Reddit Hockey League

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

Monsters
0-0-0, 0pts

2022-09-14

Wolves
0-0-0, 0pts

Team Stats
N/A	Streak	N/A
0-0-0	Home Record	0-0-0
0-0-0	Away Record	0-0-0
0-0-0	Last 10 Games	0-0-0
0.00	Goals Per Game	0.00
0.00	Goals Against Per Game	0.00
0.00%	Power Play Percentage	0.00%
0.00%	Penalty Kill Percentage	0.00%

Crunch
0-0-0, 0pts

2022-09-16

Monsters
0-0-0, 0pts

Team Stats
N/A	Streak	N/A
0-0-0	Home Record	0-0-0
0-0-0	Away Record	0-0-0
0-0-0	Last 10 Games	0-0-0
0.00	Goals Per Game	0.00
0.00	Goals Against Per Game	0.00
0.00%	Power Play Percentage	0.00%
0.00%	Penalty Kill Percentage	0.00%

Monsters
0-0-0, 0pts

2022-09-17

Thunderbirds
0-0-0, 0pts

Team Stats
N/A	Streak	N/A
0-0-0	Home Record	0-0-0
0-0-0	Away Record	0-0-0
0-0-0	Last 10 Games	0-0-0
0.00	Goals Per Game	0.00
0.00	Goals Against Per Game	0.00
0.00%	Power Play Percentage	0.00%
0.00%	Penalty Kill Percentage	0.00%

Team Leaders

Team Stats
Goals For 0 0.00 GFG	Shots For 0 0.00 Avg	Power Play Percentage 0.0% 0 GF	Offensive Zone Start 0.0%
Goals Against 0 0.00 GAA	Shots Against 0 0.00 Avg	Penalty Kill Percentage 0.0% 0 GA	Defensive Zone Start 0.0%

Team Info
	General Manager	MixtureBill
	Coach	Doug Houda
	Division	Metropolitan Division
	Conference	Eastern Conference
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	0
	Season Tickets	0
Roster Info
	Pro Team	29
	Farm Team	18
	Contract Limit	47 / 90
	Prospects	34
Team History
	This Season	0-0-0 (0PTS)
	History	112-22-24 (0.709%)
	Playoff Appearances	2
	Playoff Record (W-L)	22-16
	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 Average
1	MacKenzie Entwistle	0			X		100.00	93	46	95	65	81	74	62	59	53	64	60	66	60	52	65	0	40	61	0	23	1	900,000$
2	Alex Barre-Boulet	0	X				100.00	58	40	78	64	78	60	56	67	54	72	63	60	62	55	65	0	40	60	0	25	1	950,000$
3	Evgeny Svechnikov	0		X			100.00	79	47	82	60	87	76	71	57	53	65	61	63	61	52	63	0	40	60	0	25	2	1,200,000$
4	Karson Kuhlman	0	X				100.00	83	43	88	64	80	78	59	58	46	64	60	65	60	55	65	0	40	60	0	27	1	950,000$
5	Chris Wagner	0	X		X		100.00	99	43	75	64	81	60	51	56	46	62	61	63	61	76	84	0	40	60	0	31	1	925,000$
6	Oskar Steen	0	X				100.00	82	45	79	59	81	61	55	61	49	66	63	61	62	53	64	0	40	59	0	24	1	850,000$
7	Mitchell Stephens	0	X				100.00	62	43	83	63	80	77	50	57	75	67	55	66	58	53	62	0	40	59	0	25	1	850,000$
8	Tyce Thompson	0	X	X	X		100.00	70	54	77	63	78	60	28	61	67	68	63	63	62	52	61	0	40	58	0	23	1	500,000$
9	Radim Zohorna	0	X	X			100.00	77	50	78	54	91	61	48	61	52	65	63	62	62	52	63	0	40	58	0	26	3	1,000,000$
10	Anthony Angello	0			X		100.00	99	53	75	54	89	60	43	56	46	63	59	59	60	54	67	0	40	57	0	26	1	1,000,000$
11	Brett Leason	0			X		100.00	55	50	79	53	90	64	57	57	53	63	60	62	60	53	63	0	40	57	0	23	1	925,000$
12	Wade Allison	0	X	X	X		100.00	70	46	75	55	86	60	34	59	40	63	63	59	62	51	61	0	40	56	0	24	1	925,000$
13	Colin Miller	0				X	100.00	77	50	91	65	83	83	61	76	20	73	62	73	61	62	71	0	40	67	0	29	3	1,750,000$
14	Mike Reilly	0				X	100.00	77	44	88	63	83	83	77	68	20	66	63	72	62	58	68	0	40	66	0	29	1	1,250,000$
15	Gavin Bayreuther	0				X	100.00	74	54	85	60	83	71	54	70	20	66	56	71	58	59	68	0	40	63	0	28	2	850,000$
16	Jordan Gross	0				X	100.00	70	43	74	61	80	60	50	79	20	75	64	65	62	55	64	0	40	61	0	27	4	850,000$
17	Matt Kiersted	0				X	100.00	70	40	77	58	79	60	56	66	20	63	60	67	60	53	62	0	40	60	0	24	1	925,000$
18	Jacob Moverare (R)	0				X	100.00	64	48	81	53	87	63	47	68	20	63	59	68	60	51	61	0	40	59	0	24	1	800,000$
Scratches
1	James Hamblin (R)	0		X			100.00	50	40	75	59	77	60	42	58	40	62	63	62	62	54	63	0	40	56	0	23	2	800,000$
2	Tyler Tucker	0				X	100.00	50	61	75	51	85	60	50	66	20	63	57	68	59	55	64	0	40	58	0	22	2	750,000$
3	Brennan Menell	0				X	100.00	50	44	75	57	78	60	16	66	20	63	57	68	59	56	65	0	40	56	0	25	1	850,000$
4	Philip Kemp (R)	0				X	100.00	50	53	75	49	87	60	33	64	20	60	58	68	59	51	61	0	40	56	0	23	1	750,000$
5	Max Gildon (R)	0				X	100.00	50	42	75	54	82	60	10	66	20	62	57	68	59	52	62	0	40	55	0	23	1	800,000$

