Ligue de hockey simulé de Maniwaki

Storm
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Career Team Stat

Show or Hide Column

Filter Tips

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

#	Player Name	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP
1	Tobias Rieder			X		100.00	82	63	98	93	79	80	83	72	57	70	74	78	56	73	76	0	59	74	0
2	Chris Wagner (R)	X				100.00	95	67	90	83	86	79	83	68	68	67	69	82	52	71	73	0	59	72	0
3	Johan Larsson (R)	X	X			100.00	86	68	87	79	84	80	85	68	78	69	66	78	50	72	75	0	59	71	0
4	Anton Slepyshev (R)		X			100.00	82	72	96	80	88	77	52	70	50	68	72	74	54	69	70	0	59	69	0
5	Tyler Motte (R)		X	X		100.00	91	65	97	84	81	77	47	68	50	64	71	80	53	68	69	0	57	69	0
6	Curtis McKenzie (R)		X			100.00	80	71	67	82	87	74	35	69	50	75	62	73	50	69	72	0	59	67	0
7	Phil Varone (R)	X				100.00	69	62	94	81	79	70	35	61	65	60	62	70	50	68	66	0	59	63	0
8	Dmitry Kulikov				X	100.00	80	72	92	88	85	83	68	67	50	68	66	79	50	79	77	0	59	74	0
9	Kevin Connauton (R)				X	100.00	88	68	94	84	85	81	78	71	50	68	74	71	56	72	74	0	58	73	0
10	Joakim Ryan (R)				X	100.00	70	62	97	86	77	82	67	68	50	69	66	77	50	68	71	0	59	71	0
11	Neal Pionk (R)				X	100.00	89	68	91	82	80	88	35	75	50	84	65	77	50	65	65	0	59	71	0
12	Markus Nutivaara (R)				X	100.00	73	64	95	85	78	82	66	74	50	77	71	69	53	70	68	0	57	71	0
13	Zach Bogosian				X	100.00	82	76	77	84	89	85	35	64	50	65	62	71	50	78	85	0	59	70	0
14	Tim Heed (R)				X	100.00	61	65	94	86	77	82	35	73	50	75	70	69	62	66	64	0	59	68	0
Scratches

	TEAM AVERAGE					100.00	81	67	91	84	83	80	57	69	55	70	68	75	53	71	72	0	59	70

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
Scratches

	TEAM AVERAGE	0.00	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Travis Green	80	78	78	91	69	68	99	CAN	48	3	900,000$

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	PKM	GW	GT	FO%	FOT	GA	TA	EG	P/20	FT	S1	S2	S3
1	Kevin Connauton	Cyclone (STO)	D	35	6	33	39	-1	7	5	32	23	174	60	71	3.45%	43	506	14.47	0	3	3	2	2	0	1	1	0.00%	0	25	30	0	1.54	1	5	9	6
2	Markus Nutivaara	Cyclone (STO)	D	35	6	32	38	-2	0	0	22	24	137	67	63	4.38%	31	506	14.46	1	3	4	1	2	0	0	0	0.00%	0	26	30	0	1.50	0	5	5	7
3	Tyler Motte	Cyclone (STO)	LW/RW	35	16	15	31	4	4	0	15	21	143	41	99	11.19%	10	365	10.45	0	0	0	0	0	0	3	1	70.00%	20	22	19	1	1.70	0	7	5	8
Team Total or Average				105	28	80	108	1	11	5	69	68	454	168	233	6.17%	84	1378	13.13	1	6	7	3	5	1	4	2	70.00%	20	73	79	1	1.57	1	17	19	21

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
Team Total or Average							0.000	0.00	0								0.000

