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

Game Center
	Stars 56-20-6, 118pts	2	FINAL	5	Chiefs 31-39-12, 74pts

Team Stats
W1	Streak	L1
28-10-3	Home Record	16-18-7
28-10-3	Away Record	15-21-5
6-2-2	Last 10 Games	5-5-0
3.65	Goals Per Game	3.20
2.33	Goals Against Per Game	4.00
20.00%	Power Play Percentage	23.58%
87.18%	Penalty Kill Percentage	75.70%

Chiefs
31-39-12, 74pts

FINAL

Stars
56-20-6, 118pts

Team Stats
L1	Streak	W1
16-18-7	Home Record	28-10-3
15-21-5	Away Record	28-10-3
5-5-0	Last 10 Games	6-2-2
3.20	Goals Per Game	3.65
4.00	Goals Against Per Game	2.33
23.58%	Power Play Percentage	20.00%
75.70%	Penalty Kill Percentage	87.18%

Team Leaders
	Goals Nathan Smith 42	Assists Alexander Holtz 50

	Points Nathan Smith 91	Plus/Minus Nathan Smith 58

	Wins Spencer Knight 53	Save Percentage Spencer Knight 0.93

Team Stats
Goals For 299 3.65 GFG	Shots For 3136 38.24 Avg	Power Play Percentage 20.0% 50 GF	Offensive Zone Start 42.3%
Goals Against 191 2.33 GAA	Shots Against 2636 32.15 Avg	Penalty Kill Percentage 87.2%% 25 GA	Defensive Zone Start 39.1%

Team Info
	General Manager	Julien Desrosiers
	Division	Nord
	Conference	Ouest
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,998
	Season Tickets	300
Roster Info
	Pro Team	23
	Farm Team	18
	Contract Limit	41 / 50
	Prospects	16

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	Max Comtois		X			100.00	86	69	75	78	80	67	79	72	43	65	62	62	25	59	59	0	50	64	0	22	1	792,500$
2	Taylor Raddysh	X		X		100.00	79	45	95	74	80	63	76	68	36	62	68	69	25	53	53	0	50	64	0	23	2	750,000$
3	Sam Steel	X	X			100.00	65	41	91	81	67	63	81	63	69	66	59	68	25	62	62	0	50	63	0	23	2	1,000,000$
4	Jordan Martinook	X	X	X		100.00	79	44	88	79	71	59	77	61	42	62	59	62	25	69	71	0	50	62	0	29	2	1,500,000$
5	Tobias Rieder	X	X	X		100.00	62	41	97	83	67	58	71	55	29	51	56	83	24	69	70	0	50	62	0	28	1	1,700,000$
6	Nathan Smith	X				100.00	91	46	93	70	71	67	41	74	74	65	72	63	25	44	44	0	50	62	0	22	3	650,000$
7	Alexander Holtz (R)			X		100.00	67	42	92	78	70	62	62	70	25	67	55	69	25	44	44	0	50	61	0	19	2	925,000$
8	Alexander Volkov			X		100.00	71	43	93	76	72	55	67	54	36	58	66	66	24	59	60	0	50	60	0	24	1	864,167$
9	Egor Afanasyev (R)	X	X	X		100.00	78	76	82	70	76	58	58	58	73	55	56	65	53	44	44	0	50	58	0	20	4	789,167$
10	Filip Cederqvist (R)		X			100.00	53	50	99	65	77	68	89	51	62	46	47	48	49	58	58	0	50	54	0	21	4	850,000$
11	Jake Bean				X	100.00	67	42	88	80	69	81	80	66	25	61	50	77	75	57	58	0	50	67	0	23	2	2,450,000$
12	Ty Smith				X	100.00	69	42	89	84	64	74	83	67	25	57	49	66	25	57	57	0	50	65	0	21	3	863,333$
13	Alec Regula (R)				X	100.00	79	45	77	70	78	75	64	67	25	39	49	78	25	45	45	0	50	64	0	21	3	866,667$
14	Thomas Harley (R)				X	100.00	73	43	93	78	73	63	75	61	25	48	48	63	25	47	47	0	50	61	0	20	2	894,167$
15	Zachary Jones (R)				X	100.00	61	40	99	70	62	66	63	61	25	52	47	73	75	46	46	0	50	60	0	20	2	925,000$
16	Louie Belpedio				X	100.00	68	70	64	63	70	55	56	55	25	45	51	59	48	44	44	0	50	55	0	25	1	750,000$
17	Albert Johansson (R)				X	100.00	43	36	99	68	61	67	92	46	25	45	38	44	40	54	54	0	50	53	0	20	4	820,000$
Scratches
1	Liam Hawel (R)	X				100.00	73	72	89	62	72	49	50	49	64	52	41	62	41	44	44	0	50	52	0	22	2	525,000$
2	Linus Karlsson (R)	X				100.00	47	44	81	67	65	64	85	47	55	43	41	54	45	54	56	0	50	52	0	21	3	700,000$
3	Antonio Stranges (R)	X	X			100.00	68	60	98	62	60	51	54	45	58	42	41	58	41	44	44	0	50	50	0	19	3	560,000$
4	Niklas Nordgren (R)			X		100.00	47	43	80	64	63	36	39	37	49	35	32	43	35	50	50	0	50	43	0	21	3	700,000$
5	Joni Ikonen (R)	X				100.00	40	37	97	63	62	29	28	33	49	26	34	39	38	54	54	0	50	40	0	22	2	825,000$

