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Philadelphia Flyers
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#	Player Name	#	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP
1	Riley Nash	0	X		X		100.00	69	39	88	64	78	88	85	65	82	67	66	68	65	74	76	41	50	67	0
2	Logan Shaw	0	X		X		100.00	76	40	87	66	85	92	86	65	80	67	62	64	63	71	76	51	50	67	0
3	Liam Foudy	0	X	X	X		100.00	64	36	95	72	78	80	77	67	63	61	62	69	65	62	63	82	50	65	0
4	Malte Stromwall	0			X		100.00	64	38	85	64	73	87	72	63	69	66	60	62	65	69	71	36	50	63	0
5	Hayden Hodgson	0			X		100.00	73	45	60	62	82	76	78	61	58	65	60	62	59	67	69	37	50	63	0
6	Seth Griffith	0	X		X		100.00	62	36	80	62	67	77	86	64	74	65	57	58	61	70	72	46	50	62	0
7	Cory Conacher	0	X	X	X		100.00	56	37	89	63	65	71	62	58	63	61	59	55	61	74	76	23	50	60	0
8	Matthew Strome	0		X			100.00	79	41	83	58	88	70	78	56	55	54	53	59	53	64	66	59	50	60	0
9	Cameron Hillis	0	X				100.00	55	36	87	58	67	75	67	56	71	52	50	54	57	63	65	66	50	56	0
10	Matt Irwin	0				X	100.00	81	48	83	64	80	81	74	61	30	63	57	69	49	77	76	21	50	67	0
11	Nikolai Knyzhov	0				X	100.00	75	40	85	63	88	83	65	62	30	58	61	65	52	65	67	42	50	65	0
12	Adam Ginning	0				X	100.00	74	41	72	60	83	82	72	57	30	59	58	64	49	63	65	71	50	64	0
13	Ryan Merkley	0				X	100.00	61	39	74	66	72	83	84	64	30	63	54	56	49	63	65	81	50	63	0
14	Matt Tennyson	0				X	100.00	72	42	73	57	81	66	85	56	30	58	53	59	47	73	75	26	50	62	0
15	Steven Kampfer	0				X	100.00	63	39	79	59	72	76	70	58	30	62	55	57	48	75	77	32	50	61	0
16	Helge Grans	0				X	100.00	78	40	92	56	87	66	81	54	30	55	53	58	46	61	63	76	50	61	0
17	Jeremy Groleau	0				X	100.00	73	39	89	56	82	68	77	54	30	55	53	57	46	64	66	43	50	60	0
Scratches
1	Cameron Crotty	0				X	100.00	71	39	86	54	80	68	85	53	30	54	52	56	45	64	66	63	50	60	0
2	Cameron Gaunce	0				X	100.00	67	42	70	55	76	70	81	54	30	56	54	55	46	73	75	42	50	60	0
3	Artemi Kniazev	0				X	100.00	61	37	78	61	68	79	83	59	30	60	53	55	47	62	64	73	50	60	0

	TEAM AVERAGE					100.00	69	40	82	61	78	77	77	59	47	60	57	60	54	68	70	51	50	62

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Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
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3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
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8	`?`	Wildcard for a single, non-space character.
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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
Scratches
1	Nolan Maier	100.00	68	57	58	70	67	66	68	67	66	68	67	62	67	44	50	65	0

	TEAM AVERAGE	100.00	68	57	58	70	67	66	68	67	66	68	67	62	67	44	50	65

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Mike Vellucci	71	70	73	69	85	79	62	USA	57	9	1,000,000$

