Columbus Blue Jackets
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance

Game Center
	Cleveland Monsters 3-10-1, 7pts	5	FINAL	6	Laval Rocket 9-4-1, 19pts

Team Stats
L3	Streak	L1
2-4-1	Home Record	5-1-1
1-6-0	Home Record	4-3-0
3-6-1	Last 10 Games	5-4-1
3.86	Goals Per Game	4.57
4.57	Goals Against Per Game	3.50
26.00%	Power Play Percentage	25.42%
63.93%	Penalty Kill Percentage	76.36%

Cleveland Monsters
3-10-1, 7pts

FINAL

Calgary Wranglers
5-8-1, 11pts

Team Stats
L3	Streak	W2
2-4-1	Home Record	4-3-0
1-6-0	Home Record	1-5-1
3-6-1	Last 10 Games	3-6-1
3.86	Goals Per Game	4.00
4.57	Goals Against Per Game	4.57
26.00%	Power Play Percentage	26.83%
63.93%	Penalty Kill Percentage	75.93%

Team Leaders
	Goals Mathieu Olivier 9	Assists Jake Bean 13

	Points Mathieu Olivier 15	Plus/Minus Josh Dunne 5

	Wins Jet Greaves 3	Save Percentage Nolan Lalonde 0.938

Team Stats
Goals For 54 3.86 GFG	Shots For 508 36.29 Avg	Power Play Percentage 26.0% 13 GF	Offensive Zone Start 40.1%
Goals Against 64 4.57 GAA	Shots Against 573 40.93 Avg	Penalty Kill Percentage 63.9%% 22 GA	Defensive Zone Start 40.3%

Team Info
	General Manager	Kai
	Coach	Trent Yawney
	Division	North Division
	Conference	Eastern Conference
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,775
	Season Tickets	0
Roster Info
	Pro Team	28
	Farm Team	18
	Contract Limit	46 / 70
	Prospects	16

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	Mathieu Olivier	0		X	X		100.00	76	74	73	60	82	63	72	58	53	67	59	71	54	59	57	0	32	65	0	27	1	775,000$
2	Trey Fix-Wolansky	0		X	X		100.00	57	58	67	64	59	65	63	63	54	61	62	60	61	58	55	0	34	62	0	25	2	1,100,000$
3	Carson Meyer	0		X	X		100.00	59	52	68	60	63	61	60	57	54	60	57	63	54	60	57	0	34	60	0	27	1	775,000$
4	Josh Dunne	0	X				100.00	56	58	64	60	70	65	63	58	69	57	60	59	55	60	56	0	37	60	0	26	1	775,000$
5	Jordan Dumais (R)	0		X	X		100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	60	60	0	35	60	0	20	1	775,000$
6	Brendan Gaunce	0	X				100.00	56	53	67	58	72	60	58	57	70	57	57	60	54	66	60	0	30	59	0	30	1	775,000$
7	Owen Sillinger	0	X				100.00	55	55	65	59	59	65	63	59	70	58	57	58	54	62	57	0	31	59	0	27	1	775,000$
8	Tyler Angle	0	X				100.00	55	55	66	60	57	67	64	58	65	57	58	59	55	56	54	0	34	59	0	24	1	775,000$
9	Hunter Mckown	0	X				100.00	54	38	70	53	78	61	54	51	76	69	51	62	51	51	55	0	34	58	0	22	1	775,000$
10	Jake Bean	0				X	100.00	63	40	82	70	72	77	72	62	40	75	59	77	54	58	58	0	31	69	0	26	4	1,850,000$
11	Nick Blankenburg	0				X	100.00	66	40	78	65	71	79	61	60	40	73	62	79	54	55	58	0	31	68	0	26	1	775,000$
12	Tim Berni	0				X	100.00	65	47	71	61	69	71	67	55	40	62	56	74	54	56	56	0	34	63	0	24	1	775,000$
13	Jake Christiansen	0				X	100.00	58	48	76	61	67	66	60	56	40	63	58	66	54	57	57	0	34	61	0	25	1	775,000$
14	Denton Mateychuk (R)	0				X	100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	60	60	0	34	60	0	20	1	775,000$
15	Billy Sweezey	0				X	100.00	56	56	63	57	69	66	64	55	40	57	54	61	54	63	58	0	34	59	0	28	1	775,000$
16	Marcus Bjork	0				X	100.00	59	47	63	57	69	66	58	55	38	60	55	64	51	56	54	0	34	59	0	27	1	775,000$
17	Cole Clayton	0				X	100.00	56	55	65	57	68	63	62	56	40	55	56	59	54	56	54	0	34	58	0	24	1	775,000$
18	David Jiricek	0				X	100.00	53	53	63	56	66	62	60	57	38	57	53	56	54	49	48	0	34	56	0	21	1	1,400,000$
Scratches
1	Stefan Matteau	0	X				100.00	54	51	62	53	67	55	54	52	52	52	52	59	51	63	56	0	26	55	0	30	1	775,000$
2	Mikael Pyyhtia	0		X	X		100.00	52	49	64	52	53	56	52	52	51	53	52	57	51	52	51	0	26	53	0	23	1	775,000$
3	Cameron Butler	0		X	X		100.00	51	51	65	51	66	51	51	51	52	51	51	57	51	51	49	0	26	53	0	22	1	775,000$
4	Justin Pearson	0	X	X	X		100.00	51	51	65	51	59	52	52	51	52	51	51	57	51	58	53	0	26	53	0	26	1	775,000$
5	Stanislav Svozil	0				X	100.00	53	43	61	52	65	66	52	54	38	55	50	57	51	50	52	0	26	55	0	21	1	775,000$
6	Samuel Knazko	0				X	100.00	52	50	64	53	60	61	59	54	38	55	51	54	51	51	49	0	26	54	0	22	1	775,000$
7	Corson Ceulemans	0				X	100.00	52	51	65	52	62	53	53	51	38	51	51	57	51	49	48	0	26	53	0	21	1	775,000$

