Colorado Avalanche
Home
Roster
Players Stats
Players Info
Lines
Team Stats
Schedule
Career Team Stats

Game Center
	Nordiks 36-24-6, 78pts	6	FINAL	4	Griffins 37-27-1, 75pts

Team Stats
L1	Streak	W1
16-12-4	Home Record	21-11-0
20-12-2	Home Record	16-16-1
5-3-2	Last 10 Games	6-3-1
5.26	Goals Per Game	5.43
4.86	Goals Against Per Game	5.09
20.95%	Power Play Percentage	20.14%
73.58%	Penalty Kill Percentage	73.89%

Griffins
37-27-1, 75pts

FINAL

Nordiks
36-24-6, 78pts

Team Stats
W1	Streak	L1
21-11-0	Home Record	16-12-4
16-16-1	Home Record	20-12-2
6-3-1	Last 10 Games	5-3-2
5.43	Goals Per Game	5.26
5.09	Goals Against Per Game	4.86
20.14%	Power Play Percentage	20.95%
73.89%	Penalty Kill Percentage	73.58%

Nordiks
36-24-6, 78pts

Day 141

Roadrunners
38-20-7, 83pts

Team Stats
L1	Streak	L1
16-12-4	Home Record	21-10-1
20-12-2	Away Record	17-10-6
5-3-2	Last 10 Games	3-5-2
5.26	Goals Per Game	3.71
4.86	Goals Against Per Game	3.71
20.95%	Power Play Percentage	26.71%
73.58%	Penalty Kill Percentage	79.31%

Firebirds
22-40-3, 47pts

Day 142

Nordiks
36-24-6, 78pts

Team Stats
L1	Streak	L1
12-19-1	Home Record	16-12-4
10-21-2	Away Record	20-12-2
1-8-1	Last 10 Games	5-3-2
3.95	Goals Per Game	5.26
4.78	Goals Against Per Game	5.26
23.36%	Power Play Percentage	20.95%
73.97%	Penalty Kill Percentage	73.58%

Lions
28-38-0, 56pts

Day 146

Nordiks
36-24-6, 78pts

Team Stats
L1	Streak	L1
16-15-0	Home Record	16-12-4
12-23-0	Away Record	20-12-2
3-7-0	Last 10 Games	5-3-2
4.71	Goals Per Game	5.26
5.14	Goals Against Per Game	5.26
21.53%	Power Play Percentage	20.95%
74.05%	Penalty Kill Percentage	73.58%

Team Leaders

Team Stats
Goals For 347 5.26 GFG	Shots For 3294 49.91 Avg	Power Play Percentage 20.9% 31 GF	Offensive Zone Start 41.9%
Goals Against 321 4.86 GAA	Shots Against 2943 44.59 Avg	Penalty Kill Percentage 73.6%% 42 GA	Defensive Zone Start 38.6%

Team Info
	General Manager	Kawika
	Coach	Mick Vukota
	Division	Pacific
	Conference	Western Conference
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	0
	Season Tickets	300
Roster Info
	Pro Team	13
	Farm Team	20
	Contract Limit	33 / 50
	Prospects	21

Show or Hide Columns

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	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary
1	Tyler Motte	X			100.00	72	55	93	64	68	69	71	59	55	60	53	74	50	69	71	0	66	61	0	30	4	500,000$
2	Kyle MacLean	X			100.00	73	55	90	64	69	66	90	62	66	59	50	62	50	63	63	0	66	60	0	26	1	500,000$
3	Cole Schwindt (R)		X		100.00	70	50	99	64	73	65	63	62	67	63	50	64	50	60	61	0	66	59	0	24	1	500,000$
4	Luca Del Bel Belluz (R)	X			100.00	50	50	89	62	66	66	54	67	63	66	55	56	50	54	60	0	66	58	0	22	2	500,000$
5	Sam Morton (R)	X			100.00	56	35	89	57	65	50	51	57	50	50	64	50	52	64	74	0	66	54	0	26	1	600,000$
6	Alex Steeves (R)	X			100.00	62	50	89	53	72	50	51	62	50	50	50	50	50	60	61	0	66	53	0	26	1	500,000$
7	Emil Bemstrom	X			100.00	61	50	89	57	67	56	52	53	50	53	50	55	50	65	63	0	66	53	0	26	4	500,000$
8	Arttu Hyry		X		100.00	50	35	89	57	77	50	50	50	58	50	50	50	50	61	62	0	66	52	0	24	2	700,000$
9	Jacob Moverare			X	100.00	66	58	89	62	76	62	67	65	50	59	50	69	55	65	66	0	22	62	0	27	1	500,000$
10	Spencer Stastney			X	100.00	56	56	90	60	65	67	58	63	50	56	50	68	55	62	64	0	66	59	0	26	2	500,000$
11	Declan Carlile			X	100.00	50	35	94	57	69	50	52	50	50	50	50	50	57	62	66	0	66	52	0	25	4	500,000$
12	Luca Cagnoni (R)			X	100.00	50	44	53	57	63	50	54	50	50	50	50	50	55	53	58	0	66	51	0	21	1	0$
Scratches

