Ligue de hockey Simulée Ste-Flanelle

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

Show or Hide Column

Filter Tips

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

#	Player Name	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	TA	SP	Age	Contract	Salary Average
1	Evan Rodrigues	X	X	X		100.00	64	42	90	77	66	63	77	61	69	61	62	69	75	63	63	0	50	63	X	0	26	2	1,100,000$
2	Antti Suomela	X				100.00	79	43	93	74	64	54	71	60	72	75	57	66	25	48	48	0	50	61		0	25	3	700,000$
3	Tyler Benson		X			100.00	73	70	79	71	70	75	78	63	50	66	57	63	54	44	44	0	50	61	X	0	21	2	792,500$
4	Austin Poganski			X		100.00	80	75	90	65	75	73	77	60	50	59	57	66	54	44	44	0	50	60		0	23	4	750,000$
5	Christoffer Ehn	X	X	X		100.00	76	43	98	64	70	56	83	57	55	54	56	74	25	57	57	0	50	60	X	0	23	3	700,000$
6	Jake Evans	X		X		100.00	77	43	99	62	68	56	83	57	67	58	68	67	25	45	45	0	50	60	X	0	23	2	525,000$
7	Frederick Gaudreau (A)	X		X		100.00	71	58	96	64	65	62	66	61	69	57	59	64	59	58	58	0	50	59		0	26	1	595,000$
8	Connor Bunnaman	X				100.00	79	44	96	64	75	54	73	57	56	55	57	61	25	45	45	0	50	57		0	21	2	730,000$
9	Jonah Gadjovich		X			100.00	76	76	76	66	76	59	60	57	50	45	64	63	61	44	44	0	50	57		0	20	3	783,333$
10	Skyler McKenzie		X			100.00	70	61	90	66	61	71	75	56	50	52	56	60	53	44	44	0	50	57		0	21	2	741,666$
11	Ben Harpur (A)				X	100.00	81	74	73	65	85	64	64	56	25	44	41	69	37	59	59	0	50	61		0	24	4	750,000$
12	Gavin Bayreuther				X	100.00	74	68	86	63	72	75	83	53	25	48	43	65	40	45	45	0	50	60		0	25	3	650,000$
13	Sami Niku				X	100.00	70	54	83	75	66	68	61	61	26	58	46	66	25	48	48	0	50	60		0	22	1	525,000$
14	Chris Bigras				X	100.00	74	69	84	63	71	62	63	56	25	48	41	66	39	57	57	0	50	59		0	24	6	560,000$
15	Sebastian AhoD				X	100.00	72	63	87	62	62	71	74	55	25	54	42	62	39	44	44	0	50	58		0	23	2	750,000$
Scratches

	TEAM AVERAGE					100.00	74	59	88	67	70	64	73	58	48	56	54	65	42	50	50	0	50	60

Show or Hide Column

Filter Tips

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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Arvid Holm (R)	100.00	57	71	67	79	51	56	62	60	55	56	57	58	58	0	50	60	0
2	Hunter Miska	100.00	59	56	73	63	62	62	56	63	60	62	30	44	44	0	50	58	0
Scratches
1	Philippe Desrosiers	100.00	60	59	74	74	63	62	50	61	58	56	30	44	44	0	50	58	0
2	Landon Bow	100.00	53	69	87	86	49	54	50	56	48	48	30	44	44	0	50	56	0

