PSL - Pro Simulation League

Golden Knights
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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`)
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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

#	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	Eric Robinson		X			100.00	73	37	95	69	84	79	88	63	53	64	67	65	62	74	73	34	75	66	0
2	Walker Duehr (R)			X		100.00	78	38	92	67	82	80	83	63	56	58	64	62	66	69	71	36	75	65	0
3	Elmer Soderblom		X			100.00	86	44	85	66	99	84	83	64	59	62	61	65	58	66	67	48	75	65	0
4	Matej Blumel			X		100.00	65	37	90	68	75	77	86	67	61	63	65	62	67	66	68	59	75	65	0
5	Adam Edstrom	X				100.00	77	53	90	67	99	74	76	63	55	60	61	64	62	65	68	47	75	64	0
6	Aidan McDonough (R)		X			100.00	72	41	85	64	81	88	75	63	65	61	67	62	64	66	69	42	75	64	0
7	Ryan Lomberg		X			100.00	77	71	74	69	66	73	86	63	56	61	60	62	59	74	73	27	75	64	0
8	Olle Lycksell (R)			X		100.00	62	37	90	65	72	87	81	64	67	66	63	61	64	67	69	42	75	64	0
9	Raphael Lavoie	X				100.00	78	41	83	65	90	89	78	63	58	60	64	62	65	66	68	72	75	64	0
10	Sean Farrell	X				100.00	55	36	94	63	65	81	86	62	67	64	60	61	63	65	67	56	75	62	0
11	Luke Haymes (R)	X				100.00	66	37	91	63	76	81	86	58	64	62	59	61	57	63	65	44	75	62	0
12	Parker Ford	X				100.00	66	37	81	59	66	82	81	58	60	57	59	64	56	66	68	42	75	61	0
13	Jack Williams (R)	X				100.00	58	36	94	59	69	83	87	57	62	58	56	61	55	64	66	43	75	60	0
14	Tyler Tucker				X	100.00	74	64	67	65	78	83	84	63	30	68	60	67	51	66	68	47	75	64	0
15	Christian Wolanin				X	100.00	69	39	83	64	78	77	81	63	30	67	55	60	48	71	73	47	75	63	0
16	Kyle Capobianco				X	100.00	70	41	74	65	82	81	79	62	30	68	59	61	52	69	71	55	75	63	0
17	Mike Benning (R)				X	100.00	57	37	81	65	68	85	84	64	30	66	62	60	52	64	66	61	75	63	0
18	Jake Christiansen				X	100.00	67	38	95	64	74	79	80	65	30	66	52	61	49	67	69	37	75	62	0
19	Maksymilian Szuber (R)				X	100.00	72	42	73	61	81	70	84	58	30	60	56	59	48	64	66	48	75	61	0
Scratches
1	Bokondji Imama		X			100.00	75	85	55	58	82	68	80	56	50	52	55	57	59	70	72	39	75	59	0
2	Coulson Pitre (R)			X		100.00	65	38	92	57	74	70	76	56	53	55	54	58	56	62	64	67	75	59	0
3	Noel Nordh (R)		X			100.00	71	38	95	56	80	64	82	55	50	54	53	57	52	61	63	66	75	59	0
4	Andrew Basha (R)		X			100.00	58	41	68	56	67	65	69	55	57	54	53	52	56	61	63	74	75	56	0
5	Zack Kassian		X	X		100.00	40	40	40	40	40	40	40	40	40	40	40	40	40	40	40	40	75	42	0
6	Will Zmolek (R)				X	100.00	75	39	95	58	84	63	71	56	30	54	53	57	45	67	69	37	75	60	0
7	Samuel Knazko				X	100.00	40	40	40	40	40	40	40	40	40	40	40	40	40	40	40	40	75	42	0

	TEAM AVERAGE					100.00	67	43	81	61	75	75	78	59	49	59	58	59	56	64	66	48	75	61

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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`)
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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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Sebastian Cossa	100.00	78	87	83	98	77	76	78	77	76	78	77	64	71	82	75	66	0
2	Magnus Chrona	100.00	73	81	77	85	72	71	73	72	71	73	72	66	75	47	75	63	0
Scratches
1	Owen Say (R)	100.00	67	78	74	77	66	65	67	66	65	67	66	65	73	42	75	60	0
2	Isak Posch (R)	100.00	64	82	78	85	63	62	64	63	62	64	63	64	71	43	75	58	0
3	Vyacheslav Buteyets (R)	100.00	56	72	68	90	55	54	56	55	54	56	55	64	71	48	75	54	0