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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	Logan Shaw	Phantoms (Phi)	C/RW	65	34	38	72	23	85	5	142	166	258	80	158	13.18%	15	1634	25.15	4	14	18	48	267	2	0	2	14	265	9	2	65.68%	1626	0	2	0.88	1	3	1	0	0	7	4	5
2	Liam Foudy	Phantoms (Phi)	C/LW/RW	69	30	40	70	28	26	0	36	132	291	83	232	10.31%	21	1641	23.79	6	8	14	59	279	0	2	2	8	260	5	1	50.80%	250	0	0	0.85	1	3	0	0	0	4	10	3
3	Nikolai Knyzhov	Phantoms (Phi)	D	69	15	41	56	23	58	10	117	51	154	51	82	9.74%	63	1623	23.53	5	12	17	89	300	0	0	0	2	252	2	0	0.00%	0	0	0	0.69	0	0	1	1	0	1	2	7
4	Hayden Hodgson	Phantoms (Phi)	RW	69	24	31	55	26	105	15	161	90	193	45	159	12.44%	6	1304	18.91	8	5	13	32	228	1	0	1	5	103	3	2	58.82%	119	0	1	0.84	0	1	0	3	0	5	1	3
5	Malte Stromwall	Phantoms (Phi)	RW	64	14	32	46	2	36	0	44	92	185	62	144	7.57%	9	1322	20.67	3	12	15	30	259	0	1	1	4	145	3	0	57.42%	155	0	0	0.70	0	2	0	0	0	3	2	1
6	Seth Griffith	Phantoms (Phi)	C/RW	54	20	26	46	0	16	0	32	102	159	40	84	12.58%	7	1039	19.24	6	12	18	31	204	0	0	0	1	34	2	0	62.01%	966	0	2	0.89	0	0	0	0	0	4	2	1
7	Matthew Strome	Phantoms (Phi)	LW	69	22	19	41	16	49	5	126	47	140	31	93	15.71%	12	1330	19.28	8	5	13	25	264	0	0	0	2	87	3	2	53.42%	146	0	1	0.62	0	0	0	0	1	2	3	3
8	Ryan Merkley	Phantoms (Phi)	D	58	6	27	33	3	38	0	44	44	75	25	58	8.00%	53	1213	20.92	3	12	15	48	238	0	0	0	1	195	1	0	100.00%	1	0	0	0.54	0	0	0	0	0	0	0	1
9	Adam Ginning	Phantoms (Phi)	D	53	7	21	28	13	67	5	85	27	69	18	36	10.14%	50	1093	20.63	4	6	10	41	207	0	1	1	0	171	1	2	100.00%	1	0	0	0.51	0	0	0	1	0	1	0	0
10	Matt Irwin	Phantoms (Phi)	D	36	3	22	25	8	67	15	66	38	56	19	42	5.36%	34	775	21.55	2	6	8	35	152	0	0	0	0	117	0	0	0.00%	0	1	0	0.64	0	0	0	1	2	0	1	2