	TEAM AVERAGE				100.00	60	48	88	60	69	58	59	58	55	56	52	58	52	62	64	0	62	56

Show or Hide Columns

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	Age	Contract	Salary
1	Daniil Tarasov	100.00	65	70	68	77	72	64	64	68	64	50	74	62	63	0	80	65	0	26	4	1,999,999$
Scratches

	TEAM AVERAGE	100.00	65	70	68	77	72	64	64	68	64	50	74	62	63	0	80	65

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Mick Vukota	90	80	80	80	80	75	1	CAN	59	5	500,000$

Show or Hide Columns

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	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3

Show or Hide Columns

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

Show or Hide Columns

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	Country	Rookie	Weight	Height	No Trade	Available For Trade	Acquired By	Last Trade Date	Force Waivers	Waiver Possible	Contract	Contract Signature Date	Force UFA	Emergency Recall	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Salary Year 5	Salary Year 6	Salary Year 7	Salary Year 8	Salary Year 9	Salary Year 10	Salary Cap Year 2	Salary Cap Year 3	Salary Cap Year 4	Salary Cap Year 5	Salary Cap Year 6	Salary Cap Year 7	Salary Cap Year 8	Salary Cap Year 9	Salary Cap Year 10	No Trade Year 2	No Trade Year 3	No Trade Year 4	No Trade Year 5	No Trade Year 6	No Trade Year 7	No Trade Year 8	No Trade Year 9	No Trade Year 10	Link
Alex Steeves	Nordiks (COL)	C	26	1999-12-10	USA	Yes	202 Lbs	6 ft0	No	No	N/A		No	No	1		False	False	Pro & Farm	500,000$	50,000$	11,667$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Arttu Hyry	Nordiks (COL)	RW	24	2001-04-06	FIN	No	214 Lbs	6 ft3	No	No	Free Agent		No	No	2	2025-08-13	False	False	Pro & Farm	700,000$	70,000$	16,333$	No	700,000$	-	-	-	-	-	-	-	-	700,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Cole Schwindt	Nordiks (COL)	RW	24	2001-04-25	CAN	Yes	203 Lbs	6 ft2	No	No	N/A		No	No	1		False	False	Pro & Farm	500,000$	50,000$	11,667$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Daniil Tarasov	Nordiks (COL)	G	26	1999-03-27	RUS	No	203 Lbs	6 ft5	No	No	Free Agent		No	No	4	2025-07-23	False	False	Pro & Farm	1,999,999$	200,000$	46,667$	No	1,999,999$	1,999,999$	1,999,999$	-	-	-	-	-	-	1,999,999$	1,999,999$	1,999,999$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-	Link / NHL Link
Declan Carlile	Nordiks (COL)	D	25	2000-05-18	USA	No	192 Lbs	6 ft3	No	No	Free Agent		No	No	4	2025-07-25	False	False	Pro & Farm	500,000$	50,000$	11,667$	No	500,000$	500,000$	500,000$	-	-	-	-	-	-	500,000$	500,000$	500,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-	Link / NHL Link
Emil Bemstrom	Nordiks (COL)	C	26	1999-06-01	SWE	No	190 Lbs	6 ft0	No	No	Free Agent		No	No	4	2025-07-25	False	False	Pro & Farm	500,000$	50,000$	11,667$	No	500,000$	500,000$	500,000$	-	-	-	-	-	-	500,000$	500,000$	500,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-	Link / NHL Link
Jacob Moverare	Nordiks (COL)	D	27	1998-08-31	SWE	No	210 Lbs	6 ft3	No	No	Trade	2025-08-20	No	No	1	2025-04-09	False	False	Pro & Farm	500,000$	50,000$	11,667$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Kyle MacLean	Nordiks (COL)	C	26	1999-04-29	USA	No	194 Lbs	6 ft1	No	No	Trade	2025-08-20	No	No	1	2025-03-24	False	False	Pro & Farm	500,000$	50,000$	11,667$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Luca Cagnoni	Nordiks (COL)	D	21	2004-12-21	CAN	Yes	180 Lbs	5 ft9	No	No	Prospect		No	No	1	2025-08-04	False	False	Pro & Farm		0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Luca Del Bel Belluz	Nordiks (COL)	C	22	2003-11-10	CAN	Yes	185 Lbs	6 ft1	No	No	N/A		No	No	2		False	False	Pro & Farm	500,000$	50,000$	11,667$	No	500,000$	-	-	-	-	-	-	-	-	500,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Sam Morton	Nordiks (COL)	C	26	1999-07-28	USA	Yes	185 Lbs	6 ft0	No	No	Free Agent		No	No	1	2025-08-13	False	False	Pro & Farm	600,000$	60,000$	14,000$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Spencer Stastney	Nordiks (COL)	D	26	2000-01-04	USA	No	184 Lbs	6 ft0	No	No	N/A		No	No	2		False	False	Pro & Farm	500,000$	50,000$	11,667$	No	500,000$	-	-	-	-	-	-	-	-	500,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link / NHL Link
Tyler Motte	Nordiks (COL)	C	30	1995-03-10	USA	No	194 Lbs	5 ft10	No	No	Free Agent		No	No	4	2025-07-25	False	False	Pro & Farm	500,000$	50,000$	11,667$	No	500,000$	500,000$	500,000$	-	-	-	-	-	-	500,000$	500,000$	500,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
13	25.31	195 Lbs	6 ft1	2.15	600,000$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				40	0	0	5
2				30	0	0	5
3				20	0	0	5
4				10	0	0	5

