Online Hockey League - Schedule Pro Flyers

Roster

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	#	POS	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Corey Perry	0	RW	100.00	75	53	80	73	78	89	93	99	30	92	99	51	95	99	99	1	79	80	34	5,500,000$/2yrs
John Tavares	0	C	100.00	57	41	73	75	75	92	78	99	77	95	97	56	83	82	90	49	41	79	29	7,382,000$/2yrs
Evgeny Kuznetsov	0	C	100.00	63	12	81	78	75	86	87	90	79	99	80	69	66	66	66	69	79	76	27	6,498,000$/2yrs
Jonathan Drouin	0	C/LW/RW	100.00	38	1	91	75	72	99	80	92	63	93	93	52	77	70	70	75	79	75	24	4,630,000$/1yrs
Johnny Gaudreau	0	C/LW	100.00	1	1	89	81	65	90	75	95	79	92	94	49	78	73	73	61	79	74	26	3,906,000$/1yrs
Ivan Barbashev	0	C/LW	100.00	42	9	87	71	75	76	70	92	77	85	82	59	64	65	65	80	79	71	23	870,000$/2yrs
Boris Katchouk (R)	0	LW	100.00	72	44	76	72	88	88	85	81	50	69	74	66	70	52	52	93	62	69	19	925,000$/2yrs
Artem Anisimov	0	C/LW	100.00	39	28	90	68	77	82	84	69	74	57	71	74	78	86	87	33	79	67	31	1,343,000$/1yrs
Matt Calvert	0	C/LW/RW	100.00	57	56	76	68	73	59	65	74	25	71	71	67	64	89	91	36	79	66	29	1,070,000$/2yrs
Matt Frattin	0	LW/RW	100.00	46	29	99	79	75	77	74	73	27	63	72	52	78	90	83	29	76	66	31	1,366,000$/2yrs
Matt Luff (R)	0	RW	100.00	58	20	82	78	69	83	74	74	62	61	67	63	67	54	54	89	64	65	22	975,000$/6yrs
Hudson Fasching	0	RW	100.00	72	34	82	72	71	82	78	69	62	63	66	68	66	57	57	72	61	65	24	1,115,000$/4yrs
Tyler Myers	0	D	99.00	95	65	70	74	81	92	87	76	30	84	75	87	62	83	86	48	79	78	29	6,500,000$/4yrs
Rasmus Ristolainen	0	D	100.00	66	28	86	67	78	94	75	86	5	82	88	73	70	69	69	76	62	73	24	3,434,000$/1yrs
Thomas Chabot	0	D	100.00	64	15	85	85	73	94	94	76	5	85	71	77	67	57	57	83	79	73	22	4,426,000$/1yrs
Fedor Tyutin	0	D	100.00	72	48	63	62	79	77	83	56	30	61	53	87	55	99	99	1	79	71	36	2,431,000$/3yrs
Mark Borowiecki	0	D	100.00	99	44	63	69	74	80	75	60	5	60	50	89	57	69	69	51	79	71	30	3,026,000$/1yrs
Brady Skjei	0	D	100.00	83	41	70	75	77	75	79	80	50	88	63	70	60	61	62	74	69	70	25	2,400,000$/2yrs
Scratches
Shawn Matthias	0	C/LW	100.00	67	41	81	61	81	60	84	66	62	57	67	70	62	86	85	33	38	65	31	1,035,000$/1yrs
Christian Djoos	0	D	100.00	60	25	82	81	72	88	82	68	15	67	63	77	59	57	57	78	20	69	25	2,250,000$/3yrs

	TEAM AVERAGE		99.95	61	32	80	73	75	83	80	79	45	76	75	68	69	73	74	57	68	71

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	#	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Ben Bishop	0	100.00	83	88	89	83	85	92	96	95	95	89	81	95	97	12	79	87	32	7,125,000$/4yrs
Scratches
Matt Murray	0	95.00	87	90	90	86	87	92	93	99	94	85	89	56	56	79	66	84	25	9,250,000$/4yrs

	TEAM AVERAGE	97.50	85	89	90	85	86	92	95	97	95	87	85	76	77	46	73	86

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Dallas Eakins	84	80	98	77	92	92	39	USA	56	5	3,750,000$