	TEAM AVERAGE					100.00	67	49	89	72	70	60	67	57	43	52	51	62	37	53	53	0	50	58

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	Magnus Chrona (R)	100.00	45	57	54	89	39	44	50	46	42	43	43	50	46	0	50	50	0	21	3	560,000$
Scratches

	TEAM AVERAGE	100.00	45	57	54	89	39	44	50	46	42	43	43	50	46	0	50	50

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	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Nathan Smith	Stars (Dal)	C	82	42	49	91	58	38	0	168	190	367	99	301	11.44%	16	1515	18.48	6	9	15	30	120	0	0	0	1	76	7	0	56.91%	1685	0	1	1.20	0	0	0	0	0	7	9	7
2	Alexander Holtz	Stars (Dal)	RW	82	24	50	74	28	10	0	41	135	300	84	231	8.00%	11	1529	18.66	5	8	13	51	198	0	0	0	0	0	7	2	29.67%	91	1	1	0.97	0	1	0	0	0	2	3	5
3	Alexander Volkov	Stars (Dal)	RW	81	33	32	65	25	18	0	60	114	285	86	195	11.58%	9	1256	15.52	1	1	2	8	41	0	0	0	0	0	11	1	34.57%	81	0	1	1.03	0	0	0	0	0	4	3	6
4	Tobias Rieder	Stars (Dal)	C/LW/RW	81	27	37	64	24	8	0	38	138	311	96	202	8.68%	36	1490	18.40	4	7	11	51	210	0	0	0	3	94	3	2	34.95%	186	1	1	0.86	1	3	0	0	0	2	2	7
5	Taylor Raddysh	Stars (Dal)	C/RW	65	22	42	64	23	8	0	122	133	226	58	172	9.73%	25	1378	21.21	1	16	17	40	168	1	0	1	7	147	4	1	40.39%	1644	1	2	0.93	2	6	0	0	0	9	2	1
6	Ty Smith	Stars (Dal)	D	82	12	45	57	26	32	0	127	90	145	51	73	8.28%	104	1873	22.85	5	18	23	70	226	0	0	0	0	163	1	0	0.00%	0	0	0	0.61	0	0	0	0	0	3	2	1
7	Jordan Martinook	Stars (Dal)	C/LW/RW	81	27	28	55	25	27	5	126	104	216	60	190	12.50%	18	1552	19.16	4	6	10	26	199	0	0	0	2	64	4	1	43.87%	212	0	0	0.71	0	0	0	1	0	3	4	4
8	Egor Afanasyev	Stars (Dal)	C/LW/RW	82	20	34	54	33	74	20	147	161	238	64	171	8.40%	14	1315	16.04	0	0	0	3	23	0	0	0	1	64	4	0	57.35%	952	0	0	0.82	0	0	1	1	2	0	2	7
9	Alec Regula	Stars (Dal)	D	82	19	34	53	23	64	10	212	85	176	69	108	10.80%	141	1800	21.95	4	7	11	53	206	0	1	1	0	144	3	1	0.00%	1	0	0	0.59	0	0	0	0	2	4	3	7
10	Sam Steel	Stars (Dal)	C/LW	79	19	34	53	-3	8	0	84	194	261	83	192	7.28%	14	1645	20.83	7	7	14	43	198	0	0	0	4	141	2	1	56.34%	1278	1	0	0.64	1	6	0	0	0	2	5	4
11	Max Comtois	Stars (Dal)	LW	53	23	23	46	21	43	5	184	111	168	69	154	13.69%	10	1074	20.27	6	3	9	26	109	0	1	1	0	73	1	1	45.71%	280	0	0	0.86	1	4	0	1	0	4	6	3
12	Zachary Jones	Stars (Dal)	D	82	6	38	44	34	10	0	43	66	101	35	69	5.94%	102	1403	17.12	0	3	3	14	81	0	0	0	0	76	1	1	0.00%	0	0	0	0.63	0	0	0	0	0	1	1	2
13	Jake Bean	Stars (Dal)	D	38	8	25	33	5	16	0	66	76	95	35	68	8.42%	75	958	25.22	6	7	13	37	99	0	0	0	2	86	1	0	0.00%	0	0	0	0.69	0	0	0	0	0	2	2	1
14	Thomas Harley	Stars (Dal)	D	82	5	21	26	25	22	0	112	56	87	29	65	5.75%	74	1696	20.69	1	3	4	31	191	0	0	0	2	150	1	0	0.00%	0	0	0	0.31	0	0	0	0	0	0	1	0
15	Louie Belpedio	Stars (Dal)	D	81	1	18	19	27	70	0	184	36	49	25	24	2.04%	78	1216	15.02	0	0	0	1	7	0	0	0	0	31	1	0	0.00%	0	0	0	0.31	0	0	0	0	0	0	1	1
16	Liam Hawel	Stars (Dal)	C	76	2	8	10	6	16	10	41	59	48	9	28	4.17%	10	582	7.66	0	0	0	0	1	0	0	0	0	4	1	0	49.70%	495	0	0	0.34	0	0	0	0	2	0	0	0
17	Filip Cederqvist	Stars (Dal)	LW	79	3	5	8	12	2	0	5	19	41	14	29	7.32%	6	861	10.90	0	0	0	0	1	0	0	0	0	3	0	0	56.14%	57	0	0	0.19	0	0	0	0	0	0	0	1
18	Albert Johansson	Stars (Dal)	D	82	1	4	5	24	4	0	9	8	14	4	3	7.14%	33	969	11.82	0	0	0	0	48	0	0	0	0	38	0	0	0.00%	0	0	0	0.10	0	0	0	0	0	0	0	0
19	Linus Karlsson	Stars (Dal)	C	60	0	2	2	1	0	0	2	5	2	3	3	0.00%	1	97	1.62	0	0	0	1	18	0	0	0	0	1	0	0	41.94%	62	0	0	0.41	0	0	0	0	0	0	0	0
20	Antonio Stranges	Stars (Dal)	C/LW	31	1	0	1	-1	0	0	7	3	4	2	3	25.00%	1	109	3.54	0	0	0	0	1	0	0	0	0	19	0	0	70.00%	10	0	0	0.18	0	0	0	0	0	0	0	0
21	Niklas Nordgren	Stars (Dal)	RW	2	0	0	0	0	0	0	1	0	1	0	1	0.00%	1	35	17.91	0	0	0	0	1	0	0	0	0	0	0	0	40.00%	5	0	0	0.00%	0	0	0	0	0	0	0	0
Team Total or Average				1463	295	529	824	416	470	50	1779	1783	3135	975	2282	9.41%	779	24362	16.65	50	95	145	485	2156	1	2	3	22	1384	52	11	50.33%	7039	4	6	0.68	5	20	1	3	6	43	46	57

