Association Québécoise de Hockey Simulé

Association Québécoise de Hockey Simulé - Igloos

Home
Main
Pro League
Farm League
League
- Coaches
- Transactions
- Waivers
- Team/GM
- Trade History
- Prospects
- RSS Feed
- Unassigned
  - Players
  - Goalies
- Available For Trade
  - Players
  - Goalies
- Free Agents
  - Players
  - Goalies
History
Team's Link
- Pro Team
  - Conférence Canadienne
  - Conférence Américaine
- Farm Team
  - Conference 1
    - Bandits
    - CoolFm
    - Farmers
    - Goons
    - Grizzlies
    - Hunters
    - Husky
    - Igloos
    - Marlies
    - Marmots
    - Rockets
    - Smirnoff Ice
    - TigersCats
    - Warriors
    - Xpress
  - Conference 2
    - Barracudas
    - Bayou
    - Chiwawa
    - Outlaws
    - Predateurs
    - Raptors
    - Saguenéens
    - Scorpions
    - Snowbirds
    - Spartans
    - Supreme
    - Thugs
    - Twins
    - Vandals
    - Vipers
    - Wolves
Search
Legacy Pages
Help
- Latest STHS Client
- STHS Client Manual

Igloos

GP: 27 | W: 11 | L: 13 | OTL: 3 | P: 25
GF: 77 | GA: 89 | PP%: 22.73% | PK%: 69.57%
GM : Pierre-Luc Vezina | Morale : 90 | Team Overall : 55
Next Games #439 vs Rockets

Your browser screen resolution is too small for this page. Some information are hidden to keep the page readable.

Nunavut Mystery
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	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP
1	Cole Koepke (R)	X			100.00	82	45	92	76	66	50	40	47	30	40	54	55	50	84	71	0	90	55	0
2	Brandon Baddock		X		100.00	50	45	95	73	81	50	40	45	30	40	50	55	50	88	75	0	90	54	0
3	Marian Studenic		X		100.00	50	45	95	77	63	50	40	45	30	40	50	55	50	84	71	0	90	53	0
4	Charles Hudon	X			100.00	60	45	92	77	57	54	40	45	30	40	50	55	50	88	76	0	90	53	0
5	Paul Ladue			X	100.00	50	45	95	76	67	50	40	45	30	40	50	55	50	91	78	0	90	57	0
Scratches

	TEAM AVERAGE				100.00	58	45	94	76	67	51	40	45	30	40	51	55	50	87	74	0	90	54

Show or Hide Column

Filter Tips

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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Zach Sawchenko (R)	100.00	70	40	40	83	65	65	65	65	65	65	65	84	72	0	90	58	0
Scratches

	TEAM AVERAGE	100.00	70	40	40	83	65	65	65	65	65	65	65	84	72	0	90	58

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
John Stevens	65	65	65	65	91	75	58	CAN	57	3	1,000,000$

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	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	GA	TA	P/20	FT	S1	S2	S3
1	Alex Galchenyuk	Nunavut Mystery	LW	14	4	5	9	-3	0	0	8	11	36	12	22	11.11%	9	242	17.34	0	1	1	4	14	0	0	0	0	31	0	0	53.33%	15	10	5	0.74	0	1	2	1
2	Marian Studenic	Igloos (NUN)	RW	27	4	3	7	-2	7	5	3	16	39	18	26	10.26%	2	389	14.41	0	0	0	1	18	1	0	1	4	29	1	0	37.50%	24	6	8	0.36	1	1	1	2
3	Markus Granlund	Nunavut Mystery	RW	7	5	1	6	-6	2	0	1	6	29	9	13	17.24%	4	142	20.37	3	0	3	9	18	0	1	1	1	8	1	1	25.00%	4	5	1	0.84	0	1	1	1
4	Cole Koepke	Igloos (NUN)	LW	5	3	2	5	-1	6	0	7	3	13	3	5	23.08%	2	87	17.52	2	1	3	7	15	0	0	0	0	7	0	0	39.39%	33	2	3	1.14	0	2	1	0
5	Paul Ladue	Igloos (NUN)	D	18	0	2	2	-15	0	0	7	18	12	4	6	0.00%	16	407	22.66	0	2	2	1	31	0	0	0	0	41	0	0	0.00%	0	4	10	0.10	0	0	0	1
6	John Persson	Nunavut Mystery	D	14	0	1	1	-15	2	0	4	11	10	2	2	0.00%	19	312	22.34	0	1	1	1	23	0	0	0	0	29	0	0	0.00%	0	4	11	0.06	0	0	0	0
Team Total or Average				85	16	14	30	-42	17	5	30	65	139	48	74	11.51%	52	1582	18.62	5	5	10	23	121	1	1	2	5	147	2	1	40.79%	76	31	38	0.38	1	5	5	5