	TEAM AVERAGE						100.00	57	52	67	58	66	63	60	56	51	59	56	62	54	57	55	0	31	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	Jet Greaves	0	100.00	61	65	65	61	67	59	66	65	59	60	60	54	51	0	27	62	0	23	1	775,000$
2	Nolan Lalonde (R)	0	100.00	60	60	60	60	60	60	60	60	60	60	60	60	60	0	37	60	0	20	1	775,000$
Scratches
1	Pavel Cajan	0	100.00	54	57	57	64	63	52	63	56	53	55	55	49	48	0	33	57	0	22	1	775,000$

	TEAM AVERAGE		100.00	58	61	61	62	63	57	63	60	57	58	58	54	53	0	32	60

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Trent Yawney	65	72	65	71	85	79	63	CAN	57	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	HT	P/20	FW	FT	S1	S2	S3
1	Mathieu Olivier	Cleveland Monsters (CBJ)	LW/RW	14	9	6	15	3	34	10	50	29	42	24	51	21.43%	7	300	21.45	1	2	3	6	41	0	0	0	1	41	2	0	46.85%	111	1	1.00	0	2	1	2	2
2	Trey Fix-Wolansky	Cleveland Monsters (CBJ)	LW/RW	14	4	11	15	3	2	0	15	22	64	18	39	6.25%	3	325	23.25	2	0	2	15	43	0	0	0	4	47	0	0	50.00%	30	0	0.92	0	0	0	0	2
3	Jake Bean	Cleveland Monsters (CBJ)	D	14	1	13	14	-1	4	0	26	28	28	13	24	3.57%	28	306	21.87	1	3	4	15	45	0	0	0	0	44	0	0	0%	0	0	0.91	0	0	0	0	2
4	Nick Blankenburg	Cleveland Monsters (CBJ)	D	14	4	8	12	0	12	0	33	26	39	17	20	10.26%	25	300	21.50	1	2	3	18	45	0	0	0	0	43	0	0	0%	1	0	0.80	0	0	1	0	0
5	Tim Berni	Cleveland Monsters (CBJ)	D	14	4	7	11	-3	8	0	31	19	37	12	30	10.81%	35	282	20.20	3	2	5	22	43	0	1	1	0	44	0	0	0%	1	0	0.78	0	0	0	1	0
6	Jake Christiansen	Cleveland Monsters (CBJ)	D	14	3	7	10	-3	14	0	14	9	23	15	32	13.04%	13	280	20.07	1	4	5	13	43	0	0	0	0	43	0	0	0%	0	0	0.71	0	0	1	0	0
7	Owen Sillinger	Cleveland Monsters (CBJ)	C	14	4	6	10	-2	21	5	14	25	41	11	37	9.76%	3	206	14.78	0	1	1	0	3	0	0	0	0	0	0	0	52.67%	262	0	0.97	0	1	0	1	0
8	Carson Meyer	Cleveland Monsters (CBJ)	LW/RW	14	5	4	9	-2	6	0	23	14	42	13	26	11.90%	1	300	21.49	1	2	3	13	44	1	0	1	3	41	0	0	53.85%	26	0	0.60	0	0	0	1	0