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			40	0	0	5
2			30	0	0	5
3			20	0	0	5
4			10	0	0	5

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				60	0	0	5
2				40	0	0	5

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	0	0	5
2			40	0	0	5

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			60	0	5	0
2			40	0	5	0

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	0	5	0
2			40	0	5	0

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1		60	0	5	0			60	0	5	0
2		40	0	5	0			40	0	5	0

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			60	0	0	5
2			40	0	0	5

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	0	0	5
2			40	0	0	5

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
, ,	,

Extra Defensemen
Normal	PowerPlay	Penalty Kill
, ,		,

Penalty Shots
, , , ,

Goalie
#1 : , #2 :

Custom OT Lines Forwards
, , , , , , , , ,

Custom OT Lines Defensemen
, , , ,

Show or Hide Columns

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	Aces	3	1	1	0	0	1	0	0	11	12	-1	1	0	0	0	0	1	0	0	2	3	-1	2	1	1	0	0	0	0	0	9	9	0	3	0.500	11	20	31	0	0	139	111	95	5	116	1133	1047	1098	29	100	31	12	58	7	1	14.29%	6	1	83.33%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
2	Admirals	1	1	0	0	0	0	0	0	8	2	6	1	1	0	0	0	0	0	0	8	2	6	0	0	0	0	0	0	0	0	0	0	0	2	1.000	8	16	24	0	0	139	111	95	5	45	1133	1047	1098	29	22	6	6	17	1	0	0.00%	3	1	66.67%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
3	Americiens	1	0	1	0	0	0	0	0	3	6	-3	1	0	1	0	0	0	0	0	3	6	-3	0	0	0	0	0	0	0	0	0	0	0	0	0.000	3	6	9	0	0	139	111	95	5	43	1133	1047	1098	29	41	15	2	20	2	1	50.00%	1	0	100.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
4	Barons	3	0	2	0	0	0	0	1	9	14	-5	0	0	0	0	0	0	0	0	0	0	0	3	0	2	0	0	0	0	1	9	14	-5	1	0.167	9	17	26	0	0	139	111	95	5	172	1133	1047	1098	29	115	36	14	69	4	0	0.00%	7	2	71.43%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
5	Barracuda	4	4	0	0	0	0	0	0	22	11	11	2	2	0	0	0	0	0	0	11	5	6	2	2	0	0	0	0	0	0	11	6	5	8	1.000	22	43	65	0	0	139	111	95	5	170	1133	1047	1098	29	168	42	26	83	13	4	30.77%	12	0	100.00%	1	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
6	Broncos	4	1	2	0	0	0	0	1	19	20	-1	3	0	2	0	0	0	0	1	13	18	-5	1	1	0	0	0	0	0	0	6	2	4	3	0.375	19	37	56	0	0	139	111	95	5	181	1133	1047	1098	29	176	48	31	101	13	2	15.38%	13	3	76.92%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
7	Bruins	4	3	1	0	0	0	0	0	26	19	7	2	2	0	0	0	0	0	0	15	10	5	2	1	1	0	0	0	0	0	11	9	2	6	0.750	26	50	76	0	0	139	111	95	5	200	1133	1047	1098	29	160	28	22	64	8	3	37.50%	10	5	50.00%	1	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
8	Butter Knives	1	0	0	0	0	1	0	0	6	7	-1	1	0	0	0	0	1	0	0	6	7	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	6	12	18	0	0	139	111	95	5	69	1133	1047	1098	29	61	12	6	30	3	0	0.00%	3	0	100.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
9	Canucks	3	2	1	0	0	0	0	0	16	15	1	1	1	0	0	0	0	0	0	6	5	1	2	1	1	0	0	0	0	0	10	10	0	4	0.667	16	32	48	0	0	139	111	95	5	156	1133	1047	1098	29	125	38	8	76	5	0	0.00%	4	2	50.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
10	Fighting Pandas	3	1	1	0	0	0	1	0	17	18	-1	1	0	1	0	0	0	0	0	3	6	-3	2	1	0	0	0	0	1	0	14	12	2	4	0.667	17	30	47	0	0	139	111	95	5	145	1133	1047	1098	29	131	35	23	60	6	0	0.00%	8	4	50.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
11	Firebirds	1	1	0	0	0	0	0	0	4	1	3	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	1	3	2	1.000	4	8	12	0	0	139	111	95	5	37	1133	1047	1098	29	23	8	6	12	1	0	0.00%	3	1	66.67%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