	TEAM AVERAGE					100.00	70	47	80	59	83	66	48	63	38	65	60	65	60	55	65	0	40	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
1	Casey DeSmith	100.00	77	77	74	83	82	80	80	82	81	79	80	80	73	0	40	77	0
2	Dustin Wolf (R)	100.00	72	74	71	82	76	76	73	77	76	74	75	65	66	0	40	72	0
Scratches
1	Zane McIntyre	100.00	69	72	69	86	74	73	71	75	74	71	72	78	72	0	40	72	0
2	Jon Gillies	100.00	62	72	69	90	67	66	65	67	67	63	65	76	71	0	40	67	0
3	Pyotr Kochetkov (R)	100.00	61	64	61	85	66	65	66	66	66	62	65	68	67	0	40	65	0
4	Kyle Keyser	100.00	60	66	63	85	64	64	62	65	64	61	63	68	67	0	40	64	0

	TEAM AVERAGE	100.00	67	71	68	85	72	71	70	72	71	68	70	73	69	0	40	70

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Doug Houda	75	75	75	75	75	75	75	CAN	55	8	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	GA	TA	EG	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3

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	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3

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 Average	Salary Ave Remaining	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Link
Alex Barre-Boulet	Monsters (CBJ)	C	25	5/21/1997	No	180 Lbs	5 ft10	No	No	No	1	Pro & Farm	950,000$	939,947$	950,000$	939,947$	0$	0$	No				Link
Anthony Angello	Monsters (CBJ)	RW	26	3/6/1996	No	210 Lbs	6 ft5	No	No	No	1	Pro & Farm	1,000,000$	989,418$	1,000,000$	989,418$	0$	0$	No				Link
Brennan Menell	Monsters (CBJ)	D	25	5/24/1997	No	177 Lbs	5 ft11	No	No	No	1	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No				Link
Brett Leason	Monsters (CBJ)	RW	23	4/30/1999	No	218 Lbs	6 ft5	No	No	No	1	Pro & Farm	925,000$	915,212$	925,000$	915,212$	0$	0$	No				Link
Casey DeSmith (1 Way Contract)	Monsters (CBJ)	G	31	8/13/1991	No	181 Lbs	6 ft0	No	No	No	4	Pro & Farm	1,125,000$	1,113,095$	1,125,000$	1,113,095$	0$	0$	No	1,125,000$	1,125,000$	1,125,000$	Link
Chris Wagner	Monsters (CBJ)	C/RW	31	5/27/1991	No	191 Lbs	6 ft0	No	No	No	1	Pro & Farm	925,000$	915,212$	925,000$	915,212$	0$	0$	No				Link
Colin Miller (1 Way Contract)	Monsters (CBJ)	D	29	10/29/1992	No	198 Lbs	6 ft1	No	No	No	3	Pro & Farm	1,750,000$	1,731,481$	1,750,000$	1,731,481$	625,000$	618,386$	No	1,750,000$	1,750,000$		Link
Dustin Wolf	Monsters (CBJ)	G	21	7/1/2001	Yes	157 Lbs	6 ft0	No	No	No	3	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No	850,000$	850,000$		Link
Evgeny Svechnikov (1 Way Contract)	Monsters (CBJ)	LW	25	10/31/1996	No	208 Lbs	6 ft3	No	No	No	2	Pro & Farm	1,200,000$	1,187,302$	1,200,000$	1,187,302$	75,000$	74,206$	No	1,200,000$			Link
Gavin Bayreuther	Monsters (CBJ)	D	28	5/12/1994	No	196 Lbs	6 ft1	No	No	No	2	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No	850,000$			Link
Jacob Moverare	Monsters (CBJ)	D	24	8/31/1998	Yes	210 Lbs	6 ft3	No	No	No	1	Pro & Farm	800,000$	791,534$	800,000$	791,534$	0$	0$	No				Link
James Hamblin	Monsters (CBJ)	LW	23	4/27/1999	Yes	180 Lbs	5 ft9	No	No	No	2	Pro & Farm	800,000$	791,534$	800,000$	791,534$	0$	0$	No	800,000$			Link
