Ligue du Vieux Poele

Ducks
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Career Team Stat
Web Lines Editor

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	Nils Hoglander			X		99.00	78	43	81	86	65	62	91	68	25	62	81	64	25	63	63	0	62	70	0	23	3	1,100,000$
2	Filip Zadina		X	X		99.00	71	42	91	80	68	65	69	73	35	62	71	70	25	65	65	0	53	69	0	24	1	894,167$
3	Mavrik Bourque (R)	X				99.00	71	63	89	77	63	82	86	68	80	68	65	64	62	44	44	0	57	68	0	22	3	894,167$
4	Brett Howden	X	X			99.00	77	44	84	80	74	64	80	64	62	62	60	78	25	67	69	0	53	67	0	26	3	785,000$
5	Samuel Fagemo			X		100.00	74	68	89	70	68	75	77	68	50	59	74	65	70	45	45	0	57	67	0	24	1	795,000$
6	Dylan Holloway	X	X			99.00	89	60	78	75	74	59	76	64	58	58	70	66	25	55	55	0	62	66	0	23	2	925,000$
7	Maxime Comtois		X			100.00	71	78	56	72	78	77	81	64	50	59	62	67	59	63	63	0	61	65	0	25	1	2,037,500$
8	Sheldon Dries	X				99.00	68	64	77	64	64	74	76	68	80	59	71	66	67	58	58	0	58	65	0	30	1	750,000$
9	Zach Aston-Reese			X		100.00	75	74	76	77	74	81	87	61	50	53	60	69	57	66	68	0	61	64	0	30	1	1,725,000$
10	Jack Studnicka	X		X		100.00	81	44	91	80	65	59	70	65	67	50	57	76	25	57	57	0	55	64	0	25	3	763,000$
11	Mattias Samuelsson				X	100.00	91	47	82	74	81	80	65	63	25	51	47	90	25	60	60	0	54	70	0	24	4	4,285,714$
12	Olen Zellweger (R)				X	100.00	65	41	94	79	62	78	72	73	25	61	49	69	25	46	46	0	61	68	0	21	3	863,333$
13	Scott Perunovich				X	100.00	67	41	92	80	61	67	58	76	25	64	47	63	25	49	50	0	62	67	0	26	2	775,000$
14	Brandt Clarke (R)				X	100.00	63	41	82	78	70	62	69	72	25	59	54	65	25	45	45	0	56	66	0	21	3	863,333$
15	Oliver Kylington				X	100.00	69	42	88	81	67	71	58	61	25	51	50	75	25	47	47	0	60	65	0	27	3	750,000$
16	Ethan Bear				X	100.00	69	42	87	81	70	65	61	64	25	50	48	69	25	66	67	0	56	65	0	27	3	2,062,500$
17	Kyle Capobianco				X	100.00	75	72	82	65	72	60	60	62	25	55	52	67	49	54	55	0	53	63	0	27	1	775,000$
18	Ty Smith				X	100.00	70	64	83	65	64	69	72	59	25	53	48	64	46	58	58	0	57	61	0	24	4	775,000$
Scratches
1	Derrick Pouliot				X	100.00	72	71	74	77	71	70	74	60	25	54	48	67	46	64	64	0	45	65	0	30	2	775,000$
2	Emil Andrae (R)				X	100.00	66	64	69	65	64	56	57	53	25	51	41	57	39	44	44	0	43	56	0	22	3	903,333$

	TEAM AVERAGE					99.70	73	55	82	75	69	69	72	65	40	57	58	69	39	56	56	0	56	66

Show or Hide Column

Filter Tips

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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Casey DeSmith	100.00	66	63	63	65	74	58	69	66	65	66	73	60	61	0	55	65	0
2	Akira Schmid	100.00	60	60	58	86	65	54	69	62	65	60	90	47	48	0	59	62	0
Scratches
1	Felix Sandstrom	100.00	47	50	63	75	45	46	50	53	47	48	30	46	46	0	41	50	0

	TEAM AVERAGE	100.00	58	58	61	75	61	53	63	60	59	58	64	51	52	0	52	59

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Benoit Groulx	60	60	60	60	60	60	60	TUR	80	2	1,000,000$