General Manager
Chris Walsh

Player Stats

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	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPM	PKG	PKA	PKP	PKM	GW	GT	FO%	FOT	GA	TA	EG	HT	P/20	PSG
1	Corey Perry	Flyers	0	RW	69	21	33	54	-1	18	0	96	199	53	131	10.55%	21	20.42	1	10	11	150	0	0	0	0	3	1	36.45%	107	44	15	1	0	0.77	1	8
2	Johnny Gaudreau	Flyers	0	C/LW	69	23	28	51	4	6	0	1	235	76	168	9.79%	22	20.60	4	6	10	163	0	0	0	21	5	0	56.64%	113	70	16	1	0	0.72	2	4
3	Evgeny Kuznetsov	Flyers	0	C	69	16	33	49	-13	22	0	65	182	60	115	8.79%	27	20.04	6	12	18	161	0	0	0	106	3	3	51.57%	1524	41	16	0	0	0.71	5	8
4	Ivan Barbashev	Flyers	0	C/LW	69	17	20	37	4	4	0	16	152	52	91	11.18%	20	18.83	5	8	13	160	1	0	1	111	4	1	57.89%	399	29	17	0	0	0.57	1	3
5	Jonathan Drouin	Flyers	0	C/LW/RW	69	14	22	36	2	0	0	14	223	64	125	6.28%	19	19.24	1	10	11	161	0	1	1	114	1	1	42.57%	148	49	11	0	1	0.54	2	6
6	John Tavares	Flyers	0	C	48	12	23	35	2	24	0	38	128	44	88	9.38%	20	20.61	3	5	8	119	0	0	0	44	3	1	48.26%	1237	20	17	0	1	0.71	1	2
7	Rasmus Ristolainen	Flyers	0	D	61	7	18	25	7	10	0	44	95	44	35	7.37%	75	22.23	4	2	6	135	0	0	0	105	1	0	0.00%	0	17	36	0	0	0.37	0	0
8	Tyler Myers	Flyers	0	D	69	6	16	22	-11	60	0	197	102	41	55	5.88%	91	23.96	3	2	5	159	0	0	0	93	1	0	0.00%	0	18	58	0	0	0.27	0	0
9	Brady Skjei	Flyers	0	D	69	3	19	22	11	38	0	135	72	37	30	4.17%	67	18.01	1	1	2	131	0	0	0	2	0	0	0.00%	0	15	41	0	0	0.35	0	0
10	Thomas Chabot	Flyers	0	D	69	8	14	22	1	4	0	57	124	47	62	6.45%	104	22.35	3	0	3	170	0	0	0	109	1	1	0.00%	0	41	40	0	0	0.29	1	1
11	Matt Luff	Flyers	0	RW	51	9	11	20	-3	8	0	22	107	36	65	8.41%	10	10.81	0	0	0	0	0	0	0	0	2	0	33.33%	18	42	1	0	0	0.73	0	0
12	Artem Anisimov	Flyers	0	C/LW	69	6	9	15	-4	2	0	5	90	28	46	6.67%	18	12.52	0	0	0	14	0	0	0	21	1	1	51.83%	409	12	16	0	0	0.35	0	0
13	Matt Calvert	Flyers	0	C/LW/RW	69	6	7	13	2	14	0	20	78	30	48	7.69%	15	7.65	0	0	0	5	0	0	0	0	0	1	37.02%	181	14	11	1	0	0.49	0	0
14	Hudson Fasching	Flyers	0	RW	58	3	8	11	-3	8	0	45	66	17	45	4.55%	11	11.74	0	0	0	0	0	0	0	0	3	0	31.58%	19	9	13	0	0	0.32	0	0
15	Mark Borowiecki	Flyers	0	D	69	0	9	9	-8	38	0	134	56	24	26	0.00%	76	17.28	0	0	0	12	0	0	0	83	0	0	0.00%	0	3	48	0	0	0.15	0	0
16	Matt Frattin	Flyers	0	LW/RW	68	4	4	8	1	0	0	6	103	29	64	3.88%	7	7.46	0	0	0	2	0	0	0	0	1	0	15.38%	13	22	4	0	0	0.32	0	0
17	Fedor Tyutin	Flyers	0	D	69	1	6	7	-4	32	0	73	59	31	33	1.69%	68	17.64	0	0	0	43	0	0	0	63	0	0	0.00%	0	1	34	0	0	0.12	0	0
18	Boris Katchouk	Flyers	0	LW	28	3	4	7	4	2	0	20	53	28	38	5.66%	5	11.73	0	0	0	9	0	0	0	29	0	1	36.36%	11	12	4	0	0	0.43	0	0
19	Shawn Matthias	Flyers	0	C/LW	41	5	1	6	1	4	0	28	22	5	7	22.73%	5	7.64	0	0	0	0	0	0	0	0	0	0	38.10%	21	0	6	0	0	0.38	0	0
20	Christian Djoos	Flyers	0	D	8	0	3	3	3	0	0	2	5	2	2	0.00%	11	18.03	0	0	0	0	0	0	0	8	0	0	0.00%	0	2	6	0	0	0.42	0	0
Team Total or Average				1191	164	288	452	-5	294	0	1018	2151	748	1274	7.62%	692	16.75	31	56	87	1603	1	1	2	916	29	11	49.64%	4200	461	410	3	2	0.45	13	32

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	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Matt Murray	Flyers	45	22	15	7	0.924	2.37	2681	0	1	106	1402	609	1	2	0.824	17	44	21	8	3	5
2	Ben Bishop	Flyers	25	15	9	1	0.921	2.34	1512	0	0	59	746	335	1	1	0.643	14	25	44	5	3	2
Team Total or Average		70	37	24	8	0.923	2.36	4194	0	1	165	2148	944	2	3	0.742	31	69	65	13	6	7

Cap Hit


	Projected Total Cap Hit	77,457,000$
	Projected Cap Space	2,043,000$
	Retains And Buyout Cap Hit	0$
	Salary Cap To Date	62,383,154$
	Players In Salary Cap	22
	LTIR Players	0