Jon Gillies	Monsters (CBJ)	G	28	1/22/1994	No	223 Lbs	6 ft6	No	No	No	2	Pro & Farm	750,000$	742,063$	750,000$	742,063$	0$	0$	No	750,000$			Link
Jordan Gross (1 Way Contract)	Monsters (CBJ)	D	27	5/9/1995	No	190 Lbs	5 ft10	No	No	No	4	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No	850,000$	850,000$	850,000$	Link
Karson Kuhlman (1 Way Contract)	Monsters (CBJ)	C	27	9/26/1995	No	190 Lbs	5 ft10	No	No	No	1	Pro & Farm	950,000$	939,947$	950,000$	939,947$	0$	0$	No				Link
Kyle Keyser	Monsters (CBJ)	G	23	7/1/1999	No	179 Lbs	6 ft2	No	No	No	1	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No				Link
MacKenzie Entwistle	Monsters (CBJ)	RW	23	7/14/1999	No	184 Lbs	6 ft3	No	No	No	1	Pro & Farm	900,000$	890,476$	900,000$	890,476$	0$	0$	No				Link
Matt Kiersted	Monsters (CBJ)	D	24	4/14/1998	No	181 Lbs	6 ft0	No	No	No	1	Pro & Farm	925,000$	915,212$	925,000$	915,212$	0$	0$	No				Link
Max Gildon	Monsters (CBJ)	D	23	5/17/1999	Yes	187 Lbs	6 ft3	No	No	No	1	Pro & Farm	800,000$	791,534$	800,000$	791,534$	0$	0$	No				Link
Mike Reilly	Monsters (CBJ)	D	29	7/13/1993	No	195 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,250,000$	1,236,772$	1,250,000$	1,236,772$	0$	0$	No				Link
Mitchell Stephens	Monsters (CBJ)	C	25	2/5/1997	No	190 Lbs	5 ft11	No	No	No	1	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No				Link
Oskar Steen	Monsters (CBJ)	C	24	3/9/1998	No	199 Lbs	5 ft9	No	No	No	1	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No				Link
Philip Kemp	Monsters (CBJ)	D	23	2/12/1999	Yes	211 Lbs	6 ft3	No	No	No	1	Pro & Farm	750,000$	742,063$	750,000$	742,063$	0$	0$	No				Link
Pyotr Kochetkov	Monsters (CBJ)	G	23	6/25/1999	Yes	179 Lbs	6 ft2	No	No	No	2	Pro & Farm	850,000$	841,005$	850,000$	841,005$	0$	0$	No	850,000$			Link
Radim Zohorna (1 Way Contract)	Monsters (CBJ)	C/LW	26	4/29/1996	No	220 Lbs	6 ft6	No	No	No	3	Pro & Farm	1,000,000$	989,418$	1,000,000$	989,418$	0$	0$	No	1,000,000$	1,000,000$		Link
Tyce Thompson	Monsters (CBJ)	C/LW/RW	23	7/12/1999	No	175 Lbs	6 ft1	No	No	No	1	Pro & Farm	500,000$	494,709$	500,000$	494,709$	0$	0$	No				Link
Tyler Tucker	Monsters (CBJ)	D	22	3/1/2000	No	205 Lbs	6 ft1	No	No	No	2	Pro & Farm	750,000$	742,063$	750,000$	742,063$	0$	0$	No	750,000$			Link
Wade Allison	Monsters (CBJ)	C/LW/RW	24	10/14/1997	No	205 Lbs	6 ft2	No	No	No	1	Pro & Farm	925,000$	915,212$	925,000$	915,212$	0$	0$	No				Link
Zane McIntyre	Monsters (CBJ)	G	30	8/20/1992	No	205 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,000,000$	989,418$	1,000,000$	989,418$	0$	0$	No				Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
29	25.34	194 Lbs	6 ft1	1.62	923,276$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Alex Barre-Boulet	Tyce Thompson	Wade Allison	40	0	1	4
2	Evgeny Svechnikov	Mitchell Stephens	Oskar Steen	35	0	1	4
3	Chris Wagner	Radim Zohorna	Karson Kuhlman	17	0	2	3
4	Anthony Angello	Brett Leason	MacKenzie Entwistle	8	0	3	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Colin Miller	Jordan Gross	37	0	2	3
2	Gavin Bayreuther	Mike Reilly	33	0	2	3
3	Jacob Moverare	Matt Kiersted	22	0	3	2
4	Colin Miller	Mike Reilly	8	0	1	4