9	Josh Dunne	Cleveland Monsters (CBJ)	C	14	5	4	9	5	13	5	13	51	30	12	17	16.67%	3	288	20.58	2	1	3	5	42	0	1	1	4	38	0	0	57.27%	447	0	0.62	1	0	0	0	1
10	Jordan Dumais	Cleveland Monsters (CBJ)	LW/RW	14	2	7	9	-4	9	5	41	19	39	12	31	5.13%	2	271	19.38	0	4	4	6	43	0	0	0	0	19	0	0	35.42%	48	0	0.66	0	1	0	0	0
11	Tyler Angle	Cleveland Monsters (CBJ)	C	14	7	1	8	-1	4	0	22	11	40	14	25	17.50%	2	230	16.46	0	0	0	0	0	0	0	0	0	0	0	0	44.64%	56	0	0.69	0	0	0	0	0
12	Marcus Bjork	Cleveland Monsters (CBJ)	D	14	1	6	7	2	12	0	15	11	10	8	17	10.00%	16	168	12.02	0	0	0	0	0	0	0	0	0	4	1	0	0%	0	0	0.83	0	0	0	0	0
13	Denton Mateychuk	Cleveland Monsters (CBJ)	D	14	1	6	7	2	12	0	18	17	11	6	7	9.09%	14	177	12.70	0	0	0	0	0	0	0	0	0	13	0	0	0%	0	0	0.79	0	0	0	0	1
14	Hunter Mckown	Cleveland Monsters (CBJ)	C	14	3	3	6	-2	6	0	17	16	27	11	16	11.11%	4	213	15.23	0	0	0	1	3	0	0	0	0	0	0	1	50.00%	16	0	0.56	0	0	0	0	0
15	Brendan Gaunce	Cleveland Monsters (CBJ)	C	14	1	4	5	-3	19	5	14	32	23	7	26	4.35%	3	253	18.08	1	2	3	7	44	0	0	0	0	3	0	0	54.91%	346	0	0.39	0	0	0	0	0
16	Billy Sweezey	Cleveland Monsters (CBJ)	D	14	0	1	1	1	6	0	13	5	7	0	2	0%	9	96	6.88	0	0	0	0	3	0	0	0	0	4	0	0	100.00%	1	0	0.21	0	0	0	0	0
17	Cole Clayton	Cleveland Monsters (CBJ)	D	14	0	1	1	1	4	0	5	4	5	3	2	0%	6	86	6.15	0	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0.23	0	0	0	0	0
18	David Jiricek	Cleveland Monsters (CBJ)	D	14	0	0	0	0	0	0	0	0	0	0	0	0%	1	3	0.26	0	0	0	0	0	0	0	0	0	0	0	0	0%	0	0	0	0	0	0	0	0
Team Total or Average				252	54	95	149	-4	186	30	364	338	508	196	402	10.63%	175	4092	16.24	13	23	36	121	449	1	2	3	12	391	3	1	53.23%	1345	1	0.73	1	4	3	5	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	PS %	ST	BG	S1
1	Jet Greaves	Cleveland Monsters (CBJ)	14	3	8	1	0.882	4.76	757	2	1	60	509	0	14	0	2
2	Nolan Lalonde	Cleveland Monsters (CBJ)	2	0	2	0	0.938	3.00	80	0	0	4	64	0	0	14	0
Team Total or Average			16	3	10	1	0.888	4.59	837	2	1	64	573		14	14	2