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	Status	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap
Anton Slepyshev	Cyclone (STO)	LW	24	1995-03-23 03:26:19	Yes	221 Lbs	6 ft2	No	No	No	1	ELC	Pro & Farm	1,525,000$	0$	0$	No
Chris Wagner	Cyclone (STO)	C	27	1992-03-22 09:26:19	Yes	198 Lbs	6 ft0	No	No	No	1	RFA	Pro & Farm	637,500$	0$	0$	No
Curtis McKenzie	Cyclone (STO)	LW	27	1992-03-22 09:26:19	Yes	205 Lbs	6 ft2	No	No	No	1	RFA	Pro & Farm	1,300,000$	0$	0$	No
Dmitry Kulikov	Cyclone (STO)	D	28	1991-03-23 03:26:19	No	204 Lbs	6 ft1	Yes	No	No	3	UFA	Pro & Farm	2,500,000$	0$	0$	No
Joakim Ryan	Cyclone (STO)	D	25	1993-06-17 07:31:17	Yes	185 Lbs	5 ft11	No	No	No	2	ELC	Pro & Farm	650,000$	0$	0$	No
Johan Larsson	Cyclone (STO)	C/LW	26	1993-03-22 15:26:19	Yes	198 Lbs	5 ft11	No	No	No	1	ELC	Pro & Farm	1,462,500$	0$	0$	No
Kevin Connauton	Cyclone (STO)	D	28	1991-03-23 03:26:19	Yes	200 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	1,000,000$	0$	0$	No
Markus Nutivaara	Cyclone (STO)	D	24	1995-03-23 03:26:19	Yes	191 Lbs	6 ft1	No	No	No	1	ELC	Pro & Farm	925,000$	0$	0$	No
Neal Pionk	Cyclone (STO)	D	23	1995-07-29 07:51:12	Yes	186 Lbs	6 ft0	No	No	No	2	ELC	Pro & Farm	1,775,000$	0$	0$	No
Phil Varone	Cyclone (STO)	C	28	1990-12-04 03:26:19	Yes	193 Lbs	5 ft10	No	No	No	2	UFA	Pro & Farm	900,000$	0$	0$	No
Tim Heed	Cyclone (STO)	D	28	1991-01-27 08:56:38	Yes	185 Lbs	6 ft0	No	No	No	2	UFA	Pro & Farm	2,500,000$	0$	0$	No
Tobias Rieder	Cyclone (STO)	RW	25	1994-03-22 21:26:19	No	185 Lbs	5 ft11	No	No	No	1	ELC	Pro & Farm	2,225,000$	0$	0$	No
Tyler Motte	Cyclone (STO)	LW/RW	23	1996-03-22 09:26:19	Yes	191 Lbs	5 ft10	No	No	No	2	ELC	Pro & Farm	925,000$	0$	0$	No
Zach Bogosian	Cyclone (STO)	D	28	1991-03-23 03:26:19	No	215 Lbs	6 ft3	No	No	No	3	UFA	Pro & Farm	2,000,000$	0$	0$	No

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
14	26.00	197 Lbs	6 ft0	1.64	1,451,786$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				40	1	2	2
2				30	1	2	2
3			Tyler Motte	20	1	2	2
4				10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			40	1	2	2
2	Kevin Connauton	Markus Nutivaara	30	1	2	2
3			20	1	2	2
4			10	1	2	2

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

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	1	2	2
2	Kevin Connauton	Markus Nutivaara	40	1	2	2

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

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	1	2	2
2	Kevin Connauton	Markus Nutivaara	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			60	1	2	2
2		40	1	2	2	Kevin Connauton	Markus Nutivaara	40	1	2	2

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

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	1	2	2
2	Kevin Connauton	Markus Nutivaara	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense

Extra Forwards
Normal	PowerPlay	Penalty Kill
, Tyler Motte,	, Tyler Motte

Extra Defensemen
Normal	PowerPlay	Penalty Kill
, , Kevin Connauton		, Kevin Connauton

Penalty Shots
, , , ,

Goalie
#1 : , #2 :