	TEAM AVERAGE	100.00	57	64	75	76	56	59	55	60	55	56	37	48	48	0	50	58

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	PKS	PKM	GW	GT	FO%	FOT	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Evan Rodrigues	Phantoms (Phi)	C/LW/RW	70	40	52	92	37	18	0	60	222	357	89	242	11.20%	28	1589	22.71	5	13	18	57	189	10	210	7	3	58.69%	1944	0	3	1.16	3	6	0	0	0	5	2	6
2	Dominic Toninato	Philadelphie	C	81	40	46	86	40	24	0	165	251	333	88	238	12.01%	25	1656	20.45	8	7	15	55	208	6	161	7	1	56.78%	2087	0	1	1.04	1	7	0	0	0	7	9	4
3	Tyler Benson	Phantoms (Phi)	LW	74	31	45	76	24	42	10	133	158	310	82	221	10.00%	9	1490	20.14	6	6	12	56	195	5	111	5	2	45.97%	124	0	0	1.02	0	4	0	1	1	2	6	5
4	Joel Farabee	Philadelphie	LW	52	28	46	74	37	61	35	95	168	288	88	239	9.72%	10	1211	23.31	3	13	16	36	144	3	143	8	0	34.74%	380	2	0	1.22	0	2	2	1	4	6	5	1
5	Antti Suomela	Phantoms (Phi)	C	84	22	43	65	29	22	0	96	199	245	60	148	8.98%	15	1322	15.74	1	2	3	9	35	1	35	5	0	60.66%	788	0	0	0.98	2	2	0	0	0	1	2	3
6	Jake Evans	Phantoms (Phi)	C/RW	75	27	36	63	37	22	0	56	82	259	83	177	10.42%	14	1336	17.82	5	3	8	43	172	0	6	2	2	57.01%	221	0	1	0.94	0	0	0	0	0	0	4	3
7	Sami Niku	Phantoms (Phi)	D	82	13	49	62	24	38	0	105	82	131	61	98	9.92%	111	1771	21.60	4	13	17	61	204	1	211	1	0	0.00%	0	0	0	0.70	0	0	0	0	0	0	1	4
8	Ben Harpur	Phantoms (Phi)	D	82	12	47	59	55	94	0	268	75	104	46	94	11.54%	120	1911	23.31	5	7	12	38	225	1	190	1	0	0.00%	0	0	0	0.62	0	0	0	0	0	3	2	2
9	Christoffer Ehn	Phantoms (Phi)	C/LW/RW	59	24	30	54	16	12	0	77	88	211	66	148	11.37%	9	1081	18.33	8	5	13	41	161	2	11	3	1	56.52%	92	0	1	1.00	0	1	0	0	0	6	1	1
10	Frederick Gaudreau	Phantoms (Phi)	C/RW	82	19	34	53	11	12	0	34	156	239	66	179	7.95%	12	1173	14.31	1	1	2	8	17	1	35	3	1	55.21%	1036	0	0	0.90	0	0	0	0	0	1	1	4
11	Alexandre Carrier	Philadelphie	D	72	6	41	47	31	31	5	121	44	94	25	55	6.38%	75	1562	21.70	2	13	15	50	212	1	190	2	1	0.00%	0	0	0	0.60	0	0	1	0	0	1	2	4
12	Ryan Reaves	Philadelphie	RW	69	14	26	40	20	64	0	224	103	215	56	147	6.51%	6	1299	18.83	1	5	6	32	172	1	86	2	0	41.76%	182	0	0	0.62	2	5	0	0	0	3	3	2
13	Jonah Gadjovich	Phantoms (Phi)	LW	81	18	15	33	19	52	20	112	51	168	35	107	10.71%	13	835	10.32	0	2	2	7	22	1	71	2	0	48.33%	120	0	0	0.79	0	0	1	0	3	3	0	0
14	Austin Poganski	Phantoms (Phi)	RW	44	10	20	30	18	46	10	96	48	150	43	109	6.67%	3	756	17.19	1	1	2	19	66	2	28	0	0	43.86%	57	0	0	0.79	0	1	0	1	1	0	2	2
15	Connor Bunnaman	Phantoms (Phi)	C	74	11	14	25	6	18	0	53	76	115	37	93	9.57%	4	750	10.15	0	0	0	0	1	0	9	2	1	50.40%	379	0	0	0.67	0	0	0	0	0	1	4	1
16	Sebastian AhoD	Phantoms (Phi)	D	82	0	20	20	8	32	0	103	47	48	14	38	0.00%	73	1217	14.85	0	0	0	3	17	0	94	0	0	0.00%	0	0	0	0.33	0	0	0	0	0	0	0	0
17	Chris Bigras	Phantoms (Phi)	D	82	4	15	19	13	48	0	150	55	72	28	56	5.56%	93	1455	17.75	0	0	0	11	101	1	99	1	0	0.00%	0	0	0	0.26	0	0	0	0	0	0	0	1
18	Gavin Bayreuther	Phantoms (Phi)	D	44	5	14	19	20	14	0	69	31	65	19	23	7.69%	54	1011	22.99	2	2	4	37	110	0	114	1	0	0.00%	0	0	0	0.38	0	0	0	0	0	1	2	1
19	Skyler McKenzie	Phantoms (Phi)	LW	35	3	3	6	-5	4	0	11	22	46	17	31	6.52%	3	246	7.05	0	1	1	1	5	0	6	0	0	33.33%	30	0	0	0.49	0	0	0	0	0	0	0	0
Team Total or Average				1324	327	596	923	440	654	80	2028	1958	3450	1003	2443	9.48%	677	23682	17.89	52	94	146	564	2265	36	1819	52	12	55.15%	7440	2	6	0.78	8	28	4	3	9	40	46	44