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	GA	SA	SAR	A	ST	BG	S1	S3
1	Zach Sawchenko	Igloos (NUN)	8	4	2	1	0.842	3.62	481	29	183	89	1	8	0	1	1
2	Veini Vehvilainen	Nunavut Mystery	4	0	2	1	0.842	4.65	155	12	76	40	0	3	11	0	0
Team Total or Average			12	4	4	2	0.842	3.87	636	41	259	129	1	11	11	1	1

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
Brandon Baddock	Igloos (NUN)	RW	28	1995-03-29	No	218 Lbs	6 ft3	No	No	No	2	UFA	Pro & Farm	700,000$	0$	0$	No
Charles Hudon	Igloos (NUN)	LW	29	1994-06-23	No	190 Lbs	5 ft10	No	No	No	3	UFA	Pro & Farm	750,000$	0$	0$	No
Cole Koepke	Igloos (NUN)	LW	25	1998-05-17	Yes	196 Lbs	6 ft1	No	No	No	4	RFA	Pro & Farm	825,000$	0$	0$	No
Marian Studenic	Igloos (NUN)	RW	24	1998-10-28	No	190 Lbs	6 ft1	No	No	No	3	RFA	Pro & Farm	800,000$	0$	0$	No
Paul Ladue	Igloos (NUN)	D	31	1992-09-06	No	200 Lbs	6 ft3	No	No	No	1	UFA	Pro & Farm	2,500,000$	0$	0$	No
Zach Sawchenko	Igloos (NUN)	G	25	1997-12-30	Yes	185 Lbs	6 ft1	No	No	No	2	RFA	Pro & Farm	900,000$	0$	0$	No

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
6	27.00	197 Lbs	6 ft1	2.50	1,079,167$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				35	1	2	2
2			Marian Studenic	30	1	2	2
3				25	1	2	2
4				10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			35	1	2	2
2			30	1	2	2
3			25	1	2	2
4			10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				55	1	2	2
2			Marian Studenic	45	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			55	1	2	2
2			45	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			55	1	2	2
2		Marian Studenic	45	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			55	1	2	2
2			45	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1		55	1	2	2			55	1	2	2
2		45	1	2	2			45	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			55	1	2	2
2		Marian Studenic	45	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			55	1	2	2
2			45	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
Marian Studenic, ,	Marian Studenic,