	TEAM AVERAGE	100.00	68	80	76	87	67	66	68	67	66	68	67	65	72	52	75	60

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Jeff Blashill	70	77	78	75	79	72	71	USA	52	1	1,000,000$

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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	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	S1	S2	S3
1	Matej Blumel	Silver Knights (VGK)	RW	10	12	7	19	5	8	23	27	75	27	63	16.00%	6	221	22.10	3	0	3	13	28	0	6	3	1	57.14%	21	1	1.72	3	2	0
2	Tyler Tucker	Silver Knights (VGK)	D	10	2	11	13	6	8	35	14	17	5	6	11.76%	15	144	14.42	1	0	1	2	2	0	2	1	0	0.00%	0	0	1.80	1	1	1
3	Raphael Lavoie	Silver Knights (VGK)	C	10	7	6	13	3	14	24	41	66	12	29	10.61%	5	179	17.91	1	2	3	12	26	0	0	0	1	59.40%	266	0	1.45	1	1	1
4	Jake Christiansen	Silver Knights (VGK)	D	10	2	10	12	4	8	28	18	26	4	24	7.69%	16	246	24.67	1	1	2	17	32	1	26	0	0	0.00%	0	0	0.97	2	2	2
5	Christian Wolanin	Silver Knights (VGK)	D	10	2	8	10	-6	4	21	19	28	6	8	7.14%	17	201	20.16	1	1	2	14	19	1	23	1	0	0.00%	0	0	0.99	0	1	1
6	Kyle Capobianco	Silver Knights (VGK)	D	10	0	10	10	-6	4	31	17	11	6	7	0.00%	10	199	19.96	0	3	3	4	19	1	21	0	0	0.00%	0	0	1.00	0	0	1
7	Elmer Soderblom	Silver Knights (VGK)	LW	10	8	2	10	-2	12	27	22	38	7	19	21.05%	1	139	13.97	0	0	0	0	0	0	0	1	0	46.67%	15	1	1.43	1	2	0
8	Eric Robinson	Silver Knights (VGK)	LW	3	0	3	3	0	0	1	4	7	3	9	0.00%	0	28	9.61	0	0	0	0	0	0	0	0	0	50.00%	2	0	2.08	0	0	0
9	Ryan Lomberg	Silver Knights (VGK)	LW	10	0	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.10	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00	0	0	0
Team Total or Average				83	33	57	90	4	58	190	162	268	70	165	12.31%	70	1361	16.41	7	7	14	62	128	3	81	6	2	58.55%	304	2	1.32	8	9	6

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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	Sebastian Cossa	Silver Knights (VGK)	10	6	3	0	0.906	4.11	496	0	0	34	360	0	0	0	0.000	0	10	0	0	0	1
Team Total or Average			10	6	3	0	0.906	4.11	496	0	0	34	360	0	0	0	0.000	0	10	0	0	0	1