12	Griffins	4	3	1	0	0	0	0	0	25	24	1	2	1	1	0	0	0	0	0	12	15	-3	2	2	0	0	0	0	0	0	13	9	4	6	0.750	25	50	75	0	0	139	111	95	5	252	1133	1047	1098	29	245	67	20	120	9	3	33.33%	11	2	81.82%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
13	Ice Bats	7	4	2	0	1	0	0	0	36	29	7	2	1	1	0	0	0	0	0	11	11	0	5	3	1	0	1	0	0	0	25	18	7	10	0.714	36	68	104	0	0	139	111	95	5	343	1133	1047	1098	29	275	87	43	156	11	4	36.36%	19	4	78.95%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
14	Lions	5	3	1	0	0	1	0	0	31	28	3	2	2	0	0	0	0	0	0	13	7	6	3	1	1	0	0	1	0	0	18	21	-3	7	0.700	31	55	86	0	0	139	111	95	5	253	1133	1047	1098	29	338	87	34	131	6	0	0.00%	10	3	70.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
15	Lynx	2	1	1	0	0	0	0	0	11	12	-1	1	0	1	0	0	0	0	0	2	6	-4	1	1	0	0	0	0	0	0	9	6	3	2	0.500	11	21	32	0	0	139	111	95	5	89	1133	1047	1098	29	52	11	17	45	4	0	0.00%	6	2	66.67%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
16	Marlies	2	1	1	0	0	0	0	0	9	7	2	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	7	3	4	2	0.500	9	16	25	0	0	139	111	95	5	75	1133	1047	1098	29	86	24	7	42	9	4	44.44%	1	0	100.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
17	Quacken	3	0	2	0	0	1	0	0	13	20	-7	2	0	1	0	0	1	0	0	9	14	-5	1	0	1	0	0	0	0	0	4	6	-2	1	0.167	13	25	38	0	0	139	111	95	5	154	1133	1047	1098	29	114	38	6	81	4	1	25.00%	3	2	33.33%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
18	Roadrunners	3	2	1	0	0	0	0	0	15	9	6	3	2	1	0	0	0	0	0	15	9	6	0	0	0	0	0	0	0	0	0	0	0	4	0.667	15	29	44	0	0	139	111	95	5	131	1133	1047	1098	29	91	33	14	52	10	3	30.00%	6	3	50.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
19	Tomahawks	6	3	3	0	0	0	0	0	40	36	4	3	2	1	0	0	0	0	0	21	14	7	3	1	2	0	0	0	0	0	19	22	-3	6	0.500	40	78	118	0	0	139	111	95	5	365	1133	1047	1098	29	355	94	39	161	16	3	18.75%	18	4	77.78%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
20	Wombats	2	1	1	0	0	0	0	0	7	12	-5	1	1	0	0	0	0	0	0	6	4	2	1	0	1	0	0	0	0	0	1	8	-7	2	0.500	7	14	21	0	0	139	111	95	5	88	1133	1047	1098	29	88	21	10	48	3	0	0.00%	5	1	80.00%	1	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
21	Wranglers	4	2	2	0	0	0	0	0	19	19	0	2	1	1	0	0	0	0	0	9	10	-1	2	1	1	0	0	0	0	0	10	9	1	4	0.500	19	37	56	0	0	139	111	95	5	210	1133	1047	1098	29	177	40	32	94	13	2	15.38%	10	2	80.00%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
	Total	66	34	24	0	1	4	1	2	347	321	26	32	16	12	0	0	3	0	1	167	156	11	34	18	12	0	1	1	1	1	180	165	15	78	0.591	347	664	1011	0	0	139	111	95	5	3294	1133	1047	1098	29	2943	801	378	1520	148	31	20.95%	159	42	73.58%	3	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
	_Since Last GM Reset	66	34	24	0	1	4	1	2	347	321	26	32	16	12	0	0	3	0	1	167	156	11	34	18	12	0	1	1	1	1	180	165	15	78	0.591	347	664	1011	0	0	139	111	95	5	3294	1133	1047	1098	29	2943	801	378	1520	148	31	20.95%	159	42	73.58%	3	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
	_Vs Conference	42	22	15	0	1	3	0	1	222	208	14	17	10	5	0	0	2	0	0	94	84	10	25	12	10	0	1	1	0	1	128	124	4	50	0.595	222	425	647	0	0	139	111	95	5	2191	1133	1047	1098	29	2012	560	234	1029	88	18	20.45%	100	22	78.00%	1	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373
	_Vs Division	25	13	7	0	1	1	0	0	135	126	9	9	6	3	0	0	0	0	0	54	42	12	16	7	4	0	1	1	0	0	81	84	-3	29	0.580	135	255	390	0	0	139	111	95	5	1343	1133	1047	1098	29	1260	344	162	611	50	9	18.00%	64	15	76.56%	0	1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%	1353	939	1823	474	805	373