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Alex Barre-Boulet	Tyce Thompson	Wade Allison	60	0	0	5
2	Radim Zohorna	Mitchell Stephens	Oskar Steen	40	0	0	5

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Colin Miller	Jordan Gross	60	0	0	5
2	Gavin Bayreuther	Mike Reilly	40	0	0	5

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Mitchell Stephens	MacKenzie Entwistle	60	1	2	2
2	Evgeny Svechnikov	Karson Kuhlman	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Gavin Bayreuther	Jacob Moverare	60	1	2	2
2	Matt Kiersted	Mike Reilly	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Mitchell Stephens	60	1	2	2	Gavin Bayreuther	Jacob Moverare	60	1	2	2
2	MacKenzie Entwistle	40	1	2	2	Mike Reilly	Matt Kiersted	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Tyce Thompson	Radim Zohorna	60	1	2	2
2	Mitchell Stephens	Wade Allison	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Colin Miller	Jordan Gross	60	1	2	2
2	Mike Reilly	Gavin Bayreuther	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Alex Barre-Boulet	Tyce Thompson	Wade Allison	Colin Miller	Jordan Gross

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Karson Kuhlman	Mitchell Stephens	MacKenzie Entwistle	Colin Miller	Mike Reilly

Extra Forwards
Normal	PowerPlay	Penalty Kill
Radim Zohorna, Karson Kuhlman, MacKenzie Entwistle	Chris Wagner, Evgeny Svechnikov	Chris Wagner

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Jordan Gross, Gavin Bayreuther, Matt Kiersted	Jacob Moverare	Colin Miller, Jordan Gross

Penalty Shots
Tyce Thompson, Radim Zohorna, Wade Allison, Alex Barre-Boulet, Oskar Steen

Goalie
#1 : Casey DeSmith, #2 : Dustin Wolf

Custom OT Lines Forwards
Alex Barre-Boulet, MacKenzie Entwistle, Mitchell Stephens, Karson Kuhlman, Tyce Thompson, Evgeny Svechnikov, Evgeny Svechnikov, Oskar Steen, , ,

Custom OT Lines Defensemen
Colin Miller, Jordan Gross, Mike Reilly, Gavin Bayreuther, Matt Kiersted

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
	Total	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00%	0	0	0.00%	0	0	0	0.00%	0	0	0.00%	0	0	0.00%	0	0	0	0	0	0

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
0	0	N/A	0	0	0	0	0	0	0	0	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Last 10 Games
W	L	OTW	OTL	SOW	SOL
0	0	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
0	0	0.00%	0	0	0.00%	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
0	0	0	0	0	0	0	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
0	0	0.00%	0	0	0.00%	0	0	0.00%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
0	0	0	0	0	0

