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

Game Center
	Utica Comets 9-1-4, 22pts	4	FINAL	5	Toronto Marlies 8-5-1, 17pts

Team Stats
W1	Streak	W2
6-0-1	Home Record	5-1-1
3-1-3	Home Record	3-4-0
7-0-3	Last 10 Games	6-3-1
4.21	Goals Per Game	3.29
3.64	Goals Against Per Game	3.14
21.82%	Power Play Percentage	18.37%
89.36%	Penalty Kill Percentage	77.27%

Rochester Americans
6-6-2, 14pts

FINAL

Toronto Marlies
8-5-1, 17pts

Team Stats
L2	Streak	W2
4-3-0	Home Record	5-1-1
2-3-2	Home Record	3-4-0
5-3-2	Last 10 Games	6-3-1
4.43	Goals Per Game	3.29
4.50	Goals Against Per Game	3.14
37.74%	Power Play Percentage	18.37%
68.75%	Penalty Kill Percentage	77.27%

Team Leaders
	Goals Dylan Gambrell 7	Assists William Lagesson 10

	Points William Lagesson 13	Plus/Minus Easton Cowan 7

	Wins Matt Murray 8	Save Percentage Matt Murray 0.921

Team Stats
Goals For 46 3.29 GFG	Shots For 568 40.57 Avg	Power Play Percentage 18.4% 9 GF	Offensive Zone Start 41.8%
Goals Against 44 3.14 GAA	Shots Against 541 38.64 Avg	Penalty Kill Percentage 77.3%% 10 GA	Defensive Zone Start 41.2%

Team Info
	General Manager	Flavio
	Coach	John Gruden
	Division	North Division
	Conference	Eastern Conference
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,724
	Season Tickets	0
Roster Info
	Pro Team	34
	Farm Team	18
	Contract Limit	52 / 70
	Prospects	11

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 Average
1	Dylan Gambrell	0	X				100.00	77	40	86	66	73	63	83	58	71	69	60	77	54	61	58	0	47	67	0	28	1	775,000$
2	Kieffer Bellows	0		X	X		100.00	71	51	83	64	73	60	64	58	52	66	61	66	55	57	57	0	47	64	0	26	1	775,000$
3	Logan Shaw	0	X				100.00	55	57	65	63	69	66	64	63	55	61	61	59	58	70	62	0	47	62	0	32	1	775,000$
4	Alex Steeves	0	X				100.00	55	58	66	64	63	66	64	61	55	59	61	59	54	58	55	0	47	61	0	25	1	775,000$
5	Bobby McMann	0	X				100.00	58	56	69	62	70	63	60	58	54	58	60	62	54	62	58	0	47	61	0	28	1	775,000$
6	Joseph Blandisi	0	X				100.00	56	57	64	59	61	65	63	59	70	58	59	62	54	67	60	0	47	60	0	30	1	775,000$
7	Kyle Clifford	0		X	X		100.00	61	62	70	60	74	61	65	57	54	59	57	61	54	75	62	0	47	60	0	33	1	775,000$
8	Fraser Minten	0	X	X	X		100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	60	60	0	47	60	0	20	3	1,300,000$
9	Easton Cowan (R)	0	X	X	X		100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	60	60	0	47	60	0	19	1	775,000$
10	Braeden Kressler (R)	0	X				100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	60	60	0	47	60	0	21	1	775,000$
11	Matthew Knies	0		X	X		100.00	54	38	67	52	79	67	54	52	48	58	50	57	51	51	55	0	47	56	0	22	1	1,100,000$
12	Ryan Tverberg	0	X				100.00	51	51	65	51	59	52	52	51	52	51	51	57	51	51	49	0	47	52	0	22	1	775,000$
13	Jake Muzzin	0				X	100.00	70	40	76	66	90	77	72	57	40	64	55	79	54	77	65	0	44	68	0	35	1	775,000$
14	Simon Benoit	0				X	100.00	81	67	82	57	77	78	82	57	40	69	59	83	54	57	58	0	45	68	0	26	1	775,000$
15	Conor Timmins	0				X	100.00	66	42	83	66	76	70	61	59	40	73	56	75	54	56	57	0	47	66	0	26	1	775,000$
16	William Lagesson	0				X	100.00	59	62	72	60	71	66	65	58	40	61	57	69	54	62	58	0	48	62	0	28	1	775,000$
17	Maxime Lajoie	0				X	100.00	55	56	66	60	65	66	64	60	40	60	57	61	54	62	57	0	47	60	0	27	1	775,000$
18	Noah Chadwick (R)	0				X	100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	60	60	0	47	60	0	19	1	775,000$
Scratches
1	Josiah Slavin	0	X	X	X		100.00	54	56	67	57	64	66	64	57	55	56	58	59	54	60	56	0	26	59	0	26	1	775,000$
2	Max Ellis	0		X	X		100.00	55	55	67	57	56	60	60	56	55	55	57	61	54	56	54	0	26	57	0	24	1	775,000$
3	Zach Solow	0		X	X		100.00	56	54	66	55	57	58	58	55	55	54	55	60	54	60	56	0	26	56	0	26	1	775,000$
4	Dmitri Ovchinnikov	0	X	X	X		100.00	54	54	68	54	56	55	54	54	55	54	54	61	54	53	52	0	26	55	0	22	1	775,000$
5	David Farrance	0				X	100.00	55	54	67	58	62	63	61	57	40	57	55	60	55	58	55	0	26	58	0	25	1	775,000$
6	William Villeneuve	0				X	100.00	55	54	62	56	60	61	60	57	44	57	55	60	54	54	53	0	26	57	0	22	1	775,000$
7	Matteo Pietroniro	0				X	100.00	56	55	65	55	62	61	60	54	40	54	54	60	54	60	56	0	26	57	0	26	1	775,000$
8	Mikko Kokkonen	0				X	100.00	55	54	62	55	62	59	58	55	44	55	54	58	54	55	53	0	26	56	0	23	1	775,000$
9	Marshall Rifai	0				X	100.00	54	52	57	55	60	64	63	54	38	53	53	58	51	58	53	0	26	56	0	26	1	775,000$
10	Tommy Miller	0				X	100.00	51	51	65	53	61	59	57	52	38	52	51	58	51	56	52	0	26	54	0	25	1	775,000$
11	Topi Niemela	0				X	100.00	51	51	65	52	54	52	52	51	38	51	51	57	51	51	49	0	26	53	0	22	1	775,000$