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	Hunter Miska	Phantoms (Phi)	39	25	12	1	0.921	2.61	2321	10	5	101	1281	2	0	0.667	6	39	21	5	2	2
2	Arvid Holm	Phantoms (Phi)	23	13	9	1	0.919	2.68	1319	2	1	59	727	0	1	0.714	7	22	22	1	3	2
3	Philippe Desrosiers	Phantoms (Phi)	14	7	4	2	0.895	3.42	843	0	0	48	457	0	0	0.727	11	13	9	1	0	0
4	Landon Bow	Phantoms (Phi)	8	6	2	0	0.913	3.25	480	0	0	26	299	0	0	0.000	0	8	30	0	0	0
Team Total or Average			84	51	27	4	0.915	2.83	4963	12	6	234	2764	2	1	0.708	24	82	82	7	5	4

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	Salary Year 5	Salary Year 6	Link
Antti Suomela	Phantoms (Phi)	C	25	1994-03-17	No	172 Lbs	6 ft0	No	No	No	3	Pro & Farm	700,000$	70,000$	0$	No	700,000$	700,000$				Link
Arvid Holm	Phantoms (Phi)	G	20	1998-11-03	Yes	214 Lbs	6 ft4	No	No	No	4	Pro & Farm	845,833$	84,583$	0$	No	845,833$	845,833$	845,833$			Link
Austin Poganski	Phantoms (Phi)	RW	23	1996-02-16	No	201 Lbs	6 ft2	No	No	No	4	Pro & Farm	750,000$	75,000$	0$	No	750,000$	750,000$	750,000$			Link
Ben Harpur	Phantoms (Phi)	D	24	1995-01-12	No	222 Lbs	6 ft6	No	No	No	4	Pro & Farm	750,000$	75,000$	0$	No	750,000$	750,000$	750,000$			Link
Chris Bigras	Phantoms (Phi)	D	24	1995-02-22	No	190 Lbs	6 ft1	Yes	No	No	6	Pro & Farm	560,000$	56,000$	0$	No	560,000$	560,000$	560,000$	560,000$	560,000$	Link
Christoffer Ehn	Phantoms (Phi)	C/LW/RW	23	1996-04-05	No	181 Lbs	6 ft3	No	Yes	No	3	Pro & Farm	700,000$	70,000$	0$	No	700,000$	700,000$				Link
Connor Bunnaman	Phantoms (Phi)	C	21	1998-04-16	No	207 Lbs	6 ft1	No	No	No	2	Pro & Farm	730,000$	73,000$	0$	No	730,000$					Link
Evan Rodrigues	Phantoms (Phi)	C/LW/RW	26	1993-07-28	No	182 Lbs	5 ft11	No	Yes	No	2	Pro & Farm	1,100,000$	110,000$	0$	No	1,100,000$					Link
Frederick Gaudreau	Phantoms (Phi)	C/RW	26	1993-05-01	No	179 Lbs	6 ft0	No	No	No	1	Pro & Farm	595,000$	59,500$	0$	No						Link
Gavin Bayreuther	Phantoms (Phi)	D	25	1994-05-12	No	195 Lbs	6 ft1	No	No	No	3	Pro & Farm	650,000$	65,000$	0$	No	650,000$	650,000$				Link
Hunter Miska	Phantoms (Phi)	G	24	1995-07-06	No	170 Lbs	6 ft1	No	No	No	2	Pro & Farm	925,000$	92,500$	0$	No	925,000$					Link
Jake Evans	Phantoms (Phi)	C/RW	23	1996-06-02	No	185 Lbs	6 ft0	No	Yes	No	2	Pro & Farm	525,000$	52,500$	0$	No	525,000$					Link
Jonah Gadjovich	Phantoms (Phi)	LW	20	1998-10-12	No	209 Lbs	6 ft2	No	No	No	3	Pro & Farm	783,333$	78,333$	0$	No	783,333$	783,333$				Link
Landon Bow	Phantoms (Phi)	G	24	1995-08-23	No	214 Lbs	6 ft4	No	No	No	3	Pro & Farm	700,000$	70,000$	0$	No	700,000$	700,000$				Link
Philippe Desrosiers	Phantoms (Phi)	G	24	1995-08-15	No	195 Lbs	6 ft1	No	No	No	2	Pro & Farm	700,000$	70,000$	0$	No	700,000$					Link
Sami Niku	Phantoms (Phi)	D	22	1996-10-10	No	176 Lbs	6 ft1	No	No	No	1	Pro & Farm	525,000$	52,500$	0$	No						Link
Sebastian AhoD	Phantoms (Phi)	D	23	1996-02-17	No	170 Lbs	5 ft10	No	No	No	2	Pro & Farm	750,000$	75,000$	0$	No	750,000$					Link
Skyler McKenzie	Phantoms (Phi)	LW	21	1998-01-20	No	170 Lbs	5 ft9	No	No	No	2	Pro & Farm	741,666$	74,167$	0$	No	741,666$					Link
Tyler Benson	Phantoms (Phi)	LW	21	1998-03-15	No	192 Lbs	6 ft0	No	Yes	No	2	Pro & Farm	792,500$	79,250$	0$	No	792,500$					Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
19	23.11	191 Lbs	6 ft1	2.68	727,544$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Tyler Benson			40	1	2	2
2	Jonah Gadjovich		Jake Evans	30	1	2	2
3	Skyler McKenzie	Antti Suomela	Austin Poganski	20	1	2	2
4		Frederick Gaudreau		10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Ben Harpur	Gavin Bayreuther	40	1	2	2
2		Sami Niku	30	1	2	2
3	Chris Bigras	Sebastian AhoD	20	1	2	2
4	Ben Harpur	Gavin Bayreuther	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Tyler Benson			60	1	2	2
2	Jonah Gadjovich		Jake Evans	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Ben Harpur	Gavin Bayreuther	60	1	2	2
2		Sami Niku	40	1	2	2