Player Name	Pos	Age	Cap Hit	2019-2020	2020-2021	2021-2022	2022-2023	2023-2024	2024-2025
Artem Anisimov	C/LW	31	1,343,000$	1,343,000$
Ben Bishop	C/LW/RW	32	7,125,000$	7,125,000$	7,125,000$	7,125,000$	7,125,000$
Boris Katchouk	LW	19	925,000$	925,000$	925,000$
Brady Skjei	D	25	2,400,000$	2,400,000$	2,400,000$
Christian Djoos	D	25	2,250,000$	2,250,000$	2,250,000$	2,250,000$
Corey Perry	RW	34	5,500,000$	5,500,000$	5,500,000$
Evgeny Kuznetsov	C	27	6,498,000$	6,498,000$	6,498,000$
Fedor Tyutin	D	36	2,431,000$	2,431,000$	2,431,000$	2,431,000$
Hudson Fasching	RW	24	1,115,000$	1,115,000$	1,115,000$	1,115,000$	1,115,000$
Ivan Barbashev	C/LW	23	870,000$	870,000$	870,000$
John Tavares	C	29	7,382,000$	7,382,000$	7,382,000$
Johnny Gaudreau	C/LW	26	3,906,000$	3,906,000$
Jonathan Drouin	C/LW/RW	24	4,630,000$	4,630,000$
Mark Borowiecki	D	30	3,026,000$	3,026,000$
Matt Calvert	C/LW/RW	29	1,070,000$	1,070,000$	1,070,000$
Matt Frattin	LW/RW	31	1,366,000$	1,366,000$	1,366,000$
Matt Luff	RW	22	975,000$	975,000$	975,000$	975,000$	975,000$	975,000$	975,000$
Matt Murray	D	25	9,250,000$	9,250,000$	9,250,000$	9,250,000$	9,250,000$
Rasmus Ristolainen	D	24	3,434,000$	3,434,000$
Shawn Matthias	C/LW	31	1,035,000$	1,035,000$
Thomas Chabot	D	22	4,426,000$	4,426,000$
Tyler Myers	D	29	6,500,000$	6,500,000$	6,500,000$	6,500,000$	6,500,000$

Lines

Forward Lines
#0 - Johnny Gaudreau	#0 - John Tavares	#0 - Corey Perry
#0 - Jonathan Drouin	#0 - Evgeny Kuznetsov	#0 - Ivan Barbashev
#0 - Boris Katchouk	#0 - Artem Anisimov	#0 - Hudson Fasching
#0 - Matt Frattin	#0 - Matt Calvert	#0 - Matt Luff

Defensive Pairings
#0 - Tyler Myers	#0 - Rasmus Ristolainen
#0 - Thomas Chabot	#0 - Fedor Tyutin
#0 - Mark Borowiecki	#0 - Brady Skjei

1st Power Play Unit
#0 - Johnny Gaudreau		#0 - John Tavares		#0 - Corey Perry
#0 - Tyler Myers			#0 - Rasmus Ristolainen

2nd Power Play Unit
#0 - Ivan Barbashev		#0 - Evgeny Kuznetsov		#0 - Jonathan Drouin
#0 - Thomas Chabot			#0 - Fedor Tyutin