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
Billy Sweezey	Cleveland Monsters (CBJ)	D	28	06.02.1996	No	94 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Brendan Gaunce	Cleveland Monsters (CBJ)	C	30	25.03.1994	No	100 Kg	191 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Cameron Butler	Cleveland Monsters (CBJ)	LW/RW	22	09.06.2002	No	95 Kg	193 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Carson Meyer	Cleveland Monsters (CBJ)	LW/RW	27	18.08.1997	No	84 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Cole Clayton	Cleveland Monsters (CBJ)	D	24	29.02.2000	No	95 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Corson Ceulemans	Cleveland Monsters (CBJ)	D	21	05.05.2003	No	90 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
David Jiricek	Cleveland Monsters (CBJ)	D	21	28.11.2003	No	90 Kg	193 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	1,400,000$	1,400,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Denton Mateychuk	Cleveland Monsters (CBJ)	D	20	12.07.2004	Yes	87 Kg	180 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Hunter Mckown	Cleveland Monsters (CBJ)	C	22	18.08.2002	No	93 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jake Bean	Cleveland Monsters (CBJ)	D	26	09.06.1998	No	89 Kg	185 CM	No	No	N/A	No	No	4	False	False	Pro & Farm	1,850,000$	1,850,000$	0$	0$	No	1,850,000$	1,850,000$	1,850,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-	Link
Jake Christiansen	Cleveland Monsters (CBJ)	D	25	12.09.1999	No	88 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jet Greaves	Cleveland Monsters (CBJ)	G	23	30.03.2001	No	84 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jordan Dumais	Cleveland Monsters (CBJ)	LW/RW	20	15.04.2004	Yes	79 Kg	175 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Josh Dunne	Cleveland Monsters (CBJ)	C	26	08.12.1998	No	96 Kg	193 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Justin Pearson	Cleveland Monsters (CBJ)	C/LW/RW	26	17.05.1998	No	84 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Marcus Bjork	Cleveland Monsters (CBJ)	D	27	24.11.1997	No	96 Kg	193 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Mathieu Olivier	Cleveland Monsters (CBJ)	LW/RW	27	11.02.1997	No	99 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Mikael Pyyhtia	Cleveland Monsters (CBJ)	LW/RW	23	17.12.2001	No	75 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Nick Blankenburg	Cleveland Monsters (CBJ)	D	26	12.05.1998	No	80 Kg	175 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Nolan Lalonde	Cleveland Monsters (CBJ)	G	20	14.02.2004	Yes	84 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Owen Sillinger	Cleveland Monsters (CBJ)	C	27	23.09.1997	No	83 Kg	178 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Pavel Cajan	Cleveland Monsters (CBJ)	G	22	13.10.2002	No	84 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Samuel Knazko	Cleveland Monsters (CBJ)	D	22	07.08.2002	No	90 Kg	185 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Stanislav Svozil	Cleveland Monsters (CBJ)	D	21	17.01.2003	No	83 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Stefan Matteau	Cleveland Monsters (CBJ)	C	30	23.02.1994	No	100 Kg	188 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Tim Berni	Cleveland Monsters (CBJ)	D	24	11.02.2000	No	87 Kg	183 CM	No	No	N/A	No	No	1	False	False	Pro & Farm	775,000$	775,000$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link / NHL Link
Trey Fix-Wolansky	Cleveland Monsters (CBJ)	LW/RW	25	26.05.1999	No	87 Kg	170 CM	No	No	N/A	No	No	2	False	False	Pro & Farm	1,100,000$	1,100,000$	0$	0$	No	1,100,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Tyler Angle	Cleveland Monsters (CBJ)	C	24	30.09.2000	No	75 Kg	178 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
28	24.25	88 Kg	185 CM	1.14	847,321$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Mathieu Olivier	Josh Dunne	Trey Fix-Wolansky	33	1	2	2
2	Carson Meyer	Brendan Gaunce	Jordan Dumais	30	1	2	2
3	Tyler Angle	Owen Sillinger	Hunter Mckown	25	1	2	2
4	Mathieu Olivier	Tyler Angle	Trey Fix-Wolansky	12	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Bean	Nick Blankenburg	33	1	2	2
2	Tim Berni	Jake Christiansen	30	1	2	2
3	Denton Mateychuk	Marcus Bjork	25	1	2	2
4	Billy Sweezey	Cole Clayton	12	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Mathieu Olivier	Josh Dunne	Trey Fix-Wolansky	50	1	2	2
2	Carson Meyer	Brendan Gaunce	Jordan Dumais	50	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Bean	Nick Blankenburg	50	1	2	2
2	Tim Berni	Jake Christiansen	50	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Mathieu Olivier	Trey Fix-Wolansky	50	1	2	2
2	Josh Dunne	Carson Meyer	50	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Bean	Nick Blankenburg	50	1	2	2
2	Tim Berni	Jake Christiansen	50	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Mathieu Olivier	50	1	2	2	Jake Bean	Nick Blankenburg	50	1	2	2
2	Trey Fix-Wolansky	50	1	2	2	Tim Berni	Jake Christiansen	50	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Mathieu Olivier	Trey Fix-Wolansky	50	1	2	2
2	Josh Dunne	Carson Meyer	50	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Jake Bean	Nick Blankenburg	50	1	2	2
2	Tim Berni	Jake Christiansen	50	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Mathieu Olivier	Josh Dunne	Trey Fix-Wolansky	Jake Bean	Nick Blankenburg