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
6 - 2022-09-14	6	Monsters	-	Wolves	-
8 - 2022-09-16	21	Crunch	-	Monsters	-
9 - 2022-09-17	33	Monsters	-	Thunderbirds	-
12 - 2022-09-20	50	Canucks	-	Monsters	-
14 - 2022-09-22	63	Admirals	-	Monsters	-
16 - 2022-09-24	82	Penguins	-	Monsters	-
17 - 2022-09-25	89	Monsters	-	Wolf Pack	-
19 - 2022-09-27	98	Roadrunners	-	Monsters	-
22 - 2022-09-30	122	Bruins	-	Monsters	-
24 - 2022-10-02	139	Monsters	-	Comets	-
29 - 2022-10-07	174	Monsters	-	Eagles	-
30 - 2022-10-08	176	Eagles	-	Monsters	-
35 - 2022-10-13	214	Phantoms	-	Monsters	-
37 - 2022-10-15	231	Monsters	-	Islanders	-
40 - 2022-10-18	250	Phantoms	-	Monsters	-
42 - 2022-10-20	263	Rocket	-	Monsters	-
44 - 2022-10-22	280	Griffins	-	Monsters	-
45 - 2022-10-23	289	Checkers	-	Monsters	-
48 - 2022-10-26	307	Rocket	-	Monsters	-
50 - 2022-10-28	326	Islanders	-	Monsters	-
51 - 2022-10-29	338	Monsters	-	Admirals	-
53 - 2022-10-31	349	Silver Knights	-	Monsters	-
57 - 2022-11-04	378	Monsters	-	Moose	-
59 - 2022-11-06	394	Griffins	-	Monsters	-
61 - 2022-11-08	404	Monsters	-	Penguins	-
62 - 2022-11-09	412	Americans	-	Monsters	-
64 - 2022-11-11	424	Heat	-	Monsters	-
66 - 2022-11-13	443	Reign	-	Monsters	-
68 - 2022-11-15	455	Monsters	-	Checkers	-
70 - 2022-11-17	470	Monsters	-	Crunch	-
72 - 2022-11-19	484	Monsters	-	Bruins	-
74 - 2022-11-21	502	Stars	-	Monsters	-
75 - 2022-11-22	510	Monsters	-	Phantoms	-
78 - 2022-11-25	540	Monsters	-	IceHogs	-
82 - 2022-11-29	547	Americans	-	Monsters	-
84 - 2022-12-01	567	Monsters	-	Islanders	-
86 - 2022-12-03	584	IceHogs	-	Monsters	-
89 - 2022-12-06	601	Monsters	-	Senators	-
91 - 2022-12-08	617	Bears	-	Monsters	-
93 - 2022-12-10	629	Wolves	-	Monsters	-
94 - 2022-12-11	639	Monsters	-	Bears	-
96 - 2022-12-13	648	Monsters	-	Crunch	-
98 - 2022-12-15	662	Wolves	-	Monsters	-
100 - 2022-12-17	679	Monsters	-	Griffins	-
102 - 2022-12-19	701	Wolf Pack	-	Monsters	-
105 - 2022-12-22	717	Gulls	-	Monsters	-
107 - 2022-12-24	736	Barracuda	-	Monsters	-
109 - 2022-12-26	754	Monsters	-	Heat	-
111 - 2022-12-28	768	Monsters	-	Condors	-
113 - 2022-12-30	787	Monsters	-	Canucks	-
114 - 2022-12-31	797	Monsters	-	Firebirds	-
117 - 2023-01-03	804	Bears	-	Monsters	-
127 - 2023-01-13	829	Marlies	-	Monsters	-
128 - 2023-01-14	840	Monsters	-	Marlies	-
131 - 2023-01-17	857	Comets	-	Monsters	-
133 - 2023-01-19	872	Moose	-	Monsters	-
135 - 2023-01-21	887	Monsters	-	Stars	-
136 - 2023-01-22	901	Monsters	-	Roadrunners	-
140 - 2023-01-26	924	Wild	-	Monsters	-
142 - 2023-01-28	936	Condors	-	Monsters	-
143 - 2023-01-29	948	Monsters	-	Wild	-
145 - 2023-01-31	958	Monsters	-	Americans	-
148 - 2023-02-03	984	Firebirds	-	Monsters	-
149 - 2023-02-04	996	Monsters	-	Senators	-
152 - 2023-02-07	1013	Monsters	-	Penguins	-
156 - 2023-02-11	1045	Thunderbirds	-	Monsters	-
159 - 2023-02-14	1074	Monsters	-	Barracuda	-
161 - 2023-02-16	1089	Monsters	-	Reign	-
162 - 2023-02-17	1094	Monsters	-	Gulls	-
164 - 2023-02-19	1110	Monsters	-	Silver Knights	-
166 - 2023-02-21	1121	Monsters	-	Bears	-
169 - 2023-02-24	1147	Islanders	-	Monsters	-
170 - 2023-02-25	1159	Monsters	-	Rocket	-
173 - 2023-02-28	1176	Monsters	-	Wolf Pack	-
175 - 2023-03-02	1189	Monsters	-	Bruins	-
177 - 2023-03-04	1208	Checkers	-	Monsters	-
178 - 2023-03-05	1220	Senators	-	Monsters	-
180 - 2023-03-07	1230	Monsters	-	Marlies	-
182 - 2023-03-09	1243	Monsters	-	Comets	-
184 - 2023-03-11	1264	Wolf Pack	-	Monsters	-
187 - 2023-03-14	1287	Monsters	-	Phantoms	-
189 - 2023-03-16	1303	Penguins	-	Monsters	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	35	15
Attendance	0.00%	0.00%
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
41	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
21,058$	1,990,000$	1,990,000$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
10,529$	21,058$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	187	10,529$	1,968,923$