Goalies
#0 - Ben Bishop

Team Stats

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

#	VS Team	GP	W	L	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	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	GF%	SH%	SV%	PDO	PDOBRK
1	Sharks	1	0	1	0	0	0	0	1	3	-2	0	0.000	1	1	2	0	0	0	1	0	32	10	5	17	0	33	5	4	15	0	0	0.00%	2	1	50.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	20	13	23	7	15	7	33.3%	3.1%	90.9%	94.0	Unlucky
2	Penguins	4	1	2	0	1	0	0	8	13	-5	3	0.375	8	14	22	0	3	3	2	0	134	35	54	45	0	122	41	38	48	9	1	11.11%	19	6	68.42%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	86	51	88	36	66	31	50.0%	6.0%	89.3%	95.3	Unlucky
3	Lightning	2	0	1	1	0	0	0	4	6	-2	2	0.500	4	8	12	0	0	1	2	1	55	17	18	19	1	61	13	12	34	4	0	0.00%	6	1	83.33%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	42	24	44	18	33	16	44.4%	7.3%	90.2%	97.4	Unlucky
4	Wild	2	1	1	0	0	0	0	5	4	1	2	0.500	5	9	14	0	1	2	2	0	70	26	17	27	0	50	22	10	28	3	1	33.33%	5	1	80.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	44	27	41	16	34	16	57.1%	7.1%	92.0%	99.1	DULL
5	Bruins	2	2	0	0	0	0	0	7	3	4	4	1.000	7	11	18	0	0	4	3	0	64	26	21	17	0	55	18	8	26	6	3	50.00%	4	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	41	25	44	16	34	16	57.1%	10.9%	94.5%	105.5	LUCKY
6	Flames	1	0	0	0	1	0	0	3	4	-1	1	0.500	3	6	9	0	0	2	1	0	36	8	15	11	2	33	15	2	9	2	1	50.00%	1	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	22	14	23	9	18	8	33.3%	8.3%	87.9%	96.2	Unlucky
7	Coyotes	2	1	0	0	1	0	0	4	4	0	3	0.750	4	6	10	0	2	0	2	0	56	18	11	23	4	78	25	4	32	6	1	16.67%	2	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	43	25	44	17	35	18	42.9%	7.1%	94.9%	102.0	DULL
8	Hurricanes	6	1	4	1	0	0	0	9	15	-6	4	0.333	9	17	26	0	3	4	1	1	190	66	71	52	1	176	68	22	98	12	1	8.33%	11	3	72.73%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	132	80	127	49	101	51	40.0%	4.7%	91.5%	96.2	DULL
9	Avalanche	1	0	1	0	0	0	0	1	3	-2	0	0.000	1	1	2	1	1	0	0	0	25	14	7	4	0	27	10	14	9	3	1	33.33%	7	1	85.71%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	21	11	21	8	17	8	0.0%	4.0%	88.9%	92.9	Unlucky
10	Canadiens	2	0	2	0	0	0	0	6	8	-2	0	0.000	6	9	15	0	3	2	1	0	60	20	16	24	0	75	15	4	20	7	1	14.29%	2	1	50.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	39	22	43	17	36	17	41.7%	10.0%	89.3%	99.3	FUN
11	Blue Jackets	5	1	1	0	1	1	1	8	12	-4	6	0.600	8	13	21	0	0	6	1	2	157	52	60	44	10	151	44	14	83	10	1	10.00%	7	2	71.43%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	114	70	107	43	87	43	41.2%	5.1%	92.1%	97.1	DULL
12	Predators	1	0	1	0	0	0	0	1	3	-2	0	0.000	1	2	3	0	1	0	0	0	33	10	10	13	0	32	8	2	28	1	0	0.00%	1	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	21	13	23	8	15	7	25.0%	3.0%	90.6%	93.7	Unlucky
13	Golden Knights	1	1	0	0	0	0	0	6	1	5	2	1.000	6	11	17	0	1	2	3	0	39	8	19	12	0	30	13	6	17	4	1	25.00%	3	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	22	13	20	8	17	9	83.3%	15.4%	96.7%	112.1	LUCKY
14	Red Wings	2	0	1	0	1	0	0	7	10	-3	1	0.250	7	10	17	1	0	2	5	0	71	26	17	28	0	69	17	6	32	1	1	100.00%	4	2	50.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	44	28	43	16	32	15	42.9%	9.9%	85.5%	95.4	FUN
15	Kings	2	2	0	0	0	0	0	5	2	3	4	1.000	5	9	14	0	0	3	2	0	57	16	18	23	0	48	13	8	32	7	1	14.29%	4	1	75.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	44	25	37	17	38	20	80.0%	8.8%	95.8%	104.6	DULL
16	Sabres	1	0	0	1	0	0	0	2	1	1	2	1.000	2	3	5	0	0	1	0	1	32	11	9	11	1	30	10	4	12	4	0	0.00%	2	1	50.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	21	12	23	9	19	9	100.0%	6.3%	96.7%	102.9	DULL