	TEAM AVERAGE						100.00	59	54	68	59	65	63	62	57	50	59	57	63	55	60	57	0	39	59

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 Average
1	Matt Murray	0	100.00	76	70	71	89	79	73	81	70	82	72	83	68	68	0	50	76	0	30	2	775,000$
2	Keith Petruzzelli	0	100.00	67	66	67	86	66	65	67	66	65	67	66	63	69	0	50	67	0	25	1	775,000$
Scratches
1	Joseph Woll	0	100.00	64	59	59	72	71	57	71	62	65	57	58	59	55	0	33	64	0	26	1	1,000,000$
2	Artur Akhtyamov (R)	0	100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	0	33	60	0	23	1	775,000$
3	Dennis Hildeby	0	100.00	51	51	51	73	61	51	62	51	55	52	62	51	48	0	33	55	0	23	1	775,000$

	TEAM AVERAGE		100.00	64	61	62	76	67	61	68	62	65	62	66	60	60	0	40	64

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
John Gruden	68	75	64	68	77	72	73	USA	52	7	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	EG	P/20	PSG	PSS	FL	FT	S1	S2	S3
1	William Lagesson	Toronto Marlies (TOR)	D	14	3	10	13	-4	31	25	18	14	28	8	22	10.71%	20	317	22.69	1	1	2	11	38	0	1	1	1	33	0	2	0%	0	0	0.82	0	0	1	4	0	1	0
2	Conor Timmins	Toronto Marlies (TOR)	D	14	2	10	12	-4	2	0	32	31	35	7	27	5.71%	28	332	23.73	1	2	3	24	40	0	0	0	0	33	0	0	0%	0	0	0.72	0	0	0	0	1	1	1
3	Dylan Gambrell	Toronto Marlies (TOR)	C	14	7	4	11	0	2	0	44	54	67	20	40	10.45%	8	295	21.09	2	1	3	10	39	0	0	0	1	34	1	0	54.70%	457	1	0.75	0	3	0	0	2	0	1
4	Kieffer Bellows	Toronto Marlies (TOR)	LW/RW	14	2	7	9	0	6	0	23	22	54	9	33	3.70%	5	294	21.06	0	2	2	10	39	0	0	0	2	34	1	0	68.18%	22	0	0.61	2	3	0	0	1	0	1
5	Kyle Clifford	Toronto Marlies (TOR)	LW/RW	14	6	3	9	-4	4	0	17	11	44	9	30	13.64%	0	234	16.73	2	1	3	10	41	0	0	0	0	2	1	2	46.67%	15	0	0.77	0	0	0	0	1	1	0
6	Logan Shaw	Toronto Marlies (TOR)	C	14	3	6	9	-3	2	0	23	29	53	16	35	5.66%	5	273	19.52	0	3	3	10	40	0	0	0	4	35	0	0	43.35%	346	0	0.66	0	0	0	0	1	0	0
7	Maxime Lajoie	Toronto Marlies (TOR)	D	14	1	8	9	5	4	0	19	22	19	14	19	5.26%	23	292	20.88	0	2	2	8	39	0	0	0	1	36	1	0	0%	0	0	0.62	0	0	0	0	0	1	0
8	Easton Cowan	Toronto Marlies (TOR)	C/LW/RW	14	4	5	9	7	10	0	22	6	39	8	16	10.26%	2	164	11.75	0	0	0	0	0	0	0	0	0	0	0	0	25.00%	12	0	1.09	0	0	0	0	1	0	2
9	Alex Steeves	Toronto Marlies (TOR)	C	14	3	5	8	7	2	0	17	27	43	13	21	6.98%	2	247	17.70	0	1	1	11	40	1	0	1	2	37	0	0	44.09%	313	0	0.65	0	0	0	0	0	1	0
10	Braeden Kressler	Toronto Marlies (TOR)	C	14	1	6	7	-4	10	0	32	11	23	9	20	4.35%	2	231	16.53	1	3	4	11	40	0	0	0	0	0	0	0	31.58%	19	0	0.60	0	0	0	0	0	0	0
11	Noah Chadwick	Toronto Marlies (TOR)	D	14	0	7	7	5	18	0	32	11	28	8	15	0%	18	289	20.68	0	1	1	12	39	0	0	0	0	30	0	0	0%	0	0	0.48	0	0	0	0	0	0	1
12	Bobby McMann	Toronto Marlies (TOR)	C	14	3	2	5	-2	2	0	7	9	19	5	12	15.79%	2	218	15.59	0	0	0	0	0	0	0	0	0	3	1	0	50.00%	22	0	0.46	0	2	0	0	0	0	1
13	Simon Benoit	Toronto Marlies (TOR)	D	14	2	3	5	1	15	5	25	27	24	4	15	8.33%	23	220	15.77	0	0	0	0	1	0	0	0	0	2	0	0	0%	0	0	0.45	0	0	0	1	1	1	0
14	Fraser Minten	Toronto Marlies (TOR)	C/LW/RW	14	2	3	5	7	4	0	18	12	30	10	31	6.67%	4	172	12.32	0	0	0	0	2	0	0	0	0	0	0	0	42.86%	21	0	0.58	0	0	0	0	0	1	1
15	Jake Muzzin	Toronto Marlies (TOR)	D	14	2	2	4	1	4	0	17	19	21	7	13	9.52%	17	241	17.24	2	0	2	3	4	0	0	0	1	13	0	0	0%	0	0	0.33	0	0	0	0	0	0	0
16	Joseph Blandisi	Toronto Marlies (TOR)	C	14	1	2	3	-1	0	0	14	22	25	6	21	4.00%	1	115	8.28	0	0	0	1	2	0	0	0	0	0	0	0	56.35%	126	0	0.52	0	0	0	0	0	0	0
17	Matthew Knies	Toronto Marlies (TOR)	LW/RW	14	1	0	1	0	2	0	9	1	13	3	9	7.69%	1	112	8.05	0	0	0	0	0	0	0	0	0	0	1	0	50.00%	4	0	0.18	0	0	0	0	0	0	0
18	Ryan Tverberg	Toronto Marlies (TOR)	C	14	1	0	1	-1	0	0	2	4	3	3	6	33.33%	0	111	7.95	0	0	0	0	0	0	0	0	0	0	0	0	50.00%	8	0	0.18	0	0	0	0	0	0	0
Team Total or Average				252	44	83	127	10	118	30	371	332	568	159	385	7.75%	161	4165	16.53	9	17	26	121	411	1	1	2	12	298	6	4	48.79%	1365	1	0.61	2	8	1	5	8	7	8

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
1	Matt Murray	Toronto Marlies (TOR)	14	8	5	1	0.921	3.02	854	2	0	43	541	0	0	0	0.889	9	14	0	1	0	0
Team Total or Average			14	8	5	1	0.921	3.02	854	2	0	43	541	0	0	0		9	14	0	1	0	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

Player Name	Team Name	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Acquired By	Force Waivers	Waiver Possible	Contract	Force UFA	Emergency Recall	Type	Current Salary	Salary Average	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	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	Toronto Marlies (TOR)	C	25	10.12.1999	No	89 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Artur Akhtyamov	Toronto Marlies (TOR)	G	23	31.10.2001	Yes	76 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Bobby McMann	Toronto Marlies (TOR)	C	28	15.06.1996	No	95 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Braeden Kressler	Toronto Marlies (TOR)	C	21	05.01.2003	Yes	79 Kg	175 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Conor Timmins	Toronto Marlies (TOR)	D	26	18.09.1998	No	92 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
David Farrance	Toronto Marlies (TOR)	D	25	23.06.1999	No	86 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Dennis Hildeby	Toronto Marlies (TOR)	G	23	19.08.2001	No	106 Kg	198 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Dmitri Ovchinnikov	Toronto Marlies (TOR)	C/LW/RW	22	19.08.2002	No	74 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Dylan Gambrell	Toronto Marlies (TOR)	C	28	26.08.1996	No	84 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Easton Cowan	Toronto Marlies (TOR)	C/LW/RW	19	20.05.2005	Yes	84 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Fraser Minten	Toronto Marlies (TOR)	C/LW/RW	20	05.07.2004 06:31:35	No	87 Kg	188 CM	No	No	N/A	No	No	3	False	False	Pro & Farm	1,300,000$	1,300,000$	0$	0$	No	1,300,000$	1,300,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-
Jake Muzzin	Toronto Marlies (TOR)	D	35	21.02.1989	No	103 Kg	191 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Joseph Blandisi	Toronto Marlies (TOR)	C	30	18.07.1994	No	83 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Joseph Woll	Toronto Marlies (TOR)	G	26	12.07.1998	No	92 Kg	191 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	1,000,000$	1,000,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Josiah Slavin	Toronto Marlies (TOR)	C/LW/RW	26	31.12.1998	No	86 Kg	191 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Keith Petruzzelli	Toronto Marlies (TOR)	G	25	09.02.1999	No	84 Kg	196 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Kieffer Bellows	Toronto Marlies (TOR)	LW/RW	26	10.06.1998	No	89 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Kyle Clifford	Toronto Marlies (TOR)	LW/RW	33	13.01.1991	No	99 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Logan Shaw	Toronto Marlies (TOR)	C	32	05.10.1992	No	94 Kg	193 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Marshall Rifai	Toronto Marlies (TOR)	D	26	16.03.1998	No	86 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Matt Murray	Toronto Marlies (TOR)	G	30	25.05.1994	No	92 Kg	196 CM	No	No	N/A	No	No	2	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Matteo Pietroniro	Toronto Marlies (TOR)	D	26	20.10.1998	No	84 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Matthew Knies	Toronto Marlies (TOR)	LW/RW	22	17.10.2002	No	95 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	1,100,000$	1,100,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Max Ellis	Toronto Marlies (TOR)	LW/RW	24	18.01.2000	No	78 Kg	175 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Maxime Lajoie	Toronto Marlies (TOR)	D	27	05.11.1997	No	89 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Mikko Kokkonen	Toronto Marlies (TOR)	D	23	18.01.2001	No	91 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Noah Chadwick	Toronto Marlies (TOR)	D	19	10.05.2005	Yes	91 Kg	193 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Ryan Tverberg	Toronto Marlies (TOR)	C	22	30.01.2002	No	86 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Simon Benoit	Toronto Marlies (TOR)	D	26	19.09.1998	No	92 Kg	191 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Tommy Miller	Toronto Marlies (TOR)	D	25	06.03.1999	No	88 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Topi Niemela	Toronto Marlies (TOR)	D	22	25.03.2002	No	77 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
William Lagesson	Toronto Marlies (TOR)	D	28	22.02.1996	No	94 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
William Villeneuve	Toronto Marlies (TOR)	D	22	20.03.2002	No	83 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Zach Solow	Toronto Marlies (TOR)	LW/RW	26	06.11.1998	No	80 Kg	175 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
34	25.32	88 Kg	185 CM	1.09	806,618$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Kieffer Bellows	Dylan Gambrell	Bobby McMann	33	1	2	2
2	Braeden Kressler	Logan Shaw	Kyle Clifford	30	1	2	2
3	Fraser Minten	Alex Steeves	Easton Cowan	25	1	2	2
4	Matthew Knies	Joseph Blandisi	Ryan Tverberg	12	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Conor Timmins	William Lagesson	33	1	2	2
2	Noah Chadwick	Maxime Lajoie	30	1	2	2
3	Jake Muzzin	Simon Benoit	25	1	2	2
4	Conor Timmins	William Lagesson	12	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Kieffer Bellows	Dylan Gambrell	Alex Steeves	50	1	2	2
2	Braeden Kressler	Logan Shaw	Kyle Clifford	50	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Conor Timmins	William Lagesson	50	1	2	2
2	Noah Chadwick	Maxime Lajoie	50	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Dylan Gambrell	Kieffer Bellows	50	1	2	2
2	Alex Steeves	Logan Shaw	50	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Conor Timmins	William Lagesson	50	1	2	2
2	Noah Chadwick	Maxime Lajoie	50	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Dylan Gambrell	50	1	2	2	Conor Timmins	William Lagesson	50	1	2	2
2	Kieffer Bellows	50	1	2	2	Noah Chadwick	Maxime Lajoie	50	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Dylan Gambrell	Kieffer Bellows	50	1	2	2
2	Alex Steeves	Logan Shaw	50	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Conor Timmins	William Lagesson	50	1	2	2
2	Noah Chadwick	Maxime Lajoie	50	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Kieffer Bellows	Dylan Gambrell	Logan Shaw	Conor Timmins	William Lagesson

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Kieffer Bellows	Dylan Gambrell	Logan Shaw	Conor Timmins	William Lagesson

Extra Forwards
Normal	PowerPlay	Penalty Kill
Joseph Blandisi, Braeden Kressler, Alex Steeves	Joseph Blandisi, Braeden Kressler	Alex Steeves

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Conor Timmins, William Lagesson, Noah Chadwick	Conor Timmins	William Lagesson, Noah Chadwick

Penalty Shots
Dylan Gambrell, Kieffer Bellows, Bobby McMann, Logan Shaw, Alex Steeves

Goalie
#1 : Matt Murray, #2 : Keith Petruzzelli

Custom OT Lines Forwards
Dylan Gambrell, Kieffer Bellows, Fraser Minten, Logan Shaw, Alex Steeves, Bobby McMann, Bobby McMann, Easton Cowan, Kyle Clifford, Joseph Blandisi, Braeden Kressler

Custom OT Lines Defensemen
Conor Timmins, William Lagesson, Noah Chadwick, Maxime Lajoie, Simon Benoit

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	Belleville Senators	2	1	1	0	0	0	0	0	7	7	0	1	1	0	0	0	0	0	0	5	3	2	1	0	1	0	0	0	0	0	2	4	-2	2	0.500	7	13	20	0	0	15	18	11	2	89	178	215	162	21	83	25	18	36	10	2	20.00%	9	2	77.78%	0	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
2	Calgary Wranglers	2	1	1	0	0	0	0	0	6	7	-1	1	1	0	0	0	0	0	0	5	3	2	1	0	1	0	0	0	0	0	1	4	-3	2	0.500	6	12	18	0	0	15	18	11	2	76	178	215	162	21	74	32	10	60	6	0	0.00%	5	2	60.00%	0	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
3	Cleveland Monsters	2	1	1	0	0	0	0	0	4	6	-2	1	1	0	0	0	0	0	0	4	3	1	1	0	1	0	0	0	0	0	0	3	-3	2	0.500	4	8	12	0	0	15	18	11	2	78	178	215	162	21	61	18	34	58	6	1	16.67%	7	2	71.43%	0	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
4	Laval Rocket	2	0	1	0	0	0	1	0	6	7	-1	1	0	1	0	0	0	0	0	2	4	-2	1	0	0	0	0	0	1	0	4	3	1	2	0.500	6	9	15	0	0	15	18	11	2	90	178	215	162	21	97	33	12	52	6	1	16.67%	5	0	100.00%	1	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
5	Rochester Americans	2	2	0	0	0	0	0	0	8	3	5	1	1	0	0	0	0	0	0	4	1	3	1	1	0	0	0	0	0	0	4	2	2	4	1.000	8	15	23	0	0	15	18	11	2	82	178	215	162	21	77	20	17	52	8	2	25.00%	6	1	83.33%	0	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
6	Syracuse Crunch	2	1	0	0	0	0	0	1	8	7	1	1	0	0	0	0	0	0	1	4	5	-1	1	1	0	0	0	0	0	0	4	2	2	3	0.750	8	14	22	0	0	15	18	11	2	61	178	215	162	21	62	15	23	56	3	2	66.67%	9	2	77.78%	0	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
7	Utica Comets	2	0	1	0	0	0	1	0	7	7	0	1	0	0	0	0	0	1	0	5	4	1	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	7	12	19	0	0	15	18	11	2	92	178	215	162	21	87	18	6	57	10	1	10.00%	3	1	66.67%	0	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
	Total	14	6	5	0	0	0	2	1	46	44	2	7	4	1	0	0	0	1	1	29	23	6	7	2	4	0	0	0	1	0	17	21	-4	17	0.607	46	83	129	0	0	15	18	11	2	568	178	215	162	21	541	161	120	371	49	9	18.37%	44	10	77.27%	1	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
	_Since Last GM Reset	14	6	5	0	0	0	2	1	46	44	2	7	4	1	0	0	0	1	1	29	23	6	7	2	4	0	0	0	1	0	17	21	-4	17	0.607	46	83	129	0	0	15	18	11	2	568	178	215	162	21	541	161	120	371	49	9	18.37%	44	10	77.27%	1	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
	_Vs Conference	14	6	5	0	0	0	2	1	46	44	2	7	4	1	0	0	0	1	1	29	23	6	7	2	4	0	0	0	1	0	17	21	-4	17	0.607	46	83	129	0	0	15	18	11	2	568	178	215	162	21	541	161	120	371	49	9	18.37%	44	10	77.27%	1	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99
	_Vs Division	8	6	5	0	0	0	2	1	29	24	5	4	4	1	0	0	0	1	1	15	13	2	4	2	4	0	0	0	1	0	14	11	3	17	1.063	29	51	80	0	0	15	18	11	2	322	178	215	162	21	319	93	70	196	27	7	25.93%	29	5	82.76%	1	289	560	51.61%	260	552	47.10%	117	229	51.09%	346	234	314	106	194	99

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
14	17	W2	46	83	129	568	541	161	120	371	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
14	6	5	0	0	2	1	46	44

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
7	4	1	0	0	1	1	29	23

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
7	2	4	0	0	1	0	17	21

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

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
49	9	18.37%	44	10	77.27%	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
178	215	162	21	15	18	11	2

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
289	560	51.61%	260	552	47.10%	117	229	51.09%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
346	234	314	106	194	99

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
1	7	Toronto Marlies	1	Calgary Wranglers	4	L			BoxScore
3	18	Calgary Wranglers	3	Toronto Marlies	5	W			BoxScore
5	37	Cleveland Monsters	3	Toronto Marlies	4	W			BoxScore
7	51	Toronto Marlies	2	Belleville Senators	4	L			BoxScore
8	65	Toronto Marlies	4	Rochester Americans	2	W			BoxScore
10	84	Belleville Senators	3	Toronto Marlies	5	W			BoxScore
12	95	Toronto Marlies	2	Utica Comets	3	L			BoxScore
14	116	Laval Rocket	4	Toronto Marlies	2	L			BoxScore
17	134	Toronto Marlies	4	Syracuse Crunch	2	W			BoxScore
19	153	Syracuse Crunch	5	Toronto Marlies	4	L	X	X	BoxScore
21	168	Toronto Marlies	4	Laval Rocket	3	W	X	X	BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
22	176	Toronto Marlies	0	Cleveland Monsters	3	L			BoxScore
24	189	Utica Comets	4	Toronto Marlies	5	W	X	X	BoxScore
28	217	Rochester Americans	1	Toronto Marlies	4	W			BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	15
Attendance	12,603	6,465
Attendance PCT	90.02%	92.36%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
0	2724 - 90.80%	76,869$	538,080$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
3,242,490$	2,742,500$	2,742,500$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
91,417$	2,742,510$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	108,083$	0$

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: 14 \| W: 8 \| L: 5 \| OTL: 1 \| P: 17 GF: 46 \| GA: 44 \| PP%: 18.37% \| PK%: 77.27% GM : Flavio \| Morale : 43 \| Team Overall : 60