Toronto Maple Leafs
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Web Lines Editor

Game Center
	Calgary Wranglers 0-6-2, 2pts	4	FINAL	5	Toronto Marlies 2-6-2, 6pts

Team Stats
L1	Streak	SOW1
0-5-0	Home Record	1-2-1
0-1-2	Home Record	1-4-1
0-6-2	Last 10 Games	2-6-2
1.88	Goals Per Game	2.60
4.13	Goals Against Per Game	4.40
11.11%	Power Play Percentage	18.75%
58.33%	Penalty Kill Percentage	87.50%

Toronto Marlies
2-6-2, 6pts

FINAL

Bridgeport Islanders
3-6-1, 7pts

Team Stats
SOW1	Streak	SOL1
1-2-1	Home Record	2-2-1
1-4-1	Home Record	1-4-0
2-6-2	Last 10 Games	3-6-1
2.60	Goals Per Game	2.00
4.40	Goals Against Per Game	4.60
18.75%	Power Play Percentage	9.52%
87.50%	Penalty Kill Percentage	60.00%

Rockford IceHogs
1-8-1, 3pts

Day 24

Toronto Marlies
2-6-2, 6pts

Team Stats
L1	Streak	SOW1
1-3-0	Home Record	1-2-1
0-5-1	Away Record	1-4-1
1-8-1	Last 10 Games	2-6-2
1.60	Goals Per Game	2.60
4.30	Goals Against Per Game	2.60
0.00%	Power Play Percentage	18.75%
66.67%	Penalty Kill Percentage	87.50%

Toronto Marlies
2-6-2, 6pts

Day 26

Milwaukee Admirals
2-7-0, 4pts

Team Stats
SOW1	Streak	L5
1-2-1	Home Record	1-4-0
1-4-1	Away Record	1-3-0
2-6-2	Last 10 Games	2-7-0
2.60	Goals Per Game	1.22
4.40	Goals Against Per Game	1.22
18.75%	Power Play Percentage	0.00%
87.50%	Penalty Kill Percentage	78.95%

Abbotsford Canucks
7-2-0, 14pts

Day 28

Toronto Marlies
2-6-2, 6pts

Team Stats
W1	Streak	SOW1
4-1-0	Home Record	1-2-1
3-1-0	Away Record	1-4-1
7-2-0	Last 10 Games	2-6-2
3.22	Goals Per Game	2.60
0.78	Goals Against Per Game	2.60
12.50%	Power Play Percentage	18.75%
90.00%	Penalty Kill Percentage	87.50%

Team Leaders

Team Stats
Goals For 26 2.60 GFG	Shots For 280 28.00 Avg	Power Play Percentage 18.8% 3 GF	Offensive Zone Start 32.2%
Goals Against 44 4.40 GAA	Shots Against 492 49.20 Avg	Penalty Kill Percentage 87.5%% 1 GA	Defensive Zone Start 45.4%

Team Info
	General Manager	Nick Fournier
	Division	NORTHEAST
	Conference	Conference 1
	Captain	Cody Bass
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,921
	Season Tickets	300
Roster Info
	Pro Team	15
	Farm Team	20
	Contract Limit	35 / 250
	Prospects	35