17	Islanders	4	2	2	0	0	0	0	11	12	-1	4	0.500	11	21	32	1	3	3	5	0	145	45	46	54	0	128	43	16	62	11	4	36.36%	8	1	87.50%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	92	59	82	33	65	32	38.9%	7.6%	90.6%	98.2	Unlucky
18	Oilers	1	0	0	1	0	0	0	3	2	1	2	1.000	3	4	7	0	1	0	1	1	28	7	6	12	3	33	14	4	16	3	0	0.00%	2	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	20	11	23	8	16	8	60.0%	10.7%	93.9%	104.7	LUCKY
19	Devils	6	0	0	2	1	2	1	15	13	2	10	0.833	15	22	37	0	3	5	3	6	197	60	68	60	19	191	72	26	79	35	3	8.57%	13	2	84.62%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	144	87	130	55	104	54	52.2%	7.6%	93.2%	100.8	DULL
20	Canucks	1	1	0	0	0	0	0	3	2	1	2	1.000	3	5	8	0	1	0	2	0	36	10	13	13	0	33	9	2	15	6	2	33.33%	1	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	21	12	23	7	15	7	33.3%	8.3%	93.9%	102.3	DULL
21	Jets	2	0	1	0	0	1	0	5	6	-1	2	0.500	5	8	13	0	0	2	2	1	69	23	20	24	6	72	26	8	26	4	0	0.00%	4	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	43	26	46	17	34	17	45.5%	7.2%	91.7%	98.9	DULL
22	Stars	1	1	0	0	0	0	0	4	2	2	2	1.000	4	7	11	0	0	2	2	0	35	9	12	14	0	27	11	4	21	2	0	0.00%	2	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	23	15	18	8	17	9	66.7%	11.4%	92.6%	104.0	LUCKY
23	Maple Leafs	2	1	1	0	0	0	0	7	5	2	2	0.500	7	12	19	0	1	3	3	0	70	14	19	37	0	69	16	6	25	11	2	18.18%	3	1	66.67%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	45	28	41	16	32	16	55.6%	10.0%	92.8%	102.8	LUCKY
24	Blackhawks	1	0	0	0	0	1	0	2	1	1	2	1.000	2	2	4	0	0	0	1	3	35	13	10	10	7	24	8	6	14	3	0	0.00%	3	1	66.67%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	26	16	19	9	19	9	100.0%	5.7%	95.8%	101.5	DULL
25	Ducks	2	1	1	0	0	0	0	6	5	1	2	0.500	6	11	17	0	1	0	5	0	58	20	15	23	0	63	28	8	33	9	2	22.22%	4	0	100.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	44	27	43	16	31	16	44.4%	10.3%	92.1%	102.4	LUCKY
26	Capitals	5	4	0	1	0	0	0	23	9	14	10	1.000	23	38	61	0	3	8	11	1	159	43	64	51	1	143	43	16	86	16	2	12.50%	8	2	75.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	112	68	103	41	87	45	75.0%	14.5%	93.7%	108.2	LUCKY
27	Rangers	7	3	3	0	0	1	0	15	17	-2	8	0.571	15	26	41	0	2	4	8	2	223	78	69	74	4	237	81	26	111	5	0	0.00%	13	2	84.62%	1	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	155	97	152	59	116	58	50.0%	6.7%	92.8%	99.6	DULL
28	Senators	2	1	1	0	0	0	0	4	4	0	2	0.500	4	8	12	0	3	0	1	0	58	17	22	19	0	61	20	8	38	8	2	25.00%	4	2	50.00%	0	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	43	25	42	17	34	17	50.0%	6.9%	93.4%	100.3	DULL
	_Vs Division	37	12	12	4	3	4	2	89	91	-2	45	0.608	89	151	240	1	17	33	31	12	1205	379	432	380	35	1148	392	158	567	98	12	12.24%	79	18	77.22%	1	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	837	514	792	320	628	316	51.3%	7.4%	92.1%	99.5	DULL
	_Vs Conference	50	16	18	6	4	4	2	126	128	-2	58	0.580	126	212	338	2	24	46	46	14	1615	510	554	535	37	1568	501	206	754	139	21	15.11%	104	26	75.00%	1	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	1116	681	1075	432	851	427	50.7%	7.8%	91.8%	99.6	DULL
	_Since Last GM Reset	69	24	24	7	6	6	2	175	170	5	82	0.594	175	294	469	3	33	59	70	19	2224	702	732	761	59	2151	708	288	1049	192	31	16.15%	145	31	78.62%	1	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	1537	936	1485	594	1178	593	50.9%	7.9%	92.1%	100.0	DULL
	Total	69	24	24	7	6	6	2	175	170	5	82	0.594	175	294	469	3	33	59	70	19	2224	702	732	761	59	2151	708	288	1049	192	31	16.15%	145	31	78.62%	1	848	1672	50.72%	825	1591	51.85%	480	948	50.63%	1537	936	1485	594	1178	593	50.9%	7.9%	92.1%	100.0	DULL