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
Regular Season
11	82	45	11	0	7	10	4	5	258	164	94	41	28	2	0	2	5	2	2	132	74	58	41	17	9	0	5	5	2	3	126	90	36	127	258	427	685	2	11	70	100	77	15	3368	1177	1182	979	64	2304	798	113	1984	131	39	29.77%	44	16	63.64%	2	1125	1992	56.48%	819	1499	54.64%	601	1095	54.89%	1845	1069	1527	763	1610	795
11	82	45	11	0	7	10	4	5	258	164	94	41	28	2	0	2	5	2	2	132	74	58	41	17	9	0	5	5	2	3	126	90	36	127	258	427	685	2	11	70	100	77	15	3368	1177	1182	979	64	2304	798	113	1984	131	39	29.77%	44	16	63.64%	2	1125	1992	56.48%	819	1499	54.64%	601	1095	54.89%	1845	1069	1527	763	1610	795
Total Regular Season	164	90	22	0	14	20	8	10	516	328	188	82	56	4	0	4	10	4	4	264	148	116	82	34	18	0	10	10	4	6	252	180	72	254	516	854	1370	4	22	140	200	154	30	6736	2354	2364	1958	128	4608	1596	226	3968	262	78	29.77%	88	32	63.64%	4	2250	3984	56.48%	1638	2998	54.64%	1202	2190	54.89%	3690	2138	3054	1527	3221	1591
Playoff
11	19	11	8	0	0	0	0	0	52	49	3	11	7	4	0	0	0	0	0	31	30	1	8	4	4	0	0	0	0	0	21	19	2	22	52	88	140	1	0	15	20	13	4	794	276	265	212	41	794	287	12	434	19	5	26.32%	6	0	100.00%	0	228	453	50.33%	247	472	52.33%	151	267	56.55%	433	257	414	172	353	177
11	19	11	8	0	0	0	0	0	52	49	3	11	7	4	0	0	0	0	0	31	30	1	8	4	4	0	0	0	0	0	21	19	2	22	52	88	140	1	0	15	20	13	4	794	276	265	212	41	794	287	12	434	19	5	26.32%	6	0	100.00%	0	228	453	50.33%	247	472	52.33%	151	267	56.55%	433	257	414	172	353	177
Total Playoff	38	22	16	0	0	0	0	0	104	98	6	22	14	8	0	0	0	0	0	62	60	2	16	8	8	0	0	0	0	0	42	38	4	44	104	176	280	2	0	30	40	26	8	1588	552	530	424	82	1588	574	24	868	38	10	26.32%	12	0	100.00%	0	456	906	50.33%	494	944	52.33%	302	534	56.55%	866	515	828	345	707	355

	Monsters
	GP: 0 \| W: 0 \| L: 0 \| OTL: 0 \| P: 0 GF: 0 \| GA: 0 \| PP%: 0.00% \| PK%: 0.00% GM : MixtureBill \| Morale : 40 \| Team Overall : 61 Next Games #6 vs Wolves