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	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Spencer Knight	Dallas	76	53	16	5	0.930	2.22	4416	6	9	163	2318	1	2	0.833	18	76	0	12	9	3
2	Magnus Chrona	Stars (Dal)	11	3	4	1	0.927	2.54	543	2	0	23	316	0	0	0.667	3	6	82	0	0	0
Team Total or Average			87	56	20	6	0.929	2.25	4960	8	9	186	2634	1	2		21	82	82	12	9	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
Albert Johansson	Stars (Dal)	D	20	2001-01-04	Yes	168 Lbs	6 ft0	No	No	No	4	Pro & Farm	820,000$	0$	0$	No	820,000$	820,000$	820,000$	Link
Alec Regula	Stars (Dal)	D	21	2000-08-05	Yes	208 Lbs	6 ft4	No	No	No	3	Pro & Farm	866,667$	0$	0$	No	866,667$	866,667$		Link
Alexander Holtz	Stars (Dal)	RW	19	2002-01-23	Yes	194 Lbs	6 ft0	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$			Link
Alexander Volkov	Stars (Dal)	RW	24	1997-08-01	No	195 Lbs	6 ft1	No	No	Yes	1	Pro & Farm	864,167$	0$	0$	No				Link
Antonio Stranges	Stars (Dal)	C/LW	19	2002-02-05	Yes	168 Lbs	5 ft10	No	No	No	3	Pro & Farm	560,000$	0$	0$	No	560,000$	560,000$		Link
Egor Afanasyev	Stars (Dal)	C/LW/RW	20	2001-01-23	Yes	203 Lbs	6 ft3	No	No	No	4	Pro & Farm	789,167$	0$	0$	No	789,167$	789,167$	789,167$	Link
Filip Cederqvist	Stars (Dal)	LW	21	2000-08-23	Yes	207 Lbs	6 ft4	No	No	No	4	Pro & Farm	850,000$	0$	0$	No	850,000$	850,000$	850,000$	Link
Jake Bean	Stars (Dal)	D	23	1998-06-09	No	186 Lbs	6 ft1	No	No	No	2	Pro & Farm	2,450,000$	0$	0$	No	2,450,000$			Link
Joni Ikonen	Stars (Dal)	C	22	1999-04-14	Yes	172 Lbs	5 ft11	No	No	No	2	Pro & Farm	825,000$	0$	0$	No	825,000$			Link
Jordan Martinook (1 Way Contract)	Stars (Dal)	C/LW/RW	29	1992-07-25	No	196 Lbs	6 ft0	No	No	Yes	2	Pro & Farm	1,500,000$	600,000$	0$	No	1,500,000$			Link
Liam Hawel	Stars (Dal)	C	22	1999-04-18	Yes	183 Lbs	6 ft5	No	No	No	2	Pro & Farm	525,000$	0$	0$	No	525,000$			Link
Linus Karlsson	Stars (Dal)	C	21	1999-11-16	Yes	179 Lbs	6 ft1	No	No	No	3	Pro & Farm	700,000$	0$	0$	No	700,000$	700,000$		Link
Louie Belpedio	Stars (Dal)	D	25	1996-05-14	No	197 Lbs	5 ft11	No	No	Yes	1	Pro & Farm	750,000$	0$	0$	No				Link
Magnus Chrona	Stars (Dal)	G	21	2000-08-28	Yes	216 Lbs	6 ft6	No	No	No	3	Pro & Farm	560,000$	0$	0$	No	560,000$	560,000$		Link
Max Comtois	Stars (Dal)	LW	22	1999-01-08	No	215 Lbs	6 ft2	No	No	No	1	Pro & Farm	792,500$	0$	0$	No				Link
Nathan Smith	Stars (Dal)	C	22	1998-10-18	No	190 Lbs	6 ft1	No	No	No	3	Pro & Farm	650,000$	0$	0$	No	650,000$	650,000$		Link
Niklas Nordgren	Stars (Dal)	RW	21	2000-05-04	Yes	183 Lbs	5 ft9	No	No	No	3	Pro & Farm	700,000$	0$	0$	No	700,000$	700,000$		Link
Sam Steel	Stars (Dal)	C/LW	23	1998-02-03	No	189 Lbs	5 ft11	No	No	No	2	Pro & Farm	1,000,000$	0$	0$	No	1,000,000$			Link
Taylor Raddysh	Stars (Dal)	C/RW	23	1998-02-18	No	216 Lbs	6 ft3	No	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$			Link
Thomas Harley	Stars (Dal)	D	20	2001-08-18	Yes	190 Lbs	6 ft3	No	No	No	2	Pro & Farm	894,167$	0$	0$	No	894,167$			Link
Tobias Rieder (1 Way Contract)	Stars (Dal)	C/LW/RW	28	1993-01-09	No	186 Lbs	5 ft11	No	No	Yes	1	Pro & Farm	1,700,000$	800,000$	0$	No				Link
Ty Smith	Stars (Dal)	D	21	2000-03-23	No	175 Lbs	5 ft11	No	No	No	3	Pro & Farm	863,333$	0$	0$	No	863,333$	863,333$		Link
Zachary Jones	Stars (Dal)	D	20	2000-10-18	Yes	172 Lbs	5 ft10	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$			Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
23	22.04	191 Lbs	6 ft1	2.39	924,348$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Max Comtois	Taylor Raddysh	Tobias Rieder	40	1	2	2
2	Jordan Martinook	Sam Steel	Alexander Holtz	30	1	2	2
3	Egor Afanasyev	Nathan Smith	Alexander Volkov	20	1	2	2
4	Filip Cederqvist		Max Comtois	10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Albert Johansson	Ty Smith	40	1	2	2
2	Alec Regula	Thomas Harley	30	1	2	2
3	Zachary Jones	Louie Belpedio	20	1	2	2
4	Albert Johansson	Ty Smith	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Max Comtois	Taylor Raddysh	Tobias Rieder	60	1	2	2
2	Jordan Martinook	Sam Steel	Alexander Holtz	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Albert Johansson	Ty Smith	60	1	2	2
2	Alec Regula	Thomas Harley	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Max Comtois	Taylor Raddysh	60	1	2	2
2	Sam Steel	Tobias Rieder	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Zachary Jones	Ty Smith	60	1	2	2
2	Alec Regula	Thomas Harley	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Max Comtois	60	1	2	2	Albert Johansson	Ty Smith	60	1	2	2
2	Taylor Raddysh	40	1	2	2	Alec Regula	Thomas Harley	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Max Comtois	Taylor Raddysh	60	1	2	2
2	Sam Steel	Tobias Rieder	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Louie Belpedio	Ty Smith	60	1	2	2
2	Alec Regula	Thomas Harley	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Max Comtois	Taylor Raddysh	Tobias Rieder	Alec Regula	Ty Smith

