Seattle
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Web Lines Editor

Game Center
	Seattle 60-20-2, 122pts	4	FINAL	2	Las Vegas 33-41-8, 74pts

Team Stats
W3	Streak	L3
33-7-1	Home Record	19-19-3
27-13-1	Away Record	14-22-5
7-2-1	Last 10 Games	3-6-1
4.16	Goals Per Game	3.52
2.50	Goals Against Per Game	4.12
24.24%	Power Play Percentage	24.03%
81.51%	Penalty Kill Percentage	72.43%

Las Vegas
33-41-8, 74pts

FINAL

Seattle
60-20-2, 122pts

Team Stats
L3	Streak	W3
19-19-3	Home Record	33-7-1
14-22-5	Away Record	27-13-1
3-6-1	Last 10 Games	7-2-1
3.52	Goals Per Game	4.16
4.12	Goals Against Per Game	2.50
24.03%	Power Play Percentage	24.24%
72.43%	Penalty Kill Percentage	81.51%

Team Leaders
	Goals Ty Dellandrea 31	Assists Jake Walman 61

	Points Radim Zohorna 85	Plus/Minus Kale Clague 59

	Wins Alex Nedeljkovic 56	Save Percentage Alex Nedeljkovic 0.931

Team Stats
Goals For 341 4.16 GFG	Shots For 3404 41.51 Avg	Power Play Percentage 24.2% 64 GF	Offensive Zone Start 41.3%
Goals Against 205 2.50 GAA	Shots Against 2915 35.55 Avg	Penalty Kill Percentage 81.5%% 44 GA	Defensive Zone Start 40.7%

Team Info
	General Manager	Yvon Bergeron
	Division	Mid-Ouest
	Conference	Ouest
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	6,000
	Attendance	5,704
	Season Tickets	600
Roster Info
	Pro Team	16
	Farm Team	23
	Contract Limit	39 / 50
	Prospects	18

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	Age	Contract	Salary Average
1	Oliver Wahlstrom			X		100.00	79	59	72	80	77	62	84	69	38	62	71	60	41	58	59	0	50	64	0	21	2	925,001$
2	Nicholas Merkley			X		100.00	75	43	99	79	68	62	76	69	56	67	60	76	25	47	47	0	50	64	0	24	1	1,100,000$
3	Nicolas Aube-Kubel			X		100.00	84	54	81	76	68	56	88	60	32	63	68	68	25	61	61	0	50	63	0	25	1	1,573,000$
4	Radim Zohorna	X	X	X		100.00	79	45	91	68	85	56	64	67	37	69	62	68	25	46	46	0	50	62	0	25	3	792,500$
5	Ty Dellandrea (R)	X		X		100.00	74	71	82	75	71	74	77	64	80	59	64	65	61	46	46	0	50	62	0	21	3	863,333$
6	Kyle Rau	X	X	X		100.00	64	61	72	68	61	63	63	68	80	62	68	62	65	51	52	0	50	61	0	28	1	600,000$
7	Nolan Foote (R)		X			100.00	75	74	76	76	74	69	72	61	50	57	61	64	58	45	45	0	50	61	0	20	2	894,167$
8	Brett Murray		X			100.00	73	78	67	69	87	64	76	61	25	66	60	61	25	45	45	0	50	60	0	23	3	775,000$
9	Jansen Harkins	X	X			100.00	69	52	93	67	68	54	77	58	57	55	59	65	25	61	62	0	50	59	0	24	1	750,000$
10	Fredrik Karlstrom	X				100.00	80	73	97	66	73	60	61	57	71	49	61	65	58	44	44	0	50	58	0	23	3	700,000$
11	Jake Walman				X	100.00	73	42	93	74	65	64	64	64	25	51	48	74	25	58	59	0	50	63	0	25	1	910,000$
12	Kale Clague				X	100.00	63	41	83	76	65	70	67	69	25	56	48	66	25	50	50	0	50	61	0	23	4	1,700,000$
13	Jeremy Davies				X	100.00	69	66	76	68	66	65	68	55	25	51	43	60	41	46	46	0	50	57	0	24	1	925,000$
14	Simon Lundmark (R)				X	100.00	80	75	91	70	75	51	52	47	25	38	41	63	39	44	44	0	50	56	0	20	4	850,833$
Scratches

	TEAM AVERAGE					100.00	74	60	84	72	72	62	71	62	45	58	58	66	38	50	50	0	50	61

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	Age	Contract	Salary Average
1	Alex Nedeljkovic	100.00	69	76	73	70	68	70	69	70	67	69	75	55	56	0	50	67	0	25	3	2,800,000$
2	Spencer Martin	100.00	59	49	61	73	62	64	60	65	66	65	30	44	44	0	50	60	0	26	2	750,000$
Scratches