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	Chiefs	3	2	1	0	0	0	0	0	15	13	2	2	2	0	0	0	0	0	0	10	6	4	1	0	1	0	0	0	0	0	5	7	-2	4	0.667	15	27	42	0	0	35	70	73	1	86	319	431	387	3	84	30	16	51	10	4	40.00%	8	4	50.00%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
2	Cobra	2	1	1	0	0	0	0	0	9	11	-2	1	1	0	0	0	0	0	0	7	5	2	1	0	1	0	0	0	0	0	2	6	-4	2	0.500	9	17	26	0	0	35	70	73	1	58	319	431	387	3	55	20	10	31	6	4	66.67%	5	1	80.00%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
3	Destroyers	2	2	0	0	0	0	0	0	16	7	9	1	1	0	0	0	0	0	0	6	5	1	1	1	0	0	0	0	0	0	10	2	8	4	1.000	16	31	47	0	0	35	70	73	1	62	319	431	387	3	73	20	14	32	8	4	50.00%	7	1	85.71%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
4	Distraction	2	0	2	0	0	0	0	0	6	14	-8	2	0	2	0	0	0	0	0	6	14	-8	0	0	0	0	0	0	0	0	0	0	0	0	0.000	6	12	18	0	0	35	70	73	1	63	319	431	387	3	61	19	18	29	2	0	0.00%	9	6	33.33%	2	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
5	Dynamos	2	1	1	0	0	0	0	0	15	11	4	0	0	0	0	0	0	0	0	0	0	0	2	1	1	0	0	0	0	0	15	11	4	2	0.500	15	28	43	0	0	35	70	73	1	69	319	431	387	3	54	11	10	40	7	4	57.14%	5	2	60.00%	1	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
6	Falcons	4	4	0	0	0	0	0	0	24	13	11	2	2	0	0	0	0	0	0	14	7	7	2	2	0	0	0	0	0	0	10	6	4	8	1.000	24	43	67	0	0	35	70	73	1	127	319	431	387	3	82	28	12	62	12	8	66.67%	6	4	33.33%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
7	L'Euphorie	1	1	0	0	0	0	0	0	7	5	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	7	5	2	2	1.000	7	13	20	0	0	35	70	73	1	38	319	431	387	3	35	14	12	15	3	1	33.33%	6	3	50.00%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
8	Monkeys	5	0	5	0	0	0	0	0	10	46	-36	2	0	2	0	0	0	0	0	5	18	-13	3	0	3	0	0	0	0	0	5	28	-23	0	0.000	10	19	29	0	0	35	70	73	1	177	319	431	387	3	230	91	76	77	19	5	26.32%	18	12	33.33%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
9	Monsters	4	0	4	0	0	0	0	0	23	36	-13	2	0	2	0	0	0	0	0	13	22	-9	2	0	2	0	0	0	0	0	10	14	-4	0	0.000	23	44	67	0	0	35	70	73	1	127	319	431	387	3	160	53	60	40	9	5	55.56%	25	15	40.00%	1	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
10	Sharks	2	0	2	0	0	0	0	0	8	17	-9	0	0	0	0	0	0	0	0	0	0	0	2	0	2	0	0	0	0	0	8	17	-9	0	0.000	8	15	23	0	0	35	70	73	1	69	319	431	387	3	78	27	21	29	3	1	33.33%	8	7	12.50%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
11	Sheriefs	2	2	0	0	0	0	0	0	17	10	7	1	1	0	0	0	0	0	0	9	4	5	1	1	0	0	0	0	0	0	8	6	2	4	1.000	17	34	51	0	0	35	70	73	1	89	319	431	387	3	46	20	12	44	7	6	85.71%	6	3	50.00%	1	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
	Total	35	15	19	0	1	0	0	0	179	214	-35	17	8	8	0	1	0	0	0	91	102	-11	18	7	11	0	0	0	0	0	88	112	-24	32	0.457	179	336	515	0	0	35	70	73	1	1140	319	431	387	3	1137	387	305	529	103	48	46.60%	125	67	46.40%	5	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
13	Viking	1	0	1	0	0	0	0	0	6	8	-2	1	0	1	0	0	0	0	0	6	8	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	6	11	17	0	0	35	70	73	1	39	319	431	387	3	38	14	12	6	5	3	60.00%	6	3	50.00%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
14	Warriors	1	0	1	0	0	0	0	0	3	7	-4	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	7	-4	0	0.000	3	6	9	0	0	35	70	73	1	22	319	431	387	3	21	6	8	16	4	1	25.00%	4	3	25.00%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
15	Wildcats	4	2	1	0	1	0	0	0	20	16	4	3	1	1	0	1	0	0	0	15	13	2	1	1	0	0	0	0	0	0	5	3	2	6	0.750	20	36	56	0	0	35	70	73	1	114	319	431	387	3	120	34	24	57	8	2	25.00%	12	3	75.00%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
	_Since Last GM Reset	35	15	19	0	1	0	0	0	179	214	-35	17	8	8	0	1	0	0	0	91	102	-11	18	7	11	0	0	0	0	0	88	112	-24	32	0.457	179	336	515	0	0	35	70	73	1	1140	319	431	387	3	1137	387	305	529	103	48	46.60%	125	67	46.40%	5	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
	_Vs Conference	21	9	12	0	0	0	0	0	106	133	-27	9	5	4	0	0	0	0	0	48	58	-10	12	4	8	0	0	0	0	0	58	75	-17	18	0.429	106	198	304	0	0	35	70	73	1	670	319	431	387	3	704	239	196	318	69	31	44.93%	73	41	43.84%	2	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
	_Vs Division	13	6	7	0	0	0	0	0	52	79	-27	6	4	2	0	0	0	0	0	29	31	-2	7	2	5	0	0	0	0	0	23	48	-25	12	0.462	52	95	147	0	0	35	70	73	1	412	319	431	387	3	417	155	112	206	45	18	40.00%	36	23	36.11%	0	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
35	32	L3	179	336	515	1140	1137	387	305	529	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
35	15	19	1	0	0	0	179	214