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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 Edstrom	C	25	2000-10-12	No	232 Lbs	6 ft7	No	No	No	2	RFA	Pro & Farm	975,000$	0$	0$	No	Link / NHL Link
Aidan McDonough	LW	26	1999-11-06	Yes	190 Lbs	6 ft3	No	No	No	2	RFA	Pro & Farm	700,000$	0$	0$	No	Link / NHL Link
Andrew Basha	LW	20	2005-11-08	Yes	174 Lbs	5 ft11	No	No	No	2	ELC	Pro & Farm	865,000$	0$	0$	No	Link / NHL Link
Bokondji Imama	LW	30	1996-08-03	No	223 Lbs	6 ft1	No	No	No	3	UFA	Pro & Farm	1,499,999$	0$	0$	No	Link / NHL Link
Christian Wolanin	D	31	1995-03-17	No	190 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	750,000$	0$	0$	No	Link / NHL Link
Coulson Pitre	RW	21	2004-12-13	Yes	185 Lbs	6 ft1	No	No	No	2	ELC	Pro & Farm	858,334$	0$	0$	No	Link / NHL Link
Elmer Soderblom	LW	25	2001-07-05	No	252 Lbs	6 ft8	No	No	No	1	RFA	Pro & Farm	878,333$	0$	0$	No	Link / NHL Link
Eric Robinson	LW	31	1995-06-14	No	220 Lbs	6 ft2	No	No	No	3	UFA	Pro & Farm	1,500,000$	0$	0$	No	Link / NHL Link
Isak Posch	G	24	2002-01-31	Yes	209 Lbs	6 ft3	No	No	No	2	RFA	Pro & Farm	872,500$	0$	0$	No	Link / NHL Link
Jack Williams	C	24	2002-03-02	Yes	185 Lbs	5 ft11	No	No	No	2	RFA	Pro & Farm	923,750$	0$	0$	No	Link / NHL Link
Jake Christiansen	D	27	1999-09-12	No	199 Lbs	6 ft0	No	No	No	4	RFA	Pro & Farm	975,000$	0$	0$	No	Link / NHL Link
Kyle Capobianco	D	29	1997-08-13	No	194 Lbs	6 ft3	No	No	No	1	UFA	Pro & Farm	762,500$	0$	0$	No	Link / NHL Link
Luke Haymes	C	23	2003-07-28	Yes	192 Lbs	6 ft1	No	No	No	2	RFA	Pro & Farm	875,000$	0$	0$	No	Link / NHL Link
Magnus Chrona	G	26	2000-08-28	No	194 Lbs	6 ft4	No	No	No	2	RFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Maksymilian Szuber	D	24	2002-08-25	Yes	190 Lbs	6 ft3	No	No	No	2	RFA	Pro & Farm	859,167$	0$	0$	No	Link / NHL Link
Matej Blumel	RW	26	2000-05-31	No	202 Lbs	6 ft0	No	No	No	4	RFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Mike Benning	D	24	2002-01-05	Yes	185 Lbs	5 ft9	No	No	No	2	RFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Noel Nordh	LW	21	2005-01-25	Yes	196 Lbs	6 ft2	No	No	No	2	ELC	Pro & Farm	835,000$	0$	0$	No	Link / NHL Link
Olle Lycksell	RW	27	1999-08-24	Yes	197 Lbs	5 ft11	No	No	No	2	RFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Owen Say	G	25	2001-06-05	Yes	185 Lbs	6 ft2	No	No	No	2	RFA	Pro & Farm	872,500$	0$	0$	No	Link / NHL Link
Parker Ford	C	26	2000-07-20	No	181 Lbs	5 ft9	No	No	No	2	RFA	Pro & Farm	812,500$	0$	0$	No	Link / NHL Link
Raphael Lavoie	C	25	2000-09-25	No	217 Lbs	6 ft4	No	No	No	1	RFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Ryan Lomberg	LW	31	1994-12-09	No	184 Lbs	5 ft9	No	No	No	4	UFA	Pro & Farm	1,499,999$	0$	0$	No	Link / NHL Link
Samuel Knazko	D	24	2002-08-07	No	198 Lbs	6 ft1	No	No	No	1	RFA	Pro & Farm	847,500$	0$	0$	No	Link / NHL Link
Sean Farrell	C	24	2001-11-02	No	175 Lbs	5 ft9	No	No	No	2	RFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Sebastian Cossa	G	23	2002-11-21	No	229 Lbs	6 ft6	No	No	No	2	RFA	Pro & Farm	863,334$	0$	0$	No	Link / NHL Link
Tyler Tucker	D	26	2000-03-01	No	204 Lbs	6 ft1	No	No	No	1	RFA	Pro & Farm	925,000$	0$	0$	No	Link / NHL Link
Vyacheslav Buteyets	G	24	2002-05-29	Yes	220 Lbs	6 ft4	No	No	No	2	RFA	Pro & Farm	852,500$	0$	0$	No	Link / NHL Link
Walker Duehr	RW	28	1997-11-23	Yes	210 Lbs	6 ft2	No	No	No	2	UFA	Pro & Farm	775,000$	0$	0$	No	Link / NHL Link
Will Zmolek	D	27	1999-04-17	Yes	205 Lbs	6 ft3	No	No	No	2	RFA	Pro & Farm	700,000$	0$	0$	No	Link / NHL Link
Zack Kassian	LW/RW	35	1991-01-24	No	211 Lbs	6 ft3	No	No	No	1	UFA	Pro & Farm	1,499,999$	0$	0$	No	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
31	25.87	201 Lbs	6 ft2	2.03	917,030$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1			Matej Blumel	40	1	2	2
2		Raphael Lavoie		30	1	2	2
3			Elmer Soderblom	20	1	2	2
4	Elmer Soderblom			10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Jake Christiansen	40	1	2	2
2	Christian Wolanin	Kyle Capobianco	30	1	2	2
3	Tyler Tucker		20	1	2	2
4		Jake Christiansen	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1			Matej Blumel	60	1	2	2
2		Raphael Lavoie		40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Jake Christiansen	60	1	2	2
2	Christian Wolanin	Kyle Capobianco	40	1	2	2