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
66	78	L1	347	664	1011	3294	2943	801	378	1520	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
66	34	24	1	4	1	2	347	321

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
32	16	12	0	3	0	1	167	156

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
34	18	12	1	1	1	1	180	165

Last 10 Games
W	L	OTW	OTL	SOW	SOL
5	3	0	2	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
148	31	20.95%	159	42	73.58%	3

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
1133	1047	1098	29	139	111	95	5

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
1293	2809	46.03%	1193	2589	46.08%	589	1313	44.86%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
1353	939	1823	474	805	373

Last Played Games Show or Hide Columns

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	SO	Link
3	9	Nordiks	5	Ice Bats	3	W			BoxScore
5	18	Broncos	7	Nordiks	4	L			BoxScore
8	31	Lions	4	Nordiks	6	W			BoxScore
9	40	Nordiks	6	Ice Bats	1	W			BoxScore
12	53	Nordiks	8	Tomahawks	6	W			BoxScore
13	62	Roadrunners	6	Nordiks	4	L			BoxScore
16	73	Tomahawks	8	Nordiks	6	L			BoxScore
18	86	Nordiks	7	Griffins	5	W			BoxScore
19	92	Nordiks	5	Ice Bats	3	W			BoxScore
21	101	Nordiks	2	Barons	3	L			BoxScore
23	110	Griffins	7	Nordiks	8	W			BoxScore
26	126	Nordiks	7	Lions	8	L	X		BoxScore
28	132	Ice Bats	6	Nordiks	4	L			BoxScore
30	145	Lions	3	Nordiks	7	W			BoxScore
33	160	Nordiks	2	Ice Bats	5	L			BoxScore
35	168	Bruins	5	Nordiks	6	W			BoxScore
37	177	Nordiks	7	Tomahawks	10	L			BoxScore
39	187	Nordiks	5	Bruins	6	L			BoxScore
41	195	Admirals	2	Nordiks	8	W			BoxScore
43	202	Nordiks	9	Lynx	6	W			BoxScore
45	212	Nordiks	7	Fighting Pandas	6	W	X	X	BoxScore
47	219	Americiens	6	Nordiks	3	L			BoxScore
49	234	Nordiks	7	Fighting Pandas	6	W			BoxScore
50	242	Lynx	6	Nordiks	2	L			BoxScore
52	256	Nordiks	5	Barracuda	2	W			BoxScore
53	262	Broncos	7	Nordiks	6	L			BoxScore
56	277	Nordiks	2	Barons	5	L			BoxScore
58	285	Nordiks	7	Marlies	3	W			BoxScore
59	292	Roadrunners	2	Nordiks	7	W			BoxScore
62	306	Wombats	4	Nordiks	6	W			BoxScore
64	318	Nordiks	7	Ice Bats	6	W	X		BoxScore
67	330	Roadrunners	1	Nordiks	4	W			BoxScore
69	345	Nordiks	8	Canucks	4	W			BoxScore
70	351	Barracuda	4	Nordiks	6	W			BoxScore
74	367	Nordiks	5	Barons	6	L	X	X	BoxScore
75	374	Quacken	7	Nordiks	6	L	X		BoxScore
79	394	Nordiks	2	Canucks	6	L			BoxScore
80	398	Quacken	7	Nordiks	3	L			BoxScore
83	416	Bruins	5	Nordiks	9	W			BoxScore
85	428	Nordiks	7	Aces	3	W			BoxScore
87	438	Barracuda	1	Nordiks	5	W			BoxScore
89	448	Nordiks	8	Wranglers	5	W			BoxScore
91	455	Marlies	4	Nordiks	2	L			BoxScore