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Max Comtois	Taylor Raddysh	Tobias Rieder	Alec Regula	Ty Smith

Extra Forwards
Normal	PowerPlay	Penalty Kill
Nathan Smith, Alexander Volkov, Egor Afanasyev	Nathan Smith, Alexander Volkov	Egor Afanasyev

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Zachary Jones, Louie Belpedio, Albert Johansson	Zachary Jones	Louie Belpedio, Albert Johansson

Penalty Shots
Max Comtois, Taylor Raddysh, Sam Steel, Tobias Rieder, Nathan Smith

Goalie
#1 : , #2 : Magnus Chrona

Custom OT Lines Forwards
Max Comtois, Taylor Raddysh, Tobias Rieder, Sam Steel, Nathan Smith, Alexander Volkov, Alexander Volkov, Egor Afanasyev, Jordan Martinook, Alexander Holtz, Filip Cederqvist

Custom OT Lines Defensemen
Ty Smith, Alec Regula, Thomas Harley, Zachary Jones, Louie Belpedio

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	3	2	0	0	0	1	0	0	8	6	2	2	1	0	0	0	1	0	0	5	5	0	1	1	0	0	0	0	0	0	3	1	2	5	0.833	8	12	20	0	0	97	107	90	6	102	986	1033	1098	39	74	20	35	58	5	2	40.00%	9	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
2	Baby Hawks	4	2	1	0	0	0	0	1	9	7	2	2	2	0	0	0	0	0	0	7	2	5	2	0	1	0	0	0	0	1	2	5	-3	5	0.625	9	15	24	1	1	97	107	90	6	132	986	1033	1098	39	135	32	31	76	7	1	14.29%	13	1	92.31%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
3	Bears	2	1	1	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	5	0	5	1	0	1	0	0	0	0	0	2	4	-2	2	0.500	7	12	19	0	1	97	107	90	6	57	986	1033	1098	39	79	21	12	44	9	0	0.00%	6	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
4	Bruins	2	1	0	0	0	1	0	0	8	7	1	1	0	0	0	0	1	0	0	3	4	-1	1	1	0	0	0	0	0	0	5	3	2	3	0.750	8	16	24	0	0	97	107	90	6	75	986	1033	1098	39	62	20	8	53	6	3	50.00%	4	2	50.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
5	Cabaret Lady Mary Ann	2	2	0	0	0	0	0	0	15	4	11	1	1	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	7	1	6	4	1.000	15	25	40	0	0	97	107	90	6	133	986	1033	1098	39	63	16	14	40	4	1	25.00%	7	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
6	Caroline	2	1	1	0	0	0	0	0	7	8	-1	1	0	1	0	0	0	0	0	1	4	-3	1	1	0	0	0	0	0	0	6	4	2	2	0.500	7	13	20	0	0	97	107	90	6	81	986	1033	1098	39	56	21	6	44	8	2	25.00%	3	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
7	Chiefs	3	2	1	0	0	0	0	0	12	6	6	1	1	0	0	0	0	0	0	6	1	5	2	1	1	0	0	0	0	0	6	5	1	4	0.667	12	22	34	0	1	97	107	90	6	115	986	1033	1098	39	76	29	11	62	12	1	8.33%	3	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
8	Chill	4	2	2	0	0	0	0	0	11	12	-1	2	1	1	0	0	0	0	0	6	8	-2	2	1	1	0	0	0	0	0	5	4	1	4	0.500	11	21	32	0	0	97	107	90	6	146	986	1033	1098	39	145	53	21	89	7	1	14.29%	8	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
9	Comets	3	3	0	0	0	0	0	0	15	6	9	2	2	0	0	0	0	0	0	11	4	7	1	1	0	0	0	0	0	0	4	2	2	6	1.000	15	29	44	0	0	97	107	90	6	112	986	1033	1098	39	103	24	23	69	13	2	15.38%	8	1	87.50%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