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	L	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	Wayne Simmonds		X	X		100.00	40	35	40	63	35	80	90	40	20	40	40	40	30	88	76	11	50	0	0	36	3	1,999,999$
2	Derek Stepan	X		X		100.00	40	35	40	64	35	80	90	40	20	40	40	40	30	84	73	11	47	0	0	34	3	750,000$
3	Jonathan Dahlen		X			100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	26	2	750,000$
4	Jacob Perreault (R)			X		100.00	62	42	68	60	71	76	77	59	60	58	57	56	59	62	64	80	50	0	0	22	1	925,000$
5	Wade Allison			X		100.00	78	60	77	62	81	84	82	59	57	56	61	63	59	68	69	61	41	0	0	26	2	925,000$
6	Logan Hutsko (R)			X		100.00	40	35	40	61	35	80	90	40	20	40	40	40	30	64	66	11	50	0	0	25	1	892,500$
7	Filip Cederqvist (R)		X			100.00	74	40	84	58	83	78	80	57	54	56	56	59	55	64	66	53	50	0	0	24	1	850,000$
8	Josh Doan (R)			X		100.00	64	37	88	69	74	86	76	67	60	66	72	63	69	62	68	76	43	0	0	22	1	925,000$
9	Logan Morrison (R)	X				100.00	61	36	95	63	70	82	72	62	71	60	58	66	63	62	64	53	50	0	0	22	1	870,000$
10	Stephen Halliday (R)	X				100.00	77	39	94	62	86	71	60	58	74	65	50	61	54	63	64	62	50	0	0	22	1	950,000$
11	Viktor Neuchev (R)		X			100.00	64	37	95	58	73	79	70	56	62	59	57	55	58	61	63	66	50	0	0	20	1	870,000$
12	Danny DeKeyser				X	100.00	40	35	40	62	35	80	90	40	20	40	40	40	30	76	72	11	47	0	0	34	3	999,999$
13	Nikita Novikov (R)				X	100.00	74	41	79	59	83	63	71	56	30	60	53	58	46	61	63	49	50	0	0	21	1	867,500$
Scratches

	TEAM AVERAGE					100.00	58	39	68	60	61	78	80	52	44	52	51	52	47	65	64	43	48	0

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	Kevin Poulin	100.00	40	90	80	50	40	40	40	40	40	40	40	73	87	11	50	0	0	34	3	750,000$
2	Landon Bow	100.00	40	90	80	50	40	40	40	40	40	40	40	25	25	11	50	0	0	29	1	700,000$
Scratches

	TEAM AVERAGE	100.00	40	90	80	50	40	40	40	40	40	40	40	49	56	11	50	0

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary

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 Remaining	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
Danny DeKeyser	Toronto Marlies (TOR)	D	34	1990-03-07	USA	No	183 Lbs	6 ft3	No	No	N/A		No	No	3		False	False	Pro & Farm	999,999$	881,442$	0$	0$	No	999,999$	999,999$	-	-	-	-	-	-	-	600,000$	600,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Derek Stepan	Toronto Marlies (TOR)	C/RW	34	1990-06-18	USA	No	196 Lbs	5 ft11	No	No	N/A		No	No	3		False	False	Pro & Farm	750,000$	661,082$	0$	0$	No	750,000$	750,000$	-	-	-	-	-	-	-	750,000$	750,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Filip Cederqvist	Toronto Marlies (TOR)	LW	24	2000-08-23	SWE	Yes	196 Lbs	6 ft3	No	No	N/A		No	No	1		False	False	Pro & Farm	850,000$	749,227$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jacob Perreault	Toronto Marlies (TOR)	RW	22	2002-04-15	USA	Yes	192 Lbs	5 ft11	No	No	Trade	2024-01-02	No	No	1		False	False	Pro & Farm	925,000$	815,335$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jonathan