	TEAM AVERAGE	100.00	64	63	67	72	65	67	65	68	67	67	53	50	50	0	50	64

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	HT	P/20	PSG	PSS	FW	FT	S1	S2	S3
1	Radim Zohorna	Seattle (SEA)	C/LW/RW	79	29	56	85	30	30	0	93	151	277	65	184	10.47%	31	1383	17.52	6	22	28	50	182	0	0	0	2	9	5	1	42.29%	1719	0	1.23	0	0	0	0	3	3	4
2	Ty Dellandrea	Seattle (SEA)	C/RW	79	31	48	79	26	61	15	145	202	319	85	256	9.72%	17	1537	19.46	5	10	15	50	205	0	0	0	0	6	7	2	59.23%	2065	1	1.03	0	0	1	2	3	1	6
3	Jake Walman	Seattle (SEA)	D	82	14	61	75	41	34	0	146	103	174	64	114	8.05%	121	1912	23.32	6	17	23	85	220	0	1	1	1	191	2	0	0.00%	0	0	0.78	0	0	0	0	3	2	2
4	Kale Clague	Seattle (SEA)	D	82	16	58	74	59	32	0	98	96	151	40	104	10.60%	112	1782	21.73	5	16	21	55	207	0	0	0	0	197	6	1	0.00%	0	1	0.83	0	0	0	0	1	4	1
5	William Carrier	Seattle	LW	61	30	42	72	38	48	0	189	110	231	72	173	12.99%	16	1231	20.19	8	7	15	47	152	0	1	1	2	130	6	0	46.02%	176	0	1.17	0	3	0	0	7	6	4
6	Mason Appleton	Seattle	LW/RW	64	32	39	71	26	8	0	84	148	323	87	226	9.91%	23	1365	21.33	5	8	13	54	176	0	0	0	1	119	4	1	40.24%	169	2	1.04	3	3	0	0	5	5	4
7	Oliver Wahlstrom	Seattle (SEA)	RW	76	23	45	68	30	34	0	155	105	290	85	231	7.93%	10	1328	17.48	5	9	14	25	89	1	0	1	3	74	2	0	47.90%	119	0	1.02	2	2	0	0	2	2	5
8	Nicholas Merkley	Seattle (SEA)	RW	50	27	37	64	5	8	0	31	151	252	56	183	10.71%	14	1082	21.64	8	4	12	39	108	0	0	0	1	97	6	1	50.91%	275	1	1.18	1	3	0	0	6	3	4
9	Nolan Foote	Seattle (SEA)	LW	74	21	37	58	14	49	15	98	109	252	59	161	8.33%	10	1198	16.19	6	7	13	34	121	0	0	0	0	61	4	2	43.88%	139	0	0.97	0	0	1	2	1	4	5
10	Owen Tippett	Seattle	LW/RW	59	28	25	53	25	16	0	122	136	289	109	246	9.69%	15	1402	23.78	8	3	11	49	161	1	0	1	6	158	6	0	36.45%	502	0	0.76	1	3	0	0	4	6	4
11	Nicolas Aube-Kubel	Seattle (SEA)	RW	79	14	37	51	24	54	0	157	86	144	54	113	9.72%	53	1184	15.00	0	0	0	3	12	0	0	0	0	35	3	0	37.50%	32	0	0.86	0	0	0	0	3	3	2