10	Cougars	2	2	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	1	3	4	1.000	8	14	22	0	0	97	107	90	6	60	986	1033	1098	39	68	15	14	35	6	0	0.00%	7	1	85.71%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
11	Crunch	2	0	0	0	0	0	1	1	8	8	0	1	0	0	0	0	0	1	0	3	2	1	1	0	0	0	0	0	0	1	5	6	-1	3	0.750	8	14	22	0	0	97	107	90	6	88	986	1033	1098	39	87	23	16	58	6	3	50.00%	7	2	71.43%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
12	Heat	3	2	0	0	0	1	0	0	14	8	6	2	2	0	0	0	0	0	0	12	5	7	1	0	0	0	0	1	0	0	2	3	-1	5	0.833	14	26	40	0	0	97	107	90	6	129	986	1033	1098	39	85	33	6	72	6	2	33.33%	3	0	100.00%	1	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
13	Jayhawks	4	3	0	0	0	0	1	0	14	6	8	2	2	0	0	0	0	0	0	8	3	5	2	1	0	0	0	0	1	0	6	3	3	8	1.000	14	25	39	0	0	97	107	90	6	142	986	1033	1098	39	136	42	29	87	7	1	14.29%	11	1	90.91%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
14	Las Vegas	3	3	0	0	0	0	0	0	15	6	9	1	1	0	0	0	0	0	0	5	2	3	2	2	0	0	0	0	0	0	10	4	6	6	1.000	15	25	40	0	0	97	107	90	6	125	986	1033	1098	39	85	23	25	68	13	4	30.77%	5	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
15	Manchots	2	1	1	0	0	0	0	0	7	6	1	1	0	1	0	0	0	0	0	2	3	-1	1	1	0	0	0	0	0	0	5	3	2	2	0.500	7	11	18	0	0	97	107	90	6	81	986	1033	1098	39	46	13	6	38	7	1	14.29%	3	1	66.67%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
16	Marlies	2	2	0	0	0	0	0	0	7	2	5	1	1	0	0	0	0	0	0	2	1	1	1	1	0	0	0	0	0	0	5	1	4	4	1.000	7	12	19	0	0	97	107	90	6	69	986	1033	1098	39	49	15	14	48	17	2	11.76%	7	1	85.71%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
17	Minnesota	4	1	2	0	0	0	1	0	14	11	3	2	1	1	0	0	0	0	0	8	5	3	2	0	1	0	0	0	1	0	6	6	0	4	0.500	14	23	37	0	1	97	107	90	6	175	986	1033	1098	39	139	30	20	100	13	0	0.00%	9	4	55.56%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
18	Monarchs	3	3	0	0	0	0	0	0	17	8	9	1	1	0	0	0	0	0	0	4	2	2	2	2	0	0	0	0	0	0	13	6	7	6	1.000	17	31	48	0	0	97	107	90	6	154	986	1033	1098	39	99	29	10	86	10	4	40.00%	5	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
19	Monsters	2	1	0	0	1	0	0	0	3	1	2	1	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0	0	0	2	1	1	4	1.000	3	5	8	0	1	97	107	90	6	65	986	1033	1098	39	65	17	14	48	7	0	0.00%	7	1	85.71%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
20	Monsters	4	3	1	0	0	0	0	0	16	8	8	2	1	1	0	0	0	0	0	8	6	2	2	2	0	0	0	0	0	0	8	2	6	6	0.750	16	29	45	0	1	97	107	90	6	149	986	1033	1098	39	122	41	20	77	11	4	36.36%	9	1	88.89%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
21	Oceanics	3	1	2	0	0	0	0	0	9	8	1	2	1	1	0	0	0	0	0	8	5	3	1	0	1	0	0	0	0	0	1	3	-2	2	0.333	9	18	27	0	0	97	107	90	6	104	986	1033	1098	39	114	29	19	60	7	1	14.29%	7	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
22	Oil Kings	3	2	1	0	0	0	0	0	6	6	0	1	0	1	0	0	0	0	0	1	4	-3	2	2	0	0	0	0	0	0	5	2	3	4	0.667	6	12	18	0	0	97	107	90	6	107	986	1033	1098	39	99	33	14	58	6	2	33.33%	6	1	83.33%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
23	Phantoms	2	1	0	0	0	0	0	1	8	7	1	1	0	0	0	0	0	0	1	3	4	-1	1	1	0	0	0	0	0	0	5	3	2	3	0.750	8	14	22	0	0	97	107	90	6	64	986	1033	1098	39	72	18	14	40	11	3	27.27%	6	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