11	Cory Conacher	Phantoms (Phi)	C/LW/RW	54	12	11	23	-2	10	0	22	71	119	39	73	10.08%	7	874	16.20	1	1	2	5	45	0	0	0	0	52	2	1	56.27%	670	0	0	0.53	0	0	0	0	0	1	1	1
12	Cameron Hillis	Phantoms (Phi)	C	61	9	14	23	8	12	0	25	60	71	28	38	12.68%	9	979	16.05	0	3	3	5	58	0	0	0	1	18	1	1	60.78%	408	0	0	0.47	0	0	0	0	0	1	0	3
13	Matt Tennyson	Phantoms (Phi)	D	69	5	17	22	3	67	15	70	45	46	13	36	10.87%	40	984	14.27	3	3	6	14	102	0	0	0	1	88	1	0	0.00%	0	0	0	0.45	0	0	0	0	3	0	2	0
14	Steven Kampfer	Phantoms (Phi)	D	69	6	15	21	-8	59	5	68	38	51	17	35	11.76%	33	964	13.97	3	3	6	7	39	0	0	0	0	59	0	1	60.00%	10	0	0	0.44	0	0	0	0	1	0	0	0
15	Helge Grans	Phantoms (Phi)	D	61	3	16	19	6	24	0	54	28	27	7	22	11.11%	34	755	12.39	1	1	2	4	44	0	0	0	0	62	0	0	0.00%	1	0	0	0.50	0	0	0	0	0	0	0	2
16	Artemi Kniazev	Phantoms (Phi)	D	59	1	16	17	5	16	0	17	12	31	6	9	3.23%	19	464	7.87	0	1	1	9	15	0	0	0	0	24	0	0	28.57%	7	0	0	0.73	0	0	0	0	0	1	0	0
17	Riley Nash	Phantoms (Phi)	C/RW	10	4	7	11	7	6	0	15	28	32	13	21	12.50%	3	254	25.44	1	1	2	9	52	0	0	0	0	46	0	0	64.14%	304	0	0	0.86	0	1	0	0	0	0	2	0
18	Jeremy Groleau	Phantoms (Phi)	D	62	1	9	10	2	13	5	26	12	28	8	25	3.57%	8	368	5.94	0	1	1	0	16	0	0	0	0	30	0	0	40.00%	10	0	0	0.54	0	0	0	0	1	0	1	0
19	Cameron Gaunce	Phantoms (Phi)	D	52	3	7	10	1	42	10	58	19	33	7	23	9.09%	10	611	11.77	0	0	0	0	6	0	0	0	0	13	1	0	39.56%	91	0	0	0.33	0	0	0	0	2	1	0	0
20	Cameron Crotty	Phantoms (Phi)	D	47	6	2	8	2	20	0	36	10	25	5	17	24.00%	6	311	6.62	0	0	0	0	6	0	0	0	0	38	0	1	50.91%	55	0	0	0.51	0	0	0	0	0	1	1	1
Team Total or Average				1150	225	411	636	166	816	90	1244	1112	2043	597	1387	11.01%	439	19548	17.00	58	106	164	491	2788	3	4	7	39	2068	34	13	60.77%	4820	1	6	0.65	2	10	2	6	10	32	32	33