12	Alex Barre-Boulet	Seattle	C	35	19	25	44	29	4	0	13	81	127	23	75	14.96%	4	559	15.99	0	3	3	4	32	0	0	0	0	6	1	4	42.94%	708	1	1.57	0	0	0	0	4	2	4
13	Kyle Rau	Seattle (SEA)	C/LW/RW	78	17	27	44	32	8	0	43	137	207	64	153	8.21%	10	1114	14.29	0	0	0	4	17	0	0	0	3	77	1	0	59.33%	1018	1	0.79	0	0	0	0	2	3	3
14	Daniel Sprong	Seattle	RW	49	19	22	41	27	4	0	18	65	199	45	132	9.55%	16	860	17.56	2	5	7	23	89	0	1	1	0	31	1	0	31.58%	57	0	0.95	1	1	0	0	1	2	4
15	Travis Dermott	Seattle	D	61	7	28	35	30	10	0	98	51	126	39	89	5.56%	70	1440	23.61	5	6	11	54	170	0	0	0	0	148	0	0	0.00%	0	0	0.49	0	0	0	0	0	2	0
16	Brett Murray	Seattle (SEA)	LW	82	17	15	32	25	41	5	112	68	150	35	75	11.33%	33	1106	13.49	0	0	0	1	6	0	0	0	0	16	3	2	29.11%	79	0	0.58	0	0	0	1	2	2	1
17	Brendan Guhle	Seattle	D	56	3	16	19	29	73	15	137	37	67	18	33	4.48%	60	1099	19.63	0	3	3	13	125	0	0	0	0	91	2	0	0.00%	0	0	0.35	0	0	1	1	1	0	1
18	Jeremy Davies	Seattle (SEA)	D	41	5	13	18	13	33	5	70	25	35	8	20	14.29%	47	821	20.03	5	5	10	13	89	0	0	0	0	75	1	0	0.00%	0	0	0.44	0	0	0	1	0	1	0
19	Haydn Fleury	Seattle	D	10	0	11	11	14	12	0	28	12	23	7	9	0.00%	11	229	22.93	0	1	1	7	16	0	0	0	0	21	0	0	0.00%	0	0	0.96	0	0	0	0	0	0	0
20	Fredrik Karlstrom	Seattle (SEA)	C	48	3	7	10	5	10	0	21	19	30	7	20	10.00%	12	288	6.01	0	1	1	1	11	0	0	0	0	27	1	0	55.24%	105	0	0.69	0	0	0	0	0	0	0
21	Jansen Harkins	Seattle (SEA)	C/LW	41	2	7	9	1	12	0	35	34	38	14	45	5.26%	31	527	12.86	0	0	0	0	0	0	0	0	0	3	0	0	49.11%	112	0	0.34	0	0	0	0	0	0	0
22	Simon Lundmark	Seattle (SEA)	D	23	3	4	7	8	20	10	64	13	16	4	9	18.75%	35	398	17.34	0	1	1	4	19	0	0	0	0	30	0	0	0.00%	0	0	0.35	0	0	1	1	0	1	1
23	Danton Heinen	Seattle	LW/RW	1	0	1	1	-1	0	0	1	4	6	4	4	0.00%	0	21	21.82	0	0	0	0	0	0	0	0	0	0	0	0	100.00%	1	0	0.92	0	0	0	0	0	0	0
Team Total or Average				1310	360	661	1021	530	601	65	1958	1943	3726	1044	2655	9.66%	751	23875	18.23	74	128	202	615	2216	2	3	5	19	1610	61	14	49.68%	7276	7	0.86	8	15	4	8	48	52	55