Extra Defensemen
Normal	PowerPlay	Penalty Kill
, ,		,

Penalty Shots
, , , Marian Studenic,

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	Bandits	3	0	3	0	0	0	0	0	6	11	-5	2	0	2	0	0	0	0	0	5	9	-4	1	0	1	0	0	0	0	0	1	2	-1	0	0.000	6	11	17	0	0	17	22	38	0	65	165	212	277	0	82	35	42	31	7	0	0.00%	16	4	75.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
2	Barracudas	1	0	1	0	0	0	0	0	0	2	-2	1	0	1	0	0	0	0	0	0	2	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	17	22	38	0	10	165	212	277	0	15	10	6	7	4	0	0.00%	3	1	66.67%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
3	Bayou	2	1	1	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	3	1	2	1	0	1	0	0	0	0	0	1	2	-1	2	0.500	4	8	12	0	0	17	22	38	0	47	165	212	277	0	37	20	14	22	7	1	14.29%	7	2	71.43%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
4	Chiwawa	1	0	0	0	0	1	0	0	2	3	-1	1	0	0	0	0	1	0	0	2	3	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	2	4	6	0	0	17	22	38	0	28	165	212	277	0	18	7	2	13	3	0	0.00%	1	0	100.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
5	CoolFm	2	1	1	0	0	0	0	0	6	7	-1	1	0	1	0	0	0	0	0	1	4	-3	1	1	0	0	0	0	0	0	5	3	2	2	0.500	6	11	17	0	0	17	22	38	0	51	165	212	277	0	53	19	10	24	3	1	33.33%	5	2	60.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
6	Goons	3	2	1	0	0	0	0	0	13	9	4	2	1	1	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	7	5	2	4	0.667	13	24	37	0	0	17	22	38	0	102	165	212	277	0	82	39	20	51	14	5	35.71%	10	3	70.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
7	Hunters	4	2	2	0	0	0	0	0	12	16	-4	2	2	0	0	0	0	0	0	8	5	3	2	0	2	0	0	0	0	0	4	11	-7	4	0.500	12	22	34	0	0	17	22	38	0	76	165	212	277	0	81	29	59	50	11	4	36.36%	12	2	83.33%	1	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
8	Husky	3	1	1	0	0	1	0	0	8	9	-1	2	1	0	0	0	1	0	0	7	6	1	1	0	1	0	0	0	0	0	1	3	-2	3	0.500	8	15	23	1	0	17	22	38	0	91	165	212	277	0	57	16	13	34	5	0	0.00%	4	1	75.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
9	Marlies	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	3	5	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	5	8	0	0	17	22	38	0	21	165	212	277	0	27	6	8	20	1	0	0.00%	4	1	75.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
10	Raptors	1	0	1	0	0	0	0	0	1	3	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	3	-2	0	0.000	1	2	3	0	0	17	22	38	0	20	165	212	277	0	25	11	12	9	1	1	100.00%	6	2	66.67%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
11	Scorpions	1	1	0	0	0	0	0	0	5	4	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	5	4	1	2	1.000	5	10	15	0	0	17	22	38	0	24	165	212	277	0	20	14	10	7	3	2	66.67%	5	2	60.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
12	Smirnoff Ice	1	1	0	0	0	0	0	0	2	1	1	1	1	0	0	0	0	0	0	2	1	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	2	4	6	0	0	17	22	38	0	23	165	212	277	0	14	8	8	7	0	0	0.00%	4	1	75.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
13	TigersCats	1	0	1	0	0	0	0	0	3	6	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	6	-3	0	0.000	3	6	9	0	0	17	22	38	0	18	165	212	277	0	33	5	10	6	2	0	0.00%	5	2	60.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
	Total	27	11	13	0	0	3	0	0	77	89	-12	14	6	6	0	0	2	0	0	37	40	-3	13	5	7	0	0	1	0	0	40	49	-9	25	0.463	77	146	223	1	0	17	22	38	0	654	165	212	277	0	632	242	234	324	66	15	22.73%	92	28	69.57%	1	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
15	Vandals	1	1	0	0	0	0	0	0	5	3	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	5	3	2	2	1.000	5	10	15	0	0	17	22	38	0	31	165	212	277	0	26	9	8	13	1	0	0.00%	4	2	50.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
16	Xpress	2	1	0	0	0	1	0	0	7	7	0	0	0	0	0	0	0	0	0	0	0	0	2	1	0	0	0	1	0	0	7	7	0	3	0.750	7	14	21	0	0	17	22	38	0	47	165	212	277	0	62	14	12	30	4	1	25.00%	6	3	50.00%	0	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
	_Since Last GM Reset	27	11	13	0	0	3	0	0	77	89	-12	14	6	6	0	0	2	0	0	37	40	-3	13	5	7	0	0	1	0	0	40	49	-9	25	0.463	77	146	223	1	0	17	22	38	0	654	165	212	277	0	632	242	234	324	66	15	22.73%	92	28	69.57%	1	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
	_Vs Conference	20	8	10	0	0	2	0	0	60	71	-11	11	5	5	0	0	1	0	0	32	34	-2	9	3	5	0	0	1	0	0	28	37	-9	18	0.450	60	112	172	1	0	17	22	38	0	494	165	212	277	0	491	171	182	253	47	11	23.40%	66	19	71.21%	1	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
	_Vs Division	17	7	8	0	0	2	0	0	52	59	-7	9	4	4	0	0	1	0	0	27	28	-1	8	3	4	0	0	1	0	0	25	31	-6	16	0.471	52	97	149	1	0	17	22	38	0	432	165	212	277	0	417	152	156	220	44	11	25.00%	53	15	71.70%	1	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
27	25	L2	77	146	223	654	632	242	234	324	1	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
27	11	13	0	3	0	0	77	89