Show or Hide Column

Filter Tips

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

#	Player Name	Team Name	POS	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	S1	S2	S3
1	Sheldon Dries	Hit Man (ANA)	C	8	4	6	10	7	4	6	16	26	7	17	15.38%	1	146	18.27	0	0	0	0	12	1	25	1	0	55.73%	192	1	1.37	0	0	1	0	0
2	Filip Zadina	Hit Man (ANA)	LW/RW	8	4	3	7	2	0	10	18	29	6	26	13.79%	1	161	20.23	1	0	1	8	30	0	5	1	0	40.00%	5	0	0.86	0	1	1	1	1
3	Mavrik Bourque	Hit Man (ANA)	C	8	2	5	7	-2	2	7	24	18	3	30	11.11%	0	165	20.74	1	0	1	8	29	0	4	0	0	52.50%	200	0	0.84	0	1	1	0	0
4	Nils Hoglander	Hit Man (ANA)	RW	8	3	4	7	2	6	13	20	32	10	20	9.38%	1	159	19.94	0	1	1	10	30	0	5	0	1	33.33%	33	0	0.88	0	1	0	0	2
5	Samuel Fagemo	Hit Man (ANA)	RW	8	3	4	7	4	4	6	11	19	8	33	15.79%	3	146	18.29	2	1	3	6	30	0	2	0	1	52.94%	17	0	0.96	1	1	0	2	0
6	Brett Howden	Hit Man (ANA)	C/LW	8	1	5	6	-3	4	7	16	45	13	23	2.22%	2	167	20.92	0	3	3	14	30	0	25	0	0	53.77%	199	0	0.72	0	0	0	0	0
7	Olen Zellweger	Hit Man (ANA)	D	8	1	5	6	2	2	3	13	14	2	4	7.14%	8	149	18.74	0	2	2	9	29	0	13	0	0	0.00%	0	0	0.80	0	0	0	0	0
8	Mattias Samuelsson	Hit Man (ANA)	D	7	0	5	5	3	6	17	13	20	3	7	0.00%	15	144	20.70	0	1	1	14	30	1	25	0	0	0.00%	0	0	0.69	0	0	0	1	0
9	Dylan Holloway	Hit Man (ANA)	C/LW	8	2	2	4	-4	6	20	8	17	4	11	11.76%	3	158	19.80	1	0	1	5	19	0	24	2	0	42.11%	19	0	0.50	0	0	0	1	0
10	Ethan Bear	Hit Man (ANA)	D	7	2	2	4	0	6	7	0	6	0	3	33.33%	13	104	14.89	0	0	0	0	0	0	25	0	1	0.00%	0	0	0.77	0	0	1	0	0
11	Zach Aston-Reese	Hit Man (ANA)	RW	8	2	1	3	-5	12	10	7	18	5	16	11.11%	2	122	15.31	0	0	0	0	0	0	5	0	0	70.59%	17	0	0.49	0	0	0	0	0
12	Maxime Comtois	Hit Man (ANA)	LW	8	2	1	3	2	6	16	9	27	8	15	7.41%	4	124	15.57	0	0	0	0	0	0	2	0	0	33.33%	12	0	0.48	0	0	0	0	1
13	Scott Perunovich	Hit Man (ANA)	D	7	1	2	3	-2	4	6	4	15	3	9	6.67%	7	143	20.44	1	0	1	10	29	0	22	0	0	0.00%	0	0	0.42	0	0	0	0	0
14	Oliver Kylington	Hit Man (ANA)	D	8	1	1	2	1	6	6	6	11	3	5	9.09%	9	127	16.00	0	0	0	0	0	0	31	0	0	0.00%	0	0	0.31	0	0	0	0	1
15	Brandt Clarke	Hit Man (ANA)	D	7	0	2	2	-2	2	6	6	13	6	6	0.00%	5	117	16.76	0	1	1	6	27	0	0	0	0	0.00%	0	0	0.34	0	0	0	0	0
16	Kyle Capobianco	Hit Man (ANA)	D	8	0	1	1	2	2	15	4	4	1	2	0.00%	2	72	9.08	0	0	0	0	0	0	3	0	0	0.00%	0	0	0.28	0	0	0	0	0
17	Ty Smith	Hit Man (ANA)	D	8	1	0	1	0	2	7	2	3	0	5	33.33%	6	69	8.73	0	0	0	0	2	0	2	0	0	0.00%	0	0	0.29	0	0	0	0	0
18	Jack Studnicka	Hit Man (ANA)	C/RW	7	0	0	0	0	0	3	7	1	0	0	0.00%	2	30	4.30	0	0	0	0	0	1	27	0	0	53.85%	39	0	0.00	0	0	0	0	0
19	Emil Andrae	Hit Man (ANA)	D	1	0	0	0	-1	0	1	0	0	0	0	0.00%	2	16	16.78	0	0	0	0	0	0	2	0	0	0.00%	0	0	0.00	0	0	0	0	0
20	Derrick Pouliot	Hit Man (ANA)	D	1	0	0	0	-1	2	2	1	1	0	0	0.00%	0	21	21.88	0	0	0	0	1	0	3	0	0	0.00%	0	0	0.00	0	0	0	0	0
Team Total or Average				141	29	49	78	5	76	168	185	319	82	232	9.09%	86	2351	16.68	6	9	15	90	307	3	256	4	3	52.66%	733	1	0.66	1	4	4	5	5