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

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Jake Christiansen	60	1	2	2
2	Christian Wolanin	Kyle Capobianco	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		Jake Christiansen	60	1	2	2
2		40	1	2	2	Christian Wolanin	Kyle Capobianco	40	1	2	2

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

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Jake Christiansen	60	1	2	2
2	Christian Wolanin	Kyle Capobianco	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
		Matej Blumel		Jake Christiansen

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
		Matej Blumel		Jake Christiansen

Extra Forwards
Normal	PowerPlay	Penalty Kill
Ryan Lomberg, ,	Ryan Lomberg,

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Tyler Tucker, , Christian Wolanin	Tyler Tucker	, Christian Wolanin

Penalty Shots
, , , Elmer Soderblom,

Goalie
#1 : Sebastian Cossa, #2 :

Custom OT Lines Forwards
, , , Elmer Soderblom, , Matej Blumel, Matej Blumel, Ryan Lomberg, , Raphael Lavoie,

Custom OT Lines Defensemen
, Jake Christiansen, Christian Wolanin, Kyle Capobianco, Tyler Tucker

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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

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
10	14	W4	46	86	132	378	430	105	83	262	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
10	7	3	0	0	0	0	46	41

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
5	3	2	0	0	0	0	24	24

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
5	4	1	0	0	0	0	22	17

Last 10 Games
W	L	OTW	OTL	SOW	SOL
7	3	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
35	7	20.00%	36	6	83.33%	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
123	128	127	0	13	18	15	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
176	343	51.31%	217	422	51.42%	100	190	52.63%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
243	179	239	64	116	60

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	Link
Trade Deadline --- Trades can’t be done after this day is simulated!
1 - 2026-09-02	15	Silver Knights	5	Canucks	3	W	BoxScore
2 - 2026-09-03	31	Silver Knights	4	Wolves	2	W	BoxScore
4 - 2026-09-05	41	Senators	3	Silver Knights	5	W	BoxScore
5 - 2026-09-06	50	Americans	7	Silver Knights	3	L	BoxScore
7 - 2026-09-08	78	Silver Knights	2	Roadrunners	8	L	BoxScore
8 - 2026-09-09	85	IceHogs	5	Silver Knights	3	L	BoxScore
10 - 2026-09-11	112	Silver Knights	4	Bears	2	W	BoxScore
11 - 2026-09-12	121	Rocket	5	Silver Knights	8	W	BoxScore
13 - 2026-09-14	129	Gulls	4	Silver Knights	5	W	BoxScore
14 - 2026-09-15	159	Silver Knights	7	Wolf Pack	2	W	BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	37	17
Attendance	0	0
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
0	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
0$	2,842,792$	2,842,792$	0$