93	463	Nordiks	1	Wombats	8	L			BoxScore
96	473	Nordiks	2	Aces	6	L			BoxScore
97	482	Fighting Pandas	6	Nordiks	3	L			BoxScore
100	498	Nordiks	4	Firebirds	1	W			BoxScore
101	506	Broncos	4	Nordiks	3	L	X	X	BoxScore
103	516	Nordiks	6	Bruins	3	W			BoxScore
105	527	Canucks	5	Nordiks	6	W			BoxScore
107	540	Nordiks	4	Quacken	6	L			BoxScore
109	549	Wranglers	6	Nordiks	7	W			BoxScore
111	561	Nordiks	4	Tomahawks	6	L			BoxScore
113	571	Ice Bats	5	Nordiks	7	W			BoxScore
115	581	Nordiks	6	Lions	5	W			BoxScore
117	592	Wranglers	4	Nordiks	2	L			BoxScore
119	600	Nordiks	2	Wranglers	4	L			BoxScore
121	611	Tomahawks	2	Nordiks	8	W			BoxScore
123	622	Nordiks	6	Broncos	2	W			BoxScore
124	631	Nordiks	5	Lions	8	L			BoxScore
125	638	Butter Knives	7	Nordiks	6	L	X		BoxScore
129	656	Nordiks	6	Barracuda	4	W			BoxScore
130	660	Tomahawks	4	Nordiks	7	W			BoxScore
133	678	Aces	3	Nordiks	2	L	X		BoxScore
136	694	Nordiks	6	Griffins	4	W			BoxScore
138	700	Griffins	8	Nordiks	4	L			BoxScore
141	716	Nordiks	-	Roadrunners	-
142	723	Firebirds	-	Nordiks	-
146	740	Lions	-	Nordiks	-
148	752	Nordiks	-	Quacken	-
149	764	Nordiks	-	Griffins	-
150	767	Lions	-	Nordiks	-
Trade Deadline --- Trades can’t be done after this day is simulated!
154	786	Canucks	-	Nordiks	-
156	793	Nordiks	-	Americiens	-
158	806	Nordiks	-	Roadrunners	-
160	812	Barons	-	Nordiks	-
162	824	Nordiks	-	Admirals	-
164	833	Ice Bats	-	Nordiks	-
168	852	Aces	-	Nordiks	-
170	862	Nordiks	-	Butter Knives	-
174	880	Griffins	-	Nordiks	-
177	893	Barons	-	Nordiks	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
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	Capacity	Team Popularity
9	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Salaries Cap	Coaches Salaries
961,302$	780,000$	780,000$	500,000$0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
780,000$	570,254$	13	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	42	7,111$	298,662$

Nordiks Players Stat Leaders (Regular Season)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Nordiks Goalies Stat Leaders (Regular Season)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

Nordiks Career Team Stats

Show or Hide Columns

	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

Nordiks Players Stat Leaders (Play-Off)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Nordiks Goalies Stat Leaders (Play-Off)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

	Nordiks
	GP: 66 \| W: 36 \| L: 24 \| OTL: 6 \| P: 78 GF: 347 \| GA: 321 \| PP%: 20.95% \| PK%: 73.58% GM : Kawika \| Morale : 54 \| Team Overall : 57 Next Games #716 vs Roadrunners