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	PIM	SO	GA	SA	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Alex Nedeljkovic	Seattle (SEA)	76	56	17	2	0.931	2.42	4505	6	6	182	2629	1	1.000	5	76	0	15	5	3
2	Spencer Martin	Seattle (SEA)	4	2	1	0	0.919	3.28	183	0	0	10	123	0	0.750	8	2	36	0	0	0
Team Total or Average			80	58	18	2	0.930	2.46	4689	6	6	192	2752	1		13	78	36	15	5	3

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	Salary Year 3	Salary Year 4	Link
Alex Nedeljkovic	Seattle (SEA)	G	25	1996-01-07	No	189 Lbs	6 ft0	No	No	No	3	Pro & Farm	2,800,000$	0$	0$	No	2,800,000$	2,800,000$		Link
Brett Murray	Seattle (SEA)	LW	23	1998-07-19	No	228 Lbs	6 ft5	No	No	No	3	Pro & Farm	775,000$	0$	0$	No	775,000$	775,000$		Link
Fredrik Karlstrom	Seattle (SEA)	C	23	1998-01-12	No	196 Lbs	6 ft2	No	No	No	3	Pro & Farm	700,000$	0$	0$	No	700,000$	700,000$		Link
Jake Walman	Seattle (SEA)	D	25	1996-02-20	No	170 Lbs	6 ft1	No	No	No	1	Pro & Farm	910,000$	0$	0$	No				Link
Jansen Harkins	Seattle (SEA)	C/LW	24	1997-05-23	No	182 Lbs	6 ft1	No	No	No	1	Pro & Farm	750,000$	0$	0$	No				Link
Jeremy Davies	Seattle (SEA)	D	24	1996-12-04	No	180 Lbs	5 ft11	No	No	No	1	Pro & Farm	925,000$	0$	0$	No				Link
Kale Clague	Seattle (SEA)	D	23	1998-06-05	No	177 Lbs	6 ft0	No	No	No	4	Pro & Farm	1,700,000$	0$	0$	No	1,700,000$	1,700,000$	1,700,000$	Link
Kyle Rau (1 Way Contract)	Seattle (SEA)	C/LW/RW	28	1992-10-24	No	176 Lbs	5 ft8	Yes	No	No	1	Pro & Farm	600,000$	0$	0$	No				Link
Nicholas Merkley	Seattle (SEA)	RW	24	1997-05-23	No	194 Lbs	5 ft10	No	No	Yes	1	Pro & Farm	1,100,000$	0$	0$	No				Link
Nicolas Aube-Kubel	Seattle (SEA)	RW	25	1996-05-09	No	187 Lbs	5 ft11	No	No	No	1	Pro & Farm	1,573,000$	0$	0$	No				Link
Nolan Foote	Seattle (SEA)	LW	20	2000-11-29	Yes	196 Lbs	6 ft3	No	No	No	2	Pro & Farm	894,167$	0$	0$	No	894,167$			Link
Oliver Wahlstrom	Seattle (SEA)	RW	21	2000-06-13	No	205 Lbs	6 ft2	No	No	No	2	Pro & Farm	925,001$	0$	0$	No	925,001$			Link
Radim Zohorna	Seattle (SEA)	C/LW/RW	25	1996-04-29	No	220 Lbs	6 ft6	No	No	No	3	Pro & Farm	792,500$	0$	0$	No	792,500$	792,500$		Link
Simon Lundmark	Seattle (SEA)	D	20	2000-10-08	Yes	201 Lbs	6 ft2	No	No	No	4	Pro & Farm	850,833$	0$	0$	No	850,833$	850,833$	850,833$	Link
Spencer Martin (1 Way Contract)	Seattle (SEA)	G	26	1995-06-08	No	191 Lbs	6 ft1	No	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$			Link
Ty Dellandrea	Seattle (SEA)	C/RW	21	2000-07-20	Yes	190 Lbs	6 ft1	No	No	No	3	Pro & Farm	863,333$	0$	0$	No	863,333$	863,333$		Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
16	23.56	193 Lbs	6 ft1	2.19	1,056,802$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Radim Zohorna	Ty Dellandrea	Nicholas Merkley	40	1	2	2
2	Nolan Foote	Kyle Rau	Oliver Wahlstrom	30	1	2	2
3	Brett Murray	Jansen Harkins	Nicolas Aube-Kubel	20	1	2	2
4	Nicholas Merkley	Fredrik Karlstrom	Oliver Wahlstrom	10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Walman	Kale Clague	40	1	2	2
2	Jeremy Davies	Simon Lundmark	30	1	2	2
3	Fredrik Karlstrom		20	1	2	2
4	Jake Walman	Kale Clague	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Radim Zohorna	Ty Dellandrea	Nicholas Merkley	60	1	2	2
2	Nolan Foote	Kyle Rau	Oliver Wahlstrom	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Walman	Kale Clague	60	1	2	2
2	Jeremy Davies	Simon Lundmark	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Nicholas Merkley	Oliver Wahlstrom	60	1	2	2
2	Nicolas Aube-Kubel	Ty Dellandrea	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Walman	Kale Clague	60	1	2	2
2	Jeremy Davies	Simon Lundmark	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Nicholas Merkley	60	1	2	2	Jake Walman	Kale Clague	60	1	2	2
2	Oliver Wahlstrom	40	1	2	2	Jeremy Davies	Simon Lundmark	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Nicholas Merkley	Oliver Wahlstrom	60	1	2	2
2	Nicolas Aube-Kubel	Ty Dellandrea	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Walman	Kale Clague	60	1	2	2
2	Jeremy Davies	Simon Lundmark	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Radim Zohorna	Ty Dellandrea	Nicholas Merkley	Jake Walman	Kale Clague

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Radim Zohorna	Ty Dellandrea	Nicholas Merkley	Jake Walman	Kale Clague

Extra Forwards
Normal	PowerPlay	Penalty Kill
Brett Murray, Jansen Harkins, Radim Zohorna	Brett Murray, Jansen Harkins	Radim Zohorna

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Jeremy Davies, Simon Lundmark, Jake Walman	Jeremy Davies	Simon Lundmark, Jake Walman

Penalty Shots
Nicholas Merkley, Oliver Wahlstrom, Nicolas Aube-Kubel, Ty Dellandrea, Radim Zohorna