Show or Hide Column

Filter Tips

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

#	Goalie Name	Team Name	GP	W	L	PCT	GAA	MP	GA	SA	PS %	PSA	ST	BG
1	Casey DeSmith	Hit Man (ANA)	6	4	2	0.876	4.10	337	23	186	1.000	4	6	2
2	Akira Schmid	Hit Man (ANA)	3	1	1	0.933	2.86	147	7	104	0.000	0	2	6
Team Total or Average			9	5	3	0.897	3.72	484	30	290	1.000	4	8	8

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	Salary Year 4	Link
Akira Schmid	Hit Man (ANA)	G	24	2000-05-12	No	205 Lbs	6 ft5	No	No	No	2	Pro & Farm	850,833$	850,833$	0$	0$	No	850,833$			Link
Brandt Clarke	Hit Man (ANA)	D	21	2003-02-09	Yes	185 Lbs	6 ft2	No	No	No	3	Pro & Farm	863,333$	863,333$	0$	0$	No	863,333$	863,333$		Link
Brett Howden	Hit Man (ANA)	C/LW	26	1998-03-28	No	196 Lbs	6 ft3	No	No	No	3	Pro & Farm	785,000$	785,000$	0$	0$	No	785,000$	785,000$		Link / NHL Link
Casey DeSmith	Hit Man (ANA)	G	33	1991-08-12	No	180 Lbs	6 ft0	No	No	No	2	Pro & Farm	1,250,000$	1,250,000$	0$	0$	No	1,250,000$			Link / NHL Link
Derrick Pouliot	Hit Man (ANA)	D	30	1994-01-16	No	196 Lbs	6 ft0	No	No	No	2	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$			Link / NHL Link
Dylan Holloway	Hit Man (ANA)	C/LW	23	2001-09-23	No	202 Lbs	6 ft1	No	No	No	2	Pro & Farm	925,000$	925,000$	0$	0$	No	925,000$			Link
Emil Andrae	Hit Man (ANA)	D	22	2002-02-23	Yes	180 Lbs	5 ft9	No	No	No	3	Pro & Farm	903,333$	903,333$	0$	0$	No	903,333$	903,333$		Link
Ethan Bear	Hit Man (ANA)	D	27	1997-06-26	No	196 Lbs	5 ft11	No	No	No	3	Pro & Farm	2,062,500$	2,062,500$	0$	0$	No	2,062,500$	2,062,500$		Link / NHL Link
Felix Sandstrom	Hit Man (ANA)	G	27	1997-01-12	No	191 Lbs	6 ft2	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No				Link
Filip Zadina	Hit Man (ANA)	LW/RW	24	1999-11-27	No	189 Lbs	6 ft0	No	No	No	1	Pro & Farm	894,167$	894,167$	0$	0$	No				Link / NHL Link
Jack Studnicka	Hit Man (ANA)	C/RW	25	1999-02-18	No	172 Lbs	6 ft1	No	No	No	3	Pro & Farm	763,000$	763,000$	0$	0$	No	763,000$	763,000$		Link / NHL Link
Kyle Capobianco	Hit Man (ANA)	D	27	1997-08-13	No	196 Lbs	6 ft1	No	No	No	1	Pro & Farm	775,000$	775,000$	0$	0$	No				Link / NHL Link
Mattias Samuelsson	Hit Man (ANA)	D	24	2000-03-14	No	218 Lbs	6 ft4	No	No	No	4	Pro & Farm	4,285,714$	4,285,714$	0$	0$	No	4,285,714$	4,285,714$	4,285,714$	Link
Mavrik Bourque	Hit Man (ANA)	C	22	2002-08-01	Yes	178 Lbs	5 ft10	No	No	No	3	Pro & Farm	894,167$	894,167$	0$	0$	No	894,167$	894,167$		Link
Maxime Comtois	Hit Man (ANA)	LW	25	1999-08-01	No	214 Lbs	6 ft2	No	No	No	1	Pro & Farm	2,037,500$	2,037,500$	0$	0$	No				Link
Nils Hoglander	Hit Man (ANA)	RW	23	2000-12-20	No	185 Lbs	5 ft9	No	No	No	3	Pro & Farm	1,100,000$	1,100,000$	0$	0$	No	1,100,000$	1,100,000$		Link