24	Rocket	2	2	0	0	0	0	0	0	11	5	6	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	7	2	5	4	1.000	11	19	30	0	0	97	107	90	6	67	986	1033	1098	39	59	19	12	35	5	1	20.00%	5	1	80.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
25	Seattle	3	1	2	0	0	0	0	0	5	7	-2	1	0	1	0	0	0	0	0	1	3	-2	2	1	1	0	0	0	0	0	4	4	0	2	0.333	5	9	14	0	1	97	107	90	6	102	986	1033	1098	39	91	31	16	51	8	1	12.50%	6	1	83.33%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
26	Senators	2	1	0	0	0	0	1	0	12	7	5	1	0	0	0	0	0	1	0	4	3	1	1	1	0	0	0	0	0	0	8	4	4	4	1.000	12	19	31	0	0	97	107	90	6	75	986	1033	1098	39	81	28	14	46	7	3	42.86%	7	3	57.14%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
27	Sharks	3	2	1	0	0	0	0	0	11	5	6	2	2	0	0	0	0	0	0	9	2	7	1	0	1	0	0	0	0	0	2	3	-1	4	0.667	11	19	30	0	0	97	107	90	6	121	986	1033	1098	39	94	25	12	73	8	2	25.00%	5	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
28	Sound Tigers	2	1	1	0	0	0	0	0	6	7	-1	1	1	0	0	0	0	0	0	4	3	1	1	0	1	0	0	0	0	0	2	4	-2	2	0.500	6	11	17	0	0	97	107	90	6	71	986	1033	1098	39	76	14	8	37	6	1	16.67%	4	1	75.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
29	Spiders	2	0	2	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	1	3	-2	1	0	1	0	0	0	0	0	0	2	-2	0	0.000	1	2	3	1	0	97	107	90	6	74	986	1033	1098	39	66	23	8	46	3	0	0.00%	3	1	66.67%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
30	Thunder	2	2	0	0	0	0	0	0	5	1	4	1	1	0	0	0	0	0	0	2	0	2	1	1	0	0	0	0	0	0	3	1	2	4	1.000	5	7	12	0	1	97	107	90	6	59	986	1033	1098	39	53	17	18	38	7	0	0.00%	9	0	100.00%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
31	Wolf Pack	2	1	1	0	0	0	0	0	10	6	4	1	0	1	0	0	0	0	0	5	6	-1	1	1	0	0	0	0	0	0	5	0	5	2	0.500	10	19	29	0	1	97	107	90	6	102	986	1033	1098	39	57	25	8	43	8	2	25.00%	3	1	66.67%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
	Total	82	51	20	0	1	3	4	3	299	191	108	41	26	10	0	0	2	2	1	151	98	53	41	25	10	0	1	1	2	2	148	93	55	118	0.720	299	529	828	2	9	97	107	90	6	3136	986	1033	1098	39	2636	779	478	1779	250	50	20.00%	195	25	87.18%	1	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
	_Since Last GM Reset	82	51	20	0	1	3	4	3	299	191	108	41	26	10	0	0	2	2	1	151	98	53	41	25	10	0	1	1	2	2	148	93	55	118	0.720	299	529	828	2	9	97	107	90	6	3136	986	1033	1098	39	2636	779	478	1779	250	50	20.00%	195	25	87.18%	1	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
	_Vs Conference	44	29	9	0	0	1	3	2	169	99	70	21	15	5	0	0	0	1	0	87	49	38	23	14	4	0	0	1	2	2	82	50	32	67	0.761	169	300	469	1	5	97	107	90	6	1717	986	1033	1098	39	1404	412	257	932	125	25	20.00%	102	14	86.27%	1	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556
	_Vs Division	26	8	1	0	0	0	1	1	85	58	27	13	4	1	0	0	0	1	0	51	30	21	13	4	0	0	0	0	0	1	34	28	6	19	0.365	85	153	238	1	4	97	107	90	6	963	986	1033	1098	39	867	256	151	551	64	9	14.06%	60	7	88.33%	0	1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%	2107	1476	1782	587	1074	556