Goalie
#1 : Alex Nedeljkovic, #2 : Spencer Martin

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	Admirals	4	4	0	0	0	0	0	0	23	5	18	2	2	0	0	0	0	0	0	10	2	8	2	2	0	0	0	0	0	0	13	3	10	8	1.000	23	43	66	0	0	132	117	86	9	189	1130	1154	1101	33	142	25	35	89	8	4	50.00%	15	1	93.33%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
2	Baby Hawks	3	0	3	0	0	0	0	0	5	13	-8	1	0	1	0	0	0	0	0	0	4	-4	2	0	2	0	0	0	0	0	5	9	-4	0	0.000	5	9	14	0	0	132	117	86	9	72	1130	1154	1101	33	119	28	50	71	7	1	14.29%	12	2	83.33%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
3	Bears	2	1	1	0	0	0	0	0	9	7	2	1	0	1	0	0	0	0	0	3	5	-2	1	1	0	0	0	0	0	0	6	2	4	2	0.500	9	17	26	0	0	132	117	86	9	66	1130	1154	1101	33	58	15	8	43	9	4	44.44%	4	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
4	Bruins	2	2	0	0	0	0	0	0	10	3	7	1	1	0	0	0	0	0	0	6	1	5	1	1	0	0	0	0	0	0	4	2	2	4	1.000	10	18	28	0	0	132	117	86	9	102	1130	1154	1101	33	54	16	14	38	8	1	12.50%	6	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
5	Cabaret Lady Mary Ann	2	2	0	0	0	0	0	0	17	4	13	1	1	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	8	2	6	4	1.000	17	33	50	0	0	132	117	86	9	132	1130	1154	1101	33	66	15	12	61	2	2	100.00%	6	1	83.33%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
6	Caroline	2	1	1	0	0	0	0	0	10	4	6	1	1	0	0	0	0	0	0	9	1	8	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	10	20	30	0	0	132	117	86	9	105	1130	1154	1101	33	72	21	16	64	5	1	20.00%	8	3	62.50%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
7	Chiefs	3	3	0	0	0	0	0	0	14	6	8	2	2	0	0	0	0	0	0	10	4	6	1	1	0	0	0	0	0	0	4	2	2	6	1.000	14	24	38	0	0	132	117	86	9	132	1130	1154	1101	33	87	22	14	65	11	1	9.09%	7	1	85.71%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
8	Chill	3	3	0	0	0	0	0	0	14	7	7	1	1	0	0	0	0	0	0	4	1	3	2	2	0	0	0	0	0	0	10	6	4	6	1.000	14	25	39	0	0	132	117	86	9	118	1130	1154	1101	33	101	17	24	83	11	3	27.27%	11	1	90.91%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
9	Comets	4	2	1	0	1	0	0	0	16	11	5	2	1	1	0	0	0	0	0	7	6	1	2	1	0	0	1	0	0	0	9	5	4	6	0.750	16	28	44	0	1	132	117	86	9	165	1130	1154	1101	33	168	39	41	76	12	4	33.33%	19	6	68.42%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
10	Cougars	2	0	2	0	0	0	0	0	6	10	-4	1	0	1	0	0	0	0	0	5	7	-2	1	0	1	0	0	0	0	0	1	3	-2	0	0.000	6	10	16	1	0	132	117	86	9	85	1130	1154	1101	33	97	26	16	49	6	1	16.67%	8	2	75.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
11	Crunch	2	1	1	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	4	1	3	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	6	12	18	0	0	132	117	86	9	90	1130	1154	1101	33	73	13	8	58	6	1	16.67%	3	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
12	Heat	3	1	0	0	1	0	1	0	15	11	4	2	1	0	0	0	0	1	0	10	7	3	1	0	0	0	1	0	0	0	5	4	1	6	1.000	15	26	41	0	0	132	117	86	9	125	1130	1154	1101	33	121	39	16	65	2	0	0.00%	7	1	85.71%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
13	Jayhawks	3	2	0	0	0	1	0	0	17	13	4	2	1	0	0	0	1	0	0	10	9	1	1	1	0	0	0	0	0	0	7	4	3	5	0.833	17	28	45	0	0	132	117	86	9	134	1130	1154	1101	33	104	32	20	71	10	3	30.00%	8	3	62.50%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
14	Las Vegas	4	3	1	0	0	0	0	0	18	8	10	2	2	0	0	0	0	0	0	12	3	9	2	1	1	0	0	0	0	0	6	5	1	6	0.750	18	35	53	0	1	132	117	86	9	180	1130	1154	1101	33	125	29	34	98	15	4	26.67%	12	2	83.33%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
15	Manchots	2	1	1	0	0	0	0	0	5	3	2	1	1	0	0	0	0	0	0	5	2	3	1	0	1	0	0	0	0	0	0	1	-1	2	0.500	5	9	14	0	0	132	117	86	9	71	1130	1154	1101	33	68	14	12	50	4	0	0.00%	6	1	83.33%	1	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
16	Marlies	2	2	0	0	0	0	0	0	10	4	6	1	1	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	4	2	2	4	1.000	10	18	28	0	0	132	117	86	9	72	1130	1154	1101	33	61	17	6	38	9	2	22.22%	3	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
17	Minnesota	3	2	1	0	0	0	0	0	11	8	3	1	1	0	0	0	0	0	0	5	3	2	2	1	1	0	0	0	0	0	6	5	1	4	0.667	11	21	32	0	0	132	117	86	9	115	1130	1154	1101	33	121	29	23	77	5	0	0.00%	9	2	77.78%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
18	Monarchs	4	4	0	0	0	0	0	0	17	7	10	2	2	0	0	0	0	0	0	7	4	3	2	2	0	0	0	0	0	0	10	3	7	8	1.000	17	31	48	0	0	132	117	86	9	196	1130	1154	1101	33	130	32	16	98	14	3	21.43%	7	1	85.71%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