	Puck Time
Offensive Zone	22
Neutral Zone	17
Defensive Zone	21

	Puck Time
Offensive Zone Start	1672
Neutral Zone Start	948
Defensive Zone Start	1591

	Puck Time
With Puck	30
Without Puck	30

	Faceoffs
Faceoffs Won	2153
Faceoffs Lost	2058

	Team Average Shots after	League Average Shots after
1st Period	10.2	9.57
2nd Period	20.8	20.31
3rd Period	31.8	30.68
Overtime	32.7	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	0.5	0.64
2nd Period	1.3	1.65
3rd Period	2.3	2.67
Overtime	2.6	2.83


Even Strenght Goal	140
PP Goal	31
PK Goal	1
Empty Net Goal	3

	Home	Away
Win	20	17
Lost	12	12
Overtime Lost	4	4


PP Attempt	192
PP Goal	31
PK Attempt	145
PK Goal Against	31

	Home
Shots For	32.2
Shots Against	31.2
Goals For	2.5
Goals Against	2.5
Hits	15.2
Shots Blocked	10.3
Pim	4.2

Schedule

Date	Matchup			Result	Detail
2019-06-10	Penguins	@	Flyers	Penguins4,Flyers3 (OT)	RECAP
2019-06-12	Flyers	@	Blue Jackets	Flyers1,Blue Jackets2 (OT)	RECAP
2019-06-13	Flyers	@	Hurricanes	Flyers2,Hurricanes1	RECAP
2019-06-15	Rangers	@	Flyers	Rangers3,Flyers1	RECAP
2019-06-17	Islanders	@	Flyers	Islanders3,Flyers1	RECAP
2019-06-19	Hurricanes	@	Flyers	Hurricanes1,Flyers2 (OT)	RECAP
2019-06-20	Flyers	@	Penguins	Flyers1,Penguins3	RECAP
2019-06-22	Flyers	@	Sabres	Flyers2,Sabres1 (OT)	RECAP
2019-06-24	Flyers	@	Rangers	Flyers4,Rangers1	RECAP
2019-06-26	Capitals	@	Flyers	Capitals2,Flyers4	RECAP
2019-06-28	Blue Jackets	@	Flyers	Blue Jackets6,Flyers2	RECAP
2019-06-29	Flyers	@	Hurricanes	Flyers1,Hurricanes4	RECAP
2019-07-01	Devils	@	Flyers	Devils1,Flyers2 (SO)	RECAP
2019-07-03	Flyers	@	Devils	Flyers3,Devils2 (OT)	RECAP
2019-07-04	Flyers	@	Rangers	Flyers0,Rangers2	RECAP
2019-07-06	Capitals	@	Flyers	Capitals2,Flyers5	RECAP
2019-07-10	Kings	@	Flyers	Kings1,Flyers3	RECAP
2019-07-12	Flyers	@	Hurricanes	Flyers1,Hurricanes3	RECAP
2019-07-13	Devils	@	Flyers	Devils3,Flyers2 (SO)	RECAP
2019-07-15	Jets	@	Flyers	Jets3,Flyers4 (SO)	RECAP
2019-07-17	Flyers	@	Maple Leafs	Flyers2,Maple Leafs3	RECAP
2019-07-19	Coyotes	@	Flyers	Coyotes2,Flyers3	RECAP
2019-07-21	Flyers	@	Blue Jackets	Flyers2,Blue Jackets1	RECAP
2019-07-22	Flyers	@	Capitals	Flyers3,Capitals1	RECAP
2019-07-24	Flyers	@	Blue Jackets	Flyers0,Blue Jackets1 (SO)	RECAP
2019-07-26	Red Wings	@	Flyers	Red Wings4,Flyers3 (OT)	RECAP
2019-07-28	Devils	@	Flyers	Devils2,Flyers3 (SO)	RECAP
2019-07-31	Hurricanes	@	Flyers	Hurricanes4,Flyers2	RECAP
2019-08-01	Flyers	@	Golden Knights	Flyers6,Golden Knights1	RECAP
2019-08-04	Islanders	@	Flyers	Islanders2,Flyers3	RECAP
2019-08-06	Flyers	@	Islanders	Flyers2,Islanders4	RECAP
2019-08-07	Canadiens	@	Flyers	Canadiens6,Flyers5	RECAP
2019-08-09	Flyers	@	Stars	Flyers4,Stars2	RECAP
2019-08-12	Blue Jackets	@	Flyers	Blue Jackets2,Flyers3 (SO)	RECAP
2019-08-15	Flyers	@	Devils	Flyers3,Devils2 (OT)	RECAP
2019-08-16	Penguins	@	Flyers	Penguins1,Flyers3	RECAP
2019-08-18	Red Wings	@	Flyers	Red Wings6,Flyers4	RECAP
2019-08-20	Flyers	@	Bruins	Flyers4,Bruins2	RECAP
2019-08-21	Coyotes	@	Flyers	Coyotes2,Flyers1 (OT)	RECAP
2019-08-24	Flyers	@	Rangers	Flyers3,Rangers2 (SO)	RECAP
2019-08-25	Rangers	@	Flyers	Rangers5,Flyers1	RECAP
2019-08-28	Flyers	@	Sharks	Flyers1,Sharks3	RECAP
2019-08-29	Wild	@	Flyers	Wild3,Flyers2	RECAP
2019-08-31	Flyers	@	Wild	Flyers3,Wild1	RECAP
2019-09-02	Rangers	@	Flyers	Rangers3,Flyers4	RECAP
2019-09-03	Flyers	@	Oilers	Flyers3,Oilers2 (OT)	RECAP
2019-09-05	Blackhawks	@	Flyers	Blackhawks1,Flyers2 (SO)	RECAP
2019-09-08	Flyers	@	Canucks	Flyers3,Canucks2	RECAP
2019-09-09	Flyers	@	Devils	Flyers2,Devils3 (OT)	RECAP
2019-09-10	Maple Leafs	@	Flyers	Maple Leafs2,Flyers5	RECAP
2019-09-13	Flyers	@	Capitals	Flyers8,Capitals2	RECAP
2019-09-14	Penguins	@	Flyers	Penguins5,Flyers1	RECAP
2019-09-16	Lightning	@	Flyers	Lightning2,Flyers3 (OT)	RECAP
2019-09-19	Flyers	@	Ducks	Flyers2,Ducks4	RECAP
2019-09-20	Flyers	@	Canadiens	Flyers1,Canadiens2	RECAP
2019-09-21	Predators	@	Flyers	Predators3,Flyers1	RECAP
2019-09-24	Flyers	@	Hurricanes	Flyers1,Hurricanes2	RECAP
2019-09-25	Bruins	@	Flyers	Bruins1,Flyers3	RECAP
2019-09-27	Flyers	@	Senators	Flyers3,Senators1	RECAP
2019-09-29	Ducks	@	Flyers	Ducks1,Flyers4	RECAP
2019-10-01	Flyers	@	Lightning	Flyers1,Lightning4	RECAP
2019-10-02	Avalanche	@	Flyers	Avalanche3,Flyers1	RECAP
2019-10-05	Senators	@	Flyers	Senators3,Flyers1	RECAP
2019-10-07	Flyers	@	Flames	Flyers3,Flames4 (OT)	RECAP
2019-10-09	Kings	@	Flyers	Kings1,Flyers2	RECAP
2019-10-10	Flyers	@	Jets	Flyers1,Jets3	RECAP
Trade Deadline --- Trades can�t be done after this day is simulated!
2019-10-13	Flyers	@	Capitals	Flyers3,Capitals2 (OT)	RECAP
2019-10-14	Rangers	@	Flyers	Rangers1,Flyers2	RECAP
2019-10-17	Islanders	@	Flyers	Islanders3,Flyers5	RECAP
2019-10-19	Flyers	@	Avalanche
2019-10-20	Islanders	@	Flyers
2019-10-22	Flyers	@	Penguins
2019-10-24	Sabres	@	Flyers
2019-10-26	Flyers	@	Penguins
2019-10-27	Flyers	@	Red Wings
2019-10-29	Hurricanes	@	Flyers
2019-10-30	Flyers	@	Islanders
2019-11-01	Flyers	@	Blackhawks
2019-11-03	Capitals	@	Flyers
2019-11-05	Flyers	@	Jets
2019-11-07	Blue Jackets	@	Flyers
2019-11-09	Flyers	@	Islanders