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Mathieu Olivier	Josh Dunne	Trey Fix-Wolansky	Jake Bean	Nick Blankenburg

Extra Forwards
Normal	PowerPlay	Penalty Kill
Owen Sillinger, Hunter Mckown, Jordan Dumais	Owen Sillinger, Hunter Mckown	Jordan Dumais

Extra Defensemen
Normal	PowerPlay	Penalty Kill
David Jiricek, Denton Mateychuk, Marcus Bjork	David Jiricek	Denton Mateychuk, Marcus Bjork

Penalty Shots
Mathieu Olivier, Trey Fix-Wolansky, Josh Dunne, Carson Meyer, Jordan Dumais

Goalie
#1 : Jet Greaves, #2 : Nolan Lalonde

Custom OT Lines Forwards
Mathieu Olivier, Trey Fix-Wolansky, Josh Dunne, Carson Meyer, Jordan Dumais, Brendan Gaunce, Brendan Gaunce, Owen Sillinger, Tyler Angle, Hunter Mckown,

Custom OT Lines Defensemen
Jake Bean, Nick Blankenburg, Tim Berni, Jake Christiansen, Denton Mateychuk

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	8	8	0	1	1	0	0	0	0	0	0	5	3	2	1	0	1	0	0	0	0	0	3	5	-2	2	0.500	8	16	24	0	0	24	19	11	0	101	179	165	164	0	61	16	14	58	9	2	22.22%	6	2	66.67%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
2	Calgary Wranglers	2	0	2	0	0	0	0	0	7	10	-3	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	4	5	-1	0	0.000	7	11	18	0	0	24	19	11	0	63	179	165	164	0	79	25	21	55	9	2	22.22%	8	3	62.50%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
3	Laval Rocket	2	0	2	0	0	0	0	0	7	13	-6	1	0	1	0	0	0	0	0	2	7	-5	1	0	1	0	0	0	0	0	5	6	-1	0	0.000	7	12	19	0	0	24	19	11	0	71	179	165	164	0	93	32	24	48	6	3	50.00%	11	3	72.73%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
4	Rochester Americans	2	0	2	0	0	0	0	0	10	13	-3	1	0	1	0	0	0	0	0	5	6	-1	1	0	1	0	0	0	0	0	5	7	-2	0	0.000	10	17	27	0	0	24	19	11	0	79	179	165	164	0	101	31	26	45	3	2	66.67%	12	6	50.00%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
5	Syracuse Crunch	2	1	0	0	0	1	0	0	12	9	3	1	0	0	0	0	1	0	0	5	6	-1	1	1	0	0	0	0	0	0	7	3	4	3	0.750	12	22	34	0	0	24	19	11	0	68	179	165	164	0	68	28	16	54	10	2	20.00%	7	4	42.86%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
6	Toronto Marlies	2	1	1	0	0	0	0	0	6	4	2	1	1	0	0	0	0	0	0	3	0	3	1	0	1	0	0	0	0	0	3	4	-1	2	0.500	6	9	15	0	1	24	19	11	0	61	179	165	164	0	78	24	52	44	7	2	28.57%	6	1	83.33%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
7	Utica Comets	2	0	2	0	0	0	0	0	4	7	-3	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	4	8	12	0	0	24	19	11	0	65	179	165	164	0	93	19	35	60	6	0	0.00%	11	3	72.73%	1	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
	Total	14	3	10	0	0	1	0	0	54	64	-10	7	2	4	0	0	1	0	0	25	30	-5	7	1	6	0	0	0	0	0	29	34	-5	7	0.250	54	95	149	0	1	24	19	11	0	508	179	165	164	0	573	175	188	364	50	13	26.00%	61	22	63.93%	1	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
	_Since Last GM Reset	14	3	10	0	0	1	0	0	54	64	-10	7	2	4	0	0	1	0	0	25	30	-5	7	1	6	0	0	0	0	0	29	34	-5	7	0.250	54	95	149	0	1	24	19	11	0	508	179	165	164	0	573	175	188	364	50	13	26.00%	61	22	63.93%	1	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