Dahlen	Toronto Marlies (TOR)	LW	26	1997-12-20	SWE	No	180 Lbs	5 ft11	No	No	N/A		No	No	2		False	False	Pro & Farm	750,000$	661,082$	0$	0$	No	750,000$	-	-	-	-	-	-	-	-	702,020$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Josh Doan	Toronto Marlies (TOR)	RW	22	2002-02-01	USA	Yes	183 Lbs	6 ft1	No	No	N/A		No	No	1	2024-05-19	False	False	Pro & Farm	925,000$	815,335$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Kevin Poulin	Toronto Marlies (TOR)	G	34	1990-04-12	CAN	No	206 Lbs	6 ft2	No	No	N/A		No	No	3		False	False	Pro & Farm	750,000$	661,082$	0$	0$	No	750,000$	750,000$	-	-	-	-	-	-	-	750,000$	750,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Landon Bow	Toronto Marlies (TOR)	G	29	1995-08-24	CAN	No	220 Lbs	6 ft5	No	No	N/A		No	No	1		False	False	Pro & Farm	700,000$	617,010$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Logan Hutsko	Toronto Marlies (TOR)	RW	25	1999-01-11	USA	Yes	172 Lbs	5 ft11	No	No	N/A		No	No	1		False	False	Pro & Farm	892,500$	786,688$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Logan Morrison	Toronto Marlies (TOR)	C	22	2002-07-09	CAN	Yes	179 Lbs	6 ft0	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	870,000$	766,856$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Nikita Novikov	Toronto Marlies (TOR)	D	21	2003-07-25	RUS	Yes	196 Lbs	6 ft3	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	867,500$	764,652$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Stephen Halliday	Toronto Marlies (TOR)	C	22	2002-07-02	CAN	Yes	213 Lbs	6 ft3	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	950,000$	837,371$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Viktor Neuchev	Toronto Marlies (TOR)	LW	20	2003-10-25	RUS	Yes	165 Lbs	6 ft2	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	870,000$	766,856$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Wade Allison	Toronto Marlies (TOR)	RW	26	1997-10-14	CAN	No	205 Lbs	6 ft2	No	No	N/A		No	No	2		False	False	Pro & Farm	925,000$	815,335$	0$	0$	No	925,000$	-	-	-	-	-	-	-	-	925,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Wayne Simmonds	Toronto Marlies (TOR)	LW/RW	36	1988-07-27	CAN	No	184 Lbs	6 ft2	No	No	N/A		No	No	3		False	False	Pro & Farm	1,999,999$	1,762,886$	0$	0$	No	1,999,999$	1,999,999$	-	-	-	-	-	-	-	600,000$	600,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
15	26.47	191 Lbs	6 ft1	1.67	935,000$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				38	1	2	2
2				30	1	2	2
3				22	1	2	2
4				10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			38	1	2	2
2			30	1	2	2
3			22	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			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			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			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			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
, ,	,