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
17	8	8	1	0	0	0	91	102

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
18	7	11	0	0	0	0	88	112

Last 10 Games
W	L	OTW	OTL	SOW	SOL
6	4	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
103	48	46.60%	125	67	46.40%	5

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
319	431	387	3	35	70	73	1

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
226	525	43.05%	191	510	37.45%	321	790	40.63%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
712	429	801	327	586	265

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	Link
1 - 2019-05-06	6	Distraction	6	Cyclone	3	L		BoxScore
2 - 2019-05-07	11	Cyclone	5	Chiefs	7	L		BoxScore
4 - 2019-05-09	24	Wildcats	3	Cyclone	5	W		BoxScore
6 - 2019-05-11	34	Cyclone	2	Cobra	6	L		BoxScore
8 - 2019-05-13	45	Cyclone	3	Warriors	7	L		BoxScore
10 - 2019-05-15	57	Cyclone	0	Monkeys	7	L		BoxScore
12 - 2019-05-17	63	Falcons	3	Cyclone	6	W		BoxScore
14 - 2019-05-19	76	Chiefs	2	Cyclone	4	W		BoxScore
16 - 2019-05-21	88	Wildcats	3	Cyclone	4	W	X	BoxScore
18 - 2019-05-23	100	Cyclone	4	Monkeys	10	L		BoxScore
21 - 2019-05-26	108	Viking	8	Cyclone	6	L		BoxScore
22 - 2019-05-27	118	Cyclone	5	Falcons	2	W		BoxScore
24 - 2019-05-29	125	Cyclone	4	Dynamos	5	L		BoxScore
26 - 2019-05-31	133	Cyclone	7	L'Euphorie	5	W		BoxScore
28 - 2019-06-02	143	Monkeys	10	Cyclone	3	L		BoxScore
30 - 2019-06-04	159	Monsters	11	Cyclone	6	L		BoxScore
32 - 2019-06-06	165	Distraction	8	Cyclone	3	L		BoxScore
34 - 2019-06-08	175	Cyclone	8	Sheriefs	6	W		BoxScore
36 - 2019-06-10	182	Cyclone	5	Monsters	7	L		BoxScore
38 - 2019-06-12	195	Cyclone	7	Sharks	9	L		BoxScore
40 - 2019-06-14	203	Wildcats	7	Cyclone	6	L		BoxScore
42 - 2019-06-16	217	Chiefs	4	Cyclone	6	W		BoxScore
45 - 2019-06-19	229	Cyclone	5	Monsters	7	L		BoxScore
46 - 2019-06-20	234	Cyclone	11	Dynamos	6	W		BoxScore
48 - 2019-06-22	244	Cobra	5	Cyclone	7	W		BoxScore
50 - 2019-06-24	254	Sheriefs	4	Cyclone	9	W		BoxScore
53 - 2019-06-27	268	Cyclone	5	Wildcats	3	W		BoxScore
54 - 2019-06-28	271	Falcons	4	Cyclone	8	W		BoxScore
56 - 2019-06-30	284	Monkeys	8	Cyclone	2	L		BoxScore
58 - 2019-07-02	295	Cyclone	5	Falcons	4	W		BoxScore
60 - 2019-07-04	307	Destroyers	5	Cyclone	6	W		BoxScore
62 - 2019-07-06	314	Cyclone	10	Destroyers	2	W		BoxScore
64 - 2019-07-08	321	Cyclone	1	Monkeys	11	L		BoxScore
66 - 2019-07-10	337	Monsters	11	Cyclone	7	L		BoxScore
68 - 2019-07-12	342	Cyclone	1	Sharks	8	L		BoxScore
71 - 2019-07-15	359	Senators	-	Cyclone	-
74 - 2019-07-18	370	Distraction	-	Cyclone	-
76 - 2019-07-20	383	Sheriefs	-	Cyclone	-
78 - 2019-07-22	399	Cyclone	-	Prospects	-
79 - 2019-07-23	405	Falcons	-	Cyclone	-
81 - 2019-07-25	418	Warriors	-	Cyclone	-
83 - 2019-07-27	426	Cyclone	-	Wildcats	-
84 - 2019-07-28	437	Cyclone	-	Viking	-
86 - 2019-07-30	446	Viking	-	Cyclone	-
87 - 2019-07-31	456	Cyclone	-	Intrepides	-
89 - 2019-08-02	464	Patriotes	-	Cyclone	-
91 - 2019-08-04	474	Cyclone	-	Patriotes	-
92 - 2019-08-05	485	Intrepides	-	Cyclone	-
94 - 2019-08-07	497	Youngblood	-	Cyclone	-
96 - 2019-08-09	503	Cyclone	-	Intrepides	-
98 - 2019-08-11	518	Cyclone	-	Cobra	-
99 - 2019-08-12	523	Prospects	-	Cyclone	-
101 - 2019-08-14	537	Cyclone	-	Distraction	-
103 - 2019-08-16	544	Warriors	-	Cyclone	-
104 - 2019-08-17	555	Cyclone	-	Warriors	-
105 - 2019-08-18	562	Cyclone	-	Prospects	-
107 - 2019-08-20	569	Cyclone	-	Chiefs	-
108 - 2019-08-21	577	Patriotes	-	Cyclone	-
110 - 2019-08-23	590	Cobra	-	Cyclone	-
112 - 2019-08-25	602	Cyclone	-	Senators	-
113 - 2019-08-26	610	Youngblood	-	Cyclone	-
116 - 2019-08-29	623	Cyclone	-	Youngblood	-
117 - 2019-08-30	632	Sharks	-	Cyclone	-
118 - 2019-08-31	644	Intrepides	-	Cyclone	-
119 - 2019-09-01	654	Cyclone	-	Warriors	-
121 - 2019-09-03	663	Cyclone	-	Distraction	-
122 - 2019-09-04	671	Dynamos	-	Cyclone	-
123 - 2019-09-05	681	Cyclone	-	L'Euphorie	-
124 - 2019-09-06	689	Cyclone	-	Senators	-
126 - 2019-09-08	698	Senators	-	Cyclone	-
127 - 2019-09-09	707	Cyclone	-	Chiefs	-
128 - 2019-09-10	716	Dynamos	-	Cyclone	-
130 - 2019-09-12	730	L'Euphorie	-	Cyclone	-
Trade Deadline --- Trades can’t be done after this day is simulated!
131 - 2019-09-13	737	Cyclone	-	Youngblood	-
133 - 2019-09-15	744	Cyclone	-	Patriotes	-
135 - 2019-09-17	756	Cyclone	-	Destroyers	-
136 - 2019-09-18	762	Destroyers	-	Cyclone	-
138 - 2019-09-20	770	Cyclone	-	Sheriefs	-
139 - 2019-09-21	779	Sharks	-	Cyclone	-
140 - 2019-09-22	787	Cyclone	-	Viking	-
142 - 2019-09-24	800	L'Euphorie	-	Cyclone	-
145 - 2019-09-27	818	Prospects	-	Cyclone	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	0	0
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
24	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
1,509,163$	2,032,500$	1,631,250$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
13,921$	1,077,626$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	76	20,086$	1,526,536$