19	Monsters	2	1	1	0	0	0	0	0	6	6	0	1	0	1	0	0	0	0	0	4	5	-1	1	1	0	0	0	0	0	0	2	1	1	2	0.500	6	9	15	0	0	132	117	86	9	81	1130	1154	1101	33	92	19	6	36	5	1	20.00%	3	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
20	Monsters	3	1	0	0	0	0	2	0	13	8	5	1	0	0	0	0	0	1	0	6	5	1	2	1	0	0	0	0	1	0	7	3	4	6	1.000	13	21	34	0	1	132	117	86	9	104	1130	1154	1101	33	89	30	16	67	16	3	18.75%	8	3	62.50%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
21	Oceanics	3	2	0	0	0	0	1	0	13	7	6	2	1	0	0	0	0	1	0	10	6	4	1	1	0	0	0	0	0	0	3	1	2	6	1.000	13	23	36	0	0	132	117	86	9	132	1130	1154	1101	33	121	26	8	75	19	4	21.05%	4	1	75.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
22	Oil Kings	4	2	2	0	0	0	0	0	14	15	-1	2	2	0	0	0	0	0	0	9	4	5	2	0	2	0	0	0	0	0	5	11	-6	4	0.500	14	27	41	0	0	132	117	86	9	140	1130	1154	1101	33	139	38	58	87	12	1	8.33%	17	2	88.24%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
23	Phantoms	2	2	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	3	0	3	4	1.000	7	12	19	0	1	132	117	86	9	75	1130	1154	1101	33	102	16	6	46	6	1	16.67%	3	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
24	Rocket	2	2	0	0	0	0	0	0	8	5	3	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	3	1	4	1.000	8	13	21	0	0	132	117	86	9	101	1130	1154	1101	33	59	19	14	56	11	3	27.27%	7	3	57.14%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
25	Senators	2	2	0	0	0	0	0	0	9	2	7	1	1	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	4	1	3	4	1.000	9	17	26	0	0	132	117	86	9	77	1130	1154	1101	33	63	18	14	46	6	4	66.67%	7	1	85.71%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
26	Sharks	3	2	0	0	0	1	0	0	15	6	9	1	1	0	0	0	0	0	0	6	2	4	2	1	0	0	0	1	0	0	9	4	5	5	0.833	15	27	42	0	1	132	117	86	9	136	1130	1154	1101	33	106	31	18	68	8	4	50.00%	9	0	100.00%	1	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
27	Sound Tigers	2	1	1	0	0	0	0	0	7	6	1	1	1	0	0	0	0	0	0	4	1	3	1	0	1	0	0	0	0	0	3	5	-2	2	0.500	7	14	21	0	0	132	117	86	9	89	1130	1154	1101	33	63	18	10	43	13	4	30.77%	5	2	60.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
28	Spiders	2	2	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	2	1	4	1.000	6	10	16	0	0	132	117	86	9	75	1130	1154	1101	33	76	17	12	36	7	1	14.29%	6	3	50.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
29	Stars	3	2	1	0	0	0	0	0	7	5	2	2	1	1	0	0	0	0	0	4	4	0	1	1	0	0	0	0	0	0	3	1	2	4	0.667	7	13	20	0	0	132	117	86	9	91	1130	1154	1101	33	102	47	20	68	6	1	16.67%	8	1	87.50%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
30	Thunder	2	1	1	0	0	0	0	0	7	3	4	1	1	0	0	0	0	0	0	5	0	5	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	7	13	20	0	1	132	117	86	9	78	1130	1154	1101	33	51	11	8	48	7	0	0.00%	4	0	100.00%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
31	Wolf Pack	2	0	2	0	0	0	0	0	6	8	-2	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	4	5	-1	0	0.000	6	11	17	0	0	132	117	86	9	76	1130	1154	1101	33	85	27	12	49	4	2	50.00%	6	1	83.33%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
	Total	82	54	20	0	2	2	4	0	341	205	136	41	30	7	0	0	1	3	0	188	101	87	41	24	13	0	2	1	1	0	153	104	49	122	0.744	341	617	958	1	6	132	117	86	9	3404	1130	1154	1101	33	2915	746	567	1919	264	64	24.24%	238	44	81.51%	2	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
	_Since Last GM Reset	82	54	20	0	2	2	4	0	341	205	136	41	30	7	0	0	1	3	0	188	101	87	41	24	13	0	2	1	1	0	153	104	49	122	0.744	341	617	958	1	6	132	117	86	9	3404	1130	1154	1101	33	2915	746	567	1919	264	64	24.24%	238	44	81.51%	2	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
	_Vs Conference	43	24	13	0	2	1	3	0	177	125	52	22	15	4	0	0	1	2	0	104	62	42	21	9	9	0	2	0	1	0	73	63	10	59	0.686	177	320	497	1	3	132	117	86	9	1771	1130	1154	1101	33	1542	427	358	1033	126	26	20.63%	139	32	76.98%	0	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549
	_Vs Division	26	8	7	0	0	0	0	0	118	63	55	13	5	2	0	0	0	0	0	61	28	33	13	3	5	0	0	0	0	0	57	35	22	16	0.308	118	217	335	0	3	132	117	86	9	1131	1130	1154	1101	33	931	233	218	581	71	20	28.17%	86	13	84.88%	1	1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%	2087	1470	1796	579	1064	549