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
14	6	6	0	2	0	0	37	40

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
13	5	7	0	1	0	0	40	49

Last 10 Games
W	L	OTW	OTL	SOW	SOL
3	6	0	1	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
66	15	22.73%	92	28	69.57%	1

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
165	212	277	0	17	22	38	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
236	448	52.68%	234	436	53.67%	215	417	51.56%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
808	613	451	187	364	178

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 - 2024-04-06	4	Hunters	4	Igloos	5	W		BoxScore
3 - 2024-04-08	23	Igloos	0	Hunters	6	L		BoxScore
4 - 2024-04-09	36	Husky	4	Igloos	3	L	X	BoxScore
7 - 2024-04-12	68	Goons	1	Igloos	4	W		BoxScore
9 - 2024-04-14	91	Igloos	7	Goons	5	W		BoxScore
10 - 2024-04-15	97	Hunters	1	Igloos	3	W		BoxScore
11 - 2024-04-16	113	Igloos	4	Hunters	5	L		BoxScore
12 - 2024-04-17	126	Igloos	5	CoolFm	3	W		BoxScore
13 - 2024-04-18	139	CoolFm	4	Igloos	1	L		BoxScore
15 - 2024-04-20	158	Bandits	4	Igloos	3	L		BoxScore
16 - 2024-04-21	169	Igloos	4	Xpress	5	L	X	BoxScore
18 - 2024-04-23	189	Husky	2	Igloos	4	W		BoxScore
20 - 2024-04-25	207	Igloos	1	Husky	3	L		BoxScore
21 - 2024-04-26	222	Marlies	5	Igloos	3	L		BoxScore
23 - 2024-04-28	240	Igloos	1	Raptors	3	L		BoxScore
24 - 2024-04-29	251	Smirnoff Ice	1	Igloos	2	W		BoxScore
26 - 2024-05-01	271	Igloos	5	Scorpions	4	W		BoxScore
27 - 2024-05-02	283	Chiwawa	3	Igloos	2	L	X	BoxScore
28 - 2024-05-03	298	Igloos	5	Vandals	3	W		BoxScore
30 - 2024-05-05	315	Goons	3	Igloos	2	L		BoxScore
31 - 2024-05-06	324	Igloos	1	Bandits	2	L		BoxScore
32 - 2024-05-07	344	Barracudas	2	Igloos	0	L		BoxScore
34 - 2024-05-09	362	Igloos	3	TigersCats	6	L		BoxScore
35 - 2024-05-10	374	Igloos	3	Xpress	2	W		BoxScore
36 - 2024-05-11	385	Bayou	1	Igloos	3	W		BoxScore
38 - 2024-05-13	402	Igloos	1	Bayou	2	L		BoxScore
39 - 2024-05-14	416	Bandits	5	Igloos	2	L		BoxScore
42 - 2024-05-17	439	Rockets	-	Igloos	-
43 - 2024-05-18	454	Igloos	-	Rockets	-
44 - 2024-05-19	469	Husky	-	Igloos	-
46 - 2024-05-21	486	Igloos	-	Twins	-
47 - 2024-05-22	498	TigersCats	-	Igloos	-
48 - 2024-05-23	518	Igloos	-	Thugs	-
49 - 2024-05-24	528	Igloos	-	Snowbirds	-
50 - 2024-05-25	539	Igloos	-	Bandits	-
51 - 2024-05-26	550	Snowbirds	-	Igloos	-
53 - 2024-05-28	570	CoolFm	-	Igloos	-
55 - 2024-05-30	591	Grizzlies	-	Igloos	-
58 - 2024-06-02	612	Igloos	-	Farmers	-
59 - 2024-06-03	623	Igloos	-	Outlaws	-
60 - 2024-06-04	632	Twins	-	Igloos	-
62 - 2024-06-06	654	Igloos	-	Supreme	-
63 - 2024-06-07	667	Marmots	-	Igloos	-
64 - 2024-06-08	682	Igloos	-	Vipers	-
65 - 2024-06-09	696	Igloos	-	Barracudas	-
66 - 2024-06-10	706	Outlaws	-	Igloos	-
68 - 2024-06-12	726	Saguenéens	-	Igloos	-
70 - 2024-06-14	748	Igloos	-	Wolves	-
71 - 2024-06-15	756	Hunters	-	Igloos	-
72 - 2024-06-16	779	Igloos	-	Saguenéens	-
73 - 2024-06-17	787	Vipers	-	Igloos	-
75 - 2024-06-19	812	Raptors	-	Igloos	-
76 - 2024-06-20	824	Igloos	-	Smirnoff Ice	-
77 - 2024-06-21	837	Igloos	-	Chiwawa	-
78 - 2024-06-22	851	Vandals	-	Igloos	-
79 - 2024-06-23	861	Igloos	-	Warriors	-
81 - 2024-06-25	882	Xpress	-	Igloos	-
83 - 2024-06-27	902	Warriors	-	Igloos	-
85 - 2024-06-29	918	Igloos	-	Hunters	-
86 - 2024-06-30	928	Igloos	-	Grizzlies	-
88 - 2024-07-02	945	Wolves	-	Igloos	-
89 - 2024-07-03	956	Igloos	-	Marlies	-
Trade Deadline --- Trades can’t be done after this day is simulated!
90 - 2024-07-04	971	Farmers	-	Igloos	-
92 - 2024-07-06	991	Igloos	-	Marmots	-
93 - 2024-07-07	1005	Xpress	-	Igloos	-
94 - 2024-07-08	1017	Igloos	-	CoolFm	-
95 - 2024-07-09	1031	Thugs	-	Igloos	-
97 - 2024-07-11	1050	Igloos	-	Marlies	-
99 - 2024-07-13	1068	Igloos	-	Goons	-
100 - 2024-07-14	1077	Predateurs	-	Igloos	-
103 - 2024-07-17	1096	Scorpions	-	Igloos	-
104 - 2024-07-18	1104	Igloos	-	Predateurs	-
105 - 2024-07-19	1116	Igloos	-	Husky	-
107 - 2024-07-21	1138	Spartans	-	Igloos	-
110 - 2024-07-24	1160	Igloos	-	Spartans	-
112 - 2024-07-26	1174	Supreme	-	Igloos	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	27,874	13,964
Attendance PCT	99.55%	99.74%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
24	2988 - 99.61%	101,576$	1,422,060$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
783,386$	647,500$	647,500$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	435,070$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
2,437,817$	74	14,580$	1,078,920$