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

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Ben Harpur	Gavin Bayreuther	60	1	2	2
2		Sami Niku	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1		60	1	2	2	Ben Harpur	Gavin Bayreuther	60	1	2	2
2		40	1	2	2		Sami Niku	40	1	2	2

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

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Ben Harpur	Gavin Bayreuther	60	1	2	2
2		Sami Niku	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Tyler Benson			Ben Harpur	Gavin Bayreuther

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Tyler Benson			Ben Harpur	Gavin Bayreuther

Extra Forwards
Normal	PowerPlay	Penalty Kill
Connor Bunnaman, Antti Suomela, Austin Poganski	Connor Bunnaman, Antti Suomela	Austin Poganski

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Chris Bigras, Sebastian AhoD,	Chris Bigras	Sebastian AhoD,

Penalty Shots
, , , Tyler Benson, Antti Suomela

Goalie
#1 : Hunter Miska, #2 : Arvid Holm

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	2	2	0	0	0	0	0	0	11	3	8	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	7	1	6	4	1.000	11	19	30	0	0	130	113	78	10	81	1098	1150	1100	42	59	26	12	43	9	2	22.22%	5	0	100.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
2	Baby Hawks	2	1	1	0	0	0	0	0	7	8	-1	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	5	4	1	2	0.500	7	13	20	1	0	130	113	78	10	56	1098	1150	1100	42	87	17	16	47	7	2	28.57%	8	1	87.50%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
3	Bears	4	2	1	0	0	0	1	0	15	14	1	2	1	1	0	0	0	0	0	6	7	-1	2	1	0	0	0	0	1	0	9	7	2	6	0.750	15	25	40	0	0	130	113	78	10	146	1098	1150	1100	42	165	45	30	127	13	3	23.08%	15	4	73.33%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
4	Bruins	3	1	2	0	0	0	0	0	6	6	0	2	0	2	0	0	0	0	0	2	5	-3	1	1	0	0	0	0	0	0	4	1	3	2	0.333	6	11	17	0	0	130	113	78	10	103	1098	1150	1100	42	89	32	18	71	13	1	7.69%	9	2	77.78%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
5	Cabaret Lady Mary Ann	3	3	0	0	0	0	0	0	21	6	15	1	1	0	0	0	0	0	0	8	3	5	2	2	0	0	0	0	0	0	13	3	10	6	1.000	21	39	60	0	0	130	113	78	10	228	1098	1150	1100	42	112	27	18	64	5	1	20.00%	9	1	88.89%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
6	Caroline	4	2	1	0	1	0	0	0	18	14	4	2	1	1	0	0	0	0	0	8	6	2	2	1	0	0	1	0	0	0	10	8	2	6	0.750	18	34	52	0	0	130	113	78	10	161	1098	1150	1100	42	176	45	34	94	7	2	28.57%	16	3	81.25%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
7	Chiefs	2	0	1	0	0	0	0	1	6	8	-2	1	0	1	0	0	0	0	0	3	4	-1	1	0	0	0	0	0	0	1	3	4	-1	1	0.250	6	10	16	0	0	130	113	78	10	58	1098	1150	1100	42	69	21	24	51	6	0	0.00%	11	2	81.82%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
8	Chill	2	1	1	0	0	0	0	0	9	8	1	1	0	1	0	0	0	0	0	4	5	-1	1	1	0	0	0	0	0	0	5	3	2	2	0.500	9	16	25	0	0	130	113	78	10	73	1098	1150	1100	42	73	16	20	72	5	2	40.00%	10	2	80.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
9	Comets	2	1	1	0	0	0	0	0	9	7	2	1	1	0	0	0	0	0	0	6	2	4	1	0	1	0	0	0	0	0	3	5	-2	2	0.500	9	16	25	0	0	130	113	78	10	66	1098	1150	1100	42	59	19	14	46	4	1	25.00%	7	1	85.71%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