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
82	122	W3	341	617	958	3404	2915	746	567	1919	1	6

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	54	20	2	2	4	0	341	205

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	30	7	0	1	3	0	188	101

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	24	13	2	1	1	0	153	104

Last 10 Games
W	L	OTW	OTL	SOW	SOL
7	2	0	1	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
264	64	24.24%	238	44	81.51%	2

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
1130	1154	1101	33	132	117	86	9

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
1495	3037	49.23%	1417	2992	47.36%	658	1321	49.81%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
2087	1470	1796	579	1064	549

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
6 - 2022-10-12	9	Seattle	8	Admirals	2	AW			BoxScore
7 - 2022-10-13	20	Seattle	6	Monarchs	1	AW			BoxScore
9 - 2022-10-15	36	Las Vegas	3	Seattle	7	BW			BoxScore
11 - 2022-10-17	47	Caroline	1	Seattle	9	BW			BoxScore
13 - 2022-10-19	58	Chiefs	2	Seattle	3	BW			BoxScore
15 - 2022-10-21	73	Seattle	3	Monsters	0	AW			BoxScore
17 - 2022-10-23	87	Seattle	3	Baby Hawks	4	AL			BoxScore
19 - 2022-10-25	105	Crunch	1	Seattle	4	BW			BoxScore
21 - 2022-10-27	118	Comets	5	Seattle	3	BL			BoxScore
23 - 2022-10-29	137	Manchots	2	Seattle	5	BW			BoxScore
26 - 2022-11-01	155	Seattle	5	Heat	4	AW	X		BoxScore
28 - 2022-11-03	167	Seattle	4	Minnesota	1	AW			BoxScore
30 - 2022-11-05	184	Seattle	0	Manchots	1	AL			BoxScore
33 - 2022-11-08	205	Chill	1	Seattle	4	BW			BoxScore
36 - 2022-11-11	224	Minnesota	3	Seattle	5	BW			BoxScore
38 - 2022-11-13	243	Oceanics	4	Seattle	5	BW	X	X	BoxScore
42 - 2022-11-17	271	Wolf Pack	3	Seattle	2	BL			BoxScore
44 - 2022-11-19	286	Monarchs	2	Seattle	3	BW			BoxScore
48 - 2022-11-23	316	Sharks	2	Seattle	6	BW			BoxScore
50 - 2022-11-25	329	Seattle	2	Las Vegas	3	AL			BoxScore
52 - 2022-11-27	343	Seattle	5	Admirals	1	AW			BoxScore
54 - 2022-11-29	360	Seattle	4	Monarchs	2	AW			BoxScore
56 - 2022-12-01	374	Bears	5	Seattle	3	BL			BoxScore
58 - 2022-12-03	389	Cabaret Lady Mary Ann	2	Seattle	9	BW			BoxScore
61 - 2022-12-06	411	Rocket	2	Seattle	4	BW			BoxScore
64 - 2022-12-09	427	Seattle	6	Bears	2	AW			BoxScore
66 - 2022-12-11	444	Seattle	8	Cabaret Lady Mary Ann	2	AW			BoxScore
68 - 2022-12-13	459	Seattle	2	Thunder	3	AL			BoxScore
70 - 2022-12-15	475	Seattle	1	Caroline	3	AL			BoxScore
73 - 2022-12-18	500	Oceanics	2	Seattle	5	BW			BoxScore
75 - 2022-12-20	515	Chiefs	2	Seattle	7	BW			BoxScore
77 - 2022-12-22	530	Seattle	3	Comets	0	AW			BoxScore
83 - 2022-12-28	561	Heat	4	Seattle	6	BW			BoxScore
85 - 2022-12-30	577	Oil Kings	1	Seattle	2	BW			BoxScore
87 - 2023-01-01	594	Sound Tigers	1	Seattle	4	BW			BoxScore
89 - 2023-01-03	606	Seattle	1	Oil Kings	4	AL			BoxScore
91 - 2023-01-05	615	Seattle	4	Marlies	2	AW			BoxScore
93 - 2023-01-07	632	Seattle	4	Senators	1	AW			BoxScore
95 - 2023-01-09	646	Seattle	4	Rocket	3	AW			BoxScore
96 - 2023-01-10	651	Seattle	2	Crunch	3	AL			BoxScore
98 - 2023-01-12	665	Seattle	4	Bruins	2	AW			BoxScore
100 - 2023-01-14	687	Seattle	2	Baby Hawks	5	AL			BoxScore
102 - 2023-01-16	698	Thunder	0	Seattle	5	BW			BoxScore
103 - 2023-01-17	711	Seattle	4	Oil Kings	7	AL			BoxScore
105 - 2023-01-19	728	Spiders	2	Seattle	3	BW			BoxScore