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
77,457,000$	0$	77,457,000$	2,043,000$

Arena	Goal Horn	About us
		Name	Wells Fargo Center
		City	Philadelphia
		Capacity	18000
		Season Ticket Holders	50%

Arena Capacity - Ticket Price Attendance - %

Arena Capacity	6000	5000	2000	4000	1000
Ticket Price	125$	80$	50$	30$	225$
Attendance	208403	170248	68148	140606	34499
Attendance PCT	96.48%	94.58%	94.65%	97.64%	95.83%

Income

Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
5	17275 - 95.97%	2,278,792$	82,036,527$	18000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
77,457,000$	77,457,000$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
65,834,842$	77,457,000$	62,383,154$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
11,393,962$	25	520,558$	13,013,950$

Team Total Estimate
Estimated Season Expenses	Estimated Season Salary Cap	Current Bank Account	Projected Bank Account
13,736,760$	77,457,000$	182,009,276$	181,528,978$

Depth Chart

Rookie

Injured

Cold Streak

Hot Streak

Left Wing

Center

Right Wing

Boris Katchouk	AGE:19	CAN
Ivan Barbashev	AGE:23	RUS
Jonathan Drouin	AGE:24	CAN
Johnny Gaudreau	AGE:26	USA
Matt Calvert	AGE:29	CAN
Artem Anisimov	AGE:31	RUS
Matt Frattin	AGE:31	CAN
Shawn Matthias	AGE:31	CAN
Justin Kirkland	AGE:23	CAN
Joel Eriksson Ek	AGE:22	SWE
Patrick Newell	AGE:23	USA
David Kampf	AGE:24	CZE
Charles Hudon	AGE:25	CAN
Adam Cracknell	AGE:34	CAN

Ivan Barbashev	AGE:23	RUS
Jonathan Drouin	AGE:24	CAN
Evgeny Kuznetsov	AGE:27	RUS
Johnny Gaudreau	AGE:26	USA
John Tavares	AGE:29	CAN
Matt Calvert	AGE:29	CAN
Artem Anisimov	AGE:31	RUS
Shawn Matthias	AGE:31	CAN
Nico Hischier	AGE:17	CHE
Sam Steel	AGE:21	CAN
Christian Fischer	AGE:22	USA
Joel Eriksson Ek	AGE:22	SWE
Patrick Newell	AGE:23	USA
Ryan Lohin	AGE:23	USA
David Kampf	AGE:24	CZE
Charles Hudon	AGE:25	CAN
Reid Petryk	AGE:26	CAN
Scott Glennie	AGE:28	CAN
Adam Cracknell	AGE:34	CAN

Matt Luff	AGE:22	CAN
Jonathan Drouin	AGE:24	CAN
Hudson Fasching	AGE:24	USA
Matt Calvert	AGE:29	CAN
Matt Frattin	AGE:31	CAN
Corey Perry	AGE:34	CAN
Christian Fischer	AGE:22	USA
Nicolas Aube-Kubel	AGE:23	CAN
Joel Eriksson Ek	AGE:22	SWE
David Kampf	AGE:24	CZE
Reid Petryk	AGE:26	CAN
Scott Glennie	AGE:28	CAN
Adam Cracknell	AGE:34	CAN

Defense #1

Defense #2

Goalie

Thomas Chabot	AGE:22	CAN
Rasmus Ristolainen	AGE:24	FIN
Brady Skjei	AGE:25	USA
Mark Borowiecki	AGE:30	CAN
Tyler Myers	AGE:29	USA
Fedor Tyutin	AGE:36	RUS
Christian Djoos	AGE:25	SWE
Travis Dermott	AGE:23	CAN
Ryan Mantha	AGE:23	CAN