10	Cougars	3	2	1	0	0	0	0	0	8	7	1	1	0	1	0	0	0	0	0	4	6	-2	2	2	0	0	0	0	0	0	4	1	3	4	0.667	8	14	22	0	1	130	113	78	10	115	1098	1150	1100	42	120	22	38	88	16	2	12.50%	10	1	90.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
11	Crunch	3	2	0	0	1	0	0	0	13	7	6	2	2	0	0	0	0	0	0	9	4	5	1	0	0	0	1	0	0	0	4	3	1	6	1.000	13	25	38	0	1	130	113	78	10	138	1098	1150	1100	42	116	36	33	81	12	2	16.67%	13	1	92.31%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
12	Heat	2	2	0	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	2	0	2	1	1	0	0	0	0	0	0	6	4	2	4	1.000	8	14	22	0	1	130	113	78	10	132	1098	1150	1100	42	54	18	26	43	2	0	0.00%	8	3	62.50%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
13	Jayhawks	2	2	0	0	0	0	0	0	9	5	4	1	1	0	0	0	0	0	0	5	3	2	1	1	0	0	0	0	0	0	4	2	2	4	1.000	9	15	24	0	0	130	113	78	10	88	1098	1150	1100	42	75	16	10	44	7	1	14.29%	5	0	100.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
14	Las Vegas	2	1	1	0	0	0	0	0	8	9	-1	1	0	1	0	0	0	0	0	3	5	-2	1	1	0	0	0	0	0	0	5	4	1	2	0.500	8	16	24	0	0	130	113	78	10	57	1098	1150	1100	42	69	18	34	57	4	1	25.00%	12	2	83.33%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
15	Manchots	4	1	3	0	0	0	0	0	14	16	-2	2	1	1	0	0	0	0	0	8	8	0	2	0	2	0	0	0	0	0	6	8	-2	2	0.250	14	24	38	0	0	130	113	78	10	127	1098	1150	1100	42	146	49	73	81	11	2	18.18%	18	4	77.78%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
16	Marlies	3	0	2	0	0	0	0	1	5	9	-4	2	0	1	0	0	0	0	1	3	6	-3	1	0	1	0	0	0	0	0	2	3	-1	1	0.167	5	8	13	0	0	130	113	78	10	98	1098	1150	1100	42	106	25	32	74	8	1	12.50%	14	4	71.43%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
17	Minnesota	2	2	0	0	0	0	0	0	14	2	12	1	1	0	0	0	0	0	0	8	0	8	1	1	0	0	0	0	0	0	6	2	4	4	1.000	14	27	41	0	1	130	113	78	10	132	1098	1150	1100	42	57	10	11	55	2	1	50.00%	3	0	100.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
18	Monarchs	2	0	2	0	0	0	0	0	7	10	-3	1	0	1	0	0	0	0	0	3	4	-1	1	0	1	0	0	0	0	0	4	6	-2	0	0.000	7	13	20	0	0	130	113	78	10	73	1098	1150	1100	42	94	18	17	64	6	0	0.00%	6	2	66.67%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
19	Monsters	4	3	0	0	0	0	0	1	13	8	5	2	2	0	0	0	0	0	0	8	3	5	2	1	0	0	0	0	0	1	5	5	0	7	0.875	13	24	37	0	0	130	113	78	10	136	1098	1150	1100	42	116	32	25	101	10	2	20.00%	10	1	90.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
20	Monsters	2	0	2	0	0	0	0	0	5	8	-3	1	0	1	0	0	0	0	0	3	4	-1	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	5	8	13	0	0	130	113	78	10	73	1098	1150	1100	42	66	15	8	36	7	1	14.29%	4	1	75.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
21	Oceanics	2	1	1	0	0	0	0	0	4	6	-2	1	0	1	0	0	0	0	0	1	4	-3	1	1	0	0	0	0	0	0	3	2	1	2	0.500	4	8	12	0	0	130	113	78	10	62	1098	1150	1100	42	57	12	10	59	11	2	18.18%	5	1	80.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
22	Oil Kings	2	0	1	0	1	0	0	0	6	7	-1	1	0	0	0	1	0	0	0	4	3	1	1	0	1	0	0	0	0	0	2	4	-2	2	0.500	6	10	16	0	0	130	113	78	10	93	1098	1150	1100	42	73	19	20	50	9	1	11.11%	8	4	50.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