107 - 2023-01-21	743	Monsters	5	Seattle	6	BW	X	X	BoxScore
111 - 2023-01-25	770	Comets	1	Seattle	4	BW			BoxScore
113 - 2023-01-27	786	Heat	3	Seattle	4	BW	X	X	BoxScore
114 - 2023-01-28	797	Monsters	5	Seattle	4	BL			BoxScore
124 - 2023-02-07	816	Seattle	3	Sound Tigers	5	AL			BoxScore
126 - 2023-02-09	824	Seattle	3	Spiders	2	AW			BoxScore
127 - 2023-02-10	828	Seattle	4	Wolf Pack	5	AL			BoxScore
129 - 2023-02-12	848	Seattle	3	Phantoms	0	AW			BoxScore
131 - 2023-02-14	860	Seattle	3	Oceanics	1	AW			BoxScore
133 - 2023-02-16	876	Phantoms	2	Seattle	4	BW			BoxScore
135 - 2023-02-18	894	Cougars	7	Seattle	5	BL			BoxScore
137 - 2023-02-20	904	Seattle	3	Sharks	4	AL	X		BoxScore
140 - 2023-02-23	927	Bruins	1	Seattle	6	BW			BoxScore
143 - 2023-02-26	952	Marlies	2	Seattle	6	BW			BoxScore
145 - 2023-02-28	964	Seattle	4	Chiefs	2	AW			BoxScore
147 - 2023-03-02	978	Seattle	1	Cougars	3	AL			BoxScore
148 - 2023-03-03	984	Seattle	2	Monsters	1	AW			BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
150 - 2023-03-05	1005	Seattle	4	Monsters	3	AW	X	X	BoxScore
152 - 2023-03-07	1021	Admirals	1	Seattle	4	BW			BoxScore
154 - 2023-03-09	1035	Senators	1	Seattle	5	BW			BoxScore
156 - 2023-03-11	1049	Stars	3	Seattle	4	BW			BoxScore
158 - 2023-03-13	1063	Stars	1	Seattle	0	BL			BoxScore
161 - 2023-03-16	1090	Seattle	6	Sharks	0	AW			BoxScore
163 - 2023-03-18	1098	Oil Kings	3	Seattle	7	BW			BoxScore
166 - 2023-03-21	1130	Seattle	3	Stars	1	AW			BoxScore
168 - 2023-03-23	1142	Seattle	7	Chill	4	AW			BoxScore
170 - 2023-03-25	1152	Seattle	3	Chill	2	AW			BoxScore
172 - 2023-03-27	1173	Seattle	2	Minnesota	4	AL			BoxScore
175 - 2023-03-30	1198	Admirals	1	Seattle	6	BW			BoxScore
177 - 2023-04-01	1213	Monarchs	2	Seattle	4	BW			BoxScore
179 - 2023-04-03	1228	Jayhawks	4	Seattle	6	BW			BoxScore
180 - 2023-04-04	1238	Seattle	6	Comets	5	AW	X		BoxScore
182 - 2023-04-06	1253	Jayhawks	5	Seattle	4	BL	X		BoxScore
184 - 2023-04-08	1271	Baby Hawks	4	Seattle	0	BL			BoxScore
186 - 2023-04-10	1283	Seattle	7	Jayhawks	4	AW			BoxScore
187 - 2023-04-11	1293	Seattle	4	Las Vegas	2	AW			BoxScore
189 - 2023-04-13	1312	Las Vegas	0	Seattle	5	BW			BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	4000	2000
Ticket Price	35	15
Attendance	155,836	78,045
Attendance PCT	95.02%	95.18%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
0	5704 - 95.07%	161,584$	6,624,935$	6000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
1,761,492$	1,555,883$	1,555,883$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
8,189$	1,761,492$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	8,189$	0$

Seattle Stat Leaders (Regular Season)

Show or Hide Column

#	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

Seattle Goalies Stat Leaders (Regular Season)

Show or Hide Column

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

Seattle Career Team Stats

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

Seattle Stat Leaders (Play-Off)

Show or Hide Column

#	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

Seattle Goalies Stat Leaders (Play-Off)

Show or Hide Column

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

	Seattle
	GP: 82 \| W: 60 \| L: 20 \| OTL: 2 \| P: 122 GF: 341 \| GA: 205 \| PP%: 24.24% \| PK%: 81.51% GM : Yvon Bergeron \| Morale : 50 \| Team Overall : 61