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
2018	35	15	19	0	1	0	0	0	179	214	-35	17	8	8	0	1	0	0	0	91	102	-11	18	7	11	0	0	0	0	0	88	112	-24	32	179	336	515	0	0	35	70	73	1	1140	319	431	387	3	1137	387	305	529	103	48	46.60%	125	67	46.40%	5	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
Total Regular Season	35	15	19	0	1	0	0	0	179	214	-35	17	8	8	0	1	0	0	0	91	102	-11	18	7	11	0	0	0	0	0	88	112	-24	32	179	336	515	0	0	35	70	73	1	1140	319	431	387	3	1137	387	305	529	103	48	46.60%	125	67	46.40%	5	226	525	43.05%	191	510	37.45%	321	790	40.63%	712	429	801	327	586	265
Playoff
2017	9	4	5	0	0	0	0	0	51	57	-6	5	2	3	0	0	0	0	0	26	27	-1	4	2	2	0	0	0	0	0	25	30	-5	8	51	94	145	0	0	9	23	18	1	306	101	90	113	2	309	86	69	133	31	12	38.71%	27	17	37.04%	2	60	128	46.88%	70	135	51.85%	102	216	47.22%	199	118	188	83	155	76
Total Playoff	9	4	5	0	0	0	0	0	51	57	-6	5	2	3	0	0	0	0	0	26	27	-1	4	2	2	0	0	0	0	0	25	30	-5	8	51	94	145	0	0	9	23	18	1	306	101	90	113	2	309	86	69	133	31	12	38.71%	27	17	37.04%	2	60	128	46.88%	70	135	51.85%	102	216	47.22%	199	118	188	83	155	76