Olen Zellweger	Hit Man (ANA)	D	21	2003-09-10	Yes	174 Lbs	5 ft10	No	No	No	3	Pro & Farm	863,333$	863,333$	0$	0$	No	863,333$	863,333$		Link
Oliver Kylington	Hit Man (ANA)	D	27	1997-05-19	No	183 Lbs	6 ft0	No	No	No	3	Pro & Farm	750,000$	750,000$	0$	0$	No	750,000$	750,000$		Link
Samuel Fagemo	Hit Man (ANA)	RW	24	2000-03-14	No	189 Lbs	6 ft0	No	No	No	1	Pro & Farm	795,000$	795,000$	0$	0$	No				Link
Scott Perunovich	Hit Man (ANA)	D	26	1998-08-18	No	172 Lbs	5 ft9	No	No	No	2	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$			Link
Sheldon Dries	Hit Man (ANA)	C	30	1994-04-23	No	180 Lbs	5 ft9	No	No	No	1	Pro & Farm	750,000$	750,000$	0$	0$	No				Link / NHL Link
Ty Smith	Hit Man (ANA)	D	24	2000-03-23	No	176 Lbs	5 ft11	No	No	No	4	Pro & Farm	775,000$	775,000$	0$	0$	No	775,000$	775,000$	775,000$	Link
Zach Aston-Reese	Hit Man (ANA)	RW	30	1994-08-10	No	205 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,725,000$	1,725,000$	0$	0$	No				Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
23	25.43	190 Lbs	6 ft0	2.26	1,145,560$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Filip Zadina	Mavrik Bourque	Nils Hoglander	35	1	2	2
2	Dylan Holloway	Brett Howden	Zach Aston-Reese	35	1	2	2
3	Maxime Comtois	Sheldon Dries	Samuel Fagemo	25	1	2	2
4	Maxime Comtois	Sheldon Dries	Samuel Fagemo	5	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Olen Zellweger	Mattias Samuelsson	30	1	2	2
2	Scott Perunovich	Brandt Clarke	30	1	2	2
3	Oliver Kylington	Ethan Bear	25	1	2	2
4	Kyle Capobianco	Ty Smith	15	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Filip Zadina	Mavrik Bourque	Nils Hoglander	50	0	0	5
2	Sheldon Dries	Brett Howden	Samuel Fagemo	50	0	0	5

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Olen Zellweger	Brandt Clarke	50	0	0	5
2	Mattias Samuelsson	Scott Perunovich	50	0	0	5

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Sheldon Dries	Dylan Holloway	50	0	4	1
2	Jack Studnicka	Brett Howden	50	0	4	1

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Oliver Kylington	Scott Perunovich	50	0	4	1
2	Mattias Samuelsson	Ethan Bear	50	0	4	1

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Mavrik Bourque	50	0	5	0	Mattias Samuelsson	Oliver Kylington	50	0	5	0
2	Brett Howden	50	0	5	0	Olen Zellweger	Ethan Bear	50	0	5	0

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Sheldon Dries	Filip Zadina	50	0	2	3
2	Jack Studnicka	Maxime Comtois	50	0	2	3

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Ethan Bear	Scott Perunovich	60	1	2	2
2	Oliver Kylington	Brandt Clarke	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Filip Zadina	Mavrik Bourque	Nils Hoglander	Olen Zellweger	Brandt Clarke

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Filip Zadina	Mavrik Bourque	Nils Hoglander	Olen Zellweger	Mattias Samuelsson