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
82	118	W1	299	529	828	3136	2636	779	478	1779	2	9

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	51	20	1	3	4	3	299	191

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	26	10	0	2	2	1	151	98

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	25	10	1	1	2	2	148	93

Last 10 Games
W	L	OTW	OTL	SOW	SOL
6	2	0	0	0	2

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
250	50	20.00%	195	25	87.18%	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
986	1033	1098	39	97	107	90	6

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
1519	2994	50.73%	1408	2764	50.94%	625	1317	47.46%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
2107	1476	1782	587	1074	556

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
7 - 2022-10-13	16	Stars	3	Chill	1	AW			BoxScore
9 - 2022-10-15	35	Chill	1	Stars	2	BW			BoxScore
11 - 2022-10-17	46	Oceanics	1	Stars	5	BW			BoxScore
14 - 2022-10-20	61	Stars	5	Marlies	1	AW			BoxScore
16 - 2022-10-22	79	Stars	7	Rocket	2	AW			BoxScore
18 - 2022-10-24	92	Stars	8	Senators	4	AW			BoxScore
19 - 2022-10-25	100	Stars	5	Bruins	3	AW			BoxScore
21 - 2022-10-27	117	Bears	0	Stars	5	BW			BoxScore
23 - 2022-10-29	127	Wolf Pack	6	Stars	5	BL			BoxScore
26 - 2022-11-01	153	Monarchs	2	Stars	4	BW			BoxScore
28 - 2022-11-03	172	Stars	2	Jayhawks	1	AW	X	X	BoxScore
30 - 2022-11-05	178	Stars	2	Oil Kings	1	AW			BoxScore
33 - 2022-11-08	204	Stars	1	Oceanics	3	AL			BoxScore
36 - 2022-11-11	223	Sharks	1	Stars	4	BW			BoxScore
38 - 2022-11-13	238	Stars	5	Phantoms	3	AW			BoxScore
40 - 2022-11-15	248	Stars	3	Thunder	1	AW			BoxScore
42 - 2022-11-17	262	Stars	7	Cabaret Lady Mary Ann	1	AW			BoxScore
44 - 2022-11-19	284	Sound Tigers	3	Stars	4	BW			BoxScore
46 - 2022-11-21	298	Monsters	1	Stars	4	BW			BoxScore
48 - 2022-11-23	313	Baby Hawks	2	Stars	4	BW			BoxScore
50 - 2022-11-25	330	Oceanics	4	Stars	3	BL			BoxScore
51 - 2022-11-26	339	Stars	4	Monsters	2	AW			BoxScore
53 - 2022-11-28	350	Stars	4	Chiefs	0	AW			BoxScore
56 - 2022-12-01	371	Admirals	3	Stars	2	BL	X		BoxScore
59 - 2022-12-04	393	Minnesota	0	Stars	6	BW			BoxScore
61 - 2022-12-06	409	Marlies	1	Stars	2	BW			BoxScore
63 - 2022-12-08	423	Senators	3	Stars	4	BW	X	X	BoxScore
65 - 2022-12-10	435	Cougars	2	Stars	4	BW			BoxScore
67 - 2022-12-12	449	Stars	5	Manchots	3	AW			BoxScore
68 - 2022-12-13	456	Stars	0	Spiders	2	AL			BoxScore
70 - 2022-12-15	471	Stars	2	Bears	4	AL			BoxScore
72 - 2022-12-17	487	Stars	6	Caroline	4	AW			BoxScore
74 - 2022-12-19	502	Stars	2	Monsters	1	AW	X		BoxScore
76 - 2022-12-21	522	Oil Kings	4	Stars	1	BL			BoxScore
78 - 2022-12-23	541	Rocket	3	Stars	4	BW			BoxScore
82 - 2022-12-27	551	Stars	2	Chill	3	AL			BoxScore
84 - 2022-12-29	569	Stars	4	Minnesota	3	AW	X	X	BoxScore
86 - 2022-12-31	586	Sharks	1	Stars	5	BW			BoxScore
89 - 2023-01-03	608	Stars	5	Monarchs	2	AW			BoxScore
90 - 2023-01-04	611	Stars	3	Admirals	1	AW			BoxScore
94 - 2023-01-08	637	Cabaret Lady Mary Ann	3	Stars	8	BW			BoxScore
96 - 2023-01-10	653	Stars	2	Sound Tigers	4	AL			BoxScore
98 - 2023-01-12	663	Stars	5	Wolf Pack	0	AW			BoxScore
