6	2	0	1	0	0	1	12	15	-3	23	1.917	20	33	53	1	0	33	38	25	3	210	335	312	270	16	205	61	72	165	27	4	14.81%	26	4	84.62%	0	475	964	49.27%	410	842	48.69%	224	445	50.34%	619	416	604	202	347	173

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
26	41	W5	99	176	275	930	757	208	250	666	2	1

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
26	17	5	3	0	0	1	99	72

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
13	8	3	2	0	0	0	52	37

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
13	9	2	1	0	0	1	47	35

Last 10 Games
W	L	OTW	OTL	SOW	SOL
7	1	1	0	0	1

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
107	22	20.56%	106	18	83.02%	1

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
335	312	270	16	33	38	25	3

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
475	964	49.27%	410	842	48.69%	224	445	50.34%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
619	416	604	202	347	173

Last Played Games Show or Hide Columns

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
3	19	Marlies	2	Gulls	6	W			BoxScore
4	28	Gulls	4	Marlies	3	W	X		BoxScore
7	45	Barracuda	3	Gulls	4	W			BoxScore
8	60	Gulls	3	Marlies	0	W			BoxScore
10	71	Gulls	3	Heat	1	W			BoxScore
13	87	Rocket	6	Gulls	2	L			BoxScore
15	106	Reign	5	Gulls	4	L			BoxScore
16	113	Gulls	4	Monsters	2	W			BoxScore
20	141	Rampage	1	Gulls	4	W			BoxScore
22	154	Gulls	2	Reign	7	L			BoxScore
24	170	Gulls	4	Moose	2	W			BoxScore
26	179	Moose	3	Gulls	4	W	X		BoxScore
28	200	Gulls	3	Reign	5	L			BoxScore
30	211	Gulls	6	Monsters	5	W			BoxScore
31	221	Rocket	3	Gulls	4	W			BoxScore
34	241	Gulls	4	Wheat Kings	2	W			BoxScore
36	250	Rampage	4	Gulls	2	L			BoxScore
38	269	Gulls	4	Sound Tigers	3	W			BoxScore
40	280	Bruins	2	Gulls	4	W			BoxScore
42	295	Gulls	0	Rampage	1	L	X	X	BoxScore
44	312	Crunch	3	Gulls	4	W	X		BoxScore
46	329	Gulls	4	Heat	2	W			BoxScore
48	340	Gulls	6	Monsters	2	W			BoxScore
50	352	Rampage	1	Gulls	4	W			BoxScore
53	377	Moose	2	Gulls	4	W			BoxScore
57	402	Senators	2	Gulls	6	W			BoxScore
59	414	Gulls	-	Falcons	-
61	432	Gulls	-	Ice Hogs	-
62	442	Marlies	-	Gulls	-
65	460	Gulls	-	Moose	-
67	474	Marlies	-	Gulls	-
69	493	Gulls	-	Wild	-
71	504	Wolves	-	Gulls	-
73	521	Gulls	-	Wheat Kings	-
75	537	Reign	-	Gulls	-
78	558	Monsters	-	Gulls	-
80	567	Gulls	-	Aces	-
82	588	Gulls	-	Thunderbirds	-
84	601	Ice Hogs	-	Gulls	-
88	625	Gulls	-	Phantoms	-
89	636	Griffins	-	Gulls	-
93	660	Wolfpack	-	Gulls	-
95	673	Gulls	-	Rampage	-
97	689	Rocket	-	Gulls	-
100	709	Gulls	-	Crunch	-
102	723	Heat	-	Gulls	-
104	739	Gulls	-	Stars	-
106	753	Wolves	-	Gulls	-
107	763	Gulls	-	Oil Kings	-
110	782	Gulls	-	Barracuda	-
111	792	Wild	-	Gulls	-
114	817	Firebirds	-	Gulls	-
118	843	Bears	-	Gulls	-
120	854	Gulls	-	Wolfpack	-
122	871	Gulls	-	Admirals	-
124	886	Monsters	-	Gulls	-
127	909	Aces	-	Gulls	-
129	926	Gulls	-	Reign	-
131	940	Barracuda	-	Gulls	-
133	955	Gulls	-	Swamp Rabbits	-
135	972	Penguins	-	Gulls	-
137	984	Gulls	-	Americans	-
139	996	Gulls	-	Wolves	-
141	1011	Admirals	-	Gulls	-
144	1036	Reign	-	Gulls	-
146	1044	Gulls	-	Aces	-
149	1068	Gulls	-	Griffins	-
150	1078	Crunch	-	Gulls	-
153	1094	Gulls	-	Barracuda	-
Trade Deadline --- Trades can’t be done after this day is simulated!
154	1105	Gulls	-	Marlies	-
156	1115	Heat	-	Gulls	-
159	1140	Devils	-	Gulls	-
160	1149	Gulls	-	Swamp Rabbits	-
163	1164	Gulls	-	Heat	-
165	1178	Barracuda	-	Gulls	-
170	1207	Wheat Kings	-	Gulls	-
172	1223	Wheat Kings	-	Gulls	-
173	1234	Gulls	-	Marlies	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	200	100
Attendance	18,609	8,972
Attendance PCT	71.57%	69.02%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
26	2122 - 70.72%	497,431$	6,466,600$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Salaries Cap	Coaches Salaries
766,340$	2,175,000$	2,175,000$	250,000$0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
12,288$	685,799$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
12,933,200$	120	13,701$	1,644,120$

Gulls Players Stat Leaders (Regular Season)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Gulls Goalies Stat Leaders (Regular Season)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

Gulls Career Team Stats

Show or Hide Columns

	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

Gulls Players Stat Leaders (Play-Off)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Gulls Goalies Stat Leaders (Play-Off)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

	Gulls
	GP: 26 \| W: 20 \| L: 5 \| OTL: 1 \| P: 41 GF: 99 \| GA: 72 \| PP%: 20.56% \| PK%: 83.02% GM : Mark Donais \| Morale : 50 \| Team Overall : 61 Next Games #414 vs Falcons