23	Rocket	3	2	1	0	0	0	0	0	16	5	11	2	1	1	0	0	0	0	0	11	5	6	1	1	0	0	0	0	0	0	5	0	5	4	0.667	16	29	45	0	1	130	113	78	10	126	1098	1150	1100	42	91	24	16	72	7	3	42.86%	7	0	100.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
24	Senators	3	1	1	0	0	0	0	1	13	12	1	1	1	0	0	0	0	0	0	5	2	3	2	0	1	0	0	0	0	1	8	10	-2	3	0.500	13	24	37	0	0	130	113	78	10	146	1098	1150	1100	42	96	19	26	70	14	3	21.43%	8	2	75.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
25	Sharks	2	2	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	2	1	1	4	1.000	5	8	13	0	0	130	113	78	10	59	1098	1150	1100	42	41	17	14	48	10	1	10.00%	7	1	85.71%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
26	Sound Tigers	4	3	1	0	0	0	0	0	18	10	8	2	2	0	0	0	0	0	0	12	3	9	2	1	1	0	0	0	0	0	6	7	-1	6	0.750	18	32	50	0	0	130	113	78	10	177	1098	1150	1100	42	131	34	25	90	12	3	25.00%	10	2	80.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
27	Spiders	4	2	2	0	0	0	0	0	16	13	3	2	1	1	0	0	0	0	0	10	7	3	2	1	1	0	0	0	0	0	6	6	0	4	0.500	16	31	47	0	0	130	113	78	10	146	1098	1150	1100	42	117	29	29	93	11	5	45.45%	12	2	83.33%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
28	Stars	2	1	0	0	0	0	1	0	10	6	4	1	0	0	0	0	0	1	0	6	5	1	1	1	0	0	0	0	0	0	4	1	3	4	1.000	10	15	25	0	0	130	113	78	10	95	1098	1150	1100	42	72	23	12	44	6	0	0.00%	6	0	100.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
29	Thunder	3	2	1	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	4	0	4	2	1	1	0	0	0	0	0	4	4	0	4	0.667	8	15	23	0	1	130	113	78	10	122	1098	1150	1100	42	72	24	19	78	7	0	0.00%	7	0	100.00%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
	Total	82	45	27	0	3	0	3	4	327	239	88	41	22	16	0	1	0	1	1	168	118	50	41	23	11	0	2	0	2	3	159	121	38	106	0.646	327	587	914	1	6	130	113	78	10	3365	1098	1150	1100	42	2765	739	688	2043	252	50	19.84%	275	49	82.18%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
30	Wolf Pack	4	3	0	0	0	0	1	0	25	15	10	2	2	0	0	0	0	0	0	13	7	6	2	1	0	0	0	0	1	0	12	8	4	8	1.000	25	44	69	0	0	130	113	78	10	198	1098	1150	1100	42	107	31	24	100	11	5	45.45%	12	2	83.33%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
	_Since Last GM Reset	82	45	27	0	3	0	3	4	327	239	88	41	22	16	0	1	0	1	1	168	118	50	41	23	11	0	2	0	2	3	159	121	38	106	0.646	327	587	914	1	6	130	113	78	10	3365	1098	1150	1100	42	2765	739	688	2043	252	50	19.84%	275	49	82.18%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
	_Vs Conference	46	24	17	0	0	0	2	3	169	136	33	23	13	9	0	0	0	0	1	86	64	22	23	11	8	0	0	0	2	2	83	72	11	55	0.598	169	302	471	0	1	130	113	78	10	1747	1098	1150	1100	42	1469	409	374	1171	151	32	21.19%	148	29	80.41%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	556
	_Vs Division	28	11	3	0	0	0	1	1	119	90	29	14	7	1	0	0	0	0	0	65	41	24	14	4	2	0	0	0	1	1	54	49	5	25	0.446	119	214	333	0	0	130	113	78	10	1091	1098	1150	1100	42	958	265	240	686	75	22	29.33%	93	18	80.65%	0	1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%	2155	1512	1746	569	1065	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	106	W1	327	587	914	3365	2765	739	688	2043	1	6