	_Vs Conference	14	3	10	0	0	1	0	0	54	64	-10	7	2	4	0	0	1	0	0	25	30	-5	7	1	6	0	0	0	0	0	29	34	-5	7	0.250	54	95	149	0	1	24	19	11	0	508	179	165	164	0	573	175	188	364	50	13	26.00%	61	22	63.93%	1	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98
	_Vs Division	10	3	10	0	0	1	0	0	43	47	-4	5	2	4	0	0	1	0	0	20	22	-2	5	1	6	0	0	0	0	0	23	25	-2	7	0.350	43	76	119	0	1	24	19	11	0	380	179	165	164	0	401	131	132	249	35	11	31.43%	42	16	61.90%	0	280	539	51.95%	304	541	56.19%	132	264	50.00%	340	225	307	107	193	98

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
14	7	L3	54	95	149	508	573	175	188	364	0	1

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
14	3	10	0	1	0	0	54	64

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
7	2	4	0	1	0	0	25	30

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
7	1	6	0	0	0	0	29	34

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

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
50	13	26.00%	61	22	63.93%	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
179	165	164	0	24	19	11	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
280	539	51.95%	304	541	56.19%	132	264	50.00%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
340	225	307	107	193	98

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	Link
1	5	Utica Comets	3	Cleveland Monsters	2	L		BoxScore
4	30	Cleveland Monsters	3	Belleville Senators	5	L		BoxScore
5	37	Cleveland Monsters	3	Toronto Marlies	4	L		BoxScore
7	59	Laval Rocket	7	Cleveland Monsters	2	L		BoxScore
10	78	Calgary Wranglers	5	Cleveland Monsters	3	L		BoxScore
11	91	Cleveland Monsters	7	Syracuse Crunch	3	W		BoxScore
14	111	Syracuse Crunch	6	Cleveland Monsters	5	L	X	BoxScore
16	130	Belleville Senators	3	Cleveland Monsters	5	W		BoxScore
18	146	Cleveland Monsters	2	Utica Comets	4	L		BoxScore
21	165	Cleveland Monsters	5	Rochester Americans	7	L		BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
22	176	Toronto Marlies	0	Cleveland Monsters	3	W		BoxScore
25	195	Rochester Americans	6	Cleveland Monsters	5	L		BoxScore
26	202	Cleveland Monsters	5	Laval Rocket	6	L		BoxScore
27	210	Cleveland Monsters	4	Calgary Wranglers	5	L		BoxScore

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	15
Attendance	13,041	6,383
Attendance PCT	93.15%	91.19%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
0	2775 - 92.50%	78,883$	552,180$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
2,872,500$	2,372,500$	2,372,500$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
79,083$	2,372,490$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	95,750$	0$

Cleveland Monsters 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

Cleveland Monsters 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

Cleveland Monsters 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

Cleveland Monsters 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

Cleveland Monsters 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

	Cleveland Monsters
	GP: 14 \| W: 3 \| L: 10 \| OTL: 1 \| P: 7 GF: 54 \| GA: 64 \| PP%: 26.00% \| PK%: 63.93% GM : Kai \| Morale : 30 \| Team Overall : 59