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	Bakersfield Condors	1	0	0	0	0	0	0	1	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	4	5	-1	1	0.500	4	8	12	0	0	13	7	4	3	34	93	105	75	14	60	23	0	27	1	0	0.00%	0	0	0%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
2	Belleville Senators	1	0	0	0	0	1	0	0	3	4	-1	1	0	0	0	0	1	0	0	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	3	6	9	0	0	13	7	4	3	27	93	105	75	14	59	14	2	31	2	0	0.00%	1	0	100.00%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
3	Bridgeport Islanders	1	0	0	0	0	0	1	0	5	4	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	5	4	1	2	1.000	5	8	13	0	0	13	7	4	3	36	93	105	75	14	45	18	4	42	1	1	100.00%	2	0	100.00%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
4	Calgary Wranglers	1	0	0	0	1	0	0	0	5	4	1	1	0	0	0	1	0	0	0	5	4	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	5	10	15	0	0	13	7	4	3	45	93	105	75	14	56	13	0	24	1	1	100.00%	0	0	0%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
5	Colorado Eagles	2	0	2	0	0	0	0	0	3	8	-5	0	0	0	0	0	0	0	0	0	0	0	2	0	2	0	0	0	0	0	3	8	-5	0	0.000	3	6	9	0	0	13	7	4	3	50	93	105	75	14	95	35	0	37	5	1	20.00%	1	0	100.00%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
6	Providence Bruins	3	0	3	0	0	0	0	0	6	14	-8	1	0	1	0	0	0	0	0	4	5	-1	2	0	2	0	0	0	0	0	2	9	-7	0	0.000	6	11	17	0	0	13	7	4	3	80	93	105	75	14	148	37	8	61	4	0	0.00%	3	0	100.00%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
7	Rochester Americans	1	0	1	0	0	0	0	0	0	5	-5	1	0	1	0	0	0	0	0	0	5	-5	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	13	7	4	3	8	93	105	75	14	29	8	2	9	2	0	0.00%	1	1	0.00%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
	Total	10	0	6	0	1	1	1	1	26	44	-18	4	0	2	0	1	1	0	0	12	18	-6	6	0	4	0	0	0	1	1	14	26	-12	6	0.300	26	49	75	0	0	13	7	4	3	280	93	105	75	14	492	148	16	231	16	3	18.75%	8	1	87.50%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
	_Since Last GM Reset	10	0	6	0	1	1	1	1	26	44	-18	4	0	2	0	1	1	0	0	12	18	-6	6	0	4	0	0	0	1	1	14	26	-12	6	0.300	26	49	75	0	0	13	7	4	3	280	93	105	75	14	492	148	16	231	16	3	18.75%	8	1	87.50%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
	_Vs Conference	6	0	4	0	0	1	1	0	14	27	-13	3	0	2	0	0	1	0	0	7	14	-7	3	0	2	0	0	0	1	0	7	13	-6	3	0.250	14	25	39	0	0	13	7	4	3	151	93	105	75	14	281	77	16	143	9	1	11.11%	7	1	85.71%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51
	_Vs Division	1	0	4	0	0	1	0	0	5	4	1	0	0	2	0	0	1	0	0	0	0	0	1	0	2	0	0	0	0	0	5	4	1	1	0.500	5	8	13	0	0	13	7	4	3	36	93	105	75	14	45	18	4	42	1	1	100.00%	2	0	100.00%	0	93	218	42.66%	112	307	36.48%	67	151	44.37%	174	119	323	70	117	51

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
10	6	SOW1	26	49	75	280	492	148	16	231	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
10	0	6	1	1	1	1	26	44

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	0	2	1	1	0	0	12	18

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
6	0	4	0	0	1	1	14	26

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

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
16	3	18.75%	8	1	87.50%	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
93	105	75	14	13	7	4	3

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
93	218	42.66%	112	307	36.48%	67	151	44.37%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
174	119	323	70	117	51

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
2	9	Providence Bruins	5	Toronto Marlies	4	L			BoxScore
6	24	Toronto Marlies	4	Bakersfield Condors	5	L	X	X	BoxScore
7	33	Toronto Marlies	2	Colorado Eagles	4	L			BoxScore
10	54	Belleville Senators	4	Toronto Marlies	3	L	X		BoxScore
12	66	Toronto Marlies	1	Providence Bruins	5	L			BoxScore
14	83	Rochester Americans	5	Toronto Marlies	0	L			BoxScore
15	91	Toronto Marlies	1	Providence Bruins	4	L			BoxScore
17	108	Toronto Marlies	1	Colorado Eagles	4	L			BoxScore
20	123	Calgary Wranglers	4	Toronto Marlies	5	W	X		BoxScore
23	145	Toronto Marlies	5	Bridgeport Islanders	4	W	X	X	BoxScore
24	155	Rockford IceHogs	-	Toronto Marlies	-
26	170	Toronto Marlies	-	Milwaukee Admirals	-
28	184	Abbotsford Canucks	-	Toronto Marlies	-
31	208	Utica Comets	-	Toronto Marlies	-
34	228	Toronto Marlies	-	Laval Rocket	-
37	243	Toronto Marlies	-	Utica Comets	-
38	252	Rochester Americans	-	Toronto Marlies	-
40	276	LeHigh Valley Phantoms	-	Toronto Marlies	-
43	295	Toronto Marlies	-	Laval Rocket	-
45	310	Charlotte Checkers	-	Toronto Marlies	-
46	323	Toronto Marlies	-	Coachella Valley Firebirds	-
48	342	Chicago Wolves	-	Toronto Marlies	-
52	364	Hershey Bears	-	Toronto Marlies	-
54	371	Toronto Marlies	-	Abbotsford Canucks	-
58	390	Toronto Marlies	-	Manitoba Moose	-
60	404	Toronto Marlies	-	Rockford IceHogs	-
61	413	Calgary Wranglers	-	Toronto Marlies	-
64	432	Springfield Thunderbirds	-	Toronto Marlies	-
68	461	Toronto Marlies	-	Wilkes-Barre/Scranton Penguins	-
70	472	San Diego Gulls	-	Toronto Marlies	-
72	484	Toronto Marlies	-	Milwaukee Admirals	-
74	499	Toronto Marlies	-	Providence Bruins	-
75	507	Manitoba Moose	-	Toronto Marlies	-
78	526	Toronto Marlies	-	Bridgeport Islanders	-
80	538	Iowa Wild	-	Toronto Marlies	-
85	548	Toronto Marlies	-	Manitoba Moose	-
88	570	Abbotsford Canucks	-	Toronto Marlies	-
90	588	Toronto Marlies	-	Belleville Senators	-
91	599	Tucson Roadrunners	-	Toronto Marlies	-
94	614	Toronto Marlies	-	Grand Rapids Griffins	-
97	633	Milwaukee Admirals	-	Toronto Marlies	-
99	650	Toronto Marlies	-	Bakersfield Condors	-
100	662	Bakersfield Condors	-	Toronto Marlies	-
103	683	Toronto Marlies	-	Ontario Reign	-
104	696	Laval Rocket	-	Toronto Marlies	-
106	712	Toronto Marlies	-	Belleville Senators	-
108	726	Toronto Marlies	-	San Jose Barracuda	-
110	741	Texas Stars	-	Toronto Marlies	-
113	760	Cleveland Monsters	-	Toronto Marlies	-
115	778	Toronto Marlies	-	Hartford Wolf Pack	-
117	791	Hartford Wolf Pack	-	Toronto Marlies	-
122	823	Syracuse Crunch	-	Toronto Marlies	-
126	845	Bridgeport Islanders	-	Toronto Marlies	-
127	857	Toronto Marlies	-	Charlotte Checkers	-
142	876	San Jose Barracuda	-	Toronto Marlies	-
143	889	Toronto Marlies	-	Chicago Wolves	-
147	911	Wilkes-Barre/Scranton Penguins	-	Toronto Marlies	-
149	924	Toronto Marlies	-	Colorado Eagles	-
151	943	Belleville Senators	-	Toronto Marlies	-
153	959	Toronto Marlies	-	LeHigh Valley Phantoms	-
156	971	Belleville Senators	-	Toronto Marlies	-
Trade Deadline --- Trades can’t be done after this day is simulated!
159	991	Toronto Marlies	-	Hershey Bears	-
162	1006	Laval Rocket	-	Toronto Marlies	-
167	1024	Toronto Marlies	-	Henderson Silver Knights	-
168	1031	Toronto Marlies	-	Syracuse Crunch	-
170	1038	Toronto Marlies	-	Calgary Wranglers	-
172	1051	Rockford IceHogs	-	Toronto Marlies	-
175	1076	Utica Comets	-	Toronto Marlies	-
180	1096	Providence Bruins	-	Toronto Marlies	-
183	1112	Toronto Marlies	-	Rochester Americans	-
190	1133	Providence Bruins	-	Toronto Marlies	-
192	1146	Toronto Marlies	-	Rochester Americans	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	15
Attendance	7,758	3,925
Attendance PCT	96.98%	98.13%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
32	2921 - 97.36%	97,470$	389,878$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Salaries Cap	Coaches Salaries
167,416$	1,402,500$	1,015,202$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
7,229$	167,416$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
3,119,024$	171	7,229$	1,236,159$

Toronto Marlies 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

Toronto Marlies 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

Toronto Marlies 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

Toronto Marlies 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

Toronto Marlies 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

	Toronto Marlies
	GP: 10 \| W: 2 \| L: 6 \| OTL: 2 \| P: 6 GF: 26 \| GA: 44 \| PP%: 18.75% \| PK%: 87.50% GM : Nick Fournier \| Morale : 50 \| Team Overall : N/A Next Games #155 vs Rockford IceHogs