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
45	82	24	35	0	6	9	3	5	235	281	-46	41	10	19	0	3	4	1	4	116	143	-27	41	14	16	0	3	5	2	1	119	138	-19	80	235	443	678	2	1	54	77	95	11	1727	406	593	703	50	2044	689	791	972	226	37	16.37%	248	71	71.37%	7	628	1209	51.94%	713	1384	51.52%	643	1274	50.47%	2267	1696	1635	563	1084	535
46	82	27	37	0	8	5	1	4	237	272	-35	41	12	18	0	6	1	1	3	116	126	-10	41	15	19	0	2	4	0	1	121	146	-25	81	237	439	676	1	2	46	90	92	13	1802	463	635	686	34	1945	632	1079	1042	250	42	16.80%	289	83	71.28%	6	670	1331	50.34%	696	1362	51.10%	615	1244	49.44%	2432	1816	1425	575	1113	559
47	27	11	13	0	0	3	0	0	77	89	-12	14	6	6	0	0	2	0	0	37	40	-3	13	5	7	0	0	1	0	0	40	49	-9	25	77	146	223	1	0	17	22	38	0	654	165	212	277	0	632	242	234	324	66	15	22.73%	92	28	69.57%	1	236	448	52.68%	234	436	53.67%	215	417	51.56%	808	613	451	187	364	178
Total Regular Season	191	62	85	0	14	17	4	9	549	642	-93	96	28	43	0	9	7	2	7	269	309	-40	95	34	42	0	5	10	2	2	280	333	-53	186	549	1028	1577	4	3	117	189	225	24	4183	1034	1440	1666	84	4621	1563	2104	2338	542	94	17.34%	629	182	71.07%	14	1534	2988	51.34%	1643	3182	51.63%	1473	2935	50.19%	5508	4127	3512	1326	2562	1272

Back to the Top