Extra Forwards
Normal	PowerPlay	Penalty Kill
Sheldon Dries, Maxime Comtois, Zach Aston-Reese	Sheldon Dries, Maxime Comtois	Zach Aston-Reese

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Ty Smith, Oliver Kylington, Brandt Clarke	Ty Smith	Oliver Kylington, Olen Zellweger

Penalty Shots
Nils Hoglander, Filip Zadina, Mavrik Bourque, Samuel Fagemo, Brett Howden

Goalie
#1 : Casey DeSmith, #2 : Akira Schmid

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

Custom OT Lines Defensemen
, , , ,

Show or Hide Column

Filter Tips

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

	Overall												Home											Visitor
#	VS Team	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Bulldogs	2	2	0	0	0	0	0	0	11	8	3	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	7	5	2	4	1.000	11	19	30	0	0	7	10	12	1	91	85	119	113	6	58	15	8	39	13	2	15.38%	4	1	75.00%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
2	Phantoms	2	2	0	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	2	1	4	1.000	6	10	16	0	0	7	10	12	1	74	85	119	113	6	58	18	18	39	11	1	9.09%	9	0	100.00%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
3	Saints	2	0	2	0	0	0	0	0	5	9	-4	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	3	5	-2	0	0.000	5	9	14	0	0	7	10	12	1	70	85	119	113	6	91	33	26	36	7	1	14.29%	13	4	69.23%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
4	Titans	2	0	1	0	0	0	1	0	8	9	-1	1	0	1	0	0	0	0	0	3	5	-2	1	0	0	0	0	0	1	0	5	4	1	2	0.500	8	11	19	0	0	7	10	12	1	84	85	119	113	6	83	20	24	54	4	2	50.00%	11	3	72.73%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
	Total	8	4	3	0	0	0	1	0	30	30	0	4	2	2	0	0	0	0	0	12	14	-2	4	2	1	0	0	0	1	0	18	16	2	10	0.625	30	49	79	0	0	7	10	12	1	319	85	119	113	6	290	86	76	168	35	6	17.14%	37	8	78.38%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
	_Since Last GM Reset	8	4	3	0	0	0	1	0	30	30	0	4	2	2	0	0	0	0	0	12	14	-2	4	2	1	0	0	0	1	0	18	16	2	10	0.625	30	49	79	0	0	7	10	12	1	319	85	119	113	6	290	86	76	168	35	6	17.14%	37	8	78.38%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
	_Vs Conference	8	4	3	0	0	0	1	0	30	30	0	4	2	2	0	0	0	0	0	12	14	-2	4	2	1	0	0	0	1	0	18	16	2	10	0.625	30	49	79	0	0	7	10	12	1	319	85	119	113	6	290	86	76	168	35	6	17.14%	37	8	78.38%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51
	_Vs Division	4	4	3	0	0	0	1	0	19	17	2	2	2	2	0	0	0	0	0	7	8	-1	2	2	1	0	0	0	1	0	12	9	3	10	1.250	19	30	49	0	0	7	10	12	1	175	85	119	113	6	141	35	32	93	17	4	23.53%	15	4	73.33%	0	165	302	54.64%	151	290	52.07%	70	141	49.65%	197	138	186	57	100	51

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
8	10	W1	30	49	79	319	290	86	76	168	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	4	3	0	0	1	0	30	30

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	2	0	0	0	0	12	14

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	1	0	0	1	0	18	16

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

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
35	6	17.14%	37	8	78.38%	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
85	119	113	6	7	10	12	1

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
165	302	54.64%	151	290	52.07%	70	141	49.65%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
197	138	186	57	100	51

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 - 2024-09-17	4	Titans	5	Hit Man	3	L			BoxScore
3 - 2024-09-19	11	Hit Man	3	Phantoms	2	W			BoxScore
4 - 2024-09-20	20	Hit Man	3	Saints	5	L			BoxScore
6 - 2024-09-22	34	Bulldogs	3	Hit Man	4	W			BoxScore
8 - 2024-09-24	47	Phantoms	2	Hit Man	3	W			BoxScore
10 - 2024-09-26	55	Hit Man	7	Bulldogs	5	W			BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
14 - 2024-09-30	71	Saints	4	Hit Man	2	L			BoxScore
15 - 2024-10-01	79	Hit Man	5	Titans	4	W	X	X	BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	30	15
Attendance	0	0
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena 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$	2,634,787$	2,263,366$	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$

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