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	45	27	3	0	3	4	327	239

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	22	16	1	0	1	1	168	118

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	23	11	2	0	2	3	159	121

Last 10 Games
W	L	OTW	OTL	SOW	SOL
8	2	0	0	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
252	50	19.84%	275	49	82.18%	0

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
1098	1150	1100	42	130	113	78	10

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
1765	3209	55.00%	1438	2749	52.31%	788	1389	56.73%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
2155	1512	1746	569	1065	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
3 - 2020-10-24	13	Baby Hawks	4	Phantoms	2	L			BoxScore
8 - 2020-10-29	45	Spiders	5	Phantoms	3	L			BoxScore
11 - 2020-11-01	73	Phantoms	3	Comets	5	L			BoxScore
14 - 2020-11-04	88	Phantoms	6	Heat	4	W			BoxScore
15 - 2020-11-05	95	Phantoms	2	Oil Kings	4	L			BoxScore
18 - 2020-11-08	118	Stars	5	Phantoms	6	W	X	X	BoxScore
20 - 2020-11-10	130	Las Vegas	5	Phantoms	3	L			BoxScore
23 - 2020-11-13	151	Phantoms	5	Baby Hawks	4	W			BoxScore
25 - 2020-11-15	165	Monsters	1	Phantoms	3	W			BoxScore
26 - 2020-11-16	174	Phantoms	5	Sound Tigers	4	W			BoxScore
28 - 2020-11-18	182	Phantoms	4	Manchots	5	L			BoxScore
31 - 2020-11-21	196	Phantoms	3	Spiders	4	L			BoxScore
32 - 2020-11-22	210	Marlies	4	Phantoms	3	L	X	X	BoxScore
35 - 2020-11-25	226	Caroline	2	Phantoms	5	W			BoxScore
37 - 2020-11-27	240	Rocket	4	Phantoms	2	L			BoxScore
39 - 2020-11-29	254	Phantoms	2	Marlies	3	L			BoxScore
40 - 2020-11-30	268	Phantoms	4	Bruins	1	W			BoxScore
43 - 2020-12-03	283	Bears	3	Phantoms	4	W			BoxScore
45 - 2020-12-05	298	Phantoms	4	Senators	5	L	X	X	BoxScore
46 - 2020-12-06	307	Sound Tigers	2	Phantoms	6	W			BoxScore
49 - 2020-12-09	319	Phantoms	9	Cabaret Lady Mary Ann	2	W			BoxScore
51 - 2020-12-11	336	Phantoms	6	Caroline	5	W	X		BoxScore
53 - 2020-12-13	349	Heat	0	Phantoms	2	W			BoxScore
55 - 2020-12-15	367	Comets	2	Phantoms	6	W			BoxScore
57 - 2020-12-17	384	Phantoms	2	Monsters	1	W			BoxScore
59 - 2020-12-19	397	Cougars	6	Phantoms	4	L			BoxScore
60 - 2020-12-20	404	Phantoms	5	Rocket	0	W			BoxScore
63 - 2020-12-23	428	Marlies	2	Phantoms	0	L			BoxScore
65 - 2020-12-25	442	Jayhawks	3	Phantoms	5	W			BoxScore
67 - 2020-12-27	452	Senators	2	Phantoms	5	W			BoxScore
71 - 2020-12-31	486	Phantoms	2	Monsters	4	L			BoxScore
74 - 2021-01-03	507	Phantoms	6	Minnesota	2	W			BoxScore
75 - 2021-01-04	515	Phantoms	3	Oceanics	2	W			BoxScore
77 - 2021-01-06	528	Admirals	2	Phantoms	4	W			BoxScore
79 - 2021-01-08	540	Crunch	0	Phantoms	4	W			BoxScore
81 - 2021-01-10	558	Phantoms	4	Senators	5	L			BoxScore
83 - 2021-01-12	574	Wolf Pack	5	Phantoms	6	W			BoxScore
88 - 2021-01-17	601	Phantoms	2	Sharks	1	W			BoxScore
89 - 2021-01-18	610	Phantoms	7	Admirals	1	W			BoxScore
91 - 2021-01-20	622	Phantoms	4	Monarchs	6	L			BoxScore
93 - 2021-01-22	638	Phantoms	5	Las Vegas	4	W			BoxScore