Colton White	AGE:22	D
Brandon Hickey	AGE:23	CAN
Duncan Siemens	AGE:26	CAN
Jordie Benn	AGE:32	CAN
Yegor Rykov	AGE:19	0
Kyle Capobianco	AGE:20	CAN
Tucker Poolman	AGE:26	USA
Petter Granberg	AGE:27	SWE

Ben Bishop	AGE:32	USA
Matt Murray	AGE:25	CAN
Marcus Hogberg	AGE:24	SWE
Charlie Lindgren	AGE:25	USA
Eddie Lack	AGE:31	SWE
David Rittich	AGE:27	CZE

Draft Picks

Year	Round 1	Round 2	Round 3
10
11
12
13
14

** Conditional Draft Pick That Have Been Traded

Prospects

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

Prospect	Team Name	Draft Year	Overall Pick	Information
Benton Maass	Flyers	8	82	6.0C Defence
Daniel Brickley	Flyers	7	60	6.0B Defence
Daniel Vladar	Flyers	4	72	7.0C Goalie
Justin Kloos	Flyers	7	65	6.0C Center
Luke Green	Flyers	7	44	6.0C Defence
Mikhail Berdin	Flyers	7	70	6.0C Goalie
Noah Gregor	Flyers	7	55	6.0B Center
Noah Rod	Flyers	4	52	5.0B Right Wing
Ostap Safin	Flyers	9	54	7.0C C/W
Reilly Walsh	Flyers	9	29	7.0C Defence
Tanner Laczynski	Flyers	8	73	7.0C Center
Tyler Soy	Flyers	8	62	7.0C Center

Transactions

New Record for Team's Most Points (22) in 1 Game for Flyers!

New Record for Team's Most Assists (14) in 1 Game for Flyers!

Ryan Lohin signed with Flyers for $685,000 for 1 year(s).

Patrick Newell signed with Flyers for $685,000 for 1 year(s).

TRADE : From Flyers to Avalanche : Jack Roslovic (69), Nic Petan (69), Ryan Spooner (71), Dmitry Sokolov (P).

TRADE : From Avalanche to Flyers : John Tavares (79).

TRADE : From Canucks to Flyers : Johnny Gaudreau (74), Dmitry Sokolov (P).

TRADE : From Flyers to Canucks : Jake DeBrusk (70), Matt Read (70).

TRADE : From Flyers to Golden Knights : Bryan Little (74), Y:10-RND:3-NYR.

TRADE : From Golden Knights to Flyers : Brady Skjei (70), Y:10-RND:2-ANA.

Ben Bishop signed with Flyers for $7,125,000 for 4 year(s) / Option : 1 Way Contract .

Colton White signed with Flyers for $1,125,000 for 6 year(s).

Hudson Fasching signed with Flyers for $1,115,000 for 4 year(s).

Matt Luff signed with Flyers for $975,000 for 6 year(s).

TRADE : From Flyers to Predators : Y:11-RND:2-PHI.

TRADE : From Predators to Flyers : Ryan Mantha (68).

Injuries

No Injury or Suspension.

Game Center

Rangers37-24-10, 84pts

12Final

Flyers37-24-8, 82pts

Islanders38-26-7, 83pts

35Final

Flyers37-24-8, 82pts

Sat, Oct 19Pepsi Center

Avalanche41-20-9, 91pts

Islanders38-26-7, 83pts

Sun, Oct 20Wells Fargo Center

Flyers37-24-8, 82pts

Tue, Oct 22PPG Paints Arena

Penguins40-20-10, 90pts

Team Leaders

	GOALS Johnny Gaudreau 23			ASSISTS Corey Perry 33
	POINTS Corey Perry 54			PLUS/MINUS Brady Skjei 11
	WINS Matt Murray 22			SV% Matt Murray 0.924

Team Stats

Goals For 175 2.54 GFG	Shots For 2224 32.23 Avg	Power Play Percentage 16.1% 31 GF	Offensive Zone Start 51.2%
Goals Against 170 2.46 GAA	Shots Against 2151 31.17 Avg	Penalty Kill Percentage 78.6% 31 GA	Defensive Zone Start 48.8%

Team Info


	General Manager	Chris Walsh
	Head Coach	Dallas Eakins
	Captain
	Division	Metropolitan

Flyers Affiliation


	Team Name	Phantoms
	City	Lehigh Valley
	Head Coach	Dave Cameron
	Division	Atlantic

Arena Info


	Stadium	Wells Fargo Center
	Capacity	18,000
	Attendance	17,275
	Season Tickets	9,000

Salary Cap


	Total Cap Hit	77,457,000$
	Cap Space	2,043,000$
	Retains & Buyout	0$
	Players In Salary Cap	22

Finance


	Team Revenue	82,036,527$
	Estimated Revenue	95,292,989$
	Estimated Expenses	78,848,792$
	Operating Income	16,444,197$
	Bank Account	182,009,276$

Team Captain - Alternate Captains

Captain

Alternate Captain