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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	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Nolan Maier	Phantoms (Phi)	24	10	9	3	0.897	2.25	1333	0	3	50	484	0	1	0	0.333	6	24	0	1	2	0
Team Total or Average			24	10	9	3	0.897	2.25	1333	0	3	50	484	0	1	0	0.333	6	24	0	1	2	0

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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	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Contract	Status	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Link
Adam Ginning	D	24	2000-01-13	No	196 Lbs	6 ft3	No	No	No	4	RFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Artemi Kniazev	D	23	2001-01-04	No	178 Lbs	5 ft11	No	No	No	3	RFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Cameron Crotty	D	24	1999-05-05	No	182 Lbs	6 ft3	No	No	No	2	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Cameron Gaunce	D	34	1990-03-19	No	194 Lbs	6 ft1	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Cameron Hillis	C	23	2000-06-24	No	175 Lbs	5 ft9	No	No	No	2	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Cory Conacher	C/LW/RW	34	1989-12-14	No	185 Lbs	5 ft8	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Hayden Hodgson	RW	28	1996-03-02	No	208 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Helge Grans	D	21	2002-05-10	No	205 Lbs	6 ft4	No	No	No	3	ELC	Pro & Farm	900,000$	0$	0$	No	Link / NHL Link
Jeremy Groleau	D	24	1999-10-25	No	193 Lbs	6 ft3	No	No	No	1	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Liam Foudy	C/LW/RW	24	2000-02-04	No	188 Lbs	6 ft2	No	No	No	2	RFA	Pro & Farm	900,000$	0$	0$	No	Link / NHL Link
Logan Shaw	C/RW	31	1992-10-05	No	208 Lbs	6 ft3	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Malte Stromwall	RW	29	1994-08-24	No	191 Lbs	6 ft0	No	No	No	2	UFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Matt Irwin	D	36	1987-11-29	No	200 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	1,000,000$	0$	0$	No	Link / NHL Link
Matt Tennyson	D	33	1990-04-23	No	205 Lbs	6 ft2	No	No	No	3	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Matthew Strome	LW	25	1999-01-06	No	206 Lbs	6 ft4	No	No	No	1	RFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Nikolai Knyzhov	D	26	1998-03-20	No	222 Lbs	6 ft3	No	No	No	1	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Nolan Maier	G	23	2001-01-10	No	172 Lbs	6 ft0	No	No	No	2	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Riley Nash	C/RW	34	1989-05-09	No	188 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	1,000,000$	0$	0$	No	Link / NHL Link
Ryan Merkley	D	23	2000-08-14	No	186 Lbs	6 ft0	No	No	No	2	RFA	Pro & Farm	900,000$	0$	0$	No	Link / NHL Link
Seth Griffith	C/RW	31	1993-01-04	No	190 Lbs	5 ft9	No	No	No	2	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Steven Kampfer	D	35	1988-09-24	No	198 Lbs	5 ft11	No	No	No	3	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
21	27.86	194 Lbs	6 ft1	1.86	538,095$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Liam Foudy	Riley Nash	Logan Shaw	40	1	2	2
2	Matthew Strome	Seth Griffith	Malte Stromwall	30	1	2	2
3	Cameron Hillis	Cory Conacher	Hayden Hodgson	20	1	2	2
4	Riley Nash	Cameron Hillis	Logan Shaw	10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Matt Irwin	Nikolai Knyzhov	40	1	2	2
2	Adam Ginning	Ryan Merkley	30	1	2	2
3	Matt Tennyson	Steven Kampfer	20	1	2	2
4	Helge Grans	Jeremy Groleau	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Liam Foudy	Riley Nash	Logan Shaw	60	1	2	2
2	Matthew Strome	Seth Griffith	Malte Stromwall	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Matt Irwin	Nikolai Knyzhov	60	1	2	2
2	Adam Ginning	Ryan Merkley	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Riley Nash	Logan Shaw	60	1	2	2
2	Liam Foudy	Malte Stromwall	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Matt Irwin	Nikolai Knyzhov	60	1	2	2
2	Adam Ginning	Ryan Merkley	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Riley Nash	60	1	2	2	Matt Irwin	Nikolai Knyzhov	60	1	2	2
2	Logan Shaw	40	1	2	2	Adam Ginning	Ryan Merkley	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Riley Nash	Logan Shaw	60	1	2	2
2	Liam Foudy	Malte Stromwall	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Matt Irwin	Nikolai Knyzhov	60	1	2	2
2	Adam Ginning	Ryan Merkley	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Liam Foudy	Riley Nash	Logan Shaw	Matt Irwin	Nikolai Knyzhov

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Liam Foudy	Riley Nash	Logan Shaw	Matt Irwin	Nikolai Knyzhov

Extra Forwards
Normal	PowerPlay	Penalty Kill
Hayden Hodgson, Cory Conacher, Seth Griffith	Hayden Hodgson, Cory Conacher	Seth Griffith

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Matt Tennyson, Steven Kampfer, Helge Grans	Matt Tennyson	Steven Kampfer, Helge Grans

Penalty Shots
Riley Nash, Logan Shaw, Liam Foudy, Malte Stromwall, Hayden Hodgson

Goalie
#1 : , #2 :

Custom OT Lines Forwards
Riley Nash, Logan Shaw, Liam Foudy, Malte Stromwall, Hayden Hodgson, Seth Griffith, Seth Griffith, Matthew Strome, Cory Conacher, Cameron Hillis,

Custom OT Lines Defensemen
Matt Irwin, Nikolai Knyzhov, Adam Ginning, Ryan Merkley, Matt Tennyson

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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.
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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

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
69	73	L2	225	412	637	2047	1579	442	816	1245	0	3

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
69	30	30	4	2	0	3	225	205

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
34	17	13	0	1	0	3	118	93

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
35	13	17	4	1	0	0	107	112

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

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
346	58	16.76%	333	69	79.28%	3

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
695	657	679	26	100	73	48	6

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
1307	2155	60.65%	998	1670	59.76%	637	1020	62.45%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
1967	1446	1374	445	819	444

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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
5 - 2023-08-14	21	Monsters	0	Phantoms	4	W			BoxScore
6 - 2023-08-15	25	Senators	4	Phantoms	3	L	X	X	BoxScore
11 - 2023-08-20	38	Phantoms	1	Thunderbirds	2	L	X		BoxScore
12 - 2023-08-21	47	Phantoms	0	Wolfpack	3	L			BoxScore
13 - 2023-08-22	60	Thunderbirds	0	Phantoms	1	W			BoxScore
19 - 2023-08-28	88	Phantoms	5	Bears	1	W			BoxScore
20 - 2023-08-29	103	Wolfpack	2	Phantoms	1	L	X	X	BoxScore
25 - 2023-09-03	116	Wolfpack	2	Phantoms	3	W			BoxScore
26 - 2023-09-04	133	Phantoms	1	Thunderbirds	3	L			BoxScore
30 - 2023-09-08	143	Phantoms	2	Thunderbirds	3	L			BoxScore
33 - 2023-09-11	166	Bears	1	Phantoms	5	W			BoxScore
34 - 2023-09-12	174	Phantoms	5	Bears	3	W			BoxScore
39 - 2023-09-17	195	Phantoms	0	Rocket	4	L			BoxScore
40 - 2023-09-18	211	Phantoms	2	Senators	9	L			BoxScore
43 - 2023-09-21	224	Phantoms	3	Marlies	1	W			BoxScore
46 - 2023-09-24	242	Bruins	4	Phantoms	3	L	X		BoxScore
47 - 2023-09-25	258	Americans	3	Phantoms	0	L			BoxScore
53 - 2023-10-01	276	Phantoms	4	Wolfpack	3	W	X		BoxScore
54 - 2023-10-02	294	Phantoms	3	Wolfpack	8	L			BoxScore
55 - 2023-10-03	306	Phantoms	2	Bruins	5	L			BoxScore
60 - 2023-10-08	319	Bruins	3	Phantoms	2	L			BoxScore
61 - 2023-10-09	336	Checkers	3	Phantoms	6	W			BoxScore
65 - 2023-10-13	352	Checkers	3	Phantoms	7	W			BoxScore
67 - 2023-10-15	364	Rocket	3	Phantoms	2	L			BoxScore
68 - 2023-10-16	373	Thunderbirds	4	Phantoms	2	L			BoxScore
73 - 2023-10-21	399	Phantoms	6	Checkers	3	W			BoxScore
74 - 2023-10-22	402	Phantoms	7	Checkers	3	W			BoxScore
79 - 2023-10-27	429	Comets	3	Phantoms	6	W			BoxScore
82 - 2023-10-30	457	Bears	1	Phantoms	7	W			BoxScore
83 - 2023-10-31	466	Checkers	1	Phantoms	3	W			BoxScore
89 - 2023-11-06	489	Phantoms	0	Penguins	2	L			BoxScore
90 - 2023-11-07	503	Phantoms	9	Bears	1	W			BoxScore
95 - 2023-11-12	524	Wolfpack	4	Phantoms	1	L			BoxScore
96 - 2023-11-13	538	Bears	2	Phantoms	7	W			BoxScore
100 - 2023-11-17	559	Phantoms	1	Americans	5	L			BoxScore
102 - 2023-11-19	566	Phantoms	3	Monsters	2	W	X		BoxScore
103 - 2023-11-20	574	Phantoms	3	Monsters	5	L			BoxScore
109 - 2023-11-26	612	Penguins	4	Phantoms	1	L			BoxScore
110 - 2023-11-27	629	Marlies	2	Phantoms	8	W			BoxScore
114 - 2023-12-01	647	Bears	1	Phantoms	7	W			BoxScore
116 - 2023-12-03	659	Bears	3	Phantoms	7	W			BoxScore
117 - 2023-12-04	675	Penguins	7	Phantoms	2	L			BoxScore
123 - 2023-12-10	690	Penguins	1	Phantoms	3	W			BoxScore
124 - 2023-12-11	699	Phantoms	1	Islanders	3	L			BoxScore
128 - 2023-12-15	718	Phantoms	3	Bears	1	W			BoxScore
131 - 2023-12-18	739	Crunch	2	Phantoms	4	W			BoxScore
135 - 2023-12-22	764	Phantoms	2	Penguins	5	L			BoxScore
138 - 2023-12-25	783	Phantoms	6	Checkers	3	W			BoxScore
139 - 2023-12-26	797	Phantoms	7	Checkers	3	W			BoxScore
144 - 2023-12-31	819	Phantoms	2	Comets	3	L			BoxScore
145 - 2024-01-01	836	Islanders	3	Phantoms	1	L			BoxScore
146 - 2024-01-02	844	Bears	3	Phantoms	1	L			BoxScore
151 - 2024-01-07	862	Penguins	4	Phantoms	0	L			BoxScore
152 - 2024-01-08	872	Phantoms	1	Penguins	0	W			BoxScore
153 - 2024-01-09	884	Phantoms	3	Islanders	2	W			BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
156 - 2024-01-12	900	Phantoms	1	Penguins	3	L			BoxScore
158 - 2024-01-14	909	Phantoms	5	Bruins	4	W	X		BoxScore
159 - 2024-01-15	920	Phantoms	4	Bruins	3	W	X		BoxScore
163 - 2024-01-19	942	Thunderbirds	2	Phantoms	1	L			BoxScore
166 - 2024-01-22	969	Islanders	5	Phantoms	2	L			BoxScore
167 - 2024-01-23	976	Penguins	2	Phantoms	1	L	X	X	BoxScore
172 - 2024-01-28	998	Phantoms	6	Crunch	3	W			BoxScore
173 - 2024-01-29	1010	Phantoms	1	Penguins	5	L			BoxScore
176 - 2024-02-01	1023	Phantoms	3	Bears	4	L			BoxScore
179 - 2024-02-04	1040	Bruins	5	Phantoms	6	W			BoxScore
180 - 2024-02-05	1053	Monsters	2	Phantoms	8	W			BoxScore
181 - 2024-02-06	1063	Phantoms	3	Penguins	1	W			BoxScore
186 - 2024-02-11	1077	Phantoms	2	Bears	3	L			BoxScore
187 - 2024-02-12	1096	Islanders	4	Phantoms	3	L			BoxScore
193 - 2024-02-18	1119	Phantoms	-	Islanders	-
194 - 2024-02-19	1136	Penguins	-	Phantoms	-
195 - 2024-02-20	1147	Checkers	-	Phantoms	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	63,996	32,048
Attendance PCT	94.11%	94.26%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
2	2825 - 94.16%	70,415$	2,394,112$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
1,080,405$	113,000$	0$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	95,755$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
140,830$	3	5,708$	17,124$

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	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
Regular Season
12	82	52	19	0	3	4	1	3	260	123	137	41	30	7	0	1	1	0	2	154	55	99	41	22	12	0	2	3	1	1	106	68	38	119	260	477	737	0	18	0	96	77	83	2406	0	749	810	838	1588	471	1527	1837	517	74	14.31%	603	52	91.38%	7	1695	2732	62.04%	1368	2286	59.84%	744	1164	63.92%	2302	1643	1674	573	979	526
12	82	52	19	0	3	4	1	3	260	123	137	41	30	7	0	1	1	0	2	154	55	99	41	22	12	0	2	3	1	1	106	68	38	119	260	477	737	0	18	0	96	77	83	2406	0	749	810	838	1588	471	1527	1837	517	74	14.31%	603	52	91.38%	7	1695	2732	62.04%	1368	2286	59.84%	744	1164	63.92%	2302	1643	1674	573	979	526
13	76	46	21	0	2	1	4	2	227	137	90	38	26	11	0	0	0	1	0	123	80	43	38	20	10	0	2	1	3	2	104	57	47	107	227	410	637	0	15	0	82	78	61	2003	0	664	653	672	1513	452	1415	1644	459	78	16.99%	538	59	89.03%	5	1301	2405	54.10%	1185	2141	55.35%	528	1034	51.06%	2139	1532	1560	524	896	483
13	76	46	21	0	2	1	4	2	227	137	90	38	26	11	0	0	0	1	0	123	80	43	38	20	10	0	2	1	3	2	104	57	47	107	227	410	637	0	15	0	82	78	61	2003	0	664	653	672	1513	452	1415	1644	459	78	16.99%	538	59	89.03%	5	1301	2405	54.10%	1185	2141	55.35%	528	1034	51.06%	2139	1532	1560	524	896	483
14	76	34	26	0	1	4	8	3	195	146	49	38	18	14	0	0	2	2	2	100	78	22	38	16	12	0	1	2	6	1	95	68	27	93	195	342	537	1	8	0	64	50	72	1922	0	618	655	633	1696	490	951	1460	360	61	16.94%	361	48	86.70%	1	1379	2420	56.98%	1181	2174	54.32%	569	1092	52.11%	2054	1474	1648	521	919	478
14	76	34	26	0	1	4	8	3	195	146	49	38	18	14	0	0	2	2	2	100	78	22	38	16	12	0	1	2	6	1	95	68	27	93	195	342	537	1	8	0	64	50	72	1922	0	618	655	633	1696	490	951	1460	360	61	16.94%	361	48	86.70%	1	1379	2420	56.98%	1181	2174	54.32%	569	1092	52.11%	2054	1474	1648	521	919	478
15	82	37	36	0	0	2	5	2	249	220	29	41	23	16	0	0	0	2	0	143	104	39	41	14	20	0	0	2	3	2	106	116	-10	88	249	443	692	0	3	0	92	76	76	2576	0	874	815	874	1931	626	934	1553	429	70	16.32%	372	56	84.95%	1	1530	2746	55.72%	1218	2296	53.05%	680	1232	55.19%	2098	1505	1881	579	976	496
15	82	37	36	0	0	2	5	2	249	220	29	41	23	16	0	0	0	2	0	143	104	39	41	14	20	0	0	2	3	2	106	116	-10	88	249	443	692	0	3	0	92	76	76	2576	0	874	815	874	1931	626	934	1553	429	70	16.32%	372	56	84.95%	1	1530	2746	55.72%	1218	2296	53.05%	680	1232	55.19%	2098	1505	1881	579	976	496
16	82	51	17	0	3	4	5	2	279	150	129	41	28	6	0	1	2	4	0	163	65	98	41	23	11	0	2	2	1	2	116	85	31	124	279	506	785	1	14	0	117	93	61	2383	0	802	808	760	1789	545	1015	1511	418	65	15.55%	408	55	86.52%	3	1573	2597	60.57%	1357	2317	58.57%	689	1164	59.19%	2283	1672	1719	550	964	512
16	82	51	17	0	3	4	5	2	279	150	129	41	28	6	0	1	2	4	0	163	65	98	41	23	11	0	2	2	1	2	116	85	31	124	279	506	785	1	14	0	117	93	61	2383	0	802	808	760	1789	545	1015	1511	418	65	15.55%	408	55	86.52%	3	1573	2597	60.57%	1357	2317	58.57%	689	1164	59.19%	2283	1672	1719	550	964	512
17	72	38	20	0	5	3	3	3	238	141	97	36	17	11	0	4	1	1	2	112	64	48	36	21	9	0	1	2	2	1	126	77	49	98	238	447	685	0	13	0	109	69	52	2152	0	740	703	682	1598	448	820	1360	365	60	16.44%	352	42	88.07%	3	1413	2278	62.03%	1186	2050	57.85%	611	1019	59.96%	2021	1473	1492	479	850	457
17	72	38	20	0	5	3	3	3	238	141	97	36	17	11	0	4	1	1	2	112	64	48	36	21	9	0	1	2	2	1	126	77	49	98	238	447	685	0	13	0	109	69	52	2152	0	740	703	682	1598	448	820	1360	365	60	16.44%	352	42	88.07%	3	1413	2278	62.03%	1186	2050	57.85%	611	1019	59.96%	2021	1473	1492	479	850	457
18	69	30	30	0	4	2	0	3	225	205	20	34	17	13	0	0	1	0	3	118	93	25	35	13	17	0	4	1	0	0	107	112	-5	73	225	412	637	0	3	100	73	48	6	2047	695	657	679	26	1579	442	816	1245	346	58	16.76%	333	69	79.28%	3	1307	2155	60.65%	998	1670	59.76%	637	1020	62.45%	1967	1446	1374	445	819	444
Total Regular Season	1009	546	308	0	32	38	52	33	3121	2039	1082	504	301	143	0	12	13	20	15	1708	985	723	505	245	165	0	20	25	32	18	1413	1054	359	1331	3121	5662	8783	4	145	100	1193	934	816	28931	695	9551	9567	8944	21809	6506	14140	19975	5442	874	16.06%	5601	693	87.63%	43	19089	32511	58.72%	15988	28198	56.70%	8279	14430	57.37%	27765	20054	21328	6905	11994	6356
Playoff
11	24	16	8	0	0	0	0	0	58	44	14	11	6	5	0	0	0	0	0	26	22	4	13	10	3	0	0	0	0	0	32	22	10	32	58	100	158	0	3	0	15	19	20	587	0	177	185	178	534	146	448	523	193	23	11.92%	198	20	89.90%	2	429	813	52.77%	434	810	53.58%	167	343	48.69%	626	424	614	202	316	155
11	24	16	8	0	0	0	0	0	58	44	14	11	6	5	0	0	0	0	0	26	22	4	13	10	3	0	0	0	0	0	32	22	10	32	58	100	158	0	3	0	15	19	20	587	0	177	185	178	534	146	448	523	193	23	11.92%	198	20	89.90%	2	429	813	52.77%	434	810	53.58%	167	343	48.69%	626	424	614	202	316	155
12	9	5	4	0	0	0	0	0	21	19	2	4	3	1	0	0	0	0	0	9	8	1	5	2	3	0	0	0	0	0	12	11	1	10	21	39	60	0	0	0	7	7	7	222	0	56	67	76	204	47	193	199	92	10	10.87%	74	6	91.89%	1	188	338	55.62%	163	308	52.92%	88	145	60.69%	257	174	220	78	125	65
12	9	5	4	0	0	0	0	0	21	19	2	4	3	1	0	0	0	0	0	9	8	1	5	2	3	0	0	0	0	0	12	11	1	10	21	39	60	0	0	0	7	7	7	222	0	56	67	76	204	47	193	199	92	10	10.87%	74	6	91.89%	1	188	338	55.62%	163	308	52.92%	88	145	60.69%	257	174	220	78	125	65
13	5	1	4	0	0	0	0	0	10	12	-2	2	0	2	0	0	0	0	0	3	5	-2	3	1	2	0	0	0	0	0	7	7	0	2	10	18	28	0	0	0	2	6	2	121	0	48	40	33	114	26	99	107	42	3	7.14%	46	9	80.43%	0	99	172	57.56%	81	175	46.29%	23	57	40.35%	117	79	123	40	59	28
13	5	1	4	0	0	0	0	0	10	12	-2	2	0	2	0	0	0	0	0	3	5	-2	3	1	2	0	0	0	0	0	7	7	0	2	10	18	28	0	0	0	2	6	2	121	0	48	40	33	114	26	99	107	42	3	7.14%	46	9	80.43%	0	99	172	57.56%	81	175	46.29%	23	57	40.35%	117	79	123	40	59	28
Total Playoff	76	44	32	0	0	0	0	0	178	150	28	34	18	16	0	0	0	0	0	76	70	6	42	26	16	0	0	0	0	0	102	80	22	88	178	314	492	0	6	0	48	64	58	1860	0	562	584	574	1704	438	1480	1658	654	72	11.01%	636	70	88.99%	6	1432	2646	54.12%	1356	2586	52.44%	556	1090	51.01%	2003	1356	1917	642	1002	499