95 - 2021-01-24	650	Phantoms	4	Jayhawks	2	W			BoxScore
98 - 2021-01-27	668	Phantoms	4	Caroline	3	W			BoxScore
99 - 2021-01-28	676	Bears	4	Phantoms	2	L			BoxScore
102 - 2021-01-31	695	Thunder	0	Phantoms	4	W			BoxScore
104 - 2021-02-02	711	Bruins	3	Phantoms	1	L			BoxScore
106 - 2021-02-04	726	Phantoms	3	Chiefs	4	L	X	X	BoxScore
107 - 2021-02-05	731	Rocket	1	Phantoms	9	W			BoxScore
109 - 2021-02-07	750	Monarchs	4	Phantoms	3	L			BoxScore
112 - 2021-02-10	763	Manchots	5	Phantoms	4	L			BoxScore
122 - 2021-02-20	786	Phantoms	2	Manchots	3	L			BoxScore
123 - 2021-02-21	799	Monsters	4	Phantoms	3	L			BoxScore
125 - 2021-02-23	811	Phantoms	2	Cougars	0	W			BoxScore
128 - 2021-02-26	832	Spiders	2	Phantoms	7	W			BoxScore
130 - 2021-02-28	850	Phantoms	6	Bears	5	W	X	X	BoxScore
132 - 2021-03-02	861	Cabaret Lady Mary Ann	3	Phantoms	8	W			BoxScore
133 - 2021-03-03	869	Phantoms	1	Sound Tigers	3	L			BoxScore
135 - 2021-03-05	883	Phantoms	4	Cabaret Lady Mary Ann	1	W			BoxScore
137 - 2021-03-07	897	Phantoms	3	Thunder	1	W			BoxScore
140 - 2021-03-10	920	Monsters	2	Phantoms	5	W			BoxScore
142 - 2021-03-12	936	Phantoms	3	Monsters	4	L	X	X	BoxScore
144 - 2021-03-14	949	Oceanics	4	Phantoms	1	L			BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
147 - 2021-03-17	972	Sharks	1	Phantoms	3	W			BoxScore
150 - 2021-03-20	994	Wolf Pack	2	Phantoms	7	W			BoxScore
152 - 2021-03-22	1011	Phantoms	6	Wolf Pack	3	W			BoxScore
155 - 2021-03-25	1029	Phantoms	3	Bears	2	W			BoxScore
156 - 2021-03-26	1037	Caroline	4	Phantoms	3	L			BoxScore
158 - 2021-03-28	1057	Crunch	4	Phantoms	5	W			BoxScore
161 - 2021-03-31	1073	Bruins	2	Phantoms	1	L			BoxScore
163 - 2021-04-02	1085	Phantoms	1	Thunder	3	L			BoxScore
165 - 2021-04-04	1098	Minnesota	0	Phantoms	8	W			BoxScore
166 - 2021-04-05	1109	Oil Kings	3	Phantoms	4	W	X		BoxScore
168 - 2021-04-07	1125	Chiefs	4	Phantoms	3	L			BoxScore
171 - 2021-04-10	1149	Phantoms	4	Stars	1	W			BoxScore
172 - 2021-04-11	1159	Phantoms	5	Chill	3	W			BoxScore
175 - 2021-04-14	1179	Sound Tigers	1	Phantoms	6	W			BoxScore
177 - 2021-04-16	1196	Phantoms	2	Cougars	1	W			BoxScore
179 - 2021-04-18	1206	Phantoms	3	Spiders	2	W			BoxScore
180 - 2021-04-19	1216	Manchots	3	Phantoms	4	W			BoxScore
183 - 2021-04-22	1239	Phantoms	6	Wolf Pack	5	W	X	X	BoxScore
184 - 2021-04-23	1246	Chill	5	Phantoms	4	L			BoxScore
186 - 2021-04-25	1261	Phantoms	4	Crunch	3	W	X		BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	77,647	39,857
Attendance PCT	94.69%	97.21%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
0	2866 - 95.53%	80,866$	3,315,500$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
1,882,098$	1,382,333$	1,382,333$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
7,432$	1,882,098$	19	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	7,432$	0$

Show or Hide Column

	Overall											Home											Visitor
Year	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT