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

Game Center
	Marlies 14-4-0, 28pts	5	FINAL	1	Bears 8-7-3, 19pts

Team Stats
W2	Streak	W1
6-3-0	Home Record	5-4-0
8-1-0	Away Record	3-3-3
8-2-0	Last 10 Games	4-5-1
4.06	Goals Per Game	3.83
1.94	Goals Against Per Game	2.67
22.50%	Power Play Percentage	12.99%
87.80%	Penalty Kill Percentage	86.00%

Marlies
14-4-0, 28pts

FINAL

Checkers
13-4-1, 27pts

Team Stats
W2	Streak	L1
6-3-0	Home Record	7-2-0
8-1-0	Away Record	6-2-1
8-2-0	Last 10 Games	8-2-0
4.06	Goals Per Game	3.94
1.94	Goals Against Per Game	1.94
22.50%	Power Play Percentage	17.33%
87.80%	Penalty Kill Percentage	84.42%

Team Leaders
	Goals Sheldon Dries 11	Assists Sheldon Dries 14

	Points Sheldon Dries 25	Plus/Minus Sheldon Dries 17

	Wins Jesper Vikman 14	Save Percentage Olivier Rodrigue 1

Team Stats
Goals For 73 4.06 GFG	Shots For 424 23.56 Avg	Power Play Percentage 22.5% 18 GF	Offensive Zone Start 38.8%
Goals Against 35 1.94 GAA	Shots Against 328 18.22 Avg	Penalty Kill Percentage 87.8%% 15 GA	Defensive Zone Start 38.1%

Team Info
	General Manager	Garrett
	Coach	Greg Moore
	Division	North
	Conference	Eastern Conference
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	0
	Season Tickets	0
Roster Info
	Pro Team	26
	Farm Team	20
	Contract Limit	46 / 50
	Prospects	49
Team History
	This Season	14-4-0 (28PTS)
	History	0-0-0


	Stanley Cup	0

Show or Hide Column

Filter Tips

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

#	Player Name	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary
1	Sheldon Dries	X				100.00	68	64	77	64	64	74	76	68	80	59	71	66	67	58	58	68	50	64	0	30	1	775,000$
2	Jack Studnicka	X		X		100.00	81	44	91	80	65	59	70	65	67	50	57	76	25	57	57	64	50	63	0	25	3	875,000$
3	Chris Wagner	X		X		100.00	88	93	80	77	74	48	65	64	75	58	58	69	25	68	71	64	50	63	0	33	1	950,000$
4	Ethan Cardwell (R)			X		100.00	73	69	83	64	69	70	72	62	50	56	63	63	60	44	44	64	50	60	0	22	3	925,000$
5	Martin Chromiak (R)			X		100.00	73	68	84	60	68	74	78	58	50	53	58	62	55	44	44	61	50	59	0	22	2	820,000$
6	Ryan Dzingel	X	X	X		100.00	76	70	89	65	70	45	43	58	73	44	63	69	60	66	66	61	50	59	0	32	1	950,000$
7	Spencer Smallman			X		100.00	74	73	77	63	73	69	73	55	50	48	58	62	55	44	44	60	50	58	0	28	1	750,000$
8	Maxim Cajkovic (R)			X		100.00	73	67	87	59	67	51	50	59	50	56	58	62	55	44	44	60	50	56	0	23	3	925,000$
9	Matvei Petrov (R)		X	X		100.00	71	67	81	60	67	61	64	52	50	44	56	60	53	44	44	57	50	55	0	21	3	813,333$
10	Adam Raska			X		100.00	60	63	53	67	63	69	75	47	50	43	46	53	44	45	45	51	50	53	0	23	2	900,000$
11	Ilya Nikolayev (R)	X				100.00	76	70	90	59	70	54	56	49	61	45	47	61	45	44	44	54	50	53	0	23	3	836,667$
12	Jacob Bernard-Docker				X	100.00	73	53	89	75	70	67	83	62	25	51	48	82	25	56	56	63	50	67	0	24	1	1,208,333$
13	John Ludvig				X	100.00	87	99	62	73	77	58	63	62	25	45	50	80	25	47	47	61	50	65	0	24	2	775,000$
14	Sean Day				X	100.00	89	88	90	65	88	70	75	52	25	48	42	69	40	44	44	58	50	63	0	26	2	750,000$
15	Callan Foote				X	100.00	78	77	80	74	77	57	59	53	25	46	41	67	39	60	61	55	50	62	0	25	1	850,000$
16	Keaton Middleton				X	100.00	77	87	54	65	87	78	87	47	25	36	42	61	40	44	44	53	50	60	0	26	1	750,000$
17	Jeremy Davies				X	100.00	66	66	67	65	66	76	81	57	25	48	51	59	48	46	46	58	50	59	0	27	3	900,000$
18	Brinson Pasichnuk				X	100.00	79	74	91	68	74	59	63	48	25	40	40	63	38	44	44	53	50	58	0	26	1	750,000$
19	Joseph Cecconi				X	100.00	75	76	71	65	76	65	70	46	25	38	39	60	37	44	44	51	50	57	0	27	1	750,000$
20	Layton Ahac (R)				X	100.00	76	72	86	65	72	63	68	44	25	33	39	60	37	44	44	51	50	57	0	23	2	925,000$
Scratches
1	Tanner Dickinson (R)	X				100.00	67	57	91	60	57	52	52	52	65	42	57	58	54	44	44	56	50	53	0	22	3	878,333$
2	Zayde Wisdom (R)	X				100.00	74	69	85	62	69	59	63	44	55	38	44	59	42	44	44	51	50	52	0	22	2	896,667$
3	Mike Benning (R)				X	100.00	67	62	80	62	62	67	72	51	25	42	46	57	44	44	44	54	50	56	0	22	3	925,000$

	TEAM AVERAGE					100.00	75	71	80	66	71	63	68	55	45	46	51	64	44	49	49	58	50	59

Show or Hide Column

Filter Tips

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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary
1	Jesper Vikman (R)	100.00	59	47	59	72	64	66	58	64	68	66	30	44	44	61	50	60	0	22	3	858,000$
2	Olivier Rodrigue	100.00	58	49	61	59	62	59	54	61	59	58	30	44	44	57	50	56	0	24	2	775,000$
Scratches
1	Jiri Patera	100.00	52	52	65	80	52	52	50	56	51	51	30	44	44	52	50	54	0	25	2	775,000$

	TEAM AVERAGE	100.00	56	49	62	70	59	59	54	60	59	58	30	44	44	57	50	57

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Greg Moore	62	60	71	73	57	54	88	USA	38	1	500,000$

Show or Hide Column

Filter Tips

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

#	Player Name	Team Name	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FW	FT	S1	S2	S3
1	Sheldon Dries	Marlies (TOR)	C	18	11	14	25	17	2	0	4	46	54	16	53	20.37%	1	385	21.42	4	4	8	13	63	0	0	0	3	106	1	0	68.27%	498	0	1.30	0	1	0	0	3	2	0
2	Jacob Bernard-Docker	Marlies (TOR)	D	18	9	12	21	11	10	0	6	24	34	4	27	26.47%	13	399	22.20	4	4	8	26	60	0	1	1	0	91	2	0	0.00%	0	0	1.05	0	0	0	0	0	1	3
3	Chris Wagner	Marlies (TOR)	C/RW	18	6	13	19	9	17	5	30	23	44	15	26	13.64%	3	307	17.07	3	4	7	16	44	0	0	0	1	50	0	0	71.96%	107	0	1.24	1	1	0	1	0	2	1
4	Martin Chromiak	Marlies (TOR)	RW	18	4	13	17	10	8	0	14	21	31	19	37	12.90%	0	276	15.37	0	2	2	2	39	0	0	0	0	8	0	0	73.08%	26	0	1.23	0	0	0	0	0	2	2
5	John Ludvig	Marlies (TOR)	D	18	6	10	16	13	64	10	62	16	33	15	24	18.18%	9	353	19.66	3	3	6	21	57	1	1	2	1	68	2	0	0.00%	0	0	0.90	0	0	1	1	1	3	0
6	Ethan Cardwell	Marlies (TOR)	RW	18	6	9	15	14	12	0	12	14	38	11	25	15.79%	3	310	17.24	2	2	4	7	62	0	0	0	1	40	1	1	60.00%	25	0	0.97	0	0	0	0	0	0	2
7	Liam O'Brien	Marlies (TOR)	LW/RW	18	8	7	15	15	30	0	61	23	45	11	25	17.78%	1	338	18.80	0	3	3	7	57	1	0	1	1	60	2	0	55.00%	40	1	0.89	0	1	0	0	2	1	0
8	Sean Day	Marlies (TOR)	D	18	1	11	12	11	27	5	55	13	10	4	9	10.00%	17	365	20.32	1	1	2	9	55	0	1	1	0	81	0	0	0.00%	0	0	0.66	0	0	0	1	1	0	1
9	Spencer Smallman	Marlies (TOR)	RW	18	7	5	12	11	18	0	23	11	21	10	17	33.33%	0	250	13.90	0	1	1	2	18	0	0	0	0	0	0	0	51.92%	52	0	0.96	0	0	0	0	2	0	1
10	Ryan Dzingel	Marlies (TOR)	C/LW/RW	18	4	7	11	12	4	0	13	21	33	5	22	12.12%	3	315	17.51	0	3	3	8	52	0	0	0	2	62	1	0	69.50%	141	0	0.70	0	0	0	0	1	1	0
11	Maxim Cajkovic	Marlies (TOR)	RW	13	2	8	10	8	6	0	9	11	25	7	17	8.00%	0	165	12.77	0	0	0	0	2	0	0	0	0	0	1	0	55.00%	60	0	1.21	0	0	0	0	0	1	1
12	Jeremy Davies	Marlies (TOR)	D	18	2	7	9	10	12	0	19	11	7	5	6	28.57%	7	296	16.50	0	4	4	1	13	0	0	0	0	49	2	0	25.00%	4	0	0.61	0	0	0	0	0	0	1
13	Callan Foote	Marlies (TOR)	D	15	1	4	5	4	8	0	17	10	11	2	11	9.09%	8	286	19.09	0	1	1	9	44	0	0	0	0	48	0	1	0.00%	0	0	0.35	0	0	0	0	0	0	0
14	Jack Studnicka	Marlies (TOR)	C/RW	6	2	3	5	1	6	0	11	14	15	6	6	13.33%	1	113	18.94	1	1	2	2	21	0	0	0	1	21	0	0	65.00%	100	0	0.88	0	0	0	0	0	0	0
15	Keaton Middleton	Marlies (TOR)	D	18	0	5	5	9	40	0	57	12	10	0	6	0.00%	18	305	16.99	0	1	1	3	14	0	0	0	2	52	0	0	0.00%	0	0	0.33	0	0	0	0	0	0	0
16	Joseph Cecconi	Marlies (TOR)	D	11	1	2	3	4	18	10	11	4	3	0	2	33.33%	2	95	8.72	0	0	0	0	1	0	0	0	0	7	1	0	0.00%	0	0	0.63	0	0	1	1	1	0	0
17	Brinson Pasichnuk	Marlies (TOR)	D	11	2	0	2	4	16	0	16	7	5	4	7	40.00%	0	142	12.91	0	0	0	0	0	0	0	0	0	1	0	0	25.00%	4	0	0.28	0	0	0	0	0	0	0
18	Layton Ahac	Marlies (TOR)	D	8	0	0	0	2	2	0	4	0	2	0	0	0.00%	0	29	3.71	0	0	0	1	6	0	0	0	0	8	0	0	100.00%	1	0	0.00%	0	0	0	0	0	0	0
Team Total or Average				280	72	130	202	165	300	30	424	281	421	134	320	17.10%	86	4738	16.92	18	34	52	127	619	2	3	5	12	762	13	2	66.07%	1058	1	0.85	1	3	2	4	11	13	12

Show or Hide Column

Filter Tips

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

#	Goalie Name	Team Name	GP	W	L	PCT	GAA	MP	SO	GA	SA	PS %	PSA	ST	BG	S3
1	Jesper Vikman	Marlies (TOR)	18	14	4	0.891	1.96	1073	2	35	320	1.000	3	18	0	1
2	Olivier Rodrigue	Marlies (TOR)	1	0	0	1.000	0.00%	18	0	0	8	0.00%	0	0	18	0
Team Total or Average			19	14	4	0.893	1.93	1091	2	35	328		3	18	18	1

Show or Hide Column

Filter Tips

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

Player Name	Team Name	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Contract	Type	Current Salary	Salary Remaining	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Link
Adam Raska	Marlies (TOR)	RW	23	2001-09-25	No	178 Lbs	5 ft10	No	No	No	2	Pro & Farm	900,000$	900,000$	0$	0$	No	900,000$		Link
Brinson Pasichnuk	Marlies (TOR)	D	26	1997-11-23	No	205 Lbs	6 ft0	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Link
Callan Foote	Marlies (TOR)	D	25	1998-12-13	No	209 Lbs	6 ft3	No	No	No	1	Pro & Farm	850,000$	850,000$	0$	0$	No			Link
Chris Wagner	Marlies (TOR)	C/RW	33	1991-05-26	No	198 Lbs	6 ft0	No	No	No	1	Pro & Farm	950,000$	950,000$	0$	0$	No			Link / NHL Link
Ethan Cardwell	Marlies (TOR)	RW	22	2002-08-30	Yes	192 Lbs	5 ft11	No	No	No	3	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$	925,000$	Link
Ilya Nikolayev	Marlies (TOR)	C	23	2001-06-26	Yes	190 Lbs	6 ft0	No	No	No	3	Pro & Farm	836,667$	836,667$	0$	0$	No	836,667$	836,667$	Link
Jack Studnicka	Marlies (TOR)	C/RW	25	1999-02-18	No	172 Lbs	6 ft1	No	No	No	3	Pro & Farm	875,000$	875,000$	0$	0$	No	875,000$	875,000$	Link / NHL Link
Jacob Bernard-Docker	Marlies (TOR)	D	24	2000-06-30	No	189 Lbs	6 ft1	No	No	No	1	Pro & Farm	1,208,333$	1,208,333$	0$	0$	No			Link
Jeremy Davies	Marlies (TOR)	D	27	1996-12-04	No	181 Lbs	5 ft11	No	No	No	3	Pro & Farm	900,000$	900,000$	0$	0$	No	900,000$	900,000$	Link / NHL Link
Jesper Vikman	Marlies (TOR)	G	22	2002-03-11	Yes	179 Lbs	6 ft3	No	No	No	3	Pro & Farm	858,000$	858,000$	0$	0$	No	858,000$	858,000$	Link
Jiri Patera	Marlies (TOR)	G	25	1999-02-24	No	209 Lbs	6 ft2	No	No	No	2	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$		Link
John Ludvig	Marlies (TOR)	D	24	2000-02-08	No	200 Lbs	6 ft1	No	No	No	2	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$		Link
Joseph Cecconi	Marlies (TOR)	D	27	1997-05-23	No	209 Lbs	6 ft2	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Link / NHL Link
Keaton Middleton	Marlies (TOR)	D	26	1998-02-10	No	234 Lbs	6 ft5	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Link / NHL Link
Layton Ahac	Marlies (TOR)	D	23	2001-02-22	Yes	190 Lbs	6 ft2	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$		Link
Martin Chromiak	Marlies (TOR)	RW	22	2002-08-20	Yes	187 Lbs	6 ft0	No	No	No	2	Pro & Farm	820,000$	820,000$	0$	0$	No	820,000$		Link
Matvei Petrov	Marlies (TOR)	LW/RW	21	2003-03-12	Yes	179 Lbs	6 ft2	No	No	No	3	Pro & Farm	813,333$	813,333$	0$	0$	No	813,333$	813,333$	Link
Maxim Cajkovic	Marlies (TOR)	RW	23	2001-01-03	Yes	185 Lbs	5 ft11	No	No	No	3	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$	925,000$	Link
Mike Benning	Marlies (TOR)	D	22	2002-01-05	Yes	177 Lbs	5 ft9	No	No	No	3	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$	925,000$	Link
Olivier Rodrigue	Marlies (TOR)	G	24	2000-07-06	No	156 Lbs	6 ft1	No	No	No	2	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$		Link
Ryan Dzingel	Marlies (TOR)	C/LW/RW	32	1992-03-09	No	190 Lbs	6 ft0	No	No	No	1	Pro & Farm	950,000$	950,000$	0$	0$	No			Link / NHL Link
Sean Day	Marlies (TOR)	D	26	1998-01-09	No	240 Lbs	6 ft3	No	No	No	2	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$		Link / NHL Link
Sheldon Dries	Marlies (TOR)	C	30	1994-04-23	No	180 Lbs	5 ft9	No	No	No	1	Pro & Farm	775,000$	775,000$	0$	0$	No			Link / NHL Link
Spencer Smallman	Marlies (TOR)	RW	28	1996-09-09	No	201 Lbs	6 ft0	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No			Link / NHL Link
Tanner Dickinson	Marlies (TOR)	C	22	2002-03-05	Yes	150 Lbs	5 ft11	No	No	No	3	Pro & Farm	878,333$	878,333$	0$	0$	No	878,333$	878,333$	Link
Zayde Wisdom	Marlies (TOR)	C	22	2002-05-20	Yes	195 Lbs	5 ft10	No	No	No	2	Pro & Farm	896,667$	896,667$	0$	0$	No	896,667$		Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
26	24.88	191 Lbs	6 ft0	2.00	857,167$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Liam O'Brien	Sheldon Dries	Ethan Cardwell	35	0	2	3
2	Ryan Dzingel	Jack Studnicka	Martin Chromiak	30	0	3	2
3	Spencer Smallman	Ilya Nikolayev	Maxim Cajkovic	20	0	3	2
4	Matvei Petrov	Chris Wagner	Adam Raska	15	0	3	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jacob Bernard-Docker	John Ludvig	40	1	2	2
2	Callan Foote	Keaton Middleton	30	1	2	2
3	Jeremy Davies	Sean Day	20	1	2	2
4	John Ludvig	Jacob Bernard-Docker	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Liam O'Brien	Sheldon Dries	Ethan Cardwell	60	0	1	4
2	Ryan Dzingel	Jack Studnicka	Chris Wagner	40	0	1	4

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jacob Bernard-Docker	John Ludvig	60	0	2	3
2	Sean Day	Callan Foote	40	0	2	3

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Sheldon Dries	Jack Studnicka	60	1	2	2
2	Ryan Dzingel	Chris Wagner	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jacob Bernard-Docker	John Ludvig	60	1	2	2
2	Sean Day	Keaton Middleton	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Sheldon Dries	60	1	2	2	Jacob Bernard-Docker	John Ludvig	60	1	2	2
2	Jack Studnicka	40	1	2	2	Sean Day	Keaton Middleton	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Sheldon Dries	Jack Studnicka	60	1	2	2
2	Ryan Dzingel	Martin Chromiak	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jacob Bernard-Docker	John Ludvig	60	1	2	2
2	Sean Day	Keaton Middleton	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Jack Studnicka	Sheldon Dries	Ethan Cardwell	Jacob Bernard-Docker	John Ludvig

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Jack Studnicka	Sheldon Dries	Ryan Dzingel	Jacob Bernard-Docker	John Ludvig

Extra Forwards
Normal	PowerPlay	Penalty Kill
Ethan Cardwell, Ryan Dzingel, Martin Chromiak	Ethan Cardwell, Ryan Dzingel	Ethan Cardwell

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Jacob Bernard-Docker, John Ludvig, Sean Day	Jeremy Davies	John Ludvig, Keaton Middleton

Penalty Shots
Sheldon Dries, Ryan Dzingel, Chris Wagner, Liam O'Brien, Ethan Cardwell

Goalie
#1 : Jesper Vikman, #2 : Olivier Rodrigue

Custom OT Lines Forwards
Sheldon Dries, Ethan Cardwell, Jack Studnicka, Chris Wagner, Martin Chromiak, Ryan Dzingel, Ryan Dzingel, Matvei Petrov, Spencer Smallman, Maxim Cajkovic, Liam O'Brien

Custom OT Lines Defensemen
Jacob Bernard-Docker, John Ludvig, Sean Day, Callan Foote, Jeremy Davies

Show or Hide Column

Filter Tips

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

	Overall												Home											Visitor
#	VS Team	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Bears	2	2	0	0	0	0	0	0	8	2	6	1	1	0	0	0	0	0	0	3	1	2	1	1	0	0	0	0	0	0	5	1	4	4	1.000	8	16	24	0	0	31	21	18	3	56	140	145	130	12	30	10	24	49	7	2	28.57%	6	1	83.33%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
2	Bruins	2	0	0	0	2	0	0	0	8	6	2	1	0	0	0	1	0	0	0	4	3	1	1	0	0	0	1	0	0	0	4	3	1	4	1.000	8	13	21	0	0	31	21	18	3	63	140	145	130	12	36	16	20	55	9	5	55.56%	10	1	90.00%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
3	Checkers	2	2	0	0	0	0	0	0	6	2	4	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	0	3	4	1.000	6	11	17	0	1	31	21	18	3	45	140	145	130	12	37	14	22	38	12	1	8.33%	10	0	100.00%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
4	Islanders	2	2	0	0	0	0	0	0	14	2	12	1	1	0	0	0	0	0	0	4	1	3	1	1	0	0	0	0	0	0	10	1	9	4	1.000	14	27	41	0	0	31	21	18	3	66	140	145	130	12	23	6	20	42	2	2	100.00%	10	1	90.00%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
5	Penguins	2	2	0	0	0	0	0	0	15	3	12	1	1	0	0	0	0	0	0	8	1	7	1	1	0	0	0	0	0	0	7	2	5	4	1.000	15	26	41	0	0	31	21	18	3	49	140	145	130	12	45	9	32	64	12	5	41.67%	15	2	86.67%	2	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
6	Phantoms	2	2	0	0	0	0	0	0	12	5	7	1	1	0	0	0	0	0	0	7	5	2	1	1	0	0	0	0	0	0	5	0	5	4	1.000	12	22	34	0	1	31	21	18	3	47	140	145	130	12	34	10	43	55	5	1	20.00%	19	2	89.47%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
7	Rocket	2	0	2	0	0	0	0	0	3	6	-3	1	0	1	0	0	0	0	0	1	2	-1	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	3	5	8	0	0	31	21	18	3	27	140	145	130	12	42	6	42	39	8	0	0.00%	20	5	75.00%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
8	Wolf Pack	2	1	1	0	0	0	0	0	3	3	0	1	0	1	0	0	0	0	0	1	2	-1	1	1	0	0	0	0	0	0	2	1	1	2	0.500	3	6	9	0	0	31	21	18	3	40	140	145	130	12	33	7	48	45	14	2	14.29%	17	1	94.12%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
9	Wolves	2	0	1	0	0	0	1	0	4	6	-2	1	0	1	0	0	0	0	0	1	4	-3	1	0	0	0	0	0	1	0	3	2	1	2	0.500	4	4	8	0	0	31	21	18	3	31	140	145	130	12	48	8	49	41	11	0	0.00%	16	2	87.50%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
	Total	18	11	4	0	2	0	1	0	73	35	38	9	5	3	0	1	0	0	0	32	21	11	9	6	1	0	1	0	1	0	41	14	27	28	0.778	73	130	203	0	2	31	21	18	3	424	140	145	130	12	328	86	300	428	80	18	22.50%	123	15	87.80%	2	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
	_Since Last GM Reset	18	11	4	0	2	0	1	0	73	35	38	9	5	3	0	1	0	0	0	32	21	11	9	6	1	0	1	0	1	0	41	14	27	28	0.778	73	130	203	0	2	31	21	18	3	424	140	145	130	12	328	86	300	428	80	18	22.50%	123	15	87.80%	2	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
	_Vs Conference	18	11	4	0	2	0	1	0	73	35	38	9	5	3	0	1	0	0	0	32	21	11	9	6	1	0	1	0	1	0	41	14	27	28	0.778	73	130	203	0	2	31	21	18	3	424	140	145	130	12	328	86	300	428	80	18	22.50%	123	15	87.80%	2	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118
	_Vs Division	6	0	2	0	0	0	0	0	17	14	3	3	0	1	0	0	0	0	0	8	7	1	3	0	1	0	0	0	0	0	9	7	2	0	0.000	17	29	46	0	1	31	21	18	3	135	140	145	130	12	115	36	84	132	29	6	20.69%	40	6	85.00%	0	317	456	69.52%	269	448	60.04%	158	271	58.30%	524	386	351	116	216	118

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
18	28	W2	73	130	203	424	328	86	300	428	0	2

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
18	11	4	2	0	1	0	73	35

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
9	5	3	1	0	0	0	32	21

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
9	6	1	1	0	1	0	41	14

Last 10 Games
W	L	OTW	OTL	SOW	SOL
7	2	1	0	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
80	18	22.50%	123	15	87.80%	2

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
140	145	130	12	31	21	18	3

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
317	456	69.52%	269	448	60.04%	158	271	58.30%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
524	386	351	116	216	118

Last Played Games Show or Hide Column

Filter Tips

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

Day	Game	Visitor Team	Score	Home Team	Score	ST	OT	SO	Link
1 - 2024-09-23	11	Penguins	1	Marlies	8	BW			BoxScore
2 - 2024-09-24	23	Marlies	2	Wolf Pack	1	AW			BoxScore
4 - 2024-09-26	43	Wolves	4	Marlies	1	BL			BoxScore
6 - 2024-09-28	68	Marlies	4	Bruins	3	AW	X		BoxScore
7 - 2024-09-29	79	Wolf Pack	2	Marlies	1	BL			BoxScore
9 - 2024-10-01	96	Marlies	5	Phantoms	0	AW			BoxScore
10 - 2024-10-02	111	Islanders	1	Marlies	4	BW			BoxScore
11 - 2024-10-03	123	Marlies	3	Wolves	2	AW	X	X	BoxScore
12 - 2024-10-04	144	Checkers	2	Marlies	3	BW			BoxScore
13 - 2024-10-05	155	Marlies	7	Penguins	2	AW			BoxScore
14 - 2024-10-06	175	Rocket	2	Marlies	1	BL			BoxScore
16 - 2024-10-08	194	Marlies	10	Islanders	1	AW			BoxScore
17 - 2024-10-09	208	Bears	1	Marlies	3	BW			BoxScore
19 - 2024-10-11	228	Bruins	3	Marlies	4	BW	X		BoxScore
22 - 2024-10-14	257	Phantoms	5	Marlies	7	BW			BoxScore
23 - 2024-10-15	264	Marlies	2	Rocket	4	AL			BoxScore
24 - 2024-10-16	270	Marlies	5	Bears	1	AW			BoxScore
25 - 2024-10-17	287	Marlies	3	Checkers	0	AW			BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	0
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
0	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
0$	2,306,133$	2,306,133$	0$

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

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	1	0$	0$

Marlies Stat Leaders (Regular Season)

Show or Hide Column

#	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

Marlies Goalies Stat Leaders (Regular Season)

Show or Hide Column

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

Marlies Career Team Stats

Show or Hide Column

	Overall											Home											Visitor
Year	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT

Marlies Stat Leaders (Play-Off)

Show or Hide Column

#	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

Marlies Goalies Stat Leaders (Play-Off)

Show or Hide Column

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

	Marlies
	GP: 18 \| W: 14 \| L: 4 \| OTL: 0 \| P: 28 GF: 73 \| GA: 35 \| PP%: 22.50% \| PK%: 87.80% GM : Garrett \| Morale : 50 \| Team Overall : 59