100 - 2023-01-14	677	Heat	2	Stars	6	BW			BoxScore
102 - 2023-01-16	699	Stars	4	Las Vegas	2	AW			BoxScore
104 - 2023-01-18	716	Stars	2	Sharks	3	AL			BoxScore
105 - 2023-01-19	729	Stars	8	Monarchs	4	AW			BoxScore
107 - 2023-01-21	740	Jayhawks	2	Stars	5	BW			BoxScore
109 - 2023-01-23	753	Crunch	2	Stars	3	BW	X	X	BoxScore
111 - 2023-01-25	769	Caroline	4	Stars	1	BL			BoxScore
113 - 2023-01-27	785	Spiders	3	Stars	1	BL			BoxScore
123 - 2023-02-06	811	Admirals	2	Stars	3	BW			BoxScore
125 - 2023-02-08	820	Minnesota	5	Stars	2	BL			BoxScore
128 - 2023-02-11	837	Thunder	0	Stars	2	BW			BoxScore
131 - 2023-02-14	861	Bruins	4	Stars	3	BL	X		BoxScore
134 - 2023-02-17	880	Stars	2	Minnesota	3	AL			BoxScore
135 - 2023-02-18	887	Monsters	0	Stars	1	BW			BoxScore
139 - 2023-02-22	918	Baby Hawks	0	Stars	3	BW			BoxScore
142 - 2023-02-25	944	Stars	6	Las Vegas	2	AW			BoxScore
144 - 2023-02-27	955	Comets	2	Stars	5	BW			BoxScore
146 - 2023-03-01	970	Jayhawks	1	Stars	3	BW			BoxScore
147 - 2023-03-02	980	Stars	1	Baby Hawks	3	AL			BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
149 - 2023-03-04	997	Monsters	5	Stars	4	BL			BoxScore
151 - 2023-03-06	1008	Heat	3	Stars	6	BW			BoxScore
154 - 2023-03-09	1025	Stars	5	Crunch	6	AL	X	X	BoxScore
156 - 2023-03-11	1049	Stars	3	Seattle	4	AL			BoxScore
158 - 2023-03-13	1063	Stars	1	Seattle	0	AW			BoxScore
159 - 2023-03-14	1073	Stars	4	Comets	2	AW			BoxScore
161 - 2023-03-16	1086	Stars	3	Oil Kings	1	AW			BoxScore
163 - 2023-03-18	1105	Stars	2	Heat	3	AL	X		BoxScore
166 - 2023-03-21	1130	Seattle	3	Stars	1	BL			BoxScore
168 - 2023-03-23	1143	Manchots	3	Stars	2	BL			BoxScore
170 - 2023-03-25	1158	Comets	2	Stars	6	BW			BoxScore
173 - 2023-03-28	1182	Stars	1	Baby Hawks	2	AL	X	X	BoxScore
176 - 2023-03-31	1203	Stars	4	Jayhawks	2	AW			BoxScore
177 - 2023-04-01	1212	Stars	4	Monsters	0	AW			BoxScore
179 - 2023-04-03	1227	Chill	7	Stars	4	BL			BoxScore
182 - 2023-04-06	1252	Phantoms	4	Stars	3	BL	X	X	BoxScore
184 - 2023-04-08	1269	Las Vegas	2	Stars	5	BW			BoxScore
186 - 2023-04-10	1280	Stars	4	Cougars	1	AW			BoxScore
188 - 2023-04-12	1296	Stars	2	Chiefs	5	AL			BoxScore
189 - 2023-04-13	1307	Chiefs	1	Stars	6	BW			BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	20	5
Attendance	81,928	41,000
Attendance PCT	99.91%	100.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
0	2998 - 99.94%	44,965$	1,843,560$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
1,747,852$	1,806,001$	1,806,001$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
9,505$	1,747,852$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	9,505$	0$

Stars 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

Stars 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

Stars 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

Stars 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

Stars 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

	Stars
	GP: 82 \| W: 56 \| L: 20 \| OTL: 6 \| P: 118 GF: 299 \| GA: 191 \| PP%: 20.00% \| PK%: 87.18% GM : Julien Desrosiers \| Morale : 50 \| Team Overall : 57