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

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	0$	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
Regular Season
2019	82	36	31	0	2	5	3	5	247	243	4	41	18	15	0	1	3	2	2	120	123	-3	41	18	16	0	1	2	1	3	127	120	7	92	247	454	701	1	5	100	69	73	7	2959	943	972	1009	62	2771	796	670	1946	239	42	17.57%	280	51	81.79%	2	1589	3098	51.29%	1530	2957	51.74%	627	1222	51.31%	2001	1404	1930	590	1037	522
2020	82	43	23	0	3	8	2	3	330	286	44	41	21	11	0	3	4	0	2	159	129	30	41	22	12	0	0	4	2	1	171	157	14	107	330	608	938	2	3	126	106	93	8	3039	994	1020	984	59	2943	883	466	1960	238	54	22.69%	214	49	77.10%	2	1609	3053	52.70%	1639	3048	53.77%	764	1422	53.73%	2031	1430	1891	584	1043	529
2021	82	52	14	0	8	5	3	0	406	298	108	41	26	6	0	6	3	0	0	207	152	55	41	26	8	0	2	2	3	0	199	146	53	131	406	744	1150	1	0	0	161	135	99	3655	0	1209	1177	1215	3201	920	605	2116	278	71	25.54%	244	46	81.15%	2	1760	3356	52.44%	1584	3156	50.19%	803	1460	55.00%	2057	1486	1890	568	1013	513
2022	82	41	25	0	9	5	1	1	417	366	51	41	20	17	0	2	1	1	0	207	179	28	41	21	8	0	7	4	0	1	210	187	23	108	417	792	1209	0	0	0	172	128	107	4333	0	1386	1433	1473	4163	1191	699	2583	265	66	24.91%	293	72	75.43%	3	1663	3745	44.41%	1775	3897	45.55%	694	1519	45.69%	1827	1292	2148	581	977	462
2023	82	40	26	0	4	10	1	1	379	330	49	41	23	10	0	0	7	0	1	194	159	35	41	17	16	0	4	3	1	0	185	171	14	101	379	677	1056	4	1	0	155	125	94	3516	0	1115	1191	1188	3280	941	658	2270	281	86	30.60%	298	66	77.85%	1	1858	3291	56.46%	1794	3259	55.05%	793	1435	55.26%	2179	1582	1769	548	1006	525
2024	82	25	46	0	3	7	1	0	393	445	-52	41	16	20	0	2	2	1	0	202	210	-8	41	9	26	0	1	5	0	0	191	235	-44	65	393	733	1126	3	0	0	147	138	104	3636	0	1179	1264	1173	3634	984	725	2411	287	67	23.34%	298	82	72.48%	1	1687	3275	51.51%	1411	2780	50.76%	797	1573	50.67%	2047	1498	1936	529	957	483
2025	82	27	42	0	7	4	1	1	332	380	-48	41	15	19	0	5	0	1	1	166	178	-12	41	12	23	0	2	4	0	0	166	202	-36	75	332	626	958	4	0	0	121	117	86	3171	0	1049	1057	1041	3250	926	665	2297	267	71	26.59%	287	75	73.87%	2	1569	3025	51.87%	1477	2801	52.73%	751	1454	51.65%	2098	1518	1864	533	990	508
2026	10	7	3	0	0	0	0	0	46	41	5	5	3	2	0	0	0	0	0	24	24	0	5	4	1	0	0	0	0	0	22	17	5	14	46	86	132	0	0	13	18	15	0	378	123	128	127	0	430	105	83	262	35	7	20.00%	36	6	83.33%	0	176	343	51.31%	217	422	51.42%	100	190	52.63%	243	179	239	64	116	60
Total Regular Season	584	271	210	0	36	44	12	11	2550	2389	161	292	142	100	0	19	20	5	6	1279	1154	125	292	129	110	0	17	24	7	5	1271	1235	36	693	2550	4720	7270	15	9	239	949	824	505	24687	2060	8058	8242	6211	23672	6746	4571	15845	1890	464	24.55%	1950	447	77.08%	13	11911	23186	51.37%	11427	22320	51.20%	5329	10275	51.86%	14488	10392	13670	4001	7142	3606
Playoff
2019	5	1	4	0	0	0	0	0	14	22	-8	2	0	2	0	0	0	0	0	6	11	-5	3	1	2	0	0	0	0	0	8	11	-3	2	14	25	39	0	0	7	4	2	1	203	65	77	55	6	224	69	64	111	26	7	26.92%	22	4	81.82%	0	107	222	48.20%	79	191	41.36%	39	74	52.70%	114	77	127	34	61	31
2021	7	3	4	0	0	0	0	0	29	33	-4	4	2	2	0	0	0	0	0	19	19	0	3	1	2	0	0	0	0	0	10	14	-4	6	29	57	86	1	0	0	13	8	8	325	0	126	104	95	354	120	67	209	14	5	35.71%	33	7	78.79%	0	117	291	40.21%	156	330	47.27%	59	128	46.09%	154	109	182	48	82	38
2022	11	5	6	0	0	0	0	0	43	44	-1	5	3	2	0	0	0	0	0	23	17	6	6	2	4	0	0	0	0	0	20	27	-7	10	43	81	124	0	0	0	17	16	9	462	0	137	163	159	487	150	91	301	30	8	26.67%	37	8	78.38%	0	142	419	33.89%	149	457	32.60%	69	185	37.30%	204	135	328	83	134	59
2023	5	1	4	0	0	0	0	0	19	22	-3	2	0	2	0	0	0	0	0	5	8	-3	3	1	2	0	0	0	0	0	14	14	0	2	19	36	55	1	0	0	9	5	5	198	0	82	60	56	189	64	44	121	18	3	16.67%	20	3	85.00%	0	104	201	51.74%	86	193	44.56%	42	93	45.16%	116	82	125	35	59	28
Total Playoff	28	10	18	0	0	0	0	0	105	121	-16	13	5	8	0	0	0	0	0	53	55	-2	15	5	10	0	0	0	0	0	52	66	-14	20	105	199	304	2	0	7	43	31	23	1188	65	422	382	316	1254	403	266	742	88	23	26.14%	112	22	80.36%	0	470	1133	41.48%	470	1171	40.14%	209	480	43.